CONDITION MONITORING AND DIAGNOSTIC ENGINEERING MANAGEMENT (COMADEM 2001)
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CONDITION MONITORING AND DIAGNOSTIC ENGINEERING MANAGEMENT (COMADEM 2001) Proceedings of the 14'" international Congress 4 - 6 September 2001, Manchester, UK
Edited by
Andrew C Starr university of Manchester, UK
RajBKNRao COMADEM international
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The papers presented in these proceedings have been reproduced directly form the authors' 'camera ready' manuscripts. As such the presentation and reproduction quality may vary from paper to paper. Printed in Great Britain
For Fliss, Tom and Max, with thanks for their patience
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Preface COMADEM 2001 is the latest in a successful international conference series, which has spanned more than 14 years since its inception as a national conference in the 80s. The fields of Condition Monitoring, Maintenance Engineering, and Asset Management have progressed steadily - one need look no further than the proliferation of products, services and training which has occurred over this period. There is, however, a deeper change the sector is maturing, bringing advanced maintenance philosophies and technologies into widespread use, and demanding professional qualifications, certification and education. This conference aims to bring together industrial users, vendors, consultants and academics to exchange ideas about problems and solutions. Although each has his own obvious direct motivations, there is a shared passion in this community for the exploitation of technology and management science in the service of industry, the generation of public wealth and in the safety of our colleagues and the public. This volume contains papers from six continents, written by experts in industry and academia the world over. It contains the latest thought on: • the use of techniques such as acoustic emissions, non-destructive testing, tribology, image and vibration analysis; • applications in electric motors, gears, the maritime sector, process monitoring, reciprocating plant, structures and rotating machines; • advances in health management, asset management, quality and reliability; • technologies such as advanced signal processing, artificial intelligence, data fusion, neural networks, sensors, simulation and modelling; • the use of Fieldbus and communication systems, information technology and intelligent manufacturing. The Proceedings also contains a wealth of industrial case studies, and the latest developments in education, training and certification. The Maintenance Engineering Research Group at Manchester School of Engineering is proud to host this international event, and to be able to offer, this Proceedings to the industrial and research communities. I would like to thank the authors for their hard work in preparing the papers to a high standard, and all those individuals who have given up their time to edit and select abstracts and papers. In particular I must thank Prof Raj B K N Rao for his tireless efforts in promoting the conference and in helping with editorial. Dr Andrew StanJuly 2001
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CONTENTS Preface Bearing diagnostics in helicopter gearboxes KB. Randall
vii 1
Cost impact of misdiagnoses on machinery operation J.CPooleylll
13
Detection of rotor-stator rubbing in large rotating machinery using acoustic emissions LD. Hall and D.Mba
21
Condition monitoring of very slowly rotating machinery using AE techniques T.J. Holroyd Monitoring low-speed rolling element bearings using acoustic emissions N. Jamaludin and D. Mha Condition monitoring of rotodynamic machinery using acoustic emission and fuzzy c-mean clustering technique T. Kaewkongka, Y.H. Joe Au, R.T. Rakowski and B.E. Jones Monitoring sliding wear using acoustic emission C K. Mechefske and G. Sun
29 37
49 57
Intelligent condition monitoring of bearings in mail processing machines using acoustic emission S.M.E. Salvan, R.M. Parkin, J. Coy and W. Li
67
Health management system design: development, simulation and cost/benefit optimization G.J. Kacprzynski, M.J. Roemer, A.J. Hess and K. Bladen
75
Optimisation of SANC for separating gear and bearing signals J. Antoni and R.B. Randall
89
A review of fault detection and isolation (FDI) techniques for control and monitoring systems A. Elshanti, A. Badri and A.D. Ball
97
A monitoring and diagnostic tool for machinery and power plants, based on chaos theory M. Fontana, A. Lucifredi and P. Silvestri
103
Novelty detection using minimum variance features LB. Jack and A.K. Nandi Intelligent signal analysis and wireless signal transfer for purposes of condition monitoring S. Lahdelma and T. Pyssysalo Condition monitoring for a car engine using higher order time frequency method S.K. Lee
111 119 127
An investigation into the development of a condition monitoring/fault diagnostic system for large reversible Francis type Pump-Turbines S. Roberts and JA. Brandon Application of vibration diagnostics and suppression by using the Campbell diagram K. Takano, H. Fujiwara, O. Matushita, H. Okubo and Y. Kanemitsu A novel signal processing approach to eddy current flaw detection based on wavelet analysis LQ. Li, K. Tsukada and K. Hanasaki The wavelet analysis applied for fault detection of an electro-hydraulic servo system Z Shi, J. Wang, Y. Zhang, H. Zhao and H. Yue Advanced fault diagnosis by vibration and process parameter analysis U. SOdmersen, O. Pietsch, C. Scheer, W. Reimche and Fr.-W. Bach Partially blind source separation of the diagnostic signals with prior knowledge H. Zhang, L Qu, B. Xu and G. Wen
135
143
153
161
169
ill
Comparison of simple multi-attribute rating technique and fuzzy linguistic methods in multi-attribute decision making R. O. Buchal and C. K. Mechefske
185
Reasoning approaches for fault isolation: a comparison study A. Rakar ondD. Juricic
193
Migration to advanced maintenance and monitoring techniques in the process industry S.M.O. Fabricius, E. Badreddin and W. Kroger
201
Introducing value-based maintenance D. Perry and A. G. Starr
209
Vibration-based maintenance costs, potential savings and benefits: a case study B. Al-Najjar and I. Alsyouf
217
Balanced scorecard concept adapted to measure maintenance performance: a case study /. Alsyouf
111
Design, development and assessment of maintenance system for building industry in developing countries F. Falade
235
Using modeling to predict vibration from a shaft crack J. H. Maxwell and D.A. Rosario
243
An investigation of abnormal high pitch noise in the Train 2 compressor motor A. G.A. Rahman, S.M. al-Attas and R. Ramli
251
An approach to the development of condition monitoring for a new machine by example B.S. Payne, A.D. Ball, M. Husband, B. Simmers and F. Gu
263
Condition monitoring and diagnostic engineering - a data fusion approach P. Hannah, A. Starr and P. Bryanston-Cross
275
Teaching the condition monitoring of machines by understanding W. Bartelmus
283
A successful model for academia's support of industry's maintenance and reliability needs T.V.Byerley
291
Certification in condition monitoring - development of an international PCN scheme for CM personnel P. W, Hills and J. Thompson
297
The exploitation of instantaneous angular speed for condition monitoring of electric motors A. Ben Sasi, B. Payne, F. Gu and A. Ball
311
Discriminating between rotor asymmetries and time-varying loads in three-phase induction motors SM.A. Cruz and A J. Marques Cardoso
319
Asymmetrical stator and rotor fault detection using vibration, per-phase current and transient speed analysis B. Liang, A.D. Ball and S. Iwnicki
329
New methods for estimating the excitation force of electric motors in operation H. Ota, T. Sato, M. Taguchi, J. Okamoto, M. Nagai and K. Nagahashi
345
The development of flux monitoring for a novel electric motor B. Payne, M. Husband, B. Simmers, F. Gu and A. Ball
353
European projects - payback time I.D. Jennings
361
The use of the fieldbus network for maintenance data communication H El-Shtewi, R. Pietruszkiewicz, F. Gu and A. Ball
367
A distributed data processing system for process and Condition monitoring A.D. Jennings, V.R. Kennedy, P.W. Prickett, J.R. Turner and R.I. Grosvenor
375
The physical combination of control and condition monitoring R. Pietruszkiewicz, H. El-Shtewi, F. Gu andA.D. Ball
383
The design and implementation of a data acquisition and control system using fieldbus technologies J.R. Turner, A.D. Jennings, P.W. Prickett and R.I. Grosvenor
391
A non-linear technique for diagnosing spur gear tooth fatigue cracks: Volterra kernel approach F.A. Andrade andII Esat
399
Detection of gear failures using wavelet transform and improving its capability by principal component analysis I N. Baydar and A. Ball
411
Dynamic analysis method of fault gear equipment P. Chen, F. Feng and T. Toyota
419
Diagnosis method of gear drive in eccentricity, wear and spot flaw states P. Chen, F. Feng and T Toyota
All
Gear damage detection using oil debris analysis P.J. Dempsey
433
The generalized vibration spectra (GVS) for gearing condition monitoring M. Kljajin
441
Use of genetic algorithm and artificial neural network for gear condition diagnostics B. Samanta, K.R. Al-Balushi and S.A. Al-Araimi
449
Fault detection on gearboxes operating under fluctuating load conditions C.J. Stander, P.S. Heyns and V/. Schoombie
457
Detection and location of tooth defect in a two-stage helical gearbox using the smoothed instantaneous power spectrum /. Yesilyurt ondA.D. Ball
465
Securing the successful adoption of a global information delivery system D. Perry and A. G. Starr
473
Design of a PIC based data acquisition system for process and condition monitoring M.R. Frankowiak, R.I. Grosvenor, P.W. Prickett, A.D. Jennings andJ.R. Turner
481
Applications of diagnosing of naval gas turbines A. Charchalis
489
Diagnosing of naval gas turbine rotors with the use of vibroacoustic parameters A. Charchalis and A. Grzqdziela
495
Computer image analysis of dynamic processes E. Chrpovd, L Pfevrdtil and V. Motor
503
Inverse method of processing motion blur for vibration monitoring of turbine blade T. Kawai, M. Ito, Y. Sawa and Y. Takano
513
Artificial neural network performance based on different pre-processing techniques F.A. Andrade and 1.1. Esat
521
Fault accommodation for diesel engine sensor system using neural networks A. Badri, E. Berry, F. Gu and A.D. Ball
531
The application of neural networks to vibrational diagnostics for multiple fault conditions A.J. Hoffman, N.T. van derMerwe, P.S. Heyns, C. Schefferand C. Stander
537
Applying neural networks to intelligent condition monitoring W. Li, R.M. Parkin, J Coy, A.D. Ball and F. Gu
545
Data mining in a vibration analysis domain by extracting symbolic rules from RBF neural networks K. McGarry and J. Maclntyre
553
Application of componential coding in fault detection and diagnosis of rotating plant B.S. Payne, F. Gu, C.J.S. Webber and A.D. Ball
561
Bearing fault detection using adaptive neural networks Y. Shoo, K. Nezu and T. Tokito
571
Analysis of novelty detection properties of autoassociators S. O. Song, D, Shin and E.S. Yoon
577
Condition monitoring of a hydraulic system using neural networks and expert systems MA. Timusk and C.K. Mechefske
585
Multi-layer neural networks and pattern recognition for pump fault diagnosis L Wang, A.D. Hope and H. Sadek
593
Development of an automatedfluorescentdye penetrant inspection system T.D. Moore and A.G. Starr
599
Non-destructive fault induction in an electro-hydraulic servo system Z. Shi, D. Cui, Z. Wang, J. Wang andH. Yue
609
Identification of continuous industrial processes using subspace system identification methods R.J. Treasure and J.E. Cooper
615
Life cycle costing as a global imperative KJ. Culverson
625
Six sigma initiatives in thefieldof COMADEM RajB.KMRao
633
Monitoring exhaust valve leaks and misfire in marine diesel engines A. Friis-Hansen and T.L Fog
641
Combining vibrations and acoustics for the fault detection of marine diesel engines using neural networks and wavelets N.G. Pantelelis, A.E. Kanarachos, N.D. Gotzias, N. Papandreou and F. Gu
649
Condition diagnosis of reciprocating machinery using information theory T. Toyota, T. Niho, P. Chen and H. Komura
657
Experimental results in simultaneous identification of multiple faults in rotor systems N. Bachschmid, P. Pennacchi and A. Vania Thermodynamic diagnosis at steam turbines P. Girbig On-line vibration monitoring for detecting fan blade damage P. S. Heyns and W. G. Smit
663 673 681
A hybrid knowledge-based expert system for rotating machinery Y.B. Lee, T.W. Lee, S.J. Kim, C.K Kim and Y.CRhim Monitoring the integrity of low-speed rotating machines D.Mba and L. Hall Detecting and diagnosing faults in variable speed machines CK. Mechefske and L. Liu
689 697 709
ARMADA^'^^- advanced rotating machines diagnostics analysis tool for added service productivity / Toukonen, M. Orkisz, M. Wnek, K. Saarinen andZ Korendo
111
Condition monitoring and diagnosis of rotating machinery by orthogonal expansion of vibration signal r. Toyota, N. Niho, P. Chen and H. Komura
725
Comparison of approaches to process and sensor fault detection A. Adgar
733
The neural network prediction of diesel engine smoke emission from routine engine operating parameters of an operating road vehicle E. Berry, J. Wright, P. Kukla, F. Gu andA.D. Ball
741
Early detection of leakage in reciprocating compressor valves using vibration and acoustic continuous wavelet features M. Elhaj, F. Gu, AD. Ball, Z Shi and J. Wright
749
Inertial sensors error modelling and data correction for the position measurement of parallel kinematics machines J. Gao, P. Webb and N. Gindy
757
On-line sensor calibration verification: 'a survey' J. W. Mines, A, Gribok and B. Rasmussen
765
The applicability of various indirect monitoring methods to tool condition monitoring in drilling E. Jantunen 781 A palm size vibration visualizing instrument for survey diagnosis by using a hand-held type triaxial pickup H. Komura, K. Shibata and K. Shimomura
793
Development of an on-line reactor internals vibration monitoring system (RIDS) J.-H. Park, J.B. Park, C.-H. Hwang and K-S. Choi
801
Truncation mechanism in a sequential life testing approach with an underlying two-parameter inverse weibull model D.I. De Souza, Jr.
809
Maintenance functional modelling centred on reliability F.J. Didelet Pereira and F.M. Vicente Sena
817
An implementation of a model-based approach for an electro-hydraulic servo system A. El-Shanti, Z Shi, D. Luheng, F. Gu andA.D. Ball
825
Stochastic Petri net modeling for availability and maintainability analysis S.M. O. Fabricius and E. Badreddin
833
The dynamic modelling of multiple pairs of spur gears in mesh including friction /. Howard, S. Jia and J. Wang
841
The modelling of a diesel fuel injection system for the non-intrusive monitoring of its condition
SLiu,F.GuandA.Ball
849
Use of factorial simulation experiment in gearbox vibroacoustic diagnostics J. MqczakandS. Radkowski Online fault detection and diagnosis of complex systems based on hybrid component models 5. Manz
857 865
Measures of accuracy of model based diagnosis of faults in rotormachinery P. Pennacchi and A. Vania
873
Failure analysis and fault simulation of an electrohydraulic servo valve Z Shi, F. Gu, A. Ball and H. Yue
881
A multiple condition information sources based maintenance model and associated prototype software development W. Wang and Y. Jia
889
Plant residual time distribution prediction using expert judgements based condition monitoring information W.Wang and W.Zhang
899
Optimising complex CBM decisions using hybrid fusion methods R. Willetts, A.G. Starr, D. Banjevic, A.K.S. Jardine and A. Doyle
909
Diagnostics of honeycomb core sandwich panels through modal analysis R. Basso, C. Cattaruzzo, N. Maggi andM. Pinqffo
919
Assessment of structural integrity monitoring systems B. de Leeuw and F. P. Brennan Experimental validation of the constant level method for identification of nonlinear multi degree of freedom systems G. Dimitriadis
925
935
A comparative field study of fibre bragg grating strain sensors and resistive foil gauges for structural integrity monitoring YM. Gebremichael, B.T. Meggitt, W.J.O. Boyle, W. Li, K.T.V. Grattan, B. McKinley, L Boswell, K.A. Aames, S.E. Aasen, L Kvenild and P.Y. Fonjallaz The application of oil debris monitoring and vibration analysis to monitor wear in spur gears J.A. Barnes and A.G. Starr Identification of non-metallic particulate in lubricant filter debris G. C. Fisher and J.A. Hiltz Influence of turbine load on vibration pattern and symptom limit value determination procedures T. Galka
943 953 959
967
Study on the movement regulation of grinding media of vibration mill by noise testing Jiang Xiaohong, Pu Yapeng and Zhang Yongzhong
977
Gas turbine blade and disk crack detection using tosional vibration monitoring: a feasibility study K. Maynard, M. Trethewey, R. Gill andB. Resor
985
The flow-induced vibration of cylinders in heat exchanger W. Takano, K. Tozawa, M. Yokoi, M, Nakai and I. Sakamoto Author Index
993 1001
Condition Monitoring and Diagnostic Engineering Management Andrew G. Starr and Raj B.K.N. Rao (Editors) © 2001 Published by Elsevier Science Ltd.
BEARING DIAGNOSTICS IN HELICOPTER GEARBOXES
R. B. Randall DSTO Centre of Expertise in Helicopter Structures and Diagnostics School of Mechanical and Manufacturing Engineering The University of New South Wales Sydney 2052, Australia
ABSTRACT The paper summarises the work done by the DSTO Centre of Expertise in Helicopter Structures and Diagnostics in developing techniques for the diagnostics of bearings in helicopter gearboxes. Two major problems are the massive masking by background signals from gears etc, and the tortuous and varying transmission path for signals from internal planet bearings to fixed external transducers, A number of techniques are described for enhancing the results of envelope analysis, such as optimum bandpass filtering, and Self Adaptive Noise Cancellation (SANC) to separate the bearing signal from the background, after which further benefit can be obtained by raising the envelope signal to a higher power before analysis. In some cases, spectral correlation analysis can be of benefit. For planet bearings, it can be an advantage to window out the passage of a particular planet past the measurement point, and logarithmically convert the envelope signal to reduce the effect of the varying signal path. The techniques are demonstrated using simulated signals and signals measured on laboratory rigs and actual helicopter gearboxes.
KEYWORDS Bearing diagnostics, self adaptive noise cancellation, spectral correlation, helicopter gearboxes
INTRODUCTION A number of techniques have become well developed for diagnosing faults in rolling element bearings, but the'case of helicopter gearboxes is a particularly difficult one. Firstly, there is a very wide range of shaft speeds from the gas turbine input at several hundred Hz to the rotor output speed at a few Hz, with a multiplicity of gearmeshes cormecting the various shafts. In most machines, bearing faults show up at high frequencies, typically greater than 2 kHz, where there is usually little else in the spectrum to mask the bearing signals. However, in helicopter gearboxes, there are often a multitude of discrete frequency components, coming from harmonics of gearmesh frequencies, which extend to well over 20 kHz, and sidebands around them due to modulation by the various shaft frequencies. Secondly, for the planetary gear bearings in particular, the fault signals not only have to
be transmitted through a tortuous and varying path to reach externally mounted accelerometers, but must be transmitted through a gearmesh to the ring gear and become intimately mixed with gear signals. One of the most powerful diagnostic techniques for bearing faults is so-called envelope analysis, where it is the "envelope" of the bearing signal (obtained by amplitude demodulation) which is analysed rather than the raw signal. At the DSTO Centre of Expertise in Helicopter Structures and Diagnostics, we have developed a number of digital techniques to perform the envelope analysis as efficiently as possible, as well as developing the associated theory (Randall et al, 1996, 1997, 2001 A, Ho & Randall, 2000). It can be shown, for example, that under most conditions it is advantageous to analyse the square of the envelope (or even a higher power) as opposed to the envelope itself, as traditionally obtained by rectification and lowpass filtering (Ho & Randall, 2000). An analysis we have developed of the effects of masking signals gives a quantifiable measure of when this should be done, based on the signal-to-noise ratio of the bearing signal to any masking. A certain improvement in signal-to-noise ratio can often be achieved by optimal bandpass filtering, extracting that part of the frequency range where the spectral change as a result of the fault is a maximum; however with a helicopter gearbox there will often still be considerable discrete frequency masking. We have thus developed other techniques to remove such masking, based on the rationale that the gear-related frequency components are discrete frequency (phase-locked to shaft speeds) or can be made so by tracking analysis, while the bearing signals have certain random characteristics, as explained below. A technique known as self adaptive noise cancellation (SANC) is able to separate the random from the discrete frequency components (Ho & Randall, 2000), at least when the signals are additive. In the course of developing and testing these techniques, it was found that bearing faults do not always manifest themselves in this commonly accepted way, and that their effect is sometimes to modulate discrete frequencies such as gearmesh-related frequencies, so that the above separation techniques do not work in the normal way. Techniques are currently being developed, based on the analysis of cyclostationary signals, which hold some promise for separating the gear and bearing signals, even when they are multiplicative rather than (or as well as) additive. However, in one example on an actual helicopter gearbox, where the effect of the fault was a modulation at shaft speed rather than the characteristic "ballpass" frequency, it was found that the SANC technique was still useful in distinguishing the randomness of modulation by a bearing fault from other once-per-rev effects (such as gear tooth spacing error) which repeat exactly every revolution of the shaft (Randall & Antoni 2001 A). Another approach, which thus far has only been tested on a planet bearing rig, rather than on a planetary gearbox, is to improve the bearing signal-to-noise ratio by windowing that part of the total signal representing the passage of a particular bearing past the measurement point, and compensating for the variable path weighting by a logarithmic conversion of the envelope signal (Randall & Li, 1995). To apply this in practice will require a tacho signal from the planet carrier, but since this is usually available for rotor track and balance, it is hoped to obtain simultaneous tacho signals in future recordings from actual helicopter gearboxes. This paper gives examples of the above-mentioned techniques based on simulated signals and measurements from a number of test rigs as well as actual helicopter gearboxes.
BEARING SIGNALS A traditional way of modelling bearing faults is as a sequence of high frequency bursts representing the impulse response of the signal transmission path repeated at a rate given by the impacts of the faults interacting with the rolling elements. This sequence of bursts is often modulated at lower
frequencies representing the rate at which the fault passes through the load zone (shaft speed for an inner race fault, cage speed for a rolling element fault) which also often corresponds with the rate of fluctuation of the signal path length (McFadden & Smith, 1984). We have demonstrated that this model can be made more realistic by varying the spacings between the bursts randomly by a small percentage, as this happens in practice because of fluctuations in load angle and thus rolling radius of the rolling elements, and the tolerances of the cage, which only roughly keep the elements evenly spaced. The importance of this is demonstrated by Figure 1, which shows simulated bearing fault signals with and without a small random fluctuation (0.75%). Each burst represents the response of a single degree of freedom system (perhaps the first of a series of resonance frequencies separated out by a filtration). Note that in terms of acceleration (the usual vibration parameter measured), the direct spectrum of the original signal contains almost no information at the low harmonics of the "ballpass frequency" (that corresponding to the pulse spacing), but with no random fluctuation the latter can be detected as the spacing between the higher harmonics in the vicinity of the resonance frequency (figure 1(b)). With the random fluctuation even this is not possible as the higher order harmonics smear into one another (figure 1(e)). A recent paper (Randall et al, 2001 A) shows that the random jitter of the pulse spacing acts as a lowpass filter, whose characteristic is the Fourier transform of the probability density of the random process defining the j itter. Envelope Spectrum
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BEARING DIAGNOSTIC TECHNIQUES When the bearing signal is masked by background noise, either discrete frequency or random, this decreases the ability of envelope analysis to diagnose the fault. To illustrate this, figure 3(a) shows the envelope spectrum for a simulated inner race fault signal with roughly the same amount of fluctuation as in Fig.l, and illustrates that it now contains modulation sidebands around the harmonics of the ballpass frequency, with a spacing corresponding to the shaft speed. This is a valuable diagnostic indicator (McFadden & Smith, 1984). Figure 3(b) shows the effect of adding discrete frequency components (simulating gear signals) to the band demodulated for the envelope analysis in an amount corresponding to about three times the bearing signal power in the frequency band demodulated (mean square ratio, MSR = 0.3). Figure 3(c) and (d) show the effect of analysing the square and the fourth power of the envelope signal
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Figure 3 (a) Envelope spectrum of simulated inner race fault with 1% random jitter (b) Envelope spectrum of bearing fault and discrete noise (MSR 0.3) with no squaring. (c) Spectrum of the squared envelope (d) Spectrum of the envelope raised to the fourth power. which are seen to improve the diagnostic capability in this case. This is about the lowest value of signal-to-noise ratio for which an improvement is achieved and the benefits are even greater when it is higher, while the result deteriorates for smaller values. A similar benefit is given in the case of masking by random noise (Randall & Gao, 1996, Ho & Randall, 2000). Quite a good improvement in signal-to-noise ratio can often be achieved by simply extracting (by bandpass filtering) that part of the raw signal where the greatest change has occurred as a result of the fault development. Doing this digitally in the frequency domain gives much more flexibility than selecting the nearest of a (generally small) range of analogue filters. Moreover, as shown in (Ho & Randall, 1998, 2000), by using the envelope of the complex signal from the one-sided section of spectrum extracted, as opposed to rectifying a real signal, the number of extraneous components in the envelope signal caused by aliasing can be reduced. This procedure also gives efficiencies by simultaneously reducing the sampling frequency of the envelope signal. Self Adaptive Noise Cancellation (SANC) The changes caused by bearing faults will typically be at high frequencies representing resonances excited, and in many machines there is little else in the spectrum in this range. In helicopter gearboxes, however, there can still be masking by high frequency gear components, but these are
phase-locked to shaft speeds. In cases where the bearing signals are additive impulses (and thus having random characteristics in the high frequency range as in Fig. 1(e)), the above-mentioned SANC technique can be used to separate them from the gear signals by virtue of their different correlation lengths. Normal adaptive noise cancellation (ANC) uses two input signals, a primary signal containing a mixture of two components, and a reference signal which is highly coherent with one of the two components in the primary signal. ANC itself has been used in bearing diagnostics (Chaturvedi & Thomas 1982, Tan C.C. 1987) in cases where a primary signal was measured on a suspected faulty bearing, while the reference signal was measured on the same machine, but remote from the faulty bearing, so as to be dominated by other common signals such as those due to tooth meshing. In helicopter gearboxes, however, in particular for the planet bearing signals which are intimately mixed with gear signals no matter where they are measured externally, this approach cannot be used. In SANC, the reference signal is formed by delaying the primary signal so that the random component becomes uncorrelated. In this application, the gear components, being discrete frequency, are coherent even after delay, while the bearing components have random properties and thus a short correlation length. Ref (Ho & Randall, 1997) gave an empirically based guide to the selection of the SANC parameters for helicopter gearbox bearing diagnostics and ref (Antoni & Randall, 2001) presented at this conference, gives a better analytical basis for this selection, as well as suggesting further improvements. Figure 4 shows an example of the advantages gained on analysing actual signals from a Sea King helicopter gearbox which had both inner race and outer race faults in a planet bearing. Figure 4(a) shows a conventional envelope analysis obtained after highpass filtering the raw signal at 1 kHz (such as is done by some analogue instruments with fixed filters). The only effect which can be seen in the envelope spectrum is a couple of harmonics of 135 Hz, one of the gear rotational speeds modulating high gearmesh frequencies. Figure 4(b) shows the combined effects of a number of the techniques described above. Firstly, the signal has been digitally bandpass filtered in the range 1 3 - 1 6 kHz where the change in the raw spectrum was greatest. The resampled time signal from this band was then filtered by SANC, which removed virtually all discrete frequency masking (including the 135 Hz effects seen in Fig. 4(a)) to the extent that a further enhancement could be gained by analysing the squared envelope signal. The corresponding spectrum in Fig. 4(b) now clearly shows both the planet bearing outer race frequency of 170 Hz and the inner race frequency of 210 Hz. In another case from a Seahawk helicopter, with an extensive inner race fault in a bevel pinion support bearing, the ballpass frequency itself did not show up in envelope spectra (Gao & Randall, 1998). This is possibly because regular vibration monitoring was not used and the fault was first detected at quite an advanced stage because of a chip detector warning. The main effect of the spall (extending almost halfway around the race) is to give a modulation at the pinion speed, presumably because of the varying load at the gear mesh caused by the changing support. Even though the carrier frequency is presumably the discrete gearmesh frequency, the modulation varies somewhat with each revolution of the shaft because of the different positions of the rollers each time, once again introducing a degree of randomness. Figure 5 illustrates how SANC was useful here also in demonstrating the random character of the signal (in some frequency bands) and thus making it evident that the modulation source was not perfectly periodic. In this way it differs from gear tooth spacing error or localized gear fault, which repeats every revolution. Figure 5(a) and (b) show the effect of applying SANC to a signal bandpass filtered in the range 3kHz - 6kHz. After SANC, the shaft speed component is reduced by 20:1 (note the change of scale) showing that in that band it represented discrete frequency modulation of the discrete frequency carrier. However, Fig. 5(c) and (d) show that for the demodulation band 7kHz - lOkHz, the SANC
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Figure 4(b) - Envelope Power Spectrum of the squared envelope after SANC
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Figure 5 - Envelope spectra of inner race fault before and after SANC.
2000
removes many discrete frequency components, but hardly reduces that at shaft speed, presumably because in this band the modulation was dominated by the bearing fault. Cyclostationary Analysis In a new study (Randall & Antoni, 200IB), an alternative approach is suggested, which also should distinguish the modulation associated with a bearing fauh from that associated with a gear fault, even if they are at the same frequency, as in the above case. It is based on the properties of cyclostationary processes, which lie somewhere between periodic and stationary random signals. A general nonstationary signal can be characterised by its autocorrelation function, defined by: R^{t,x) = E[x{t-xl2)x{t
+ xl2)]
(1)
where E[,] is the expected value or statistical mean, and where in general this varies with both central time / and time shift x. For stationary signals, there is no variation with t and it is normally written as R^ (t). For cyclostationary signals there is a periodic variation with t, and there is an advantage in performing a Fourier transform with respect to the two time variables to give the so-called spectral correlation function defined by: 5^(a,/)= 3
3 [RAt.-^)}
(2)
t-^a T->/
With cyclostationary signals, the Fourier transform with respect to t gives discrete frequency components (cyclic frequency), even if that with respect to x gives narrrow-band or broad-band random spectra (vs normal frequency). Bearing signals are approximately cyclostationary, but differ in two respects (Randall & Antoni, 200IB). Firstly, because the fault frequencies tend to be modulated by non-commensurate frequencies (eg inner race frequency by shaft speed), they are not periodic, but rather quasi-periodic. Secondly, because they do not have a defined mean period, their autocorrelation function is not truly periodic with t, which leads to a smearing of higher harmonics, as seen in the envelope spectrum of Fig. 1(f). However, with limited slip of about 1%, which is typical, they are close to periodic, and the envelope spectra are almost discrete frequency as in Fig. 1(f). Figure 6(a) shows the spectral correlation of a signal from a bearing with an inner race fauh. It is seen to be spread out in the normal frequency direction, but effectively discrete in the cyclic frequency direction. In Ref (Randall et al, 2001 A), it is shown that the integral of the spectral correlation over all frequency is equal to the Fourier transform of the mean squared signal, effectively the spectrum of the squared envelope. Figure 6(b) represents both, and can be seen to be that of a typical inner race fault (cf Fig. 3(a)). At first glance there does not appear to be any advantage in the spread of the spectral correlation over normal fi-equency, but it can in fact be used to distinguish between periodic and cyclostationary signals. A periodic signal has discrete components in both frequency directions, whereas cyclostationary signals are distributed in t h e / direction. Since stationary random signals do not change with t, they have a component only at zero cyclic frequency (the normal power spectrum). Thus, if the spectral correlation is evaluated for a cyclic frequency coinciding with one of the discrete components other than zero, it will not be contaminated by additive noise, but on the other hand will be continuous for cyclostationary signals, and localised for periodic signals. Figure 7 shows how this can be applied to the same signals as in Fig. 5. It compares signals from a Seahawk helicopter with and without the inner race bearing fault, evaluated for a = zero and the shaft speed. For the former, there is little difference because of masking by stationary noise, while with the latter the increase and smoothing at many frequencies, due to the fault, points to the presence of a cyclostationary component, presumably a bearing fault (a gear fault would repeat periodically at the shaft speed).
100
150
200
250
300
Cyclic frequency (Hz) Figure 6 (a) Spectral correlation and (b) integrated spectral correlation for the demodulated signal of an inner race fault in the spectral band [2800 Hz; 3300 Hz]. BPFI = 120 Hz. Shaft speed = 9.5 Hz.
0
Normalised frequency
0
Normalised frequency
Figure 7 Spectral correlation at particular values of cyclic frequency a with and without bearing fault (a) a = 0 (b) a = shaft speed Q
Planet Bearing Faults A technique has also been developed to assist with the effects of the varying signal transmission path from a faulty planet bearing to an externally mounted transducer. The latter gives some weighting to the signal which without any correction gives an artificial reduction in effective length of envelope signal analysed (the signal being strongest when the faulty bearing is closest to the transducer). Experiments carried out on a planet bearing rig (without gears) showed that making a logarithmic conversion of the envelope signal changed the multiplicative weighting effect to an additive one, thus effectively extending the length of valid signal and improving the resolution of the resulting spectrum.
Spectra
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Figure 8
(i) 2000
(a) Time function of the bearing fault signal i^/\/-Yywv''''^-JS/\i,'''')^^ 100 0 20 40 (b) Spectrum of the bearing fault signal (c) Time function of the bearing fault and gear signal soo (d) Spectrum of the bearing fault and gear signal (J) (e) Time function of the bandpass filtered bearing °i0 20 40 60 80 100 fault signal. (f) Spectrum of the bandpass filtered bearing fault signal (g) Windowed, logarithmic envelope of the SANC output. (h) Envelope spectrum of the bearing fault and gear signal (i) Envelope spectrum of the bandpass filtered combined signal. (j) Envelope spectrum of the windowed, logarithmic envelope with zero padding.
On the other hand, this meant that the signal from the faulty bearing was now no longer so dominant in passing the transducer, and so should be selected out by an appropriate window function (eg onethird of the carrier revolution in the case of three planets). This of course reduces the resolution again, but overall still gives an improvement over the naturally occurring weighting function, over which the analyst has no control. Figure 8 illustrates this for signals obtained from the above-mentioned planet bearing rig, but with artificially added extraneous gear noise (Li, 1995). A combination of bandpass filtering and SANC was used to minimise the effects of the latter. Figure 8(a) shows the time signal due to an outer race fault in one of three planet bearings as measured at one external point on the annulus. The amplitude modulation of the pulses is due both to the varying signal path and to the fact that an outer race fault in a planet bearing passes through the load zone once for every relative rotation of the outer to the inner race (in contrast to a rotating shaft bearing where it is the inner race which passes through the load zone once every revolution; see Fig. 3(a)). Figure 8(b) represents the spectrum of this signal which is seen to contain a series of resonance peaks extending at least up to 11 kHz. Figure 8(c) shows the masking introduced by adding a judicious amount of artificial gear noise, and 8(d) the corresponding spectrum where the discrete frequency gear related components are now up to 20 dB higher than the bearing spectrum at some frequencies. Figure 8(e) shows the reduction in masking given by the bandpass filtering evident in the corresponding spectrum of Fig. 8(f). This band was chosen as being roughly where the gear signal had least dominance over the bearing signal. Figure 8(g) shows the windowed logarithmic envelope obtained after removing much of the residual discrete masking by using SANC, and choosing the part of the signal corresponding to the passage of the faulty bearing. Figures 8 (h) to (j) illustrate the increasing benefit given by thf
various procedures discussed above. Figure 8(h) is the envelope spectrum of the raw signal (i.e. that of 8(c)), and shows only the effect of a gear rotational speed of 150 Hz. Figure 8(i) shows the effect of bandpass filtering alone (Fig. 8(e)), where the ballpass frequency, outer race, (BPFO) of 40 Hz is just visible, but the 150 Hz gear frequency still dominant. Finally, Figure 8(j) shows the result of analysing the windowed logarithmic envelope signal of Fig. 8(g). It is now evident that the gear effect at 150 Hz has been removed by the SANC, and that the BPFO of 40 Hz and its second harmonic at 80 Hz are now quite pronounced, as are the modulation sidebands with a spacing of approx. 5 Hz. The width of the frequency components is due to the short window dictated by the parameters of the problem, but the diagnosis is much clearer than when the logarithmic envelope is not used. It has not yet been possible to apply this technique to helicopter gearbox signals, as it requires a tacho signal corresponding to the planet carrier speed, and this was not available with previously obtained signals. However, since this usually corresponds to the rotor speed, such a tacho signal is normally available for rotor track and balance, and it is hoped that signals obtained in future corresponding to bearing faults will have the appropriate tacho signal recorded at the same time. CONCLUSIONS A suite of techniques is now available for detecting and diagnosing bearing faults in helicopter gearboxes, based on vibration analysis, in spite of the difficulties involved. Digital techniques of envelope analysis provide maximum flexibility in choosing the optimum frequency band to demodulate with an almost ideal filter characteristic, at the same time achieving efficiencies by reduction in the sampling rate. The envelope signal obtained can very easily be raised to a higher power before analysis, and this is shown to be an advantage when the bearing signal-to-noise ratio is over a certain value. If this is not achieved by simple bandpass filtration alone, SANC is a useful technique for removing additive masking signals from gears to improve the bearing signal-to-noise ratio. If the gear signals are modulated by the bearing signals, spectral correlation may provide an alternative means of separating them. Finally, for planet bearings in particular it can be advantageous to perform a logarithmic conversion of the envelope signal, after which a window function can be used to separate out that part of the signal corresponding to the passage past the transducer of a particular bearing.
ACKNOWLEDGMENTS This work was supported by the Australian Government's Defence Science and Technology Organisation (DSTO) under the Centre of Expertise scheme. The Seahawk helicopter gearbox signals were supplied by the (US) Naval Air Warfare Center, Aircraft Division (NAWCAD), made under the Helicopter Integrated Diagnostic System (HIDS) program. The HIDS data were acquired at their Helicopter Transmission Test Facility, formerly of Trenton, New Jersey, and now relocated to Patuxent River, Maryland. The author would also like to thank his present and former colleagues Yuejin Li, Yujin Gao, Dominique Ho, and Jerome Antoni, from whom many of these results were obtained.
REFERENCES Chaturvedi, G.K. & Thomas, D.W., (1982) Bearing Fauh Detection Using Adaptive Noise Cancelling, Transactions of the ASME, 104, (April), pp.280-289. Gao, Y. & Randall, R.B., (1998) Detection of helicopter Pinion Bearing Fault by Vibration Analysis, ISASTI, Jakarta, Indonesia, Sept.
10
Ho, D. & Randall, R.B., (1997) Effects of Time Delay, Order of FIR filter and Convergence Factor on Self Adaptive Noise Cancellation, International Congress on Sound and Vibration, Adelaide, pp 15-18, Ho, D. & Randall, R.B,, (1998) Improving the Efficiency of SANC in its Application to Bearing Diagnostics, Comadem '98, Launceston, Australia, Dec. 8-11, pp 371-380. Ho, D. & Randall R.B., (2000), Optimisation of Bearing Diagnostic Techniques Using Simulated and Actual Bearing Fault Signals", Mechanical Systems and Signal Processing, 14 (5), pp763-788. Li, Y., (1995) Diagnostics of Planetary Gear Bearings. PhD Thesis, School of Mechanical and Manufacturing Engineering, The University of New South Wales, Sydney, Australia, Dec, McFadden, P.D. & Smith, J.D., (1984) Model for the Vibration Produced by a Single Point Defect in a Rolling Element Bearing, Journal of Sound and Vibration, 96(1), pp. 69-82. Randall, R.B. & Li, Y., (1995) Modified Envelope Analysis for Diagnostics of Planetary Gear Bearings, Machine Vibrations 3, Springer, pp.185-191. Randall, R.B. & Gao, Y., (1996) Masking Effects in Digital Envelope Analysis of Faulty Bearing Signals, 6th Int. Conf on Vibrations in Rotating Machinery, IMechE, Oxford, pp351-359. Randall, R.B., (1997) Developments in Digital Analysis Techniques for Diagnosis of Bearings and Gears, The 5^^ International Congress on Sound and Vibration, Adelaide, Vol. 1, pp 133-149. Randall, R.B., Antoni, J. & Chobsaard, S., (2001A) The Relationship between Spectral Correlation and Envelope Analysis of Cyclostationary Machine Signals - Application to Bearing Fault Diagnostics. Mechanical Systems and Signal Processing, to be published. Randall, R.B. & Antoni, J., (2001B) Separation of Gear and Bearing Fault Signals in Helicopter Gearboxes, 4th International Conference on Acoustical and Vibratory Surveillance Methods and Diagnostic Techniques. To be presented October, 2001, Compiegne, France. Tan C.C, (1987) An Adaptive Noise Cancellation Approach for Condition Monitoring of Gearbox Bearings, International Tribology Conference, Melbourne 2-4 Dec, pp.360-365,
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Condition Monitoring and Diagnostic Engineering Management Andrew G. Starr and Raj B.K.N. Rao (Editors) © 2001 Elsevier Science Ltd. All rights reserved.
COST IMPACT OF MISDIAGNOSES ON MACHINERY OPERATION John C. Poolev III Steven R. Murray Amtec Corporation 500 Wynn Drive, Suite 314 Huntsville, AL 35816
ABSTRACT: Machinery Diagnostics are performed under conditions of extraneous noise and linear/non-linear coupling from internal/external sources, which are the underlying causes of misdiagnoses in machinery health diagnostics. The consequence of these misdiagnoses is False Alarms and False Safes, which derive the candidate diagnostic system's cost impact. The optimal cost effectiveness of each candidate diagnostic system is achieved by minimizing misdiagnosis. Although optimal selection of distress features in operational machinery facilitates causality mapping to specific internal defects, nevertheless, misdiagnoses among likely candidate diagnoses occur. The gist for identifying misdiagnoses in candidate machinery diagnostic systems is to transform every candidate's diagnosis likelihood into a probability of correct diagnosis and its associated misdiagnosis. These misdiagnoses are comprised of False Alarms and False Safes, which specify their cost impact.
KEYWORDS: Cost impact. False Alarm, False Safe, Correct Diagnosis, Misdiagnosis
INTRODUCTION The correct diagnosis of the presence and nature of faults in noisy environments is predicated on the specification of unique operational performance parameter identifiers for the targeted dynamic machinery. Although optimal selection of anomaly distress features enables causality mapping to specific defects, nevertheless, misdiagnoses still occur in candidate diagnostic systems. Therefore, the evaluation of a diagnostic system's effectiveness requires a method for evaluating misdiagnosis cost impact [1]. The cost effectiveness survey of candidate diagnostic systems to the end-user's target machinery is the ultimate defining criteria in electing the most viable candidate diagnostic system. However, this cost effectiveness evaluation is predicated on the candidate system having met the diagnostic measure of effectiveness (MOE) and measure of performance (MOP) required by the target machinery application environment and dynamic operational environment. These MOE's and MOP's are employed during the down-selection of potential candidates because the metrics of the target system must be determined on a case-by-case basis. Once this down-selection process has been completed, then all qualified candidates should be subjected to cost-effectiveness evaluation so the end-user is assured that his selection is optimized on MOE, MOP and Cost. Every candidate system must arbitrate among operational condition calls. This arbitration produces False Alarms (FA) and False Safes (FS). In turn, each candidate's cost-effectiveness can be readily evaluated against its peers once its FA and FS probability for a specific end-user target machinery has been determined. This cost-effectiveness evaluation is driven by the end-user's maintenance cost (Cmaim), number of machines under analysis, and cost of unscheduled shut-down caused by machinery failure (Cfaii). The non-recurring cost of the diagnostic system must be factored into the end-user's selection process, but his must be considered on a case-by-case basis. The end-user can map the MOP and MOE qualified candidates into this cost-sensitivity matrix to optimally select the best diagnostic system. The probabilities that constitute the basis for the cost survey of candidate diagnostic systems are the probability of Correct Diagnosis (Pcd) and Probability of Incorrect Diagnosis (Pcd)- Pcd and p^^, are not statistically independent. That is, their conditional probability Pr (Pcd I Pcd) ^^ 0; however, the elements of Pcd and Pcd under certain conditions are statistically independent by Eqn. 1: Pcd=F{PN,PF}, Pcd = F{Pfa,Pfs}
Where:
PN is Probability of Nominal Pfais Probability of False Alarm Pfs is Probability of False Safe PF is Probability of Fauh
The conditional probability of Pfs and Pfa is zero, meaning they are statistically independent.
14
(1)
CONDITIONAL RISK ANALYSIS The likelihood probability that a machinery operational diagnostic pattern x is a member of the Nominal class (xsoui) is written as P(a)]/x). However, if the classifier decides that x is a member of Fault class (x8U32) when actually xeooj, the classifier incurs a loss (Ljj). Therefore, the expected loss incurred in assigning x to class N(a)i) is given by Eqn. 2: ri(x) = Li,, P(a)i/x) + L2,i P(u)2/x)
(2)
and the loss in assigning x to F(uj2) is given by Eqn. 3: r2(x) = Li,2 P(coi/x) + L2,2 P(a)2/x)
(3)
The losses associated with Ljj are as follows: Li I = No loss for Correct Nominal classification of x, XSPN, L2,i = Loss for assigning X8U) i, (Nominal) when actually XEUO 2 (False Safe: Pis), Li,2 = Loss for assigning xeoo 2 (Fault) when actually xeo) 1 (False Alarm: Pfa), and L2,2 = Loss for Correct Fauh classification of x, xePp,
The classification with the minimal conditional risk (r) is best classification in a Bayesian sense. Thus X is assigned to class ooj if ri(x) < rj(x). However, the optimal classification (Pcd) is the candidate that reduces the total conditional risk (r T), as shown in Eqn. 4: rT=2ri(x)
(4)
The candidate diagnostic system with the minimal total conditional risk rr will be the elected candidate for this target machinery application on a cost basis.
COST ANALYSIS OF CONDITIONAL RISK The Conditional Risk for binary hypotheses of Nominal versus Fault Operation is well known in estimation theory [1]. The conditional risk cost estimation is as follows: Eqn. 5 is utilized to derive the elements of the Cost-Sensitivity Matrix: Annualized Cost = F{PF, PN, Pfa, Pfs, Cmaint, Cfaii, Niaii, Nmach} A preliminary version of this analysis was presented in a COMADEM2000 paper [2].
15
(5)
Definition of Terms: PF = Probability that an imminent failure is correctly detected before failure, This is the same as a probability of correct detection. PN - Probability that a machinery component is in a nominal operating condition. necessarily the same as 1- PF, due to differences determined using a loss matrix.
This is not
Pfa = Probability that a nominal component will be classified as a fault (false alarm). A false alarm classification will require corrective maintenance to be performed to fix the component classified as faulty, and therefore leads to a significant cost impact. Pfs = Probability that a component with an imminent failure will be classified as nominal (false safe). A false safe classification occurs when a diagnostic system is unable to detect an fault. This fault will lead to loss of the component, as well as damage to other systems relying on that component. For critical components, this could lead to loss of the entire machine and the lives of the crew, and is therefore the most significant cost driver. Cmaint = Average cost of a single replacement incident including parts, labor, downtime, and further analysis. This is the average cost regardless of whether the maintenance is scheduled or discovered by a diagnostic system. This was derived from a study by NSWC on the CH53 helicopters [3]. Cfaii = Average cost of a component failure. This includes both the cost of maintenance as above, as well as the impact this provides on the fleet (such as loss of a critical machine). In addition, this Cfaii factors in the loss of an entire machine when critical components fail. This was derived from a study by NSWC on the CH53 helicopters [3]. The value chosen was derived using a conservative average risk assessment hazard (RAC III) and a 50% probability of the hazard propagating to a Class B mishap (an average value for these mishaps was used - $600k). (See Figure 3.0-1.) Nfaii = The average number of failures used in the cost sensitivity cube was 136. This was derived from a study by NSWC on the CH53 helicopters [3]. Nmach - A total of 124 CH53 helicopters were included in this cost sensitivity analysis, This was derived from a study by NSWC on the CH53 helicopters [3]. Analysis Period = One year. All cost variables are considered aimualized.
/ Aircraft \ B / Historical \ | ^ ^ ^ L ^
I
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\
Incident
MHV
/
B
Aircraft Mishaps Classes
m
Class of Mishaps Class A - Destruction of Aircraft ($25M) Class B - Extensive Damage Aircraft ($200K - IM) a a s s C - Moderate Damage Aircraft ($ IOOK) Risk Assessment Code (RAC) of Hazard RAC I - 50% probability propagate Class A
RAC II - 25% probability propagate Class A RAC III - 50% probability propagate Class B RAC IV - 25% probability propagate Class B RAC V - 50% probability propagate Class C
Figure 3.0-1: Categorization of Aircraft Historical Safety Incidents 16
Using the Annualized Cost equation, a Cost Sensitivity Cube can be derived that will allow for all possible permutations of the probabilities, from which the most cost effective system can be determined (Figure 3.0-2). Two examples are presented in the tables following the Cost Sensitivity Cube. For these tables, Tables 3.0-1 and 3.0-2, one of the probabilities was set to a constant value to represent a single slice of the cube.
Figure 3.0-2: Cost Sensitivity Cube ( F {Pp, Pfa, Pfs} )
TABLE 3.0-1 COST SENSITIVITY MATRIX FOR PF, Pfa WITH Pfs = 0.0
0.5 0.6 0.7 0,8 09 10
0.0
0.1
2,996.828 3.596.194 4.195.559 4J34.925 5,394.290 5,993,656
3,543,308 4.142.674 4,742,040 5.341,405 5,940.771 6.540.136
0.3 4,636,269 5.235.635 5,835.000 6,434.366 7,033.732 7.633,097
0,5
0.7
5.729.230 6.328,596 6,927,961 7.527.327 8.126.692 8,726,058
6.822.191 7.421.556 8,020,922 8.620,288 J,219,653 9,819,019
0.8
0.9
1.0
7,368,671 7,968.037 8,567.402 9,166,768 9,766,134 10,365,499
7,915,152 8,514,517 9,113,883 9.713,248 10,312.614 10,911.980
8,461,632 9,060,998 9,660.363 10,259,729 10,859,094 11,458,460
TABLE 3.0-2 COST SENSITIVITY MATRIX FOR PF, Pfs WITH Pfa = 0.0 0 1 4,080.000 2,996.828 7.076,828 3.596,194 7,676,194 4,195.559 8,275,559 4,794,925 8.874,925 5.394.290 9.474,290 5.993.656 0,073,656 3.656 10,073,65.6 0.0
0.0 0.5 06 0.7 0.8 0.9 1.0
0^ 12.240,000 15.236.828 15,836,194 16,435,559 17.034,925 17,634 ..290 18,233.656 18,23.3.656
0.5 20.400.000 23.396.828 23.996,194 24.595,559 25,194,925 25,794.290 26.393,656
0.7 28,560.000 31,556.828 32,156,194 32,755,559 33.354.925 33.954.290 34.553,656
0.8 32,640,000 35,636,828 36.236,194 36,835,559 37,434,925 38,034,290 38,633.656 38,633.656
09 36.720,000 39,716,828 40,316,194 40,915,559 41,514,925 42,114,290 42.713,656 42.713,656
1 0 40,800,000 43,796,828 44.396,194 44,995,559 45,594,925 46,194,290 46,793,656 46,793,65
$46,793,656 is the cost for running the system to complete failure (i.e , PF = 1 and Pf, = I; no diagnostic system)
17
By holding the PF constant at a set value, the Pfa and Pfs values can be examined. For Tables 3.0-3, 3.0-4, and 3.0-5, Pp is set to 0.0, 0.5, and 0.9, respectively.
TABLE 3.0-3 COST SENSITIVITY MATRIX FOR Pfa, Pfs WITH PF = 0.0 \ 0.0 0.1 02 03 0.4 05 06 07
00 0 546,480 1,092.961 1,639.441 2,185.922 2,732,402 3.278.882 3.825.363
01 4,080.000 4.626,480 5,172.961 5.719,441 6,265,922 6.812,402 7,358.882 7,905,363
02 8.160,000 8,706.480 9,252.961 9,799,441 10.345.922 10,892.402 11.438,882 11,985.363
0.3 12.240.000 12,786,480 13,332.961 13,879,441 14,425.922 14,972.402 15,518,882 16.065.363
04 16.320.000 16,866.480 17.412,961 17,959.441 18,505,922 19.052,402 19,598,882 20.145.363
05 20.400.000 20,946,480 21,492.961 22,039,441 22,585.922 23.132,402 23,678.882 24,225,363
06 24.480.000 25,026.480 25.572,961 26,119,441 26,665.922 27,212.402 27.758.882 28.305.363
07 28.560.000 29,106,480 29,652.961 30.199 441 30,745,922 3''.292,402 31.838,882 32,385,363
0.2 03 04 0.5 0.6 07
4,089.789 4.636.269 5.182,750 5,729.230 6,275,710 6,822,191
8,169,789 8.716,269 9,262.750 9.809.230 10.355,710 10,902,191
12,249,789 12.796,269 13,342.750 13.889.230 14.435,710 14.982,191
16,329.789 16.876,269 17,422.750 17.969.230 18,515.710 19,062.191
20.409.789 20,956.269 21,502,750 22.049.230 22.595.710 23,142.191
24,489,789 25.036,269 25.582,750 26.129.230 26.675,710 27,222,191
28,569,789 29,116,269 29,662,750 30,209,230 30.755,710 31,302.191
32,649.789 33,196.269 33.742,750 34.289.230 34,835.710 35,382,191
TABLE 3.0-5 COST SENSITIVITY MATRIX FOR Pfa, Pfs WITH PF = 0.9
\
0.0 0^ 02 03 0.4 05 0.6 0.7
0.0 5,394,290 5.940,771 6,487.251 7,033.732 7,580.212 8.126.692 8,673,173 9.2^9.653
0.1 9,474,290 10.020.771 10.5fi7,251 11,113,732 11,660,212 12.206.692 12.753,173 13,299,653
02 13.554.290 14,100,771 14,647,251 15,193.732 15,740,212 16.286,692 16,833.173 17.379,653
0.3 17,634,290 18.180 771 18,727,251 19,273,732 19,820,212 20.366,692 20.913 173 21.459,653
04 21.714.290 22.260.771 22,807.251 23.353.732 23.900.212 24,446.692 24.993.173 25.539.653
05 25,794,290 26.340,771 26,887,251 27.433,732 27,980,2^2 28,526,692 29,073,173 29,619,653
0.6 29,874,290 30,420,771 30,967,251 31.513,732 32,060,212 32,606,692 33,153,173 33,699,653
0.7 33,954,290 34,500.771 35,047,251 35,593,732 36,140,212 36,686.692 3^.233,173 37,779,653
The non-recurring cost (NRC) of the candidate diagnostic system must be factored into the candidate diagnostic system election process. The NRC cost must be significantly less than the cost in Table 3.02 for values of PF = 1.0 and Pfs = 1.0, which is the cost of allowing the target machinery to run to failure ($46,793,656). Once a candidate diagnostic system's PF, Pfa, and Pfs has been determined, the Cost Sensitivity Cube will allow the user to easily pinpoint the system's annualized recurring cost. For example, with a PF = 0.5, Pfa = 0.3, Pfs = 0.4, an annual recurring cost of $20,956,269 would be found as shown in Table 3.04.
CONCLUSION In conclusion, once the candidate diagnostic System has met the MOP and MOE requirements, what remains is to evaluate the candidate diagnostic system on cost. Ideally, a candidate diagnostic system should be evaluated from the target machinery failure experience. However, a pratical substitute machinery can be leveraged by target machinery personnel to estimate operational cost of employing
18
the candidate diagnostic system in their machinery appHcation. This cost analysis will go a long way in justifying the installation of a candidate diagnostic system to upper eschelon management.
REFERENCES 1.
George J. Klir, "Statistical Modeling with Imprecise Probabilities", AFRL-IR-RS-TR-1998166 Final Technical Report, SUNY Binghamton, Air Force Research Laboratory Information Directorate, Rome Research Site, Rome, NY, August 1998.
2.
J. Pooley, S. Murray, "Cost Impact of Ambiguities in Machinery Diagnosis", Proceedings of COhdADEM 2000, 13^^ International Congress on Condition Monitoring and Diagnostic Engineering Management, Houston, TX, December 2000, pp. 69-74.
3.
E. Davis, A. Schor, J. Hacunda, R. Pileggi, M. Ross, "Cost / Benefits Analysis for Integrated Mechanical Diagnostics", Draper Laboratory, April 1997.
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Condition Monitoring and Diagnostic Engineering Management Andrew G. Starr and Raj B.K.N. Rao (Editors) © 2001 Elsevier Science Ltd. Allrightsreserved.
DETECTION OF ROTOR-STATOR RUBBING IN LARGE ROTATING MACHINERY USING ACOUSTIC EMISSIONS LDHall',DMba' ' School of Engineering, Cranfield University, UK ABSTRACT Light intermittent rubbing between the central rotor and the surrounding components of fast rotating machinery such as a large-scale turbine unit can escalate into severe vibration and can cause costly damage. Although conventional vibration analysis remains an important condition monitoring technique for such rubbing, the nondestructive measurement of acoustic emissions (AE) activity at the journal bearings of such plant is evolving into a viable complementary detection approach, especially adept at indicating the early stages of rubbing. Due to acoustic attenuation and the significant levels of background acoustic noise exhibited by operational turbine units within the AE band of interest, the signal processing employed to suppress all but the rub signature becomes a vital consideration. This paper presents aspects of the signal processing scheme implemented within a AE monitoring system for the detection of shaft-seal rubbing. AE data measured from wide band commercial AE sensor is considered. The filtering scheme pertains to a combination of bandpass filter, a form of matched filter and a smoothing filter employing a discrete wavelet transform. To demonstrate the detection performance, rub signatures generated upon a low-noise laboratory test rotor are mixed with authentic AE noisefromthe low-pressure bearings of an operational steam turbine unit. KEYWORDS Acoustic Emissions, rubbing, large-scale rotating machinery INTRODUCTION Non-destructive diagnosis of frictional rubbing within very slow-speed rotating machinery (Mba et. al [1999]) or the detection of defects within machinery accommodating rolling-element bearings (Tandon et. al [1999]) is commonly achieved using AE condition monitoring. Reasons for this include the obvious unsuitability of
21
conventional vibration analysis at very low rotation speeds, relatively low levels of background noise and the possibility of direct measurement upon the shaft. In contrast, application of AE to the detection of rubbing in faster and larger rotating machinery has been less researched. This is primarily attributed to the acoustic attenuation incurred by the rub signal of interest, higher levels of background noise and the proven success of conventional vibration analysis within the audible range. However, it is proposed that AE measurement within a lOOkHz-SOOkHz band can provide a valuable complementary tool for diagnosing rubbing in fast rotating plant such as turbine generators, as reported previously in (Sato [1990]) and (Board [2(XX)]). Essentially, light frictional contacts between the central shaft and surrounding stationary components, such as the seals or end-glands within a turbine unit, will cause microscopic perturbation and a transient release of broad band strain energy referred to as stress waves (SW) or acoustic emissions (AE). This AE signal propagates across the shaft surface via Rayleigh wave motion (Pollard[1977]). However, in real operational machinery it is often only practical to take AE measurements on the bearing housing. Consequentiy, AE signals originating from the rotating shaft will incur significant attenuation and perturbation across the transmission path to an AE receiver attached at the bearing housing, especially at higher frequencies, due to frequency-dependent absorption, geometric spreading losses and reflection at interfaces. Moreover, significant levels of AE noise are exhibited at the bearings of large-scale turbines within the AE band occupied by the rub signatures of interest. Sources of AE noise in turbines include the turbulent motion of expanding steam or gas and the lubricating oil within the journal bearings. Therefore, these factors dictate a low rub signal-to-noise ratio and reduce the probability of rub detection. This paper examines a signal processing scheme capable of introducing SNR gain to the measured AE response prior to some form of display. The filtering techniques were assessed using rub signatures from an effectively noise-free laboratory test rig and real AE noise taken from an operational turbine unit. It is assumed that realistic rub signature plus turbine noise scenarios can be approximated by linear superposition. RUBBING IN LARGE-SCALE ROTATING MACHINERY A number of factors are attributed to the onset of light rubbing in rotating plant (Muszynska [1989]). These include thermal effects, foundation movement, component movement, rotor unbalance or misalignment. Regardless of the exact relationship between cause and effect, the existence of rubbing is important in that it can often develop into significant mechanical vibration and costly rotor damage. The two main categories of rubbing can be identified. Primarily partial rubbing, as considered within this paper, constitutes intermittent rub events occurring instantaneously within the period of the shaft rotation. Secondly continuous rubbing involves more sustained contact between shaft and surrounding components and will often follow from the partial rub state.
22
Clearly, the physical mechanisms that govern the exact nature of rubbing or the evolution of the rub state into more serious vibration within a large-scale turbine unit are complex and involve rotor dynamics (e.g. forces and stiffness) and thermal effects. However, one relevant mechanisms by which light partial rubbing can escalate into more serious vibration within large-rotating machinery is highlighted here. This involves a partial rub contact occurring periodically at a constant shaft location. Such 'once per revolution' partial rubbing induces an increase in friction and a differential heat gradient upon the shaft, leading to a local thermal expansion that causes the shaft to bow. This thermallyinduced bending adds to the imbalance of the rotor and increases the synchronous vibration which in turn, results in stronger rubbing and heating, so that the vibration escalates and this can eventually cause damage to the rotor. In some cases, such rubbing at one high stress location or *high spot' upon the shaft can lead to similar thermal stress and bending at other locations along the shaft and a spiralling motion occurs. Further, extreme thermal expansion can potentially cause plastic deformation and permanent bowing of the shaft, rendering it unsuitable for further operation. EXPERIMENTAL The dimensions of the laboratory test rig with which AE rub signatures were taken is depicted in Figure-1. The shaft is supported by two journal bearings and the rub simulation mechanism is positioned between the third and forth of five steel discs shrink fitted on to the central shaft that rotates at up to -2400rpm. An acoustic waveguide is employed to ensure direct acoustic contact between the AE sensor face and the inner bearing housing. Figure-2 illustrates how periodic partial rubs are simulated on the test rig for stationary labyrinth seal fixtures. Essentially, a non-concentric dummy shaft fixture is attached to the rotor so as to rub against a supported steel labyrinth seal fixture on every shaft rotation. The reaction pressure exerted by the seal upon the incident shaft was set by masses applied to the rub fixture. These masses applied a force of HON throughout the AE measurements. The AE signal measurement system used incorporated wideband piezoelectric WD sensors (Physical Acoustic Corp®) differentially connected to a 20/40/60 dB gain preamplifier for measurement within the lOOkHz-lMHz band. The separate pre-amp incorporated a plug-in analogue high-pass filter to suppress low frequency acoustic noise components. The signal output from the pre-amp was connected directly to the commercial acquisition card that occupies an ISA slot within a host Pentium PC. This AE DSP card, alsofromPhysical Acoustic Corp® provided up to an 8MHz sampling rate and incorporates 16-bit precision ADCs giving a dynamic range of more than 85dB. Moreover, an extended local memory allowed sequential recording of signals containing up to 256,000 samples. This corresponded to continuous measurement over more than 0.06 sees at a sampling rate of 4MHz, allowing continuous measurement of over three shaft rotations of a 50Hz turbine. Prior to the ADC, the card employs an 8th order Butterworth anti-aliasingfilterwill a 3dB roll-off at 1.2MHz.
23
p^^'^^^^'^^^^^^^'il^ A - A
Figure-1: The test rig
Figure-2: The Partial Rub simulator SIGNAL PROCESSING SCHEME After recording many rub signatures from the laboratory test rig and AE noise from the bearings of operational turbines units, signal processing techniques capable of improving the rub SNR ratio were considered. As significant spectral overlap and statistical similarity was observed between the rub signatures and the AE noise, conventional bandpass filtering was not an adequate filtering technique. Also, adaptive filteriiig structures proved ineffectual due to the absence of any significant spatial noise correlation between jMimary and reference sensors attached to the bearing housing of real turbines. Consequently, a matchedfilteringapproach was adopted to induce the necessary SNR gain. Conventional matched or correlation filtering involves cross-correlation of the measured response with stored replicas of the signal of interest, most often via multiplication in the frequency domain (i.e. fast correlation). In established matched filter applications such as radar and sonar, the known signal modulation is simple (e.g. an FM chirp) and matched filtering can provide an optimum gain. However, partial rub signatures are extremely complex and die performance of correlation filtering was significantly degraded when realistic levels of detail were assumed widiin the replica signature.
24
Therefore a simpler form of correlation filter was implemented in which Autoregressive (AR) model coefficients for bandlimited candidate replica rub signatures, calculated via Yule-walker equations, are used directly as the impulse response coefficients within a linear filter structure. Such a filtering technique is referred to as AR spectrally matched filtering and provided increased sensitivity to components within the input AE response that have a fine-scale shape similar to that of the stored partial rub signatures. Prior to this form of low-order correlation filter, both the stored replicas and the input response was bandlimited to a 100kHz-450kHz pass band via an zero-phase elliptical filter structure. The perceived advantage of the AR technique is its ability to store a suitably generalised rub signature template, derived from averaging the AR coefficients from numerous candidate signatures, that will robustly provide more SNR gain than merely bandpass filtering.
After filtering, the AE response is transformed into acoustic intensity via an energy integration process. In this integration process, the window size or integration period is the key issue and should be chosen so as to average out unwanted noise without diminishing rub signal peaks. However, it was evident that further to genuine partial rub transient peaks, a variety of other spurious AE bursts or time-discrete transient features can occur within the measured AE data. Therefore, it was desirable to smooth-out spurious features without significantly distorting the genuine filtered AE data. Techniques considered for such smoothing of transients within the AE response included low-order median filtering, Savitsky-Golay smoothing and wavelet threshold filter. Of these, a hard threshold wavelet technique proved to be the most powerfiil for smoothing AE transients. Figure-3 is a schematic of the combined signal processing chain. iNTPirr
AE RESPONSE
^
'^
Bandpass Filter
->
AR Matched Filter
^ w
Integration + Smoothing
^ w
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Figure-3: Signal Processing Schematic
25
RESULTS To demonstrate the performance of the signal processing chain, three partial rub signatures are added to AE noise from a low pressure bearing of a steam turbine unit operating at full load so as to be completely masked by inspection in the time domain, as shown in figure-4a. These were injected with a period of 0.02 sees to simulate once-perrev partial rubbing upon a real turbine. For this relatively high noise scenario, the SNR was calculated in terms of power as 0.5dB. However, after applying the AR matched filter, derived dkectly from a number of candidate signatures from the test rig, a visible SNR gain or recognition differential is introduced corresponding to 5.6dB, as shown in figure-4b. To examine the filter performance over a range of input SNR scenarios, further input SNRs were applied and die filter gain plotted. Figure-5a depicts the AR filter performance in terms of filter gain against the range of input SNR scenarios. As indicated, the recognition differential increases with respect to the SNR of the input, although this reaches a maximum level at -'16.2dB. It is noted that the scenario depicted in figure-4 corresponds to a relatively low gain upon this curve. Intuitively, this relationship between input SNR scenario and filter gain follows as the larger the signal component is within the input, the better the correlation with the fine-scale shape of the replica template. To compare the performance of the AR filter with that of bandpass filtering alone, different combinations of bandpass filters were applied to the AE data. Figure-5b shows one of the better results achieved for a standard FIR filter structure. As indicated, the performance is well below that of the AR matched filtering technique.
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26
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Figure-5: Recognition differential for (a) the AR matched filter (b) a bandpass FIR filter To illustrate the wavelet-based smoothing, once-per-rev partial rub signatures were again buried in turbine noise so as to be completely masked in the time domain. This low SNR scenario was then AR filtered and integrated as depicted in figure-6a. As evident by this AE response, the three rub signatures are now clearly visible. However, other transient *noise* features are also present, and these effectively increase the probability of the diagnostician effecting a false alarm. Therefore, the wavelet filter was applied to smoothout such transients. Essentially, the DWT filter involved transforming tihe time-domain response shown in figure-6a into corresponding wavelet coefficients, using the Daubechie-20 basis function. Then, only wavelet coefficients that exceeded the magnitude of the wavelet domain standard deviation were inverse transformed back into the time domain, with all coefficients below this threshold set to zero. The effect that this hard threshold DWT filtering technique had upon the AE response is shown in figure6.b. Clearly, the filter manages to remove the unwanted transient spikes completely and produce a smooth noise response. Moreover, it is important to note that this filter did not significantly reduce the magnitude of the genuine rub signatures, and therefore does not significantly diminish the probability of rub detection. In contrast, other smoothing filters were unable to smooth out the unwanted transient features without significantly reducing the amplitude of real rub signatures. uInsmoothedfllt«routput
1r 0.0 0.6 0.7
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(a) Unsmoothed iniegraled AE intensity
(b) D-20 Hard threshold smoothed response
Figure-6: Filtered response (a) before (b) after hard threshold D-20 filtering
27
CONCLUSIONS Clearly, scope exists for AE-based detection of partial shaft-seal rubbing within largescale turbine units. This paper demonstrates that, assuming that turbine noise can be approximated as additive, a relatively simple signal processing scheme can be employed to introduce an adequate SNR gain or recognition differential. In particular, the correlation filtering technique in which Autoregressive model coefficients are used to directly derive non-recursive filter coefficients proved effective. The success of this AR filtering technique implies that there is a fundamental difference between the fme-scale shape of rub signal-plus-noise and noise scenarios. Moreover, it is shown that hard threshold discrete wavelet filtering can be useful for smoothing the output AE energy response, so as to remove spurious signal features without too severe reduction in the rub signal amplitude. From this stage, addition signal processing gain may be possible for periodic once-perrev partial rub features via integration across rotation periods or appropriate timefrequency display. Clearly, the detection performance of the signal processing scheme can be quantified more formally by investigating noise and signal-plus-noise amplitude statistics and an appropriate detection criterion established (i.e. Neyman-Pearson). In parallel with rub detection at an acoustic display, a data processing scheme for automatic rub detection and more detailed rub diagnosis has been developed.
REFERENCES Board (2000), Stress wave analysis of turbine engine faults. Aerospace Conference, IEEE Proceedings (Cat. No.00TH8484), voi.6, pp79-93 Donoho (1995), Denoising by soft thresholding, IEEE transactions on Information Theory, vol 41, pp613 Haykin S (1984), An Introduction to Adaptive Filters, Macmillan Publishing Mba D Bannister R H (1999), Condition monitoring of low-speed rotating machinery using stress waves: Parti and Part 2, Proc Instn Mech Engrs, Vol 213, Part E, ppl53 Melton R (1982), Classification of NDE waveforms with Autoregressive models, Journal of Acoustic Emissions, voU, no4, pp266 Muszynska (1989), Rotor-Stationary element rub-related vibration phenomena in rotating machinery-Literature Survey, The Shock and Vibration Digest, vol21, no3, pp3-l 1 Pollard (1977), Sound Waves in Solids- Applied Physics Series, Pion Limited Sato I (1990), Rotating machinery diagnosis with acoustic emission techniques, Electrical EngngJapan, 110(2), 115-127 Tandon and Choudhury (1999), A review of vibration and acoustic measurement methods for the detection of defects in rolling element bearings, Tribology International, vol32, pp469 Turin, An Introduction to Matched filters, IRE Trans, Inform. Theory, vol6, 1960, pp311 West Venkatesan (1996), Detection and Modeling of Acoustic Emissions for Fault diagnostics, IEEE, 0-8186-7576-4/96
28
Condition Monitoring and Diagnostic Engineering Management Andrew G. Starr and Raj B.K.N. Rao (Editors) © 2001 Published by Elsevier Science Ltd.
CONDITION MONITORING OF VERY SLOWLY ROTATING MACHINERY USING AE TECHNIQUES Dr. Trevor J. Holroyd Holroyd Instruments Ltd Via Gellia Mills, Bonsall, MATLOCK, DE4 2AJ, UK tel: +44 (0)1629 822060, email:
[email protected]
ABSTRACT In industry it is often very slowly rotating machinery which is the most critical to the production process as well as being the largest and the highest value. These factors combine to increase the economic requirement for Condition Based Maintenance and hence the importance of suitable means of Condition Monitoring. However slow rotational speeds result in reduced energy loss rates from damage related processes and because of this Condition Monitoring techniques which detect energy loss tend to be more difficult if not impossible to apply. Perhaps surprisingly this is not the case for the Acoustic Emission (AE) technique which is well suited to detecting very small energy release rates. As a result AE is able to detect subtle defect related activity from machinery, even when it is rotating very slowly. In this paper a new AE based signal processing approach is introduced which can provide simple but sensitive means of detecting the presence and evolution of faults in very slowly rotating machinery. These developments have frirther led to the creation of what is believed to be the first easily retro-fitted and affordable on-line monitoring module for very slowly rotating machinery.
KEYWORDS AE, Acoustic Emission, Bearings, CM, Condition Monitoring, Rotating Machinery, Slow Speed.
INTRODUCTION It is well known that the application of traditional Vibration monitoring techniques becomes progressively more difficult to apply as the speed of rotation of a machine decreases. The reasons for this are fourfold : a) the energy release ratesfromdefects reduces as speed reduces.
29
b) the associated defect repetition frequencies become very low and difficult to detect amongst background noise. c) very long time records need to be digitised and further processed. d) slowly moving structures are often very massive and stiff However it is often the case that the most critical aspect of an industrial process operates at the slowest speed and under the highest loads. Examples are mills (eg sugar, paper and steel), rotating kilns, settlement tank scrapers etc.. The associated machinery is usually highly specialised and represents a high capital investment. Because of this such machinery is a prime candidate to benefit from Condition Monitoring. In particular this has provided a strong impetus for researchers and product developers to devise innovative signal processing methods in an attempt to apply vibration based CM [eg Ratcliffe (1990), Murphy T., Strackeljan et. al (1999)]. Whilst it is not possible to conunent on the practicality, range of applicability or effectiveness of all such methods it is fair to say that at the present time end users in industry who work in the field of Condition Monitoring with slowly rotating machinery know that there is currently no simple way for them to apply vibration techniques. Although it may be that newer and more complex vibration based techniques can be widely effective on slowly rotating machinery this has yet to be adequately demonstrated to end users and because of this we have concluded that an unsatisfied requirement exists in industry which we have sought to address. Instead of pursuing an ever more complex approach to overcoming the low signal to noise ratios of the vibration technique our long term aim has been to create an easy to install, easy to interpret and low cost approach to monitoring such slow speed machinery. This paper describes the development of an AE based instrument to achieve these objectives for everyday use in industry by shop-floor personnel.
BACKGROUND TO AE FROM SLOWLY ROTATING MACHINERY For both impact and frictional source processes the signal strength at source reduces with increasing frequency. Because of this it is usual for AE sensors to be of a resonant design so that their output is magnified by the mechanical Q of the piezoelectric detection element. Typically reported detection frequencies for such AE sensors fall somewhere within the range 50 kHz to 500 kHz. By contrast vibration measurements usually use broadband accelerometers which are typically used in their region of flat frequency response well below the accelerometer resonance. Using appropriate AE instrumentation higher speed machinery gives rise to a readily detectable continuous AE signal with transient variations superimposed upon it due to such processes as momentary rubbing, slip-stick and discrete impacts from defects in surfaces which make contact in the loaded zone. In particular the high signal to noise ratio (SNR) of defect activity for AE compared with vibration is a recurrent theme very widely reported in the literature and is the principal reason why AE monitoring can be so successfully carried out in the time domain [Holroyd (1999), Holroyd (2000)]. For the case of very slowly rotating machinery the continuous AE signal typically drops below the limit of detectability and defect presence manifests itself as isolated bursts of AE activity as shown in the example in Figure 1. For very slowly rotating machinery the 'mark to space' ratio of the presence of detectable AE activity can be extremely low making some forms of signal processing inappropriate. However since each individual burst of activity is observable directly in the time domain signal it follows that defect detection need not necessarily be hindered by such low mark to space ratios.
30
t I
20
6
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o J
time (msecs)
•
Figure 1 : AE envelope waveform from a defective slowly rotating bearing. When monitoring slowly rotating machinery it is important not to overlook the need to include a statistically significant sample of the waveform in the analysis. At very slow speeds this means very long time records. Although in principle it would be possible to digitally store very long samples of the AE waveform and post process the signals, in practice such an approach would (with current technology) be too costly and impractical for widespread use. Instead some form of on-line processing is required to autonomously reduce the information content of the signal whilst retaining that part of the information which is of principal interest. A wide variety of signal processing methods are commonly used in AE monitoring including distributions of AE parameters (eg peak amplitude in an amplitude distribution), locations plots (essentially distributions as a function of time difference of wavefront arrivals at an array of AE transducers) and trend plots of a processed AE parameter (such as AE count rate, energy etc.). For example Rogers [Rogers (1979)] positioned two AE sensors diametrically opposite each other on the inside face of a crane slew ring. The detected activity was presented in the form of an amplitude distribution and in the form of a linear location of its source position. In contrast Miettinen & Pataniitty [Miettinen & Pataniitty (1999)] used a trend of AE count rate as a means of providing long term monitoring of the AE activity from 16 wheel bearings rotating at 8 rpm and supporting a kiln. A quite different approach has been reported by Mba et. al. [Mba et. al. (1999), Mba et. al. (1999)] who has published extensive work describing the in-depth analysis of AE signals from Rotating Biological Contactor bearings rotating at speeds as slow speed as 0.6 rpm in terms of their Auto Regressive Coefficients. As a result of the lack of comparative data in the literature it is difficult to rank the relative merits of the different approaches that have been reported (and it will be seen that this paper is similarly deficient in this regard). Consequently it is difficult from an end-users perspective to know what method(s) to choose for particular applications. On the one hand it is self evident that no single signal processing approach can comprehensively describe all aspects of the AE activity from a slowly rotating bearing whilst on the other hand economics will dictate that all techniques cannot be simultaneously applied. In addition it is also apparent to the AE practitioner that the signal processing techniques described above generally involve a degree of operator dependency in setting up the signal processing as well as significant expertise in the interpretation of the output. Such techniques are good for the investigative or diagnostic stage of a monitoring exercise since an experienced operator will then be at hand. As an alternative approach our aim in the work reported in this paper was to try and be more pragmatic, accepting the limitations of a simpler signal processing procedure, yet providing a more direct and 31
readily understandable output. In this way we hoped to develop an affordable front-line condition monitoring tool for slowly rotating machinery which removed the need for an experienced operator but nevertheless was able to clearly identify when a machine had problems and enable a simple trend indicative of its deterioration to be generated. A NEW APPROACH Back in 1997 at Holroyd Instruments we considered the nature of the AE signals generated by slowly rotating machinery and developed new signal processing algorithms which we believed would be of relevance to monitoring very slowly rotating machinery. Although specific details of these algorithms are not made available for commercial reasons part of the external functionality of one of the parameters, 'Extent®', can be described as being to characterise the detectable AE activity in terms of the percentage of the rotational cycle where activity of concern is detected. In particular the software algorithm for Extent® was intentionally developed so that it does not require a once per rev signal from the machine since this facilitates easier application and retrofitting to existing machinery installations. The only information which needs to be inputted into this algorithm is the approximate period of rotation which is essential in order to ensure that a statistically significant length of signal is processed during the measurement.
APPLICATION EXAMPLES Initially the software algorithm for Extent® was incorporated within special versions of the MHCMemo portable CM instrument in order to allow third parties to evaluate its effectiveness on their slowly rotating industrial machinery. Some of the results of this 'beta-trialling' by third parties are described below: Turntable bearings This application is based in a foundry where there are a series of turntables which rotate at 6 rpm. Of the many readings taken on a large nimiber of bearings it was found that measurements had been made at various stages in advance of 10 catastrophic bearing failures.
Readings shown were taken over several months on 10 bearings.
s I tlJ
-^^50-
^250
-I'OO
Days to Failure
Figure 2 : Sensitivity of Extent® ' to proximity of failure in turntable bearings. 32
Figure 1 shows a composite plot for all the Extent® readings leading up to the failure of these 10 bearings. It is clear from this plot that Extent® provides a simple instant means of recognising the difference between a good and a defective bearing and is trendable up to the point of failure. Figure 2 also suggests that advanced warning of around 100 days might be expected for this particular application. Rotating Kiln support wheels This application is also based in heavy industry and concerns measurements made at the 8 wheel bearing positions of the 4 wheels supporting a rotating kiln. These wheels rotate at 7.5 rpm. Figure 3 shows three sets of readings for the Extent® value for measurements at each of the bearing positions. From the first two sets of readings, which were taken on the same day before and after greasing, it is clear that the reading at bearing #7 is noticeably higher. Simultaneous vibration diagnostics suggested (correctly as it turned out) there was no problem with this bearing and so it was decided to continue running. However 10 weeks after these measurements the wheel supported by bearings #7 and #8 catastrophically collapsed as a result of a fatigue crack grovdng in the axle shaft adjacent to bearing #7. The third set of readings were taken after the kiln assembly was rebuilt with a new axle. The previously high E value measured at bearing #7 has dramatically reduced to a more normal value. The reading at bearing #8 also shows a reduction.
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. • • • • | -
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•
!:•• 1 / . .
Mi:-, .'
'V-
m
X
m
0
dHHIII^K...,,._._ji
Figure 3 : Sensitivity of' Extent®' to shaft cracking in a wheel axle. In this application it is likely that the AE monitoring was picking up the crack closure and crack opening noise caused by rubbing of the interlocking fatigue crack faces. It isfiillyunderstandable why such activity would not be detected by vibration monitoring and illustrates well the benefits of signal detection methods that are not reliant upon pre-calculated defect repetition frequencies. Other applications Over the 4 year period in which this new slow mode of AE monitoring has been beta-trialled a number of other successes have been reported including sprocket bearings rotating at 2 rpm (detection of bearing damage and water ingress into the grease) and support bearings on cylinder dryers rotating at
33
approximately 10 rpm (a worn bearing was immediately picked out from a one-off set of measurements on 10 bearings during a sales demonstration). To date our experience is that bearings running normally and in good condition typically have Extent® values of less than 5. With this knowledge we have had good success in carrying out one-off measurements (ie sales demonstrations) and fmding rogue bearings on a wide range of very slowly rotating machines. In the light of our experiences and those of our customers who have been evaluating it, we have finally developed our first commercial product incorporating slow mode monitoring.
IMPLEMENTATION AS A STANDARD SYSTEM Although the demonstration trials described above were carried out with the analysis software implanted in a portable instrument it is clear that monitoring very slowly rotating bearings can be a time consuming and tedious task for the operator if a portable instrument is used. For this reason it was decided that the first product incorporating the new slow mode should be an on-line monitoring system which has the product name MHC-SloPoinf^^. This is a compact DIN rail mounting module which forms a complete monitoring system requiring only an external DC power supply and an external AE sensor. It has built in dual intelligent alarms and data logging. The data logging feature enables trends to be instantly observed whenever an alarm is triggered. In keeping v^th the concept of an easy to retrofit instrument the unit is able to be simply wired using screw terminals and takes a similar form to industry standard process monitoring and control modules.
DISCUSSION Clearly the currently reported signal processing approach deals with only one aspect of the AE signal and can be supplemented by or supplemental to other signal processing methods (such as those Referenced in this paper). This is because the work reported here has not been aimed at producing the most sophisticated AE instrument possible for monitoring slowly rotating bearings, nor producing a diagnostic tool for use by Condition Monitoring specialists. Instead the path has been deliberately chosen to investigate simpler to use and easier to interpret signal processing methods than those which have been previously adopted when applying either AE or Vibration techniques to slowly rotating machinery. An obvious concern when using the AE technique to monitor slowly rotating bearings is that background noise will mask the detection of defects. So far our experience is that defects are able to be readily detected even in heavy industry under noisy (and dirty) site conditions. Although it would be wrong to suggest that the monitoring system described here has total immunity to such noise we are confident that it is applicable in the majority of industrial uses for which it will be required. We further note that the measurements described were all spot readings (ie single measurements) and it is self evident that the use of more averaging over longer time periods would give a further improvement in signal to noise ratio. Different signal characterisations are likely to be of greater or lesser importance for different fault modes. In the examples to date the ' Extent® ' value has been the most widely relevant AE signal characterisation we have evaluated. However it is noted that on its own this parameter may well be inappropriate in the case of, say, a bearing race with an isolated crack in it. It is for this reason that a selection of different AE signal characteristics are required and a strategy should be adopted whereby alarms are set on each of them. The system that has been created, the MHC-SloPoinf^^, performs four such signal characterisations. Importantly its design pays great attention to consistency and long term 34
stability of these signal characterisations since this is very relevant to measurement integrity over the long periods it often takes for very slowly rotating machinery to degrade.
CONCLUSIONS 1
AE techniques can be successfully applied to monitoring the condition of very slowly rotating machinery.
2
A wide range of signal processing methods can be employed to detect the presence and amount of damage although cost and complexity limit their application.
3
A new signal processing method which is both simple to use and easy to interpret has been devised and tested over a 4 year period on numerous machines at a large number of test sites,
4
Indications are that such simple methods can be both effective and widely applicable for detecting the presence and amount of damage.
References Holroyd T. (1999). Acoustic Emission - Looking for a big change in a small signal, Proceedings of Integrating Dynamics, CM & Control for the 21st Century ISBN 90 5809 1120, published by Balkema, 499-502. Holroyd T. (2000), The Acoustic Emission & Ultrasonic Monitoring Handbook Coxmoor Publishing Company, ISBN 1 90189 207 7, 2000. Mba D., Bannister R. H., & Findlay G. E. (1999). Condition Monitoring of slow speed rotating machinery using stress waves - part 1. IMechE Jnl of Process Mechanical Engineering (part E), 213, 153-170. Mba D., Bannister R. H., & Findlay G. E. (1999). Condition Monitoring of slow speed rotating machinery using stress waves - part 2. IMechE Jnl. of Process Mechanical Engineering (part E), 213, 171-185. Miettinen J. & Pataniitty P. (1999). Acoustic Emission in Monitoring Extremely Slowly Rotating Rolling Bearings., Proceedings ofCOMADEM '99, ISBN 1 901892 13 1, published by Coxmoor, 289297. Murphy T. (year unknown), The development of a data collector for low-speed machinery. Reference unknown. Ratcliffe G. A. (1990). Condition Monitoring of rolling element bearings using the enveloping technique. Proceedings of IMechE seminar 1990-1 Machine Condition Monitoring, ISBN 0 85298 712 9, 55-65. Rogers L, M. (1979). The appHcation of vibration signature analysis and acoustic emission source location to on-line condition monitoring of anti-friction bearings. Trihology International, April, 5159.
35
Strackeljan J., Lahdelina S., Vuoto V. & Behr D.(1999). Vibration monitoring of slowly rotating bearings using higher derivatives and a fuzzy classifier. Proceedings of Condition Monitoring '99, ISBN 901892115, published by Coxmoor, 375-386. Acknowledgements Extent® is a Registered Trademark of Holroyd Instruments Limited. Patents pending on signal processing methods and means described in this paper.
36
Condition Monitoring and Diagnostic Engineering Management Andrew G. Starr and Raj B.K.N. Rao (Editors) © 2001 Elsevier Science Ltd. Allrightsreserved.
MONITORING LOW-SPEED ROLLING ELEMENT BEARINGS USING ACOUSTIC EMISSIONS N. Jamaludin\ and D. Mba^ 'Dept of Mechanical and Material Engineering, Faculty of Engineering, UKM, 43600 Bangi, Selangor, Malaysia. ^School of Engineering, Cranfield University, Cranfield, Beds. MK43 OAL.
ABSTRACT The most established technique for monitoring the integrity of rolling element bearings is vibration analysis. However, this success has not been mirrored at low rotational speeds of less than 20 rpm. At such speeds the energy generated from bearing defects might not show as an obvious change in signature and thus become undetectable using conventional vibration measuring equipment. This paper presents an investigation into the applicability of acoustic emission for detecting early stages of bearing damage at a rotational speed of 1.12 rpm. Furthermore, it reviews work undertaken in monitoring bearings rotating at speeds below 20 rpm. Investigations were centered on a bearing test-rig onto which localised surface defects were seeded by spark erosion. Analysis of acoustic emission signatures associated with bearing outer race, inner race and roller defects, showed that the uniqueness of the defect signature patterns could be utilised to provide early fault diagnosis. KEYWORDS Acoustic emissions, auto-regressive coefficients, K-means clustering, slow-speed rotating machinery, rolling element bearings, spark erosion. INTRODUCTION Monitoring bearing degradation by vibration analysis is an established technique and the various methods of analysis have been widely published (McFadden [1990], Ractliffe [1990], Harker et al [1989], Bannister [1985], Setford [1992], Berry [1992]). However, at low-rotation speeds there are numerous difficulties that have been detailed (Canada et al
37
[1995], Murphy [1992], Robinson et al [1996]). The main problems include selecting the optimum measurement parameter, instrument limitations and sensor requirements. REVIEW OF MONITORING TECHNIQUES APPLIED TO BEARINGS ROTATING AT LESS THAN 20RPM. Canada et. al. (1995) developed a Slow Speed Technology (SST) system for measuring vibrations on low-speed rotating machinery. It was based on separating the high frequency noise of the machine from the low frequency signatures of interest, furthermore, claims that this method could be applied at speeds as low as 10 rpm were made. Robinson et. al (1996) built on the SST method described earlier (Canada et. al. [1996]). More relevant to this paper is the research on acoustic emission (AE) and its applicability for monitoring low-speed bearings. Sato (1990) investigated the use of AE to monitor low-speed rotating bearing damage by simulating metal wipe in journal bearings at 5.5 rpm. It was observed that acoustic bursts were generated as a result of slight metallic contact and the amplitude of the waveform became larger with increasing metal wear. McFadden et. al. (1983) used AE sensors to monitor a fault that was simulated by a fme scratch on the inner raceway of a bearing rotating at 10 rpm. The AE transducer appeared to respond to minute strains of the bearing housing caused by the concentrated loading of each ball passing the defect. For this investigation, acoustic emissions are defined as the transient waves generated by the interaction of two surfaces that are in relative movement, in this instance, rubbing between metal surfaces in contact (Green [1955]). This makes it an ideal tool for application to condition monitoring of low-speed rotating rolling element bearings. TEST-RIG AND MEASURING EQUIPMENT A bearing test-rig was designed onto which surface defects were seeded by spark erosion. The rig consisted of a motor/gearbox system, two support slave bearings, a test bearing and a hydraulic cylinder ram, see figure 1. The motor provided a rotational speed of 1.12 rpm. The test bearing was a split Cooper spherical roller, type 01B65 EX, with a bore diameter of 65mm and 12 rollers in total. This type of bearing was chosen due to its ability to be disassembled without removing the slave bearings, thereby allowing easy assess to the test bearing. The support bearings were of a much larger size than the test bearing. A radial load of 55KN was applied to the top of the test bearing via a hydraulic cylinder ram supported by a 'H' frame. A high viscosity grease, lOOOCst at 40°C, was used in the test bearing. The process of data acquisition involved fixing a receiving transducer onto the test bearing. A schematic diagram illustrating the data acquisition system used throughout all experimental tests is shown in figure 2. All instrumentation employed was supplied by PAC (Physical Acoustics Corporation). A commercially available wide-band piezoelectric type sensor (type WD) with an operating frequency range between 100 kHz and 1000 kHz was used. A dual-channel 8-bit analogue-to-digital converter (ADC) Rapid Systems R2000 was used for data acquisition. The electronic noise level on the ADC
38
system, with 60dB amplification, had a peak voltage of 30 mV. The sampling rate employed for all tests was 5MHz. 510mm
Hydraulic ram
bearing Motor/gear box unit
280mm 540mm Figure 1
Schematic diagram of the bearing test-rig
Acoustic emission sensor, lOOkHztolMHz
^
W
Pre-amplifier, 60 dB gain
r COMPUTER Post processing
Figure 2
^ ^
Analogue-to-digital converte r (ADC)
Post-amnlifier and
povver source for pre- amplifier
Schematic diagram of data acquisition system
EXPERIMENTAL PROCEDURE AND RESULTS Defects seeded by spark erosion on the outer and inner races, and on a roller element, (22, 23, 24, and 25) resulted in surface damage that resembled pitting. The defect size on each component was approximately 3,0mm wide and an effective depth of 75|im. Operational baseline measurements To obtain operational background noise, the test-rig was run without any seeded fault. Continuous data was recorded with a pre-trigger level set above 30mV, A typical signature with a corresponding frequency spectrum can be seen in figure 3. Observations and analysis showed that the maximum amplitude for this condition did not exceed
39
170mV. Therefore, on tests with seeded defects the pre-trigger level was set slightly above 170mV in order that AE's associated with defects could be measured. Time aignaiure of oilier race defect
Time signature of a good bearing
0.1
0.2
0.3
0/4
0.5
0.0
0.7
0.S
0.9
ai
Frequency, Mega-Hertz
Figure 3
02
0.3 0.4 as ce 0.7 Fraquancy, Mega-Hertz
0.8
0L9
Typical time signature with corresponding frequency spectrum for a good bearing and outer race defect
Seededfault simulation (Spark Erosion) Faults seeded with this technique resulted in acoustic emission activity above the operation baseline trigger level (170mV). The signatures were correlated to the fault condition by monitoring the position of bearing components at the time of acquisition, i.e., signatures were generated only when the seeded fault was within the loaded region of the bearing. Typical AE signatures for the various defect conditions, with corresponding frequency spectra, are displayed in figures 3 to 4. AE signatures for each simulated fault condition were recorded for several revolutions of the shaft until 30 data sets were obtained. Time signature of roiier defect
Time eignature of inner race defect
ai
0.2
0.3
0.4
OL$
O.«
0.7
0.8
as
ai
1
Frequency, Mega-Hertz
Figure 4
0.2 0.3 a4 as 0.6 0.7 Frequency, Mega-Hertz
as
0.0
1
Typical time signature with corresponding frequency spectrum, inner race and roller defect
40
CLASSIFICATION AND SIGNAL PROCESSING Typical acoustic emission features, amplitude and energy, were employed to identify and classify AE signatures associated with seeded defects and background noise. The maximum amplitude and energy of each AE signature were extracted and plotted against the associated AE signature, see figures 5 and 6. To aid interpretation, a polynomial fit based on a 5^ order model was applied. Peak Amplitude: Defects and good bearing • • 4+
5
10
IS
20
Outer race defsct Inner race defect RoTler defect Good
25
30
Individual AE signature Figure 5
Peak amplitude values for all defects
This classification process was applied to experimental data where all the influencing factors are controlled. It was therefore though prudent to establish a relatively more robust technique for classifying and grouping the various fault conditions. The philosophy behind this was that on-site, the interpretation of signatures from real operational bearings might not be distinguishable by extracting parameters such as amplitude and energy. This is due to the very random nature of noise that could be generated on bearings operating under varying loads and environments. Since the defects simulated in this paper originate from different parts of the bearing, the associated AE signature will have "^characteristic features that are unique to their particular transmission path. As it has been shown (Mba et al [1999]) that AR coefficients can represent the shape of a signature, classification based on an AR model was undertaken.
41
Energy: Defects and good bearing
0
5
10
15
20
Individual AE signature
Figure 6
Energy values for all defects
The computation of AR coefficients is derived from linear prediction and a review of parametric models such as AR has been detailed (Kay et al [1981], Makhoul [1975], Haykin [1984]). Application of the Forward Prediction Error and Akaike Information Criteria (26,27) aided in selection of the optimal AR model order; a 15^'' order model was employed. The process of classifying these coefficients employed a cluster technique known as K-means (Everitt [1974]). This is a non-hierarchical technique that measures the Euclidean distances between the centroid value of the AR coefficients associated with each signature. The resuhs were displayed on dendrograms (Everitt [1974]). AutoRegressive coefficients associated with AE signatures from inner, outer and roller defects were compared with those from operational background noise by clustering, see figures 7 to 9. Furthermore, signatures from all fault conditions, and background noise, were mixed and clustered, see figures 10 to 12. Figures 11 and 12 are close-up views of figure 10.
42
Classification using the centroid value of AR coefBcients, ORD and noise Ml
o - Outer race defect
SIS
n - noise 022
Cluster 2 Outer race defect signatures
013
s? oU 03
i
012 n2»
Ouster 1, noise
S!. Hi •4
Si ns n13
0
0£
0.4
0.6
0.8
1
Euclidean distance between centroid vaiu^ of AR coefficients associated with individual AE's "At
Figure 8
Classification of AE's associated with background noise and outer race defects. Classification using the centroid value of AR coefficients, RD and noise
Euclidean distance between centroid values of AR coefficients
Figure 9
with individual AE's
Classification of AE's associated with background noise and roller defects.
43
Classifkation using the ceatroid value of AR coefficients, All defects and noise
0
0.1
0.2
0.3
0.4
0.5
0.«
0.7
0.8
0.9
Euclidean distance between centroid values of AR coefficients associated with individual AE's
Figure 10
Classification of AE's associated with background noise and all fault conditions, see figures 11 and 12 for close-up view of clusters 1 and 2 respectively.
DISCUSSIONS Acoustic emissions emitted from defect simulations were attributed to the relative movements between mating components. Signatures associated with operational baseline data for the test-rig showed the maximum amplitude to be in the order of 170mV. By setting a trigger level above this value, and undertaking individual fault simulations, only signatures relating to a specific fault condition were captured. During simulation of the outer race defect (ORD), AE activity occurred approximately every ten seconds. This corresponded to the calculated outer-race passage frequency of 5.65 rpm. Furthermore, AE signatures associated with the inner-race defect (IRD) and roller defect (RD) were only detected for a certain period of time, i.e., did not occur continuously throughout one complete revolution of the cage. This phenomenon was due to the fact that AE was only generated when the IRD and RD were in the loading zone. Comparisons of the amplitude and energy levels associated with individual AE's showed trends that were distinguishable. The lowest amplitude and energy values were associated with operational noise. Furthermore, a gradual increase in amplitude and energy levels was evident for inner-race, roller and outer-race defects respectively. A polynomial fit was employed to highlight this trend.
44
Classification using the centroid value of AR coefficients, All defects and noise
o - Outer race defect i • Inner race defect r - Roller defect n - noise
Cluster 1
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
Euclidean distance lictween centroid values of AR coefficients associated with individual AE's
Figure 11
Close-up view of cluster 1 in figure 10
The increasing values of amplitude and energy were attributed to the closeness of the source of defect to the sensor. For instance, as the sensor was placed on the bearing housing, it was expected that the greatest amplitude/energy of AE should come from the outer race (under constant load conditions). Signature from the inner race defect had more interfaces to overcome before reaching the receiving sensor, thus it was not surprising that attenuation has played a vital role in reducing its strength. Acoustic emission values of amplitude and energy emitted by the roller defect were scattered between corresponding levels of the inner and outer race and was attributed to the position of roller defect at the time of data acquisition, for instance, a higher level of amplitude/energy was expected when the roller defect made contact/rubbed with the outer race. This mechanism explains the scatter of roller defect amplitude and energy values between the inner and outer race values. Whilst the results already presented clearly show that extracting amplitude/energy values from AE could help indicate bearing deterioration for a particular bearing type, a more robust system of classification was investigated, as explained earlier. Clustering of AR coefficients associated with background noise did not result in any clear grouping, indicative of the random nature of noise. By comparing classifications of background noise against individual fault simulations, it was evident that the AR cluster technique showed two distinct groupings.
45
Classification using the centroid value of AR coefficients. All defects and noise o - Outer race defect i - Inner race defect r - Roller defect n - noise
Inner race and roller group
Cluster 2
Noise group
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
Euclidean distance between centroid values of A R coefHcients associated with individual AE's
Figure 12
Close-up view of cluster 2 in figure 10
The classification of all defect simulation signatures with background noise resulted in well-defined clusters. These clusters showed background noise to be quite distinct (see figure 11), however, there was some overlap between inner and outer race and roller defect signatures. Within cluster 2, figure 12, two clear groups were distinguishable; the first comprised soley noise signatures and the second was predominately a mixture of inner race and roller defect signatures. Cluster 1 of figure 11 was predominantly a group of outer race and roller defect signatures. This mixture of signatures can be attributed to the position of the roller defect at the time of data acquisition. Thus, a roller defect signature generated due to rubbing against the outer race will have characteristics that mirrored closely to the outer race defect signature. With the exemption of a few signatures, most roller defect signatures were grouped with either inner or outer race defect signatures. It is interesting to note that this phenomenon was also observed with amplitude and energy classification. Given the scenario of an operational bearing with a fault, it is probable that AE signatures measured could contain background noise that was of similar amplitude/energy levels as the fault signature, consequently, we would be unable to differentiate the signatures by observing amplitude and energy values. However, because operational background noise is random in nature, the shape of its signatures will also be random. However, the shape of AE signatures associated with a fault would generally be of similar pattern. Thus, if
46
clustering of the AR coefficients associated with all AE signatures yielded distinct group clusters, this would be evident of early signs of deterioration. If no distinct groups were evident, the bearing would be passed as defect free, as clustering of AR coefficients associated with random shaped signatures (background noise) will result in no clearly defined groups. CONCLUSION Investigations into the application of the acoustic emission technique to condition monitoring of low-speed rotating element bearings have proven successful. Results of the seeded mechanical faults on the test-rig showed that acoustic emissions were generated from rubbing of mating components. Classification of defect signatures with autoregressive (AR) coefficients has been shown to aid in determining the mechanical integrity of the bearing. Furthermore, typical AE parameters such as amplitude and energy can provide valuable information on the condition of a particular low-speed rotating bearing. REFERENCES Bannister, R. H. (1985). A review of rolling element bearing monitoring techniques. Instn Mech Engrs conference on condition monitoring of machinery and plant, pp 11-24. Berry, J. E., (1992). Required vibration analysis techniques and instrumentation on low speed machines ( particularly 30 to 300 RPM machinery ), Technical Associates of Charlotte Inc., Advanced Vibration Diagnostic and Reduction Techniques. Canada, R.G., and Robinson, J.C., (1995). Vibration measurements on slow speed machinery. Predictive Maintenance Technology National Conference (P/PM Technology), Vol. 8, no. 6. Indianapolis, Indiana, pp 33-37. Everitt, B. (1974). Cluster analysis. Published on behalf of the Social Science Research Council by Heinemann Educational Books New York: Halsted Press. ISBN 0 435 822977. Green, A. P. (1955). Friction between unlubricated metals: a theoretical analysis of the junction model. In Proc. Of the Royal Society of London, A, Vol. 228. pp 191-204. Haykin, S. 1984 Introduction to adaptive filters. Macmillan Publishing Company, New York. ISBN 0 - 02 - 949460 - 5. Harker, R. G. and Sandy, J. L. (1989). Rolling element bearing monitoring and diagnostics techniques. Transactions of the ASME, Journal of Engineering for Gas Turbines and Power. Vol. 111. pp 251-256 Kay, S.M, and Marple, S.L Jr. (1981). Spectrum analysis - A modern perspective. Proceedings of the IEEE, Vol. 69, No. 11. pp 1380-1419 Makhoul, J. 1975 Linear prediction: A tutorial review. In Proc. Of the IEEE, Vol. 63, No. 4. pp 561-580. Mathew, J. and Alfredsoa, R.J. (1984). The condition monitoring of rolling element bearings using vibration analysis. Journal of Vibration, Acoustic, Stress and Reliability Design, Transactions of ASME, Vol. 106. pp 447-453. Mba, D., Bannister, R.H., and Findlay, G.E. (1999). Condition monitoring of low-speed rotating machinery using stress waves: Part I. Proceedings of the Instn Mech Engrs, Vol. 213, Part E.pp 153-170,
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Mba, D., Bannister, R.H., and Findlay, G.E. (1999). Condition monitoring of low-speed rotating machinery using stress waves: Part H. Proceedings of the Instn Mech Engrs, Vol. 213, Part E.pp 171-185, McFadden, P. D. (1990). Condition monitoring of rolling element bearings by vibration analysis. Proceedings of the Instn Mech Engrs seminar on machine condition monitoring, pp 49-53. McFadden, P. D., and Smith, J. D. (1983). Acoustic emission tranducers for die vibration monitoring of bearings at low speeds. Report no, CUED/OMech/TR29, Muq)hy, T.J., (1992). The development of a data collector for low-speed machinery. 4th international Conference on Profitable Condition Monitoring, hK Group Ltd., 8-10 Dec, Stratford-upon-Avon, UK. pp 251-258. Ractliffe, G. A. (1990). Condition monitoring of rolling element bearings using enveloping technique. Proceedings of the Instn Mech Engrs seminar on machine condition monitoring, pp 55-65. Robinson, J.C, Canada, R.G., and Piety, R.G. (1996). Vibration Monitoring on Slow speed Machinery: New Methodologies covering Machinery from 0.5 to 60Qq)m. Proc. 5th International Conference on Profitable Condition Monitoring - Fluids and Machinery Performance Monitoring, pp 169-182, brf Group Ltd., Publication 22, Harrogate, UK. Sato, L (1990). Rotating machinery diagnosis with acoustic emission techniques. Electrical engineering in Japan, Vol. 110, No. 2. pp 115-127. Setford, G. A. W. (1992). Bearings-condition monitoring, condition measurement and condition control. 4th international Conference on Profitable Condition Monitoring, bJf Group Ltd., pp 231-240,8-10 Dec., Stratford-upon-Avon, UK.
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Condition Monitoring and Diagnostic Engineering Management Andrew G. Starr and Raj B.K.N. Rao (Editors) © 2001 Elsevier Science Ltd. Allrightsreserved.
CONDITION MONITORING OF ROTODYNAMIC MACHINERY USING ACOUSTIC EMISSION AND FUZZY C-MEAN CLUSTERING TECHNIQUE T Kaewkongka, Y H Joe Au, R T Rakowski and B E Jones The Brunei Centre for Manufacturing Metrology, Brunei University, Uxbridge, Middlesex UB8 3PH, UK Phone: (+44) 01895 274000 ext. 2608, E-mail:
[email protected]
ABSTRACT This paper describes a method of bearing condition monitoring using the fuzzy c-mean clustering technique applied to the pre-processed acoustic emission parameters. Acoustic emission (AE) events were detected by a piezoelectric transducer mounted on the bearing housing of a test rig. AE parameters were extracted from the events and used as characteristic features to represent a machine operating condition. In this experiment, four machine conditions that may happen to a rotodynamic machine were investigated and they corresponded to (a) a balanced shaft, (b) an unbalanced shaft, (c) a shaft with misaligned supportive bearings and (d) a shaft running in a defective bearing. During training, the fuzzy c-mean clustering technique was applied to establish the centres of the four clusters. For testing, a minimum distance classifier was used to classify an AE event from an unknown condition into one of the four conditions. The recognition rate was 97.22 percent. KEYWORDS Acoustic emission, condition monitoring, ftizzy c-mean clustering technique, minimum distance classifier, rotodynamic machinery. INTRODUCTION Rolling element bearings are perhaps the most ubiquitous machine elements in engineering as they can be found in almost all-rotating machines. With ever growing competition in industry, tiiese bearings are considered critical components because any malfunction, if not detected in time, leads to catastrophic failure and hence losses due to machine downtime and other forms of damage. It is evident that a reliable condition monitoring system is highly desirable so that it will reduce the cost of these consequences and enhance the overall equiprnent effectiveness. Basically there are two approaches to bearing maintenance: (1) statistical bearing life estimation and (2) bearing condition monitoring and diagnostics [1]. Statistical bearing life estimation predicts the
49
fatigue life of a bearing. However, its application has limitations, since unusual operating conditions often occur and can severely decrease a bearing's life. In this situation, estimating a bearing's life based on standard operating conditions is unrealistic. The other approach - bearing condition and diagnostics - can be more reliable because it gives up-to-date information about the condition of a bearing. The more popular condition monitoring techniques for bearings are based on vibration and acoustic emission analyses. Previous research [2,3,4] has demonstrated that AE monitoring is superior to vibration monitoring in that the former can detect subsurface crack growth whereas the latter can at best detect a defect only when it emerges on the surface of a structure. Acoustic emission (AE) is a natural phenomenon of sound generation in a material under stress. If the material is subjected to stress, a sudden release of strain energy takes place in the form of elastic wave. Each release of energy results in an AE event which often lasts no longer than a millisecond. A rotation bearing can produce AE events each time a surface defect comes into contact with other elements. These AE events are high-fi-equency transients with frequency components typically in the range from 100 kHz to 1 MHz. An AE event is characterized using parameters such as ring-down count, rise time, event duration, energy and peak amplitude. A threshold is used in order to ehminate 'noise' and only events that rise above the threshold are counted. Evidently, the threshold level affects the value of some of these parameters. A typical example is the event duration. By definition, it is the time that the envelope of an AE event is above the threshold. When the threshold level is high, the time will be shorter. The peak amplitude of an AE event is the maximum excursion of the corresponding voltage signal from the zero level. The energy of an AE event is the energy contained in the corresponding voltage signal and, strictly speaking, is not the true energy of the event itself. Energy is calculated using the formula T
Energy o<:^y^{t)dt
(1)
0
The objectives of the reported work are: 1. To represent the AE events from the four different machine operating conditions (detailed below) in terms of their event duration, peak amplitude and energy in a three-dimensional space; 2. To estabMsh the centres of the clusters for the four conditions in this three-dimensional space using the fuzzy c-mean clustering technique; and 3. To classify an AE event from an unknown condition by computing the minimum Euclidean distance of this event from the respective centres.
APPARATUS AND EXPERIMENTS Experiments were conducted on a rotodynamic test rig consisting of a rotating shaft driven by a DC motor at 20 rev/sec. The two bearings were a FAG 20205K.T.C3 which is a self-aligning single-row taper-bore roller bearing and a FAG 6304 ball bearing. They were mounted in bearing housings which in turn were attached to a base plate. The test rig provides facilities to produce the four machine operating conditions characterised by: a) the rotating shaft dynamically balanced (referred to as 'balanced shaft'), 50
b) the rotating shaft dynamically unbalanced in one plane to the extent of 65x10'^ kg.m at mid-span of the shaft (referred to as 'unbalanced shaft'), c) the shaft with misalignment achieved ft"om moving one bearing laterally by 1 mm relative to the other (referred to as * misaligned shaft') and d) the roller bearing seeded with a defect on the outer raceway of 1mm diameter produced with an electric discharge pen (referred to as 'defective bearing'). The methodology for machine condition monitoring and recognition is as shown schematically in Figure 1.
m
Ai M i i ^
•
Data preprocessing
Fuzzy - c mean clustering 1 technique
AET5500 1 ^r Minimum distance classifier
reampHfie^A AE sensor U
Figure 1: The methodology for machine condition monitoring and recognition.
Acoustic emission instrumentation Acoustic emission was measured with a microprocessor-based system AET5500 from Acoustic Emission Technology Corp. (AET), USA. A wide band transducer (of model WD, PAC) was attached to the side of the roller bearing housing via a silicone-gel couplant. The AE signalfi-omthe transducer was amplified to 60 dB and bandpass filtered - lOOkHz to 1 MHz - with a PAC preamplifier before entering the AET5500 for AE parameter extraction. The threshold level on the AET5500 was chosen to be IV (floating). With a floating threshold, it can adjust itself automatically such that background noise is excluded. The peak amplitude of the AE event was expressed in dB with 0 dB corresponding to 1 mV at the preamplifier output.
Pre-processing of data For each condition, twelve recordings each of about 30-second duration were made and they captured the AE parameters of event duration, peak amplitude and energy. These recordings were then divided into two sets of 3 and 9 each. The first set served as the reference generated from the training exercise whereas the second set provided the test samples for vahdating the classifier obtained from the training process. Both the training sets and the testing sets were processed as follows: 1. Sort the AE events in each set in the descending order of the event duration. 51
2. Discard the first ten AE events in the sorted list as they may contain outliers which, if included, would distort the characteristics the AE events in a sub-set 3. Select the next five AE events from the remaining list for the subsequent clustering analysis. Fuzzy c-mean clustering Fuzzy c-mean is an iterative technique for data clustering. The user decides on the number of clusters that a data set is to be separated into, initialises a proximity matrix and defines an error threshold for the stop condition of the iteration [5]. The five AE events partitioned fi-om the sorted list can be displayed as points on a three-dimensional graph using the event duration, peak anq)litude and energy as the three orthogonal axes. As there are four different conditions, it is expected that there will be four clusters taking up different regions in the three-dimensional space. The proximity matrix contains the membership values of an individual AE event that it does or does not belong to a particular machine condition. The initial values assigned to this matrix is arbitrary and binary logic values are generally used. Thus full membership is represented by 1 whereas nonmembership by 0. As there are four different machine conditions and five AE events, the proximity matrix is a 4-by-20 matrix. The first iteration generates the estimated locations of the cluster centres and a refined proximity matrix in which the membership values become fiizzified, that is they now have values between 0 and 1. With each subsequent iteration, the estimate for the cluster centre locations will be more and more accurate and the proximity matrix will be updated. The iteration will stop when the change in the norm of the proximity matrix from its previous iteration becomes less than the designated error threshold. The cluster centres returned from the last iteration are taken to be the *best' estimates. Specifically, the fuzzy - c mean algorithm consists of the following step [5]: 1. Fix the number of c-cluster centres and a threshold e for the stop condition in step 4. Initialise the proximity matrix \]^^\ 2. Update the c-cluster centres {v,-'^^} according to the current proximity matrix, using ^JJk=iMik
'^kj
(2)
where //^^is the membership of the k^ data point in the ith class, and m' is the weighting parameter (the arbitrary value of m' = 2 was used). 3. Update the proximity matrix for the r* iteration, U^^^ according to previous cluster centres, using (3)
Mjr= where d^,^ = [X7=i i^kj -^ij ) ^ \
is the distance measured.
4. If the objective function, as defined below, is less than the threshold e, then stop; else, go to step 2. l^/C+i) -U^'^\ < £ Otherwise set r = r+1 52
(4)
The results that emerge from the application of the fuzzy c-mean clustering technique are shown as three-dimensional graphs in Figures 2 and 3. Figure 2 shows the AE events, 20 in total, for the four different machine conditions from the training sets. Figure 3 shows the same AE events but this time with clusters identified and their centres computed.
'^ ^--A ^^^^ \xnA "ISOO •_V—^'^''^^^O ^200 ^^00 600 ^^ ^^^ Event duration
100
Figure 2: AE events for the four different machine conditions from the training sets.
Clustering of balanced, unbalanced, misaligned and defective bearing 95.
V- ----r
-----
c X i 4^
r-
o
-if
Q. ow\l
-0_4
Balanced shaft Misaligned shaft Unbalanced shaft Defective bearing
\
. -\
1600 400
600
800 Event duration
Figure 3: AE events with clusters identified and their centres computed.
53
Minimum distance classification A minimum distance classifier [6] was used to determine the machine condition to which a particular AE event belongs. It works by computing the Euclidean distances of the AE event (expressed as a point in the three-dimensional space of event duration, peak amplitude and energy) from the centres of the clusters for the four machine conditions. The AE event is considered to belong to the cluster whose centre is closest. In other words, the distance is defined as an index of similarity so that the minimum distance is equivalent to the maximum similcirity. Thus if the four centres have co-ordinates (Xii, Xi2, Xis) where i = 1, 2, 3, 4, and the AE event (yl,y2,y3), the minimum Euclidean distance is then D = minf VU-i -y^f+
(^.2 - ^'2)' + (^«3 " J2)' ]
(5)
RESULTS AND DISCUSSION Classification results of 36 AE events from the test samples of all four types of machine conditions are given in Table 1. It shows the output values from the minimum distance classifier using Eqn. 5. The events were classified correctly 35 times out of 36, a recognition rate of 97.22 percent. The only error occurs when the unbalanced shaft condition was misclassified as that due to a defective bearing (55.139 in row 4, column 2). It can also be noted that the normal machine condition of 'balanced shaft' is very distinctive from the other three abnormal conditions, as their distance values are all very small in comparison with others. For example, in the column headed 'Balanced shaft', the first value in each sub-section always turns out to be significantly smaller than the rest. This means that using this approach there is very little risk of raising a false alarm.
CONCLUSION The methodology described in this paper has been shown to work well for discriminating the four different operating conditions on the rotodynamic test rig. The recognition rate achieved was 97.22 percent. In addition, the probability of misclassifying a good balanced shaft condition is extremely low. The method involves sorting the AE events according to event duration, identifying clusters and locating their centres in the three-dimensional space of event duration, peak amplitude and energy using the ftizzy c-mean technique, and classifying using a Euclidean minimum distance classifier. Overall, the method has the advantage that it is simple and efficient to implement and can be readily adapted to include other abnormal operating conditions as may be identified on a rotodynamic machine.
54
TABLE 1 MINIMUM DISTANCE CLASSIHCATION RESULTS
Bearing conditions Testing set #1 Balanced shaft Unbalanced shaft Misaligned shaft Defective bearing Testing set #2 Balanced shaft Unbalanced shaft Misaligned shaft Defective bearing Testing set #3 Balanced shaft Unbalanced shaft Misaligned shaft Defective bearing Testing set #4 Balanced shaft Unbalanced shaft Misaligned shaft Defective bearing Testing set #5 Balanced shaft Unbalanced shaft Misaligned shaft Defective bearing Testing set #6 Balanced shaft Unbalanced shaft Misaligned shaft Defective bearing Testing set #7 Balanced shaft Unbalanced shaft Misaligned shaft Defective bearing Testing set #8 Balanced shaft Unbalanced shaft Misaligned shaft Defective bearing Testing set #9 Balanced shaft Unbalanced shaft Misaligned shaft Defective bearing
Balanced shaft
Unbalanced shaft
Misaligned shaft
Defective bearing
L47Q1 670.6 478.6 861.81
807.9 136.28 328.4
507.8 164.1 28.076 355.41
1162.3 490.6 682.74 2^§M
510.81 314.9 10429 497.41
1165.3 339.8 644.71
4.3499 821.35 516.47 1003.8 28,284 839.19 468.16 1002.3
SSiW 810.9
iSAm 290.31 197.11 778.76
32:Si
478.58 332.83
338.71 195.59
mM6
802.85
495.8
iStM 1133.2 321.93 693.09 HS;9
17054 841.6 400.91 1038.9
35Jf2
502.82 335.11
405.84 232.27
imw 532.5
70J57 773.04 421.84 1031.2
736.04 33,841 384.97 224.54
435.92 266.61 84^38 524.8
1090.5 388.11 739.38
i5.9S2 761.76 372.86 1028.2
790.71 44;958 433.85 22L56
490.61 255.5 133 J 7 521.71
1145.1 399.37 788.28 133.04
40.244 868.07 391.57 1222.8
766.44 6L403 415.12 416.14
466.34 361.74 115 A 716.4
1120.9 293.05 769.55
4;2SS3 1046.9 461.38 1108.1
804.99 240.23 345.31 30L4
504.82 540.5 45;76S 601.72
2.7767 905.94 477.31 108L2
809.05 99:297 329.39 274.57
508.92 399.5
55
29Sm 574.8
1157.3 319.61 760.26 122.2
i2»;9
6mm 1159.4 114.21 699.74
ism 1163.5 255.2 683.82 79,909
ACKNOWLEDGEMENTS The authors gratefully acknowledge the support of the Royal Thai government and the Department of Physics, Faculty of Science, Chulalongkom University in awarding a Ph.D. scholarship. The authors also v^sh to thank Corns, Middlesborough UK, which has kindly provided advice and equipment for experimentation, the support of EPSRC (Grant GR/M44200) and the nine industrial collaborators including the UK National Physical Laboratory within the INTErSECT Faraday Partnership Flagship Project (1998-2002) entitled "Acoustic Emission Traceable Sensing and Signature Diagnostics (AESAD)"(Project website: http://www.brunel.ac.uk/research^cmm/aesad/).
REFERENCES [1] [2] [3] [4] [5] [6]
Y. Li, S. Billington, C. Zhang, T. Kurfess, S. Danyluk and S. Liang. (1999). Adaptive prognostics for rolling element bearing condition. Mechanical Systems and Signal Processing 13:1,103-113. T. Kaewkongka, Y. H. Joe Au, R. T. Rakowski and B. E. Jones. (2001) Continuous wavelet transform and neural network for condition monitoring of rotodynamic machinery IEEE Instrumentation and Measurement Technology ^3^ 1962-1966. C. James Li and S. Y. Li. (1995). Acoustic emission analysis for bearing condition monitoring. Wear 185, 67-64. N. Tandon and B. C. Nakra. (1992). Comparison of vibration and acoustic measurement techniques for the condition monitoring of rolling element bearings Tribology International 25:3, 205-212. Timoty J. Ross. (1995). Fuzzy logic with engineering applications, MaGraw-Hill, Inc. Julius T. Tou. (1974). Pattern recognition principles, Addison-Wesley Publishing Company.
56
Condition Monitoring and Diagnostic Engineering Management Andrew G. Starr and Raj B.K.N. Rao (Editors) © 2001 Elsevier Science Ltd. Allrightsreserved.
MONITORING SLIDING WEAR USING ACOUSTIC EMISSION C.K. Mechefske' and G. SW ^Department of Mechanical Engineering, Queen's University Kingston, Ontario, Canada, K7L 3N6 ^Department of Mechanical & Materials Engineering, University of Western Ontario London, Ontario, Canada, N6A 5B9
ABSTRACT Acoustic emission (AE) was used in this study to monitor the wear process of a steel ball sliding on a sapphire disk and on a steel disk under different wear situations. All tests were done with lubricant that contained zinc dialkyldithiophosphate (ZDDP), a common anti-wear lubricant additive. Changes in test conditions and acoustic emission activity were measured across the mild-to-severe wear transition. The intent was to characterize these changes in wear behavior in terms of acoustic emission characteristics and to determine the usefulness of acoustic emission measurements for the study of wear. It is shown that there is a systematic relationship between acoustic emission and wear rate. In the time domain signals, the AE counts per meter of distance traveled by the ball on a disk were small when the wear rate was mild and large when the wear rate was severe. In the frequency domain signals, the variation in the AE signal spectrum was low when the wear rate was mild and high when the wear rate was severe. It appears that the energy released is primarily in the frequency range of 100-500 kHz. These resuhs suggest that AE has the potential to become a useful wear rate monitoring technique. KEYWORDS Acoustic emission, condition monitoring, diagnostics, sliding wear, lubricant additive zinc dialkyldithiophosphate. INTRODUCTION Acoustic emission (AE) is generated at the sites of material wear. Given that it is a direct result of the wear process, using this parameter for continuously monitoring wear and for basic studies of the wear process is an attractive possibility. In order to determine whether effective boundary lubrication can be distinguished from poor boundary lubrication conditions using acoustic emission measurements, a unique tribometer was used in which the conditions of lubrication could be rapidly and reliably changed from good to bad. Extremely high frequency vibration measurements (up to 1 MHz) were made using a non-contact laser vibrometer. Changes in wear rate and acoustic emission activity were measured across the mild-severe wear transition in sliding wear. The intent was to characterize these changes in wear behavior in terms of acoustic emission characteristics and to determine the usefulness
57
of acoustic emission measurements for the study of wear. Successful discrimination would also have significant potential for machine condition monitoring and for lubricant evaluation purposes. LITERATURE REVIEW Acoustic emission in the sense used here refers to elastic waves produced by microscopic deformations occurring in materials as they are stressed and which comprise part of the elastic energy released during deformation. Sometimes called stress wave emissions, acoustic emissions are associated with dislocation movements, crack growth and deformation of inclusions. Detectable emission may be observed in a wide variety of materials in different modes of deformation and can be detected at solid surfaces with appropriate instrumentation [1]. It is recognized that under moderate load and sliding speed conditions ceramics exhibit mild wear, characterized by essentially micro-cracking of the sub-surface. In some situations changing the contact parameters can produce severe wear regimes with a drastic increase of the wear rate[2]. Alternatively, layers of low mechanical strength formed from compacted smaller fragments and particles or more coherent thin layers of material with enhanced mechanical properties can protect the rubbing surfaces[3]. Kholodilov and Rapoport [4] studied both friction and wear of polymers sliding on steel using AE measurements. As specimen surface roughness was increased and adhesion and fatigue wear mechanisms gave way to abrasion, the AE count rate increased. Results showing a linear relation between total AE counts and the amount of wear were also presented. It has also been found [5,6] that tribological interactions between fibrous networks and polymer surfaces result in the generation of AE signals. The intensity of such signals was found to depend upon transducer contact pressure and rubbing velocity as well as on specimen material mechanical behavior. Several time domain techniques have been developed to analyze AE signals. The most common techniques are; i) the count rate method, in which a threshold signal value (above the noise level) is selected and the number of threshold crossings per unit time is determined, and ii) amplitude distribution analysis, in which several discrete thresholds are chosen and the relative number of events occurring within each threshold interval is obtained[7]. Frequency domain signal analysis techniques that were applied in this study include; i) frequency spectra analysis, which yields the frequency components of the recorded AE signal, ii) the RMS of Spectral Difference (Rdo) of AE signals from different sliding speeds, which is the root mean square of the difference between two spectra when the amplitude of the spectral coefficients are given logarithmically [11], and iii) the Sum of Squares of Spectral Difference (So), which is the square root of the product of the sum and absolute difference of two spectra [14]. EXPERIMENTAL EQUIPMENT AND METHOD These particular tests were carried out on a ball-on-disk tribometer under conditions of pure sliding, i.e. a stationary ball being loaded against a rotating disk (see Figure 1). The ball-on-disc tribometer system is powered by an electric motor that provides the rotating movement and torque to a saphire (or steel) disc through a flexible coupling. Sliding speeds varying between 0 to 800mm/s can be obtained. The ball (sample) holder is attached to a rigid arm which is linked to the rig support by a horizontal pivot. The arm is balanced by a counterweight. Force sensors were used to measure the normal load and the friction load. Vibration signals were detected at the ball holder using a non-contact Polytec laser vibrometer. The output signals from the laser vibrometer were collected using a Hewlett-Packard 54645 deep storage oscilloscope. Matlab was the principal softwear used for signal analysis.
58
The base oil used in the study was Solvent SON (kinematic viscosity 28.31 cSt at 40°C). The antiwear additive in the oil is based on zinc dialkyldithiophosphate (ZDDP). The material of the test ball is 52100 stainless steel. The Rockwell C hardness is 64 and the surface roughness is 0.1 jiim. Specimen preparation was completed by degreasing with hexane and 5 minutes ultrasonic bath in detergent solution and IPA alcohol degreaser. The wear behaviour was observed through the saphire disc using a microscope and a CCD video camera. WEAR PROPERTIES Wear protection in motor oils is usually achieved by the use of antiwear additives in the oil that are based on zinc dialkyldithiophosphate (ZDDP). Wear protection by ZDDPs has been associated with the formation of films largely composed of inorganic, amorphous phosphates [8]. When the additive is working optimally the wear rate is less than 1 ^im^/Nm. Wear surfaces are coated by uneven glassy films averaging 0.3 jj,m thick with load carrying asperities, typically 20 [im in diameter, that form and decay with a half life of about two minutes (see Figure 2 (d)). Additive debris appears to be fine granular particles of about 50 nm and of essentially the same composition as the thick glassy antiwear films [9]. When the additive is performing less satisfactorily, wear rates can be greater than 25 jam^/N.m, which is the maximum rate allowable to pass a Sequence V test. At such rates the wearing surfaces have a sparse antiwear film (see Figure. 3 (a)). Ball holder v Test ball
I "
'
Load and laser focus point ^
CCD Camera Figure 1: Ball-on-disk tribometer. The two different wear situations representing effective and ineffective additive performance were achieved by changing the rotating speed of the disk. The temperature (room) and load (4 kg) were held constant. In this experiment sliding speeds of 5, 10, 20 40 mm/s are considered as low speeds. Figure 2 (a) shows the wear surface situation at low disk speed. Figure 2 (b) and (c) show the wear surface of test ball at faster sliding speeds (50mm/s and 70mm/s) where the ZDDP additive started to be effective. The lubricant protection film covered area increases with sliding speeds above these. This stage is the transition stage from poor to effective lubrication. Figure 2 (d) shows the surface situation at sliding speeds of 80, 120, 240, 480 mm/s. These are considered high sliding speeds. At a high sliding speed the additive was effective. ACOUSTIC EMISSION IN WEAR The investigation of wear phenomena by AE monitoring has several attractions. Acoustic emissions are generated in a deforming body by the deformation process and so are direct indications of the actual wear event. The characteristics of the AE signal, such as frequency, amplitude, duration, growth and decay rates are determined by material properties. These signal characteristics are much different than phenomena generated by test or production machines and their components. In real time monitoring applications of AE, the use of time domain analysis is common. Measured AE parameters in time domain analysis include: (a) AE events, which can be defined as a detected AE
59
signal; (b) AE counts, or the number of times an acoustic emission signal exceeds a pre-set threshold voltage during a test; (c) AE count rate, or the time rate at which AE counts occur; (d) AE peak amplitude, or the peak voltage of the largest excursion attained by the signal waveform during an AE event; and (e) AE energy, defined as the electrical energy pre-set in an AE signal which is believed to correspond to the energy released by an AE source [10]. Two main types of acoustic emission are usually recognized. For a discrete short-term event such as an increment of crack propagation in a brittle material, an emission burst lasting typically tens of microseconds can be identified. For deformation proceding steadily, as in large-scale yielding of a ductile material, multiple individual emission waves of low amplitude merge and overlap, producing continuous emission. A continuous signal can be defined as one in which the average time between emissions of similar amplitude is less than the duration of the burst type emission. Quantitative data are most fi-equently expressed in terms of the root mean square (rms) value of the signal or in terms of measurements known as ring-down counts which record the number of times the acoustic emission signal exceeds a pre-set trigger voltage level (or a weighted count based on a number of pre-set trigger voltage levels). The former is held to be more appropriate for continuous emissions and the latter more suited to burst emissions. In each case, the measurement is closely related to the energy of AE activity [11-13].
(a) Poor lubrication
(b) ZDDP starts to be effective (Sliding speed of 50mm/s)
(c) Wear surface at sliding speed of 70mm/s
(e) Effective lubrication
Figure 2: Wear surface situations RESULTS AND DISCUSSION In this experiment acoustic emission frequencies up to 1.0 MHz have been recorded. All time domain signals were filtered using a 100-1000 kHz band pass filter. The sampling frequency was 4 MHz and signal processing involved 4000 data points collected over a sampling interval of 1 millisecond. The frequency range under investigation in this study is therefore well above the frequency range normally used to detect and diagnose mechanical deterioration in machinery.
60
Time domain AE signals at different sliding speeds and lubricatian conditions. Figure 3 shows the time domain AE signal while monitoring the wear behavior of a test ball on the sapphire disk. Figure 3 (a) represents a poor lubricant situation (there is no lubricant film on the wear siirface). Figure 3 (b) and (c) indicate that the wear surfaces are partly covered by lubricant film. Figure 3 (d) is an AE signal representing wear behavior in an effective lubricant situation. It is clear that there is more AE activity when the lubricant situation is poor. Figure 4 shows the time domain AE signal while monitoring the wear behavior of the test ball on the steel disk. The wear occurring on the surface cannot be seen directly in this case. However, it is again clear that there is more AE activity during low sliding speed wear (poor lubrication) than during high sliding speed wear (good lubrication). The comparison of wear rate and AE signals In this section, the peak level, RMS level and count rate of AE signals are examined in the time domain in order to investigate any correlation between the wear process and the AE signals. Figure 5 shows the variation in AE during monitoring of the wear behavior of a test ball on the sapphire and steel disk. The sliding speed changes from 5mm/s to 480mm/s. Figure 5 (a) shows the variation of the peak level at different sliding speeds and wear situations. It can be seen that faster sliding speeds lead to higher AE peak levels. Figure 5 (b) shows the variation in RMS level at different sliding speeds and wear situations. The results indicate that faster sliding speeds leads to higher RMS levels. AE signal intensity is proportional to strain rate. It is therefore reasonable to find that higher energy AE signals are generated at higher sliding speeds. j
'0m (a) Sliding speed of 40mm/s
(b) Sliding speed of 60mm/s
0lmm0. Time (Seconds)
(c) Sliding speed of 70mm/s
(d) SHding speed of 80mm/s
Figure 3: Time domain AE signals during wear on the sapphire disk The count rate was used to analyze AE signals using threshold values above the noise level. Since the amplitude of a continuous signal increases gradually with sliding speeds, the count rate obtained for a fixed threshold value is affected by both continuous and burst signals. If a threshold value is set above
61
the dominant amplitude, the change in burst signals with sliding distance can be examined by observing the variation of the count rate. Figure 5 (c) shows the variation of the AE count rate at different sliding speeds for wear on sapphire and the steel disk. It can be seen that the AE count rate decreases as the sliding speed increases. These results are consistent with those presented in Figure 4. The wear surface situation could not be directly observed during the wear of the steel disk. However, wear on the steel disk had the same trend as wear on sapphire disk. It is considered that effective lubricant also exists during high speed sliding wear on the steel disk. Sheasby [9] investigated the wear rate of a steel ball on a sapphire disk with ZDDP additive at constant load and temperature. Figure 5 (d) shows that the wear rate decreases as sliding speed increases.
(b) Sliding speed of 60mm/s
.4!
uMm
Time(Seconds)
(c) Sliding speed of 70mm/s
(d) Sliding speed of 80mm/s
Figure 4: Typical time domain AE signals during wear on the steel disk Frequency domain signals analysis During different wear situations the frequency characteristics of the acoustic emissions generated are quite distinct. From the results it is clear that the frequency range of 100-500kHz represents the range where there is the most significant difference between signals representing different wear situations. While the overall amplitude of the frequency spectra is higher for the effective wear situation, the response is relatively flat with minimal variation. The response in different frequency ranges is also different at different wear situations. Frequency spectra representing different wear situations are shown in Figures 6 and 7. Figure 8 shows the RMS of Spectral Difference of spectra generated from AE signals representing different sliding speeds. It can be seen that for both disk types there is a relatively rapid decrease in the Rdo value ft-om speeds where there are poor lubricant conditions (sliding speeds 5, 10, 20, 40 mm/s) until speeds are reached where effective lubrication conditions are known to exist (sliding speed 80, 120, 240, 480 mm/s). Figure 9 shows the Sum of Squares of Spectral Difference of spectra generated from AE signals representing different sliding speeds, was applied in this section. Similar to Figure 8, there is a decreasing trend as the sliding speed increases and the wear conditions change from poor to
62
effective. Both of these plots show objective comparisons of the frequency content of the AE signal spectral responces and the sliding speed which is directly sorrelated with wear condition.
-Sapphire disk, ZDDP lubricant '
-Sapphire disk. ZDDP lubricant
X
20
50
70
-Steel disk, ZDDP lubricant
Steel disk, ZDDP lubricant
120
50
Sliding speed (mm/s)
70
120
Sliding speed (mm/s)
(b) RMS level (sapphire and steel disk)
(a) Peak level (sapphire and steel disk)
-Steel disk, ZDDP lubricant
-Sapphire disk, ZDDP lubricant
0.01 i20
50
120
70
480
Sliding speed (mm/s)
Sliding speed (mm/s)
(c) AE count rate (sapphire and steel disk)
(d) Wear rate of different sliding speeds
Figure 5: Comparison of AE and wear rate
-120
-120
-130
-130
-140 ffi
2.-150 E -160 O) (Q
?,-^70 o
m
-140
fffi
•imfi \ph ."fh
S-150
IE -160 O)
m
l.-I^OH o
-180
-180 I
-190
-190
-200
-200 Frequency (Hz)
Frequency (Hz)
Figure 7: Frequency spectra from AE signal during wear on sapphire disk under good lubricant conditions (sliding speed 240mm/s)
Figure 6: Frequency spectra from AE signal during wear on sapphire disk under poor lubricant condition (sliding speed 20mm/s)
63
-Sapphire disk, ZDDP lubricant
- Sapphtre disk, ZDDP lubricant
•
^ Steel disk, ZDDP lubricant
S
10 20 40 50 60 70 «
1O2O«ISOa07<0aoi2O3404IO S i d n g speed ( m m / l )
Steel disk, ZDDP lubricant
Sliding speed (mm/s)
Figure 8: RMS of Spectral Difference at different sliding speeds
Figure 9: Sum of Squares of Spectral difference at different sliding speeds
CONCLUSIONS Acoustic emission signals generated during wear of a steel sample on sapphire and steel disks were analyzed in the time and frequency domains. The increase of AE peak level and RMS value parallel the increase of sliding speed. This indicates that peak level and RMS level represent the wear surface strain rate. Changes in AE count rate parallel the variations in wear rate across the poor-effective lubrication transition. In general, AE count rate is low when lubrication is effective and high when lubrication is poor. Such a correlation between wear and AE signal could be very useful in the characterization of wear mechanisms. These correlations were also seen in the frequency domain. The occurrence of a poor wear situation is characterized by the intensification of wear mechanisms resulting in high AE activity. The results obtained show that AE has the potential to become a useful wear rate monitoring technique. ACKNOWLEDGEMENTS This work was made possible throught a grant from the Natural Sciences and Engineering Research Council of Canada. REFERENCES 1 2 3 4 5 6 7
8
S. Lingard, C. W. Yu and C. F. Yau, (1993) Sliding wear studies using acoustic emission. Wear, 162 597-604. V.A.Belyi, O.V. Kholodilov and A.I. Sviridyonok, (1981) Acoustic spectrometry as used for the evaluation of tribological systems. Wear, 69 309-319 L. S. Rapoport, Y.S. Petrov and V. E. Vainberg, (1982) An acoustic emission study of the real polymer-metal frictional contact area, Sov. J. Frict. Wear, 3 30-33 L. S. Rapoport, Y. S. Petrov and V. E. Vainberg, (1981) Study of the dynamics of metal friction processes by the acoustic emission method, 50v. J. Prict. Wear, 2 91-94. S. V. Filatov, (1982) Acoustic emission in the abrasive wear of metals, Sov. J. Frict. Wear, J 138140. M. K. Tse, J. Leifer and A. F. Lewis, (1985) AE of flexible disk magnetic media systems, J. Acoust. Emiss., 4 (2-3) l25-\29. S.-S. Cho and K. Komvopoulos, (1997) Correlation Between Acoustic Emission and Wear of Multi-Layer Ceramic Coated Carbide Tools, Journal of manufacturing Science and Engineering, Vol. 119 238-246 P.A. Willermet, D.P. Dailey, R.O. Carter III, P.J. Schmitz, W. Zhu, (1995) Mechanism of formation of antiwear films from zinc dialkyldithiophosphates, Tribology International 28 111-\%1.
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9
J.S. Sheasby, T.A. Caughlin, S. Terranova and A. Cohen. (1996) An examination of oil debris to give insight into boundary lubrication. Tribology Series 31: The Third Body Concept: Interpretation of Tribological Phenomena. Ed D. Dowson, et al., Elsevier 685-693. 10 A. G. Beattie, (1983) Acoustic emission, principles and instrumentation, J. Acoust. Emiss., 2 95128. 11 C.K. Mechefske and J. Mathew, (1992) Fault Detection and Diagnosis in Low Speed Rolling Element Bearings, Part I: The use of Parametric Spectra, Mechanical Systems and Signal Processing, Vol. 6, No. 4 297-307
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Condition Monitoring and Diagnostic Engineering Management Andrew G. Starr and Raj B.K.N. Rao (Editors) © 2001 Elsevier Science Ltd. All rights reserved.
INTELLIGENT CONDITION MONITORING OF BEARINGS IN MAIL PROCESSING MACHINES USING ACOUSTIC EMISSION S. M. E. Salvan^; R. M. Parkin^; J. Coy^and W. Li' ' Mechatronics Research Centre Holywell Building, Holywell Way Loughborough University Leicestershire LEI 1 3UZ, UK ^ Post Office Consulting, Technology Centre Wheatstone Road, Dorcan SWINDON, SN3 4RD
ABSTRACT The Post Office possesses high-speed mail sorting machines. The handling system contains a large number of bearings. This paper present the first experiments undergone to detect a damaged bearing among healthy ones. However the present study is limited to the detection of a simpler source. Actually, types of events and their causes producing stress waves will be presented briefly in the first part of this paper. In the second part, some methods found in the literature will be suggested with the aim to apply them in the experiments. Hence, several concepts such as triangulation using difference of sound wave arrival time. Neural Networks (NN) for pattern recognition and classification purposes, Fuzzy method to allow a degree of imprecision in rolling element pattern shapes classification associated with Wavelet Network used for its resolution flexibility in time and frequency domains will be introduced. In the last part, the experiments made until now will be explained. They will involve triangulation trials and the methods used to determine the sound wave velocity thus the AE source location. Some suppositions and propositions will be formulated after the limitations statement.
KEYWORDS Acoustic emission, Rolling bearing, Condition monitoring. Fuzzy Wavelet Networks, Triangulation, Artificial Neural Network
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INTRODUCTION In a typical industrial system, 2in acoustic noise, according to Rimlyand V. I. et al (2001), arises due to a combination of the rolling element bearings rotation and the vibration of the supporting structure. In such a system, rolling elements can produce pulses of very short duration owing to the interaction of defects. This corresponds to a flaw coming in contact with another rolling element bearing surface while rotating. The previously cited pulses have particular lengths that excite the natural frequency of bearing elements and housing structures by matching their respective wavelengths. The excitation of the natural frequencies, being a broadband phenomenon, produces stress waves under the form of resonances even with frequency above 100 kHz. Hawman M.H. et al. (1988), Tan C. C (1990) and Tandon N. et al. (2000) indicate that a masking effect on the bearing signature resonances induced by low frequency range vibrations exists. They affirm that these events are generated by other machine components. This phenomenon is said to act under 10 kHz for Tan C. C (1990) and 50kHz for Tandon N. et al. (2000). However the latter unlike Hawman M.H. et al. (1988) assert that these resonances are rarely significantly altered even if the way in which they are affected on assembly into a full bearing and mounting in a housing is difficult to quantify. On the other way, monitoring the increase in the level of vibration in the high-frequency range of the spectrum is an effective method of predicting the condition of rolling element bearings because of a better Signal to Noise Ratio (SNR). It is reported as a successful method by several investigators such as Mathew J. et al. (1984), Reif Z. et al. (1989) and Broderick J.J. et al. (1989). All the types of vibroacoustical sources are summarised in figure I as indicated by Cempel C. (1991). Possible resonance phenomena
Acoustical emission in microareas Vibrations in subareas Volumetric vibrations -±,.± 1^ r=^.-:^ Cavitation, friction M
•
in kinematic^airs Gear, rolling bearing elements < • Pulsation of the medium Shafts-rotors, slide bearings Supporting structure <
•
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Frequency f^ lOOkHz IMHz Microfai lures
Figure 1: Type and area of vibroacoustical and triboacoustical phenomenon
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PREPARATION Triangulation Tandon N. et al. (2000) and Cempel C. (1991) indicate that by introducing two AE sensors in a system one can measure the difference of arrival time of signals from an AE source at the sensors (figure 2). Ri and R2: Measurement points S: AE source point
sx^ij D2
^-4 R2
Figure 2: determination of the position of an AE source by triangulation The speed of a sound wave given by Holroyd T. (2000) equals to: C=
E(\~v) p{\ + v)(\-2v)
(1)
with the young's modulus E; the Poisson's ratio v; the material density p (kg.m') and C (m/s). Triangulation by Neural Network In a recent report Grabec I. et al. (1998) use sensory NN to improve the triangulation method. The authors' principal goal is to measure the difference of arrival time of an AE wave between two sensors, determine the position of unknown AE source by interpolating it with a library of known signals location. To the sensory neural network technique is applied an information processing system to map the source coordinates. In their work, the authors extend this technique for AE bursts signals to AE continuous emission. Packets of burst of AE signals, above 50 kHz, superpose each other leading to continuous emission (Beattie A. G. (1983)). The measurement in continuous emission is achieved with correlation functions. Fuz^ Wavelet Network (FWN) An FWN is composed of Wavelet Neural Network (WNN). The NN included inside is then improved to allow a certain degree of imprecision. This degree of imprecision is also called Fuzzy concept. Moreover, the wavelet bases permit to capture different behaviour (local or global) in the spectra of a monitored signal. This is an interesting property since Ho D. W. C. et al. (2001) indicate that wavelets with coarse resolution can capture the global (low frequency) behaviour easily, while the wavelets with fme resolution can capture the local behaviour (higher frequency) of the function accurately. The Fuzzy NN can then compare the different behaviours with a series of signatures memorised under the form of
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a library (c/as example Billington S. A. et al. (1997) for classification of bearing condition with NN). Since a bearing signature is not always identical to each other, the Fuzzy properties will accept a degree of imprecision such as our brain is doing for pattern recognition.
EXPERIMENTS Since this project is at its early beginnings, our first goal is to understand how an acoustic wave propagates throughout the propagation medium. This should be made with the simplest AE signal possible. The velocity of the acoustic wave has to be defined with all the critical parameters able to influence it. All the propagation paths have to be also determined. With the same type of signal it is also proposed to test the triangulation efficiency. Passed these stages, tests with a typical bearing should be undergone and compared with equivalent test on bearing with a well-defined defect. The application to multiple bearing systems should be presented in future papers. Several experiments were made with an Integrated Mail Processing machine for training. The aim was to manipulate the AE measurement techniques before to design a test rig permitting to continue tests with a minimum of uncontrolled parameters. For these experiments, an AE system from Vallen (http://vallen.de/) was used (ref AMSY4 with 15 channels). For our needs, a high pass filter riddling under lOOkHz was installed after a resonant sensor. The plate supporting the bearings in the machine acted as the propagation medium and an AE sensor was fixed directly on it with typical wood glue as an acoustical bonding. As in all the other mail processing machines, this base plate is made of aluminium alloy and is 10 mm thick. In order to understand the propagation of an AE signal in the plate, a method, presented by Holroyd T. (2000), using a pencil lead break was used. This technique has the particularity of presenting a simple decaying AE signal easier to process and decompose. A typical pencil break signal obtained is presented with the figure 3. Chan.1 Set: 18 Index: 18 2 14:47:06 85.7789 Timelps] 0.3-
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Figure 3: Typical pencil lead break signal Then a second sensor was implemented to locate a source with the triangulation method. On the plate, the two AE sensing elements were placed at 780 mm from each other. The pencil AE source was placed close to the AE sensor connected to the channel 2. Then when breaking the pencil lead, the AE signal was recorded and the resuhs are shown in figure 4-a and 4-b (time domain) and figures 5-a and 5-b (frequency domain).
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Chan:1 Set: 31 Index: 31 2 15:14:44 884.7720 Time [psl
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Figure 4: recorded pencil break AE signal situated close to "b" and at 780 mm away from "a" Chan:1 Set: 31 Index: 31 2 15:14:44 884.7728 Frequency [kHz] 1
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Figure 5: a) frequency spectrum of 4-a b) frequency spectrum of 4-b
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dM
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From figure 4, one can observe that amplitude of 4-a has been attenuated compare to 4-b after the AE wave travelled in the propagation medium for 780 mm and at least four propagation paths (Times: 0 ^s, 55 us, 80 \is and 110 ^is) in 4-a. To determine the correlation of the distance made by the acoustic wave and its speed, one can take the equation 1 with the parameters in aluminium alloys (Callister W. D., Jr (1994) and Holroyd T. (2000)) equal to: E= 6.9 10^ MPa; p= 2.7 and v= 0.33. Thus the sound speed in this material is found to be 6215.9 m/s. On the other hand, Ascher R. C. (1997) and Beattie A. G. (1983) affirm that the speed of sound in aluminium is, depending on metallurgical condition, included between 6320 m/s and 6420 m/s. When performing the difference of arrival time of the two previous waves, the 780 mm path is found to be proceeding in 99.5 \is. In contrast, the acoustic wave should reach the sensor "a" after 125 jis according to equation 1 whereas it should happen between 123 |AS and 121 ^s with the Ascher R. C. (1997) and Beattie A. G. (1983) affirmation. Hence, even by taking the smallest difference between the theory and the experiment (21.5 ^is giving an error of location of 138.6 mm), it represents an error of 21.6% and is obviously not acceptable. In addition, the acoustic wave in practice arrives sooner than the time given theoretically. The reason for these conflicting values, according to Yoshioka T. (1992) and Grabec I. et Al (1998), is due to a very complex structure of the correlation of a stochastic AE phenomenon, consequence of the wave propagation in the plate and the source mechanism. Yoshioka T. (1992) adds that in a method determining an area bounded by two hyperbolic curves, such as in the present one, it is necessary to take into account errors relating to the difference of the acoustic arrival times. Moreover, always according to him, a two-channel location mode cannot always locate an AE source due to the already cited disturbance of the waveform, and has a lower resolution than a one-channelmode location. In alumina steel, the coefficient dV/dT which indicates the sound velocity against the temperature have little effect since Ascher R. C. (1997) values it to approximately -0.3 m.s'^°C"^ The effect of pressure on this velocity is to be determined. In addition all the parameters in equation 1 (E, v and p) has to be refined to match the propagation medium qualities. With wavelet transforms, one can recognize harmonic and signals given by different paths by making a wavelet acting as the system recognition in Adaptive Noise Cancellation (ANC) or adaptive filter. For example, in the figure 6, the last three peaks at say 30 kHz would be adaptively frequency filtered with time after this recognition made by the wavelet. Various other improving techniques introduced by Alfredson R. J. et al. (1985) and Ho D. et al. (2000) such as enveloping, masking and match filtering are suggested in order to suppress interference (such as the one induced by secondary propagation paths) and increase SNR. All these methods will be investigated in the fiiture.
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Figure 6: Wavelet transform of a signalfromGu et al. (2000) Hence, every elements cited will be investigated during future experiments in order to define the most critical parameters. In addition, several tests will be conducted with a higher number of AE sensors connected with a neural network taking into consideration bearing signature since all the bearings present in the in-situ machine are not identical The implementation of triangulation by NN should be able to locate a signal according to his propagation time and its pattern shape, hi addition, FWN will be investigated because of the resolution capability of Wavelets in both time and frequency domains. These should permit to extract the modes both in the time andfrequencydomain indicating a growing defect in a rolling element bearing signature. CONCLUSIONS The acoustic wave coming from the break of a pencil lead throughout the propagation path in aluminium was recorded to understand the propagation of a simple decaying acoustic signal. Then an attempt to locate the position of the source by computing the difference of sound wave propagation given by two separated sensors was made. This technique is called triangulation. The failure to obtain the precise location was presumably due to incorrect parameters in the sound velocity equation and the use of an inefficient technique. It is proposed to update the previously cited parameters. In addition, other processing techniques such as triangulation by NN, FWN and wavelet transforms as well as adaptivefilteringand ANC are though to be applied. ACKNOWLEDGEMENTS We would like to thank Dr Jackson M. R. for his advices and guidance. We would also like to thank Rowland C. from Vallen UK for his help and for the AE processing material he provided during the previous experiments. Many thanks also to Callahan T. from Post Office UTC for his help and information. Finally, we would like to thank Dr Holroyd T. from Holroyd Instruments Ltd and Post Office UTC for their continued funding.
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REFERENCES - Alfredson R. J. and Mathew J. (1985). Frequency domain methods for monitoring the condition of roUing element bearings. Mechanical engineering transactions - Institution of engineers, Australia 10:2,108-112 - Ascher R. C. (1997), Ultrasonic sensors for chemical and process plant. Institute of Physics Publishing Ltd - Beattie A. G. (1983). Acoustic Emission, principles and instrumentation. Acoustic Emission Group, Journal of Acoustic Emission. 95-129 - Billington S. A. et al. (1997). Roller bearing defect detection with multiple sensors. Proceedings of the 1997 ASME International mechanical engineering congress and exposition, Dallasl6-21 November 1997. Fairfield, NJ: ASME, 31-36 - Broderick J. J., Burchill R. F. and Clark H. L. (1972). Design and fabrication of prototype system for early warning of impending bearing failure. MTI Report MTI-71 TR-1 (preparedfor NASA) - Callister W. D., Jr (1994), Materials science and engineering, an introduction, third edition, John Wiley & sons, Inc, USA - Cempel C. (1991), Vibroacoustic condition monitoring, EUis Horwood series in Mechanical Engineering - Gu F., Li W., Ball A. D. and Leung A. Y. T. (2000). The condition monitoring of diesel engines using acoustic measurements part 1: acoustic modelling of the engine and representation of the acoustic characteristics. SAE 2000 World congress, March 6-9 2000, Detroit, Michigan, USA. SP-1514 - Grabec I., Kosel T. and Muzic P. (1998), Location of continuous AE sources by sensory neural networks. Ultrasonics 36:1-5, 525-530, ISSN: 0041-624X - Hawman M.H. and Galinaitis W.S. (1988). Acoustic Emission Monitoring of rolling element bearings. Ultrasonic Symposium, IEEE, 885-889 - Ho D. and Randall R. B. (2000). Optimisation of bearing diagnostic techniques using simulated and actual bearing fault signals. Mechanical systems and signal processing 14:5, 763-788, ISSN: 08883270 - Ho D. W. C , Zhang P. A. and Xu J. (2001). Fuzzy wavelet networks for function learning. IEEE Transactions on Fuzzy Systems 9:1, 200-211, ISSN: 1063-6706 - Holroyd T. (2000), Acoustic Emission & Ultrasonic, Coxmoor Publishing Company's, UK http://vallen.de/ - Mathew J. and Alfredson R.J. (1984). The condition monitoring of rolling element bearings using vibration Analysis. Tran ASME, J Vibr, Acoust, Stress Reliab. Design - Reif Z. and Lai M. S. (1989). Detection of developing bearing failures by means of vibration. ASME Design EngDiv, (Publ) DE - Rimlyand V. L, Kondratiev A. I., Kazarbin A. V. and Dobromyslov M. B. (2001). Ultrasonic diagnostic system for rotating bodies. Journal of Sound and Vibration, letters to the editor 240:3, 581 586, ISSN: 0022-460X - Tan C.C. (1990). Application of Acoustic Emission to the detection of bearing failures. The institution of Engineers, Australia Tribology Conference, Brisbane 3-5 December 1990, 110-114 - Tandon N. and Choudhury A. (2000). A review of vibration and acoustic measurement methods for the detection of defects in rolling element bearings. Tribology International, Elsevier 32:8, 469-480, ISSN: 0301-679X. - Yoshioka T. (1992). Detection of rolling contact sub-surface fatigue cracks using Acoustic Emission technique. Lubrication Engineering 49:4, 303-308
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Condition Monitoring and Diagnostic Engineering Management Andrew G. Starr and Raj B.K.N. Rao (Editors) © 2001 Elsevier Science Ltd. Allrightsreserved.
HEALTH MANAGEMENT SYSTEM DESIGN: DEVELOPMENT, SIMULATION AND COST/BENEFIT OPTIMIZATION Gregory J. Kacprzynski^ Michael J. RoemerVAndrew J. Hess^, Ken Bladen^ impact Technologies, LLC 125 Tech Park Drive, Rochester, NY 14623 ^NAWCAD 22195 Elmer Road, Building 106, Suite 228, Patuxent River, MD 20670-1534
ABSTRACT Health management is a philosophy that merges component and system level health monitoring, consisting of diagnostic and prognostic technologies, with the operations and maintenance arenas. The aspects of health management, in particular health monitoring system design, have not traditionally been an integral aspect of the overall system design process even though this is when the majority of total lifecycle costs are dictated. This paper presents a software tool being developed that extends the traditional Failure Mode, Effects and Criticality Analysis (FMECA) to create a virtual environment in which Health Monitoring architectures aimed at reducing life cycle costs can be evaluated early in the design stage and optimized from a cost/benefit standpoint. This health monitoring system design tool allows for the choice and placement of sensors and diagnostic/prognostic technologies to be determined from traditional FMECA information or detailed models at subsystem and system levels. This approach also introduces a collaborative, web-enabled environment for enhanced realization of conponent design requirements and the diagnostic and prognostic technologies themselves. A simplified exannple of a Health Management system cost/benefit analysis on an aircraft electromechanical valve is provided for illustration of the concepts introduced. KEYWORDS Health Management Design, Diagnostics, Prognostics, Sensor placement, cost/benefit optimization, System modeling
INTRODUCTION The application of "health" or "condition" monitoring systems serves to increase the overall reliability of a system through judicious application of intelligent monitoring technologies. A consistent health management philosophy integrates the results from the health monitoring system for the purposes of optimizing operations and maintenance practices through, 1.) Prediction, with confidence bounds, of
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the Remaining Useful Life (RUL) of critical components, and 2.) Isolating the root cause of failures after the failure effects have been observed. If RUL predictions can be made, the allocation of replacement parts or refurbishment actions can be scheduled in an optimum fashion to reduce the overall operational and maintenance logistic foo^rints. Fault isolation is a critical component to maximizing system availability and minimizing downtime through more efficient troubleshooting efforts. Aside from general exceedence warnings/alarms, health monitoring initiatives mostly take place after in-field failures (and substantial costs) have been incurred. To address this issue, this paper proposes the concept of a Health Management Virtual Test Bench or a software tool that is not only used for health monitoring system design but also for system validation, managing inevitable changes from infield experiences, and evaluating system design trade-offs (Figure 1).
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Design S^e
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f FMBCAs, Modeling, Cost/BemjU Analysis
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Continuous Design improvement & support Figure 1 - Health Management with System Design Because an initial system FMECA is performed during the design stage, it is a perfect link between tfie critical overall system failure modes and the health management system designed to help mitigate those failure modes. Hence, a key aspect of the process presented links this traditional FMECA analysis with health management system design optimization based on failure mode coverage and life cycle cost analysis. ROLE OF FMECA IN HEALTH MANAGEMENT FMECA's historically contain 3 main pieces of information as described below: 1) A list of failure modes for a particular component 2) The effects of each failure mode rangingfix)ma local level to the end effect 3) The criticality of the Failure mode (I - IV), where (I) is the most critical While this type of failure mode analysis is beneficial in getting an initial (though generally unsubstantiated) measure of system reliability and identifying candidates for redundancy, there are several areas where fundamental improvements can be made so that FMECA's can assist in health monitoring design. Four shortcomings of traditional FMECA's are:
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1) Traditional FMECA does not address the precursors or symptoms to failure modes. To move maintenance from reactive to proactive, it is important to focus on both system and conponcnt level indications that the likelihood of a substantial failure mode has increased. Failure mode symptoms that occur prior to failure are these indications. An example of failure mode symptoms associated with a bearing would be an increase in spike energy or an increase in the oil particulate coimt. 2) Traditional FMECA does not address the sensors and sensor placement requirements to observe failure mode symptoms or effects, 3) Traditional FMECA does not address health management technologies for diagnosing and prognosing faults. 4) Traditional FMECA typically focuses on subsystems independentiy. With these shortcomings in mind, a new approach has been developed that extends far beyond traditional FMECA capability and used in the design of health monitoring and management systems. APPROACH TO HEALTH MANAGEMENT DESIGN Figure 2 provides an overview of the approach to health management system design optimization. A basic description of each block will be given first, then details associated with each block will follow. First, a Function Block diagram of the system must be created that models the energy flow relationships between components. This functional block diagram provides a clear vision of how components interact with each other across subsystems. On a parallel path, a tabular FMECA is created that corresponds to a traditional FMECA except it contains failure mode symptoms, as well as sensors and diagnostic/prognostic technologies. Alternately, a system response model may be used for assessing sensor placements and observability of simulated failure modes thus offsetting the manual burden of creating the FMECA. Finally, maintenance tasks that address failure modes are included. The information from the Functional Block diagram and the tabular FMECA is automatically combined to create a graphical health management environment that contains all of the failure mode attributes as well as health management technologies. The graphical health management environment simply a sophisticated interface to a relational database. Once the graphical healtii management system has been developed, attributes are assigned to the failure modes, connections, sensors and diagnostic/prognostic technologies. The attributes are information like historical failure rates, replacement costs, false alarm rates etc., which are used to generate a fitness function for assessing the benefits of the health management system configuration. The '*fitness" function criteria include system availability, reliability, and cost. Some of these attributes must be manually determined if known, while others are related to the attributes of the diagnostic/prognostic technologies can be determined from independent measures of performance and effectiveness tests or from pre-developed databases. Finally, the health management configuration is automatically optimized from a cost/benefit standpoint using a genetic algorithm approach. The net result is a configuration that maintains the highest system reliability to cost/benefit ratio.
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FunctionaJ Block Diagram
Figure 2 - Architecture of PHM Design tool FUNCTIONAL BLOCK DLVGRAM The Function Block Diagram (FBD) contains an integrated representation of how components, subsystems and systems interact with one another. It is not a simulation, only a hierarchical map of physical energy flows (i.e. torque transfer, current, pressure). This energy flow map serves as the backbone for the health management design environment because it contains the failure mode symptoms and effects as well as C£^turing their temporal paths. Figure 3 shows an example of a functional flow diagram at a "system" level. One could select any of the components to reveal specific interactions between its associated subsystem components.
System or Subsystem Fluid Mechanical Electrical
Figure 3 - Functional Block Diagram Layout ENHANCED FMECA As previously mentioned, with this q)proach, traditional FMECA analyses were enhanced with the addition of sensors, health monitoring technologies and failure symptoms. Figure 4 shows an exanple
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of an enhanced FMECA performed on a portion of a fuel system for a F-lOO engine created by Penn State ARL and Impact Technologies, L _._. DEM. Modito:
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Figure 4 - Tabular FMECA of a F-lOO Fuel System As with traditional a FMECA, the failure mode is provided along with its effects (ranked from top to bottom as primary, secondary, tertiary, etc.). The Criticality or Frequency of Occurrence of the failure mode is ranked from A to E where: A = Frequent, B = Probable, C = Occasional, D = Remote, E = Improbable In practice, this Criticality letter would be associated with a specific probability of failure range. The Severity of the failure mode is ranked from I-IV where: I - Catastrophic, II-Critical, III - Marginal, IV - Negligible The first FMECA enhancement is that failure mode symptoms have been added to the "effects"column and are shaded in blue (or light gray). Failure mode symptoms are events that can be observed prior to the failure mode occurring or when the failure mode is in a very early stage of development. Subsequent effects may or may not be downstream failure modes. In the case where an effect is a downstream failure mode, the failure mode of focus could be considered a failure mode precursor. The "Component" column identifies the component immediately affected by the failure mode while "Module" is the subsystem in which the component resides. This fimctional relationship is crossreferenced with the functional block diagram. In a similar fashion, the "Sensor" column lists the sensor that can observe the symptom or effect while "S_ModuIe" is the subsystem in which the sensor resides and "S_Component" is the conqjonent it is linked to. All sensors in this example are required for control or safety purposes. Finally, *T)iagnostics" and "Prognostic" column have been added. The 'Diagnostics" column describes if there are any discrete diagnostic (Built in Test (BIT)) or continuous processing algorithms that can observe the symptom or effect. The "Prognostics" column describes any prognostic algorithms that can be used to obtain a RUL prediction on the failure mode.
RESPONSE MODELS In some cases, a model of a subsystem may be developed that can provide valuable insight into where sensor are likely to have the most observational quality on failure modes. This optional level of
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fidelity allows for detailed, physics-based subsystem modeling, to be used for examining PHM tradeoffs. Such trade-offs at this level would include analyzing the number of sensors required, location of the sensors and associated algorithms. This type of model would be integrated in tfie overall HM design environment thus far discussed where cross-system influences can be examined and accounted for (Figure 5). Integrated System HM Model
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Figure 5 - Response model integratioii in the overall HM model One such system response model for a hydraulic system developed by Dr. Jacek Stecki et al. of Monash University is shown in Figure 6. This model illustrates how the system model may be perturbed to simulate how the effects of certain modes propagate in time and space. Sensor / algorithm combinations can be examined for their abihty to detect the perturbations.
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Figure 6 - Example of a detailed system response model HEALTH MANAGEMENT ATTRIBUTES To autonomously evaluate the cost/benefit of a HM system configuration, all aspects of the system must ultimately be assigned, or modify, a dollar value so that a cost function can be generated and optimized. Some of these "attributes" are more easily derived that others. The attributes assigned
within a HM system and their respective icons are linked to Failure modes (F/FM), Sensors (eye), Effects, Diagnostics (Stoplight-discrete, x-y plot - continuous). Prognostics (stethoscope) and Maintenance Tasks (M). A short list of these attributes is shown in Figure 7. Some of the less obvious attributes are described next. Observational Quality: OQ 3 1 Failure rate: ^ (ffilMJ Probability of Propagation: Pj? Life Cycle Cost (per unit): LLCS ! « . Detection Confidence: DiX; l U % False Positive: DJ?7> Development Cost: DDAIC
H j Detection Confidence: iX: ^ % False Positive: Z)/?'? Development Cost (per unit): DAIC
™ Accuracy: iM BSJ Development Cost (per unit): P>1/C
SUBI^ '•""'
Failure rate (Pf) Criticality (C)
Scheduled Hardware Repl. Cost (SHRC) Scheduled Downtime (SD) Scheduled Labor Cost (SLC) Unscheduled penalty factor (UPF)
Figure 7 - Short Ust of HM attributes Sensors
Sensors are defined in the model as components for measuring physical quantities such as temperatures, pressures and currents. The ''Observational Quality" attribute of a particular sensor is a measure of the sensitivity with which it is able to pick up a physical signal linked to a particular failure mode. For example, an accelerometer stud mounted on top of a bearing casing may have a better observational quality than one magnetically mounted some distance away. Diagnostic and Prognostic Attributes Diagnostics can be either discrete or continuous. Discrete diagnostics are traditionally algorithms that produce 0 or 1 depending on if a threshold has been exceeded. Many types of Built In Tests (BITs) can be classified as Discrete Diagnostics. An example of a discrete diagnostics is an Exhaust Gas Temperature (EGT) reading that has exceeded a predetermined level. Continuous diagnostics are algorithms designed to observe transitional effects and diagnose a failure mode based on the method and rate in which the effect is changing. Continuous diagnostics are usually associated with observing the severity of failure mode syn:q)toms. Examples of continuous diagnostics would be a spike energy monitor for identifying low levels of bearing race spalling or an A.I. classifier for diagnosing that a valve is sticking. The "Detection Confidence score (0-1) (DDC)", and **% false positive score (0-1) - (DFP)" can be used to simultaneously account for truenegative and true-positive characteristics. Finally, Prognostic algorithms can use a combination of sensor data, a-priori knowledge of a failure mode and diagnostic information to predict the time to a failure or degraded condition with confidence bounds. Prognostic algorithms are linked directly to failure modes in the graphical FMECA model. Prognostics do not have an attribute associated with false alarms. The "Prognostic Accuracy" accounts for the early detection quality of the technology. A physical prognostic model (i.e. based on an FE
81
model) would ideally have a higher prognostic accuracy than an experienced-based model (i.e. WeibuU distributions of historical failure rates). More details on model fidelity are discussed in [2]. A valid concern is how the technical attributes of diagnostic and prognostics technologies can be determined. One method is addressed in [1], whereby algorithms are test objectively fi"om performance and effectiveness standpoints using transitional run to failure data. Of course in the absence of this type of information, and with a new sensor/algorithm combination, an educated guess may be the only option.
COST FUNCTION The health management design environment configuration and attributes contain a sufficient amount of information to generate and evaluate a "fitness" fimction. This fitness function is of the form: For each Failure Mode - FM(i) Step 1) Probability of Failure * Severity ^Consequential Cost ofFM(i) -^(Downstream Failure Mode Consequential Costs) * Probability of Propagation Step 2) *HMrisk reduction attributed to FM(i) Step 3) + Cost associated with False Alarms on FM(i) Step 4) + Total Cost of all HM technology The Consequential Cost (CC) is die sum of the direct and indirect costs required to address a particular fault/failure mode (i.e. repair, replace, inspect) ranging from quantifiable repair and labor costs, to less concrete costs such as the effect on system availability. Clearly, only a small aspect of all the possible factors are addressed here and it is purposely left ambiguous. If the probability of failure multiplied by consequential costs is defined as risk, health monitoring reduces risk by providing a probability that a particular failiu-e mode can be prevented by 1) either detecting an '^upstream" fault/failure mode or 2) prognosing when a fault/failure mode will occur. Unfortunately, the health monitoring adds development and hardware costs as well as the potential for false alarms. At the system-wide level, the benefits of the health monitoring technologies in terms of risk reduction must offset the costs and risk of the technology addition. Specifically, the formulation is as follows (using the acronyms defined in Figure 7): Steps 1 and 2 =
Z
FM^ FM,
YlDC
Y,OQ{\-SPf) Y.(^Qi\-SPf) ^ Y[PA^ NsensorsD NsensorP
PMjf
{PfS(CC'M)'Pph
The "Rolled Up" costs =
Pf'SiCQ'Pp
n
r Y.OQ Dcn
NsensorsD
1— NsensorsP
J
82
\
\ PA
2L^olled_Up
Step 3:
^(l-Pf)'S
1-
Y[(i-spf)-Y[(i-FP)
CC
Finally Step 4 = +Y,^IC+YDAIC+Y^PAIC S
D
P
HM DESIGN OPTIMIZATION The goal of the HM system optimization is to maximize the risk reduction provided by the design while minimizing costs. The optimization of the previously described cost function will operate between two boundaries; a "maximum" HM system configuration that includes the "wish list" of all potential sensors and associated algorithms that achieve complete failure mode coverage and a "minimum" configuration that is necessary for safety and control. The optimization algorithm will examine random configuration variations and calculate the "fitness" or cost for each. A genetic algorithm optimization scheme was chosen for the HM optimization because genetic algorithms are better configured to handle optimization problems with little regard for non-linearity, dimensionality or function complexity in general. Potential cost functions generated in the HM environment can include hundreds of independent variables and thus makes it impractical to utilize traditional optimization techniques such as gradient decent or other derivative-based algorithms. While the details of the optimization are outside the scope of this paper, it is important to note that there will be no clear "winner", rather many different HM system configurations will be suggested that the designer can evaluate on the basis of additional criteria. More on this subject can be found in [7]. COLLABORATIVE DESIGN ENVIRONMENT Before an example is given, it is important to address the design environment and associated architecture to enable the entire process. A collaborative work environment is being implemented in this program to allow a number of domain experts to operate applications from different locations, potentially on different operating systems, while sharing and maintaining the same data. For instance, the HM Design Tool will be used to perform advanced con^onent simulation models, FMEA and Cost/Benefit Models simultaneously at various locations. By utilizing the Intemet and standard data formats such as XML, data and applications will be accessible individually through web-based servers, and managed through an integration layer, which will control the communications protocol and access privileges (Figure 8).
83
Figure 8 - Design of Collaborative Work environment HM DESIGN EXAMPLE A simple, yet realistic example of a Health Management design evaluation is shown next. In this example, an electrically actuated control valve concept is addressed for an aerospace application. Recall that a HM design model has many hierarchies ranging from the component level to the system level. For brevity, this example will consider, but not illustrate, the far-reaching system effects of various valve failure modes nor will the cost function for this model be complete. The purpose of the example is to introduce the HM design and optimization process. The top portion of Figure 9 shows a Line Replaceable Unit (LRU) level Functional model of a Load Control Valve (LCV) that is used to regulate discharge air from an Auxiliary Power Unit (APU). Compressed air from the APU is used for main engine starts, environmental control and several other functions. The "in" and "out" bars on the left and right of the model are used to propagate signals, flows, and effects between levels.
Shuttle Valve
Pressure Regulator
Flapper CV
Torque Motor
Actuator
Butterfly Valve LCV Diagp<M
JLM Retnov«/R«phc« LCV
APU EOT, Speed CoaimaDd, Motor Current, Actuator Ponlioa
LCV I I Diapiofdc : Algol illini 1
Figure 9 - Functional Model and HM design for LCV The bottom portion of Figure 9 shows the unit level maintenance task (denoted by the "U*') to remove/replace the IXV. Also shown are the candidate health monitoring algorithms that have the potential to detect a valve degrading in performance and allow for proactive maintenance. Algorithm #1 trends the relationship between LCV command, motor current, and the actual actuator position. In this scenario, the LVDT used to monitor the actuator position is a candidate sensor. Algorithm #2
84
trends the APU's exhaust gas temperature and speed with respect to the LCV command. All the sensors used for Algorithm #2 are available for "free" because they are required for control purposes. Figure 10 shows the HM design at the torque motor level. Contained at this level is a failure mode of torque motor, the effects of such a failure, and maintenance tasks on the motor. Also shown is an existing Built-in-Test (BIT) based on the torque motor current. This BIT is either 0 or 1 and can provide no prognostic capabiHty or truly isolate a failure mode.
LCVBir
01—m-
Repdr/Replacc motor
Seized/Damaged
LCV BIT
m
= Built In Test in Vehicle System Integrated Controller based on sensed motor current draw Figure 10 - HM design at tlie Torque motor level
Figure 11 illustrates the HM design at the actuator where the LVDT would physically exist. Note that due to the cause and effect relationship, failure of the actuator position to function could be the result of a torque motor problem or an actuator failure mode. Finally, Figure 12 is the HM design for the butterfly valve. Many upstream failure modes can cause it to malfunction creating potentially creating more critical downstream failure modes such as insufficient avionics cooling, inability to start the main engines, etc. Clearly, such a model should continue through system interactions until end effects are reached.
Actuator damaged
Figure 11 - HM design for Actuator
85
Repak/Rcplacc Vahre
No load control ofAPU
-mm)-
No/reduced supply auto ECS and ATS
Failed Closed
Figure 12 - HM design for Butterfly valve Figure 13 provides a concise illustration of some of the attributes assigned to the HM elements in Figures 9-12 that were used in evaluating the cost function. Other, "expensive" fault/failure modes such as inability to start the main engines and inadequate avionics cooling were also included. For brevity, the details of the cost function analysis will not be given. In this simple study, the LVDT sensor and algorithm #1 where found to not provide enough risk reduction for the cost, rather, algorithm #2 should be implemented. There are, of course, a number of variable contributing to this result the most dominate being the fact that algorithm #2 uses existing sensors even though it provides lower diagnostic confidence and was assigned higher development costs.
LCV (LRU)
Remove/Replace ICV
SHRC: WOO SD: 2 SLC: 300 UPF: 2
m DC: 0.9 DFP: 0.1 DMC: 200|#2DC: 0 7 DFP: 0.05 DAIC:400\ All
LCV Torque { Motor
M Seized/Damaged
Pf: O.OI C: 3
LCV Actuator
Pp:0S5
Actuator damaged
Pf: 0 01 C: 4
Repair/Replace motor
Motor Currentl
ID LCV BIT
SHRC: 300 SLC: 100
SD:0 UPF: I
Repair/Replace Actuator
SHRC: 200 SLC: 100
SD: 0 UPF: 1
OQ:a6 DC: 0.7 SPfO DFP: 0.2 LLCS: 0 DDAIC: 100 LVDT Pp: 1 OQ:0.9 SPf: lE-2 LLCS: 1000\
Figure 13 - Costs and probabilities for the HM design
CONCLUSION An approach has been presented that extends traditional FMECA and system modeling capabilities to aid in the design of complex health management systems. This approach utilizes a graphical and collaborative design environment where failure modes, failure mode symptoms/effects, sensors, and diagnostic/prognostic technologies are represented. The health management system configuration can be optimized from a cost/benefit through analysis of the fitness attributes on HM system building blocks. The ultimate objective of this approach was to form a methodology and environment which 86
enables effective health management practices by mitigating or preventing failure modes while still keeping sensor and diagnostic/prognostic technology costs at a minimum. ACKNOWLEDGEMENTS We would like to acknowledge the contributions of Carl Byington of Impact, Dr. Jacek Stecki of Monash University, Rob Campbell of Penn State ARL, and the support of Andy Hess and Dr. William Scheuren of DARPA in this ongoing project.
REFERENCES [1] Orsagh R.F. and Roemer, M.J. "Development of Metrics for Mechanical Diagnostic Technique Qualification and Validation", COMADEM Conference, Houston TX, December 2000. [2] Roemer, M. J. and Kacprzynski, G.J., "Advanced Diagnostics and Prognostics for Gas Turbine Engine Risk Assessment," Paper 2000-GT-30, ASME and IGTI Turbo Expo 2000, Munich, Germany, May 2000. [3] Lewis, E., Introduction to Reliability Engineering, John Wiley & Sons, New York, 1987 [4] Roemer, M. J., and Atkinson, B., "Real-Time Engine Health Monitoring and Diagnostics for Gas Turbine Engines," Paper 97-GT-30, ASME and IGTI Turbo Expo 1997, Orlando, Florida, June 1997. [5] Brooks, R. R., and Iyengar, S. S, Multi-Sensor Fusion, Copyright 1998 by Prentice Hall, Inc., Upper Saddle River, New Jersey 07458 [6] Canada, J, and Sullivan, W, Capital Investment Analysis for Engineering and Management, Copyright Prentice Hall 1996 [7] Yukish, Michael, "Simulation Based Design and Lifecycle cost estimating", 54* Proceedings of the Society for Machinery Failure Prevention Technology (MFPT), Virginia Beach, VA, May 2000.
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Condition Monitoring and Diagnostic Engineering Management Andrew G. Starr and Raj B.K.N. Rao (Editors) © 2001 Published by Elsevier Science Ltd.
OPTIMISATION OF SANC FOR SEPARATING GEAR AND BEARING SIGNALS J. Antoni and R. B. Randall School of Mechanical and Manufacturing Engineering The University of New South Wales Sydney 2052, Australia
ABSTRACT SANC (self adaptive noise cancellation) has been found to be a useful technique for separating gear and bearing signals, to vastly improve the diagnostics of the latter, often masked by the strong gear signals. Gear signals are composed primarily of sinusoidal components phase-locked to shaft speeds (with a long correlation length), whereas bearing signals can be called "pseudo-periodic", with a small random variation of the apparent period because of slip (and thus with a short correlation length). An adaptive filter separates the two components on the basis of the correlation length. In earlier work, rules were given for the optimum selection of the three main SANC parameters, filter order, delay time, and a "forgetting factor" used in the adaptation. These were determined and checked empirically. The current paper uses a different approach based on linear prediction, where the gear signal is assumed to be the predictable part, and the bearing signal the difference between the actual and predicted signal. Using this approach, it is possible to obtain analytical expressions for factors defining the optimisation of the process, and thus put the previous recommendations on a more solid theoretical basis, even though the agreement between them is good. Methods are also suggested to achieve further optimisation of the process, by determining an optimal set of initial filter coefficients from the signal itself, and by using a varying forgetting factor to improve the accuracy of the final separation without greatly extending the analysis time.
KEYWORDS Bearing diagnostics, self adaptive noise cancellation, linear prediction, gearboxes
INTRODUCTION SANC (self adaptive noise cancellation) has been found to be a useful technique for separating gear and bearing signals, to vastly improve the diagnostics of the latter, often masked by the strong gear signals (Ho & Randall, 1998). This is particularly the case in helicopter gearboxes, where shaft speeds range from several hundred Hz at the input to a few Hz at the rotor output. The associated gearmesh related frequencies encompass the whole of the audible range and beyond, making it difficult to find a range dominated by the bearing signals (which is often possible with simpler machines). SANC is an 89
extension of ANC (adaptive noise cancellation) where a mixture of two signals can be separated if a reference signal exists which is coherent with one of the two components. An adaptive filter learns the transfer function between the coherent signals, allowing the coherent part to be subtracted from the mixture. In SANC, the reference signal is a delayed version of the primary signal, and relies on one of the components being deterministic (with a long correlation length) and the other being random (with a short correlation length). This applies to mixtures of gear and bearing signals, where the former are composed primarily of sinusoidal components phase-locked to shaft speeds, whereas the latter can be called "pseudo-periodic", with a small random variation of the apparent period because of slip. In reference (Ho & Randall, 1997), a number of guidelines were given to optimise the use of SANC. In particular the importance of setting the three main parameters of the algorithm was highlighted. Some "rules of the thumb" were given and justified by experimental results and simulations. The present paper presents a new approach to optimising SANC, and gives results which complement the previous ones and make them more precise as well as efficient.
SANC ALGORITHMS The SANC algorithm is similar to what is known as the adaptive line enhancer (ALE) in the signal processing community (Zeidler, 1978). A great deal of literature has been written on the subject, from which relevant improvements can be extracted. It is convenient to rephrase the principle of SANC from the viewpoint of prediction theory, the objective being to separate the gear signals from the bearing signal. While the gear signals are theoretically perfectly periodic, at least for a constant speed and over a possibly very long period, the bearing signals experience some randomness. Consequently, the gear signals are believed to be predictable, i.e. their value at any time can be exactly predicted from a set of past values, whereas for the bearing signals the prediction is less and less accurate as the data becomes old. This is the key idea used in SANC, where the gear signals are predicted from their past samples, old enough so that the bearing signals cannot (or very poorly) be recovered. From the prediction viewpoint, the bearing signals are estimated by the prediction error on the vibration signal. More precisely, if the measured vibration signal is written as a sum of the gear and bearing contributions
^(0 = ^ , ( 0 + ^,(0
(1)
then the bearing signal is estimated by
1,(0 = ^ ( 0 - ^ ( 0 = X(t)- E[X{t)\Xit-
(2) plX{t - p-\\...X(t
- p- L + \)]
where X(t) stands for the optimal linear prediction ofX{t) at time t from its past values at times t-p, t(p^l), ...t-(p+L-l) with;? and L positive integers. In the simplest case, the prediction is sought in a linear form, so that X{l) is estimated from a linear combination of its past values:
X{t) = J^y^Mf-p-0
(3)
where the w, are coefficients of a filter of length L to be found. Because in practice L can be very large (a few hundreds), the solution for the w, is sought through a recursive scheme which can be compared to an on-line "stochastic" gradient search, the descent being done towards the values of w, that minimise the prediction error (Widrow, 1985):
90
w';' = wf + jue,X(k -p~i),
i = 0, ...,L-]
(4)
Therefore the three following parameters are of importance: 1) thQ delay p 2) the/e«g//z of the filter Z 3) ihQ forgetting factor for the gradient search ju Further guidelines are given here as to setting the correct length L of the filter and to accelerating the search by optimising ju and introducing relevant initial values for w, which starts the search closer to the global optimum to be found. Setting the length of the filter The principle of SANC is to track narrow-band signals such as sinusoids or pseudo-sinusoids in broadband noise. As such, the adaptive filter responds by forming a transfer function equivalent to that of a narrow-band filter centred at the frequencies of the sinusoidal components in the signal. In theory, when the signal is free of noise, a 2A^-long filter is what is required to track N distinct sinusoids, because each of them is only parameterised by an amplitude and a phase. However, when some additive noise is present, the filter has to be sharp enough around each of the sinusoids in order to reject it. In other words, SANC may then be interpreted as a comb-filter in the frequency domain. From this viewpoint, it is clear that there is not really an optimal length for the filter, in the sense that there exists a value that achieves the minimum prediction error. Rather, the prediction error decreases steadily when increasing the filter length, the filter keeping on becoming narrower and narrower around the sinusoidal frequency components. Hence, the longer the length L of the filter, the better is its resolution. However, the value of Z also introduces a trade-off on the level of noise in the estimated coefficients w,. It has been shown in (Widrow, 1985) that as L is increased, the noise on the w,^s is increased proportionally. So in general, a compromise should made for the choice of Z. One possible way of measuring the accuracy of the SANC filter is to assess its ability to separate two closely spaced sinusoids in noise. The filter must have enough coefficients to resolve the spacing between the two components, i.e. it has to act on the two sinusoids independently. From this viewpoint, a formula was found which places a lower bound on the length L of the SANC filter. It is essentially derived from the results in reference (Zeidler, 1978). Zeidler et al. proved that as L becomes large, the adaptive line enhancer filter estimated on a signal containing A^ sinusoids at frequencies/„ can be simply expressed as: ^^
j . exp[2^X/„;^ - / ) ] ! - exp[2;5r-(/„ + f)L] tf
L + 2allal
l-exp[2;5-(/„ ± / ) ]
where p is the time delay of thefilter,CTQthe /?MS value of the additive (uncorrelated) noise and a„ the RMS value of sinusoid number n. In other words, for large i , the filter becomes a linear superposition of perfectly resolved bandpass filters centered at frequencies/,. From Eqn.(5), each bandpass filter is found to have an FRF magnitude 1
K(/)| =L -+ 2allal / . - I - / . r r . I rr
^ L + la'^la,
sin[;r(/-/„)i] sin[;r(/-/„)] SHIITZ^I
/ — /
M
D,[27t{f-f„)]
(6)
where Di is the Dirichlet function with parameter L. Incidentally, Formula (6) can be compared with that of the comb-filter of same order (synchronous average over iC = Ll/,J periods of length !//„), i.e.
\WiJ)\=fD,[2n(fim
(7)
An example of a single band-pass filter as given by Eqn. (6) is displayed in Figure (1), for Z = 100 and fn = 0.2.
0.9
.
•
•
•
1
08
D
0.7
[27i(f-.2)) 100
S M
0.5
03 0.2
L.
1ft
0.1
-wN^wwWWVWv 111 . 015
0.2
0.25
0.3
0.35
Normalised frequency
Figure 1: Single bandpass filter centered on a given discrete frequency component. The gain of the SANC filter f o r / = / , is given by L
\wM- L^IG'JG:
(8)
which tends to unity as L tends to infinity and the bandwidth of the main lobe is simply 2/L on the normalised frequency scale. In order to separate two sinusoids, two simple band-pass filters must therefore be centred on each frequency peak. Their bandwidth must be narrow enough so that the frequency gains do not overlap, at least on the frequencies of the sinusoids. Then, for two sinusoids separated by AB Hertz and sampled at a rate Fg, the minimum SANC filter length is (9)
A5
This formula is in perfect accordance with the experimental results presented in (Ho & Randall, 1997) where the optimal filter length for a set of A^ harmonically related sinusoids in noise was found to tend towards the period length for large A^. Recall that formula (9) assumes L>2NXo be quite large, typically of the order of a few hundreds. For the sake of completeness, these results are reported here in Figure (2). The asymptotic trend to the predicted value of Equation (9) is very clear.
92
Order vs No. Discrete Components
Samples in One Period 625 450 400 200 100 63
12 16 20 24 No. Discrete Components
Figure 2: Minimum filter length vs number of harmonically related sinusoidal components at different frequency spacings. From Ho & Randall (1997). To test Formula (9), a synthesised signal was made by summing seven sinusoids with complex modulation and some additive white noise of equal power. The magnitude of the raw Fourier transform of the synthesised signal is shown in Figure (3). Figure (4) shows the magnitude of the estimated frequency response W(f) of the SANC filter obtained on the synthesised signal. Here the exact minimal length was used to separate the closely spaced sidebands around the frequency component of each sinusoid. As demonstrated by the figure, the achieved resolution was just good enough to put independent frequency gains on each discrete frequency component, in accordance with Eqn (9). Setting the forgetting factor It is usually difficult to set the value of the forgetting factor ju. As a matter of fact, this choice faces a delicate trade-off If ju is chosen too small, then it will take extremely long for the algorithm to converge. On the other hand, if ju is set too large, the algorithm will diverge. By trial and error, one solution is to choose JU somewhere in between, but not too small so as to achieve fast convergence. But then SANC will converge around a rough solution and will keep on moving a lot around this solution due to normal random fluctuation in the prediction error. It has been shown that the prediction error of SANC remains proportional to ju as the adaptation time goes to infinity. Also, the noise in the estimate of the SANC filter coefficients is proportional to jU (Widrow 1985). Therefore, to improve the performance of SANC, it is clear that the forgetting factor should be diminished so that a finer solution can be obtained. This makes sense since a decrease in ju will result in an increase in integration time during the adaptive process. These considerations suggest choosing an exponentially time varying ju, which decreases from a fairly high value slowly to zero: ju(t) = A Qxp{-at)
(10)
Now two questions remain: how to choose A and a ? Obviously these two parameters are signal dependent. It must be said that no straightforward results exist for the choice of a, so here again this is left to the good judgment of the user. Typical values may be as small as 10"^ On the other hand, fortunate results do exist for setting ^. Indeed, it has been shown that convergence is guaranteed as long as (Widrow 1985):
93
•Wh
A
•tsl
J
•20^
J
s4 ill ill I I ill III III •xl
li i l i L
LI 1 1 . 1 U L J iu
I ul
0.1
. li
0L12
I 111
ai4
ait
i l l
au
J
0
u
002
OM
OM
0.1
0.12
0.14
0.16
0.16
02
Nomhialised frequency
Normalised frequency
Figure 4: SANC frequency response function evaluated on its minimal length.
Figxire 3: Magnitude spectrum of the synthesised signal.
JU < JUimx = ^ 2 /Imax
(11)
where Zmax is the largest eigenvalue of the data autocorrelation matrix and a the RMS value of the signal. Therefore, setting A so that ^0) is "close" to (but strictly smaller than) jUnmx is a relevant choice. Figure (5) shows that Zmax can be well estimated from an autocorrelation matrix of moderate size, much smaller then the actual length L of the SANC filter. Here the experiment was run on a synthesised signal made of seven sinusoids with complex modulation plus a white random noise with equal power. The maximum eigenvalue was computed for different sizes L of the autocorrelation matrix. The ratio Amax/L stabilised very quickly (say L = 300) to a limit value, which could then be used for adapting a much longer SANC filter. The time history for the adaptation of a given coefficient w, with an exponentially decaying forgetting factor is displayed in Figure (6.a). The first value of // was set very close to /Umax (75%), hence enabling a rapid search at the beginning of adaptation, and then decreased exponentially towards zeros (.01% of /Umax) to rcfinc the estimation. For comparison, Figure (6.b) displays a similar experiment, but for a constant forgetting factor. There, JU was set at S% of jUmax to achieve a good compromise between speed of convergence and accuracy of the estimation. However, the estimates still experience large fluctuations and the four epochs used in the adaptation (the same signal was passed four times in SANC) are clearly noticeable. Figures (7) and (8) show the simultaneous adaptation of some of the filter coefficients Wi (/ = 320,.. .,480) for the two cases. Similar conclusions hold. Finally, it was found that on real signals a conservative starting value for JLI{0) could be as low as 10% of jUmax in order to avoid any risk of divergence. It is believed that the reason for this is partly due to the non-stationary nature of the actual vibration signals. This means that there may be some time intervals where the rms value a becomes locally very high so that Equation (11) is violated. Indeed, the presence of spiky additive noise with a kurtosis much higher than that of a normal distribution has often been observed on actual measurements. Another issue of concern is the setting of the initial values for the coefficients w, of the SANC filter before starting the adaptation. Usually they are set to zero or to some small random values. Indeed, any initial values would theoretically lead to the same solution, as the optimum of the quadratic error
94
Time
V^-WY^"\A^^ 200
«X)
600
800
1000
1200
1400
Dimension L of the ACR matrix
Time
Figure 5: Maximum eigenvalue of the autocorrelation matrix with respect to the size of the matrix.
Figure 6: Adaptation history of a given coefficient in the SANC filter. (a) exponentially decaying forgetting factor. (b) constant decaying factor.
Coefficient index
Adaptation time
Adaptation time
Figure 8: Adaptation history of a set of adjacent coefficient in the SANC filter. Constant forgetting factor.
Figure 7: Adaptation history of a set of adjacent coefficient in the SANC filter. Exponentially decaying forgetting factor. Setting the initial values of the filter coefficients
hyper-surface is global. But in practice, this choice greatly influences the speed of convergence to the global solution. The closer the initial values are to the global optimum, the faster the descent to it. A good initial vector of coefficients can be supplied to the SANC by directly and quickly solving Equation (3) in the frequency domain. In other words, a first good guess for Wi is given in the frequency domain by the /fl-type frequency filter (Randall 1987) which takes the Fourier transform of the delay signal X{t-p) as its input and that ofX{t) as its output. Based on this idea, an algorithm is easily conceived which estimates the frequency response function W{f), i.e. the Fourier transforms of Wt, by averaging adjacent short-time Fourier transforms of length L. This is summarised by the following expression, where the Z,-long STFT of X{t) (properly windowed), taken at time k, is denoted by X^(f;k) and that of the delayed signal by Xf{f;k):
'Z^,(f;k)Xf(f;k) }Y( f) = _i
(12)
Y,Xt(f;k)Xf(f;k) 95
Figure (9) shows the estimated frequency response W(f) obtained on a synthesised signal made of seven sinusoids with complex modulation and additive white noise of equal power. The (minimum phase) inverse Fourier transform of it was then fed to the SANC as initial starting values. Figure (10) shows the Fourier transform of w, after convergence of SANC. It is similar to Figure (4). As can be seen, the overall shape of the filter has remained unchanged, but the useless zeros have been removed to yield a more optimal prediction filter. As mentioned previously, any initial values for the tap weights of the SANC filter would have led roughly to the same result as displayed in Figure (10) or (4), but the proposed method will generally converge much faster, and consequently a smaller value for ^0) could then be used when starting the adaptation. Finally, it must be pointed out that the STFT method and the SANC method are different ways of solving the same equations. That is to say, the STFT method should be able to reach directly to the same optimal solution as the SANC, but mth substantial gain in computation time and computation efficiency. However, for the STFT to be optimal, it requires cunning v^ndowing of the short-time sequences combined with some zero-padding to counteract the effects of circular convolution. These aspects will be investigated in more detail in future work.
dB
002
004
01
0.12
014
016
0.18
02
t2
Normalised frequency
014
016
018
02
Normalised frequency
Figure 9: Initial frequency response function of the SANC filter given by the STFT method.
Figure 10: Frequency response function after adaptation of SANC.
ACKNOWLEDGEMENT This work was supported by the Australian Government's Defence Science and Technology Organisation (DSTO) via the Centre of Expertise in Helicopter Structures and Diagnostics. REFERENCES Ho, D. and Randall R.B., (1997) Effects of time delay, order of FIR filter and convergence factor on self adaptive noise cancellation, Proc 5th Int Cong on Sound and Vibration, Adelaide, 15-18 December, Vol.2, pp 945-952. Ho, D. and Randall, R,B., (1998) Improving the efficiency of SANC in its application to bearing diagnostics, Comadem '98, Launceston, Australia, pp 371-380. Widrow, B et al, (1985). Adaptive Signal Processing, Prentice-Hall Inc., Englewood Cliffs, N.J. Zeidler, J et al., (1978) Adaptive Enhancement of Multiple Sinusoids in Uncorrelated Noise, IEEE Trans, on Acoustic, Speech and Signal Processing, pp. 241-255.
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Condition Monitoring and Diagnostic Engineering Management Andrew G. Starr and Raj B.K.N. Rao (Editors) © 2001 Elsevier Science Ltd. All rights reserved.
A REVIEW OF FAULT DETECTION AND ISOLATION (FDI) TECHNIQUES FOR CONTROL AND MONITORING SYSTEMS A. Elshanti, A. Badri and A. D. Ball MERG, Manchester School of Engineering, The University of Manchester, Oxford Road, M13 9PL, UK Email: ali elshanti@hotmaiLcom Email: abdellatefbadri(S).hotmail.com
Phone: 0044-161-2754308
ABSTRACT A fault in any control and monitoring system can develop into a failure that may cause system shutdown. Therefore, faults must be detected in the early stages and the faulty component should be isolated to prevent fauh propagation to the system, hence fault detection and Isolation (FDI) is an important aspect for any control and monitoring system. FDI techniques are becoming more common in many fields due to the benefits that they can provide such as safety and maintainability. There are several FDI techniques that can be used to achieve this aim. However, each technique has its capabilities and limitations, which make it suitable only for certain applications. The basic aim of this paper is to give abroad review of the various FDI techniques. The paper is structured as follows: firstly, classification of FDI techniques. It then describes the evaluation measurements concerning these techniques. Next, gives a review of the concepts of each technique. Finally, it highlights the capabilities, limitations, and the applications of each technique.
KEYWORDS FDI techniques, knowledge based methods, direct analysis methods, neural network and fuzzy logic methods, model-based methods INTRODUCTION Research in fault detection and isolation (FDI), has been increasing world wide in both sides, theory and application, this started in the early 70's. Researchers such as Patton and Chen (1998) and Frank and Ding (1997). • Fault detection and isolation "diagnosis" can meet the requirement in two aspects: 97
Detecting faults at early stage, which allow operators to adjust system parameters to over come the faults. •
Predicting and supervising of the faults and its development will leave enough time for the maintenance engineers to take measurement in order to avoid unexpected system breakdown.
Finally, fault detection and diagnosis has been taken very important part in ensuring the safety and reliability of control system. Researches for fault detection and diagnosis has been widely carried out in order to find a good method of extracting and classifying fault information effectively. Many methods have been developed and the main four methods are as follows: •
Knowledge based methods.
•
Direct analysis methods
•
Neural network and fuzzy logic methods
•
Model-based methods
METHODS OF FAULT DETECTION AND ISOLATION Now let us give a brief description of each method: Knowledge based methods Knowledge-based approaches may be divided into shallow diagnostic reasoning techniques and deep diagnostic reasoning techniques. These methods originates from applications in which exact information about the system is hard to obtain such as in medical applications. Shallow diagnostic reasoning techniques can be subdivided into databases and expert systems. The most commonly way to implement a shallow diagnostic reasoning technique, is to use look-up tables of process condition versus faults. The foundation of deep diagnostic reasoning techniques is a deeper model of the process than the look-up tables used in shallow knowledge based approaches. There are different approaches to achieve diagnosis within the deep reasoning concept such as: constraint suspension technique and governing equations technique. Both Isermann (1991) and Frank (1991), give a broad out line of the knowledge based method. Freyermuth (1991) and Uchiyama_T, Kallweit_S, and Siekmann_H. (1995), have carried out similar works on this subject. The knowledge based methods open a new dimension of possible fault diagnosis for complex systems. These methods depend on conclusion drown from the human experiences and the operational history of the system. The knowledge based diagnosis methods are the convenient methods for large scale and complex system. However, in most cases of implementations, these methods are combined with analytical models. Isermann provides us with its framework as shown in fig. 1.
98
People with computer
Heuristic Knowledge
Analytical Knowledge
T
T Process data
Process —•
^ w
Data processing
Inference mechanism —•
- •
Solution proposal
Fig. 1 On line knowledge based fault diagnosis
Direct analysis methods They are often based on signal processing. This is divided into two methods the first one is in time domain and the second one is in frequency domain. The use of spectrum, spectrum and wavelet found very effective in fault symptom extraction and isolation. They are suitable for vibration signal analysis and diagnosis. For example, Rao (1996), C. Guhmann (1991), McFadden (1996), Fengshou, A. Ball (1995) and Kahraman (1993). In this method, there is only one problem when failures or faults occur in sensors, the signals taken from them will be false, according to this, the diagnosis will be completely wrong. Neural network andfuzzy logic methods In these methods both linear and non-linear systems can be simulated. The neural network is trained by the normal input signals. The trained neural network give the differences between fauhy signals and normal signals when fauhs or failures occurs. Frequently, a threshold is given for the neural network to determine whether or not the alarm should be given. This method is made suitable for a wide range of application due to its high classification capability. Fuzzy logic methods are mostly used in two cases: The first case is in the diagnosis of logic program, e.g. digital control program or machine tool program, as described by Barschdorff (1992), Zhanqun Shi, Hong Yue, Qinghe Liu (1998)and Le, Watton and Pham (1997). The second case is in some systems when the systematic performance can not be represented quantitatively. In this case fuzzy logic method can give a fuzzy representation and based on some
99
fuzzy thresholds and fuzzy criteria, one can implement the diagnosis satisfactorily as stated by Kiupel and Frank (1997). Fig.2 shows the basic idea of neural network isolator.
Residual 1
Input layer
Hidden layer
Output layer
Fig.2 neural network isolator
The residuals can be feed into the neural network for training, either if they were zero or not. The network will be trained under the normal condition to describe the dynamic behavior of the system. The neural network run in parallel with the system during the trained and normal modes. If an abnormal change occurs in the system, the trained neural network will be very sensitive to give the prediction and the alarm. When the threshold is selected and chosen properly, there will be no false alarm Model-based methods Model-based diagnosis can be defined as the detection, isolation and determination of the characterisation of faults in components of a system from a comparison of its available measurements, with a priori information represented by the system's mathematical model. They use the analytical model of a system to run in parallel or redundancy with the real system. The deviation or residual is produced by comparing the output of the real system or process with the output of the analytical model (sometimes model-based observer). This residual or deviation can be used to detect and isolate faults when they occurred in systems or processes. The model-based method has a major advantage, which is, it can detect and isolate not only faults or failures which have occurred in systems and processes but also faults which have occurred in sensors and actuators. Furthermore, appropriately designed diagnostic systems are very sensitive to incipient and small faults. This is the reason why the model-based methods developed so actively and rapidly. Modelbased methods can be classified into different approaches. Patton (1997) sub-divided mode-based FDI techniques (using quantitative models) into the following approaches:
•
State estimation approaches (state or output observers) Parameter estimation approaches Parity equation approaches
100
Output -•
Input SYSTEM
Residual generation residuals Decision Making Fault Information Model-based fault diagnosis
CONCLUSIONS An overview of the different approaches to fault detection and isolation has been given including classification and the basic concepts of each method with its application. The advantages and disadvantages of different methods have been mentioned. There is a wide range of FDI methods, however, non of the methods overviewed solves the remaining problem of completeness. It can be stated that model-based methods are the most frequently applied methods for fault detection, especially for the process and sensor faults. Nevertheless, the importance of combined methods and neural network and fuzzy logic for FDI is noticeably growing.
References Barschdorff D. (1992). Comparisons of neural and classical design algorithms, IFAC symposia, 408415. Fengshou Gu and Andrew Ball (1995). Using of the smoothed pesdo-winger Ville distribution in the interpretation of monitored vibration data, Maintenance, No.2, 16-23. Frank P. M and Kiupel N, Fuzzy supervision and application to learn production. International journal of systematic science, Vol. 24, 1935-1944. Frank P. M. (1991). Fault diagnosis in dynamic systems using analytical and knowledge based redundancy- survey and some new results, Automation, Vol. 26, 459-474. Frank P. M. and Ding, X. (1997). Survey of robust residual generation and evaluation methods in observer-based fault detection systems, J, of process control 7:6,403- 424. Freyermuth B. (1991). Knowledge based incipient fault diagnosis of industrial robots, IFAC Symposia Series, 369-376. Guhmann C. and Filbert D. (1991). Fault diagnosis of electric low-power motors by analysing current signal, IFAC Symposia Series, 141 -146. Isermann R. and Freyermuth, B. (1991). Process fault diagnosis based on process model knowledgepart I, principle for fauh diagnosis with parameter estimation, Trans. Of ASME, J. of Dynamic systems, Measurement and control. Vol. 113, 621 - 6 2 6 .
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Kahraman A. (1993). Effect of axial vibration on the dynamics of a helical gear pair, Trans. OfASME, J. of Vibration and Acoustics, Vol. 115, 33-39. Kiupel N. and Frank P. M. (1997). A fuzzy FDI decision-making system for the human operator, IF AC symposia, 731 -736. Le T. T, Watton J. and Pham D. T. (1997). An artificial neural network based approach to fault diagnosis and classification of fluid power systems, oflnstn. Mech.Engns, Vol.21 lipart 1, 307- 317. McFadden P. D. and Smith J. D. (1996). Model for the vibration produced by a signal point defect in a rolling bearing, J. of Sound and Vibration, No.l, 69-82. Patton R. J. and Chen J. (1998). Proceedings of IFAC Symposium on Fault Detection, Supervision and Safety for Technical Processes-SAFE-PROSS'97, Perg amon. ISBN 0-08-04238117. Rao B.R.K. (1996). Handbook of condition monitoring, Elsevier Advanced Technology, UK. Uchiyama_ T, Kallweit_ S. and Siekmaim_ H. (1995). Knowledge-based fault diagnosis for pump stations by process modelling, ASME, Fluids Engineering Division (Publication) FED, Vol.222, 117121. Zhanqun Shi, Hong Yue and Qinghe Liu (1998). Fault diagnosis of electro-hydraulic servo systems based on neural networks, proc. OflCAIE '98, China.
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Condition Monitoring and Diagnostic Engineering Management Andrew G. Stair and Raj B.K.N. Rao (Editors) © 2001 Elsevier Science Ltd. Allrightsreserved.
A MONITORING AND DIAGNOSTIC TOOL FOR MACHINERY AND POWER PLANTS, BASED ON CHAOS THEORY M. Fontana, A. Lucifredi, P. Silvestri Dip. di Meccanica e Costruzione delle Macchine - University of Geneva, via airopera Pia, 15A, 16145 Geneva, Italy - e-mail:
[email protected]
ABSTRACT The paper is an activity report of a research still under development en machinery and power plants diagnostics. Chaos theory seems to be apt to provide useful information supplementing the traditional methods for monitoring rotating machinery or industrial plants. The paper illustrates a diagnostic tool developed by the authors based en chaos quantifiers: they are able to detect the presence of nonlinearities from the signal of the examined system, and therefore the existence of a condition of anomalous (chaotic) operation, which, in the case of rotating machinery, could be caused for example by bearings hydrodynamic instability, rotor cracks or rotor rubbing. A powerful tool to detect chaos, largely considered in our study, is the value of the fractal dimension of the attracter. Different modules for fractal dimension calculation have been developed. All the modules have been assembled into a diagnostic toolbox, written in Matlab®. The present article reports the results obtained by a systematic use of the toolbox on experimental data coming from a rotor model, which made possible to operate at will in typically chaotic unstable conditions. The experimental activity has been developed carrying out tests, at different speeds, in hydrodynamic instability conditions (oil-whirl) and in rotor rubbing conditions. From the described results, chaos tracking technique seems to be an interesting tool to be developed and integrated to the traditional methods in future real-time monitoring/diagnostic systems for machinery.
KEYWORDS Diagnostics, monitoring, chaos, nonlinearities, fractal dimension, diagnostic software, experimental data.
INTRODUCTION Nowadays vibration analysis is one of the most used methods for machinery and power plants diagnostics and monitoring. Often anomalous conditions of these mechanical systems show typical nonlinear behaviors that, to some extent, limit the application of most of the traditional methods. Analyses based on chaos theory make possible to identify the rise of nonlinear abnormal operating conditions, supplying qualitative and quantitative information [1,2]. Such analyses could be integrated effectively 103
to traditional monitoring procedures for machinery, with the aim of finding with greater precision and timeliness the rise of a nonlinear misoperation, e.g., in the case of rotating machinery, rubbing between fixed parts and moving parts, bearing hydrodynamic instability, appearance of a rotor crack. The paper reports resuhs of a research under development. It describes an original diagnostic toolbox mainly based on chaos quantifiers, which in some cases makes possible, by processing signals taken fi-om the system under examination, to detect at an earlier time than with traditional methods the presence of anomalous chaotic operating conditions, by means of methods suggested in chaos theory. The software has been developed in Matlab® platform, with a particular attention to a user-friendly graphical interface. The toolbox is made up by a number of modules, providing the user with all the most important methods of analysis based on chaos theory defined in literature. Although in the past many researchers have suggested the use of chaos analysis for the examination of non-linear phenomena, still nowadays a general-purpose commercial software supporting these analyses for real mechanical systems is missing. Important difficulties in the development of the software were related to the translation of sophisticated mathematical methods into practical engineering programs and to the type of data, coming from real acquisitions rather than from mathematical models. The software has been tuned by analyzing experimental data acquired firom a rotor model, the Bently Nevada Rotor Kit, in presence of rubbing between rotor and fixed parts and of hydrodynamic instability in bearings (oil whirl).
DIAGNOSTIC TOOLBOX Different methods of analysis based on the chaos theory have been implemented in a toolbox; particular attention was paid to the dialogue with the user, automating the greatest possible number of procedures and making "transparent" their execution. As data input, it is possible to perform data acquisition, to process experimental data, to recall a previous workspace or to examine computer-simulated data. Various choices are available: filtering, symbolic analysis, traditional methods, and chaos quantifiers. Experimental signals are always affected by noise. Noise can modify the chaos quantifiers; a wrong filtering can cause the same problem. Special filtering software for chaotic signals is under development. In particular a specific filtering software for signal noise reduction based on iterative SVD decomposition, according to the method suggested by Shin, Hammond and White [3] has been developed (and tested on experimental signals coming fi'om an instrumented model of rotor); singular value decomposition is used iteratively to distinguish the deterministic signal from the noise. The algorithm makes possible to eliminate noise in a time series keeping unmodified its chaotic component; under certain conditions the method may be used almost blindly, even in the case of a very noisy signal. It is not only applicable to chaotic signals but also to ordinary deterministic waveforms. The diagnostic toolbox operates on experimental data; computer-generated data were used for the preliminary tuning of programs. Chaos quantifiers evaluation is the most significant section available in the toolbox. Fig. 1 shows a number of indicators presently available in the software. It can be divided in two subsections, the first one referring to qualitative methods to detect chaos and the second one implementing quantitative methods asfi-actaldimensions of the attractor or Lyapunov exponents.
THE CHAOS BASED MONITORING WINDOW The chaos window collects all the chaos quantifiers developed in the toolbox: it is divided into different sections. A first preliminary section contains basic traditional methods for general use: e.g. it plots the time history, the orbit and the FFT, which are usefiil to obtain general information on the processed signal and support data for chaos analysis. Another section is devoted to chaos qualitative methods as the plot of the pseudo phase plane, Poincare maps, return time analysis and velocity statistical distribution [4, 5].
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Fig. 1 - Chaos quantifiers window; the case of information dimension calculation Finally a section is dedicated to quantitative methods based on the evaluation of the fractal dimension of the attractor or on Lyapunov exponents. Hereafter we report a description of the chaos quantifiers present in this window and of some results for each type of quantifier, obtained by processing data coming from an experimental rotor model able to generate at will all the various instabilities of interest, the Bently Nevada Rotor Kit. It is a versatile and compact model, which simulates several categories of lateral shaft vibration, found in large rotating machinery. Various vibration characteristics may be observed by changing rotor speed, shaft bow, rotor stiffness, amount and angle of unbalance, shaft rub or hitting condition, bearings operation conditions, etc. The rotor kit is equipped with proximity probes (displacement transducers). Two different operating conditions were mainly experimented: rubbing (using a preload frame) and oil whirl (installing a transparent bearing) [6]. Further information and a more detailed description of the experimental facility are reported in [4, 5]. FFT The calculation of the FFT can be done directly from the chaos window of the toolbox. FFT allows to detect the rising of chaotic behavior: this appears with the transition from a FFT having a series of isolated peaks to a FFT with a wide band of frequency. Fig.2, for the same rotor speed level, reports the graphic output for absence of rubbing (a) and with rubbing (b). Pseudo phase plane Every time a new series of data is loaded in memory, the toolbox automatically computes the FFT to characterize the frequency of the main component present in the signal and to calculate consequently an optimal value of the time delay H needed to reconstruct the pseudo phase plane (x(t), x(t+H)), according to the methods described in [4]. The pseudo phases plane is ftandamental to characterize the system, as it is closely related to the phase plane. The representation of the system in the phase plane completely defines the behavior and contains information regarding sensitivity to initial conditions and stability, elements that supply information on the possible chaotic behavior of the system. 105
<
Frequency main componenl = 41.5152 Hz o
Frequency mam component = 167 0612 Hz
-
0 03
0 025 5t i i 0.02
1
-
1 0,015
.
001
0.005
U.U
i j J v l . . i . J.-.J. . 150
200 250 300 Frequency (HzJ
350
150
400
200 260 300 Frequency (Hz]
350
400
450
500
a) Fig.2 - a) FFT n=2500 rpm, no rubbing b) EFT n=2500 rpm, high level of rubbing
b)
Fig.3 - Pseudo-phase plane; a) n=2500 rpm, no rubbing; b) n=2500rpm, high level of rubbing Trajectory and orbit The software draws trajectory and orbit of the observed system. Trajectory is the time history of the motion of the system (along one or two geometric directions) while orbit is the curve representing the system evolution in a space whose coordinates are the geometric directions. Chaotic analyses executed on trajectories or orbit require curves traced in normalized coordinates; that means that the curves will always have the same horizontal and vertical size. Poincare map The program makes possible the graphical reconstruction of the Poincare map [1], derived from the attractor represented in the pseudo phase plane, at different sampling frequencies. This tool is used as a descriptor to observe the evolution of chaos in the considered system. Statistical distribution of velocity The toolbox can use a time series to calculate the instantaneous velocity in every acquisition interval; the calculated values are then reported in a statistical diagram. The diagram gives useful information about the presence of nonlinearities in the observed system [4]. The values of instantaneous velocity may also be reported as a function of time, thus obtaining another useful diagram showing in which way nonlinearities act on the system. Both diagrams demand short times of computation, linearly increasing with the number of points of the series; they can therefore supply information in real time on the state of the system. Return time Return time analysis is a useful procedure to obtain a qualitative information about the conditions of a physical system and to discover the presence of chaotic instability. 106
This analysis, implemented in the diagnostic toolbox through the development of dedicate modules, may be used to fmd hidden periodicities in a time series which could not be easily recognized with traditional methods. More details are reported in [4]. Periodicities appear as series of decreasing peaks in a diagram; chaotic conditions cause the arising of extra peaks and the shift from linear decay to exponential decay of peaks height. Chaos quantifiers A large portion of the toolbox is dedicated to quantitative methods for chaos analysis. These algorithms measure the fractal dimension of the attractor (previously reconstructed from the time series under examination) in order to evaluate its chaotic properties. A large number of algorithms has been developed for measuring the dimension of theoretical fractals; an extensive research has allowed selecting the ones which best fit to the analysis of chaotic motion in mechanical systems. Subsequently these methods have been changed and adapted in order to overcome the problems connected with the analysis of real fractals (i.e. attractors generated by time series measured on a physical system, rather than from mathematical equations). At present the toolbox contains modules which compute Higuchi, Katz, Hausdorff, capacity, correlation, information and pointwise dimension; their definitions are reported in detail in [1, 2, 7, 8, 9]. The toolbox contains also modules for the calculation of two different kinds of entropy: the relative entropy and the K entropy. These two parameters, based on the statistical definition of entropy given by Boltzmann, are strictly correlated to the stability and chaotic nature of the system. Chaos quantifiers can be calculated directly from the main window of the program. Higuchi, Katz, Hausdorff dimension A high speed of execution is the main advantage of Higuchi and Hausdorff algorithms, which require a number of elementary operations directly proportional to the number of points of the attractor. The expression of fractal dimension given by Higuchi involves the measurement of the length of the attractor line, done at increasing resolution [10]. This method exploits the propriety of self-similarity (fractals maintain an unchanged appearance when the scale of measurement is changed). Hausdorff dimension is based on the idea that the measure of a fractal is zero or infinite if the measurement is done with a wrong unit, and gives a finite result only when the measure unit has exactly the same geometrical dimension of the fractal. The fractal dimension proposed by Katz examines the relative distances of points of the time series, comparing them to the values obtained from a linear (non-fractal) curve, similar to the attractor. Katz module demands greater times of calculation than the two previous ones, but always directly proportional to number of the sampled data. Capacity dimension (box counting dimension) The module developed for box counting dimension evaluation firstly defines a matrix representing the grid used to divide the attractor. Every element of the matrix is equal to zero if the correspondent cell of the grid does not contain any point; otherwise has a positive value. Starting from this matrix, the algorithm calculates the fractal dimension according to the definition reported in literature [1]. From the tests carried out it was possible to conclude that it represents a good compromise between precision of the resuh and duration of computation time. The number of operations to be executed has a linear dependency from the number of points of the attractor and a quadratic dependency from the grid mesh. Fig.4 reports the graphic output of the toolbox for typical non-chaotic (a) and chaotic conditions (b). Information dimension The algorithm is based on building a matrix related to a grid that divides the attractor, in analogy to box counting dimension. In this case the value of the elements corresponding to non-empty squares of the grid is equal to the number of points of the attractor which are found in the relative cell. The matrix is then divided by the number of points which compose the attractor, so the software evaluates a second matrix whose elements represent the probability for a generic point of the attractor to be found in any of the cells. A diagram in the main window is plotted using different colors, so to put in evidence zones of the pseudo phase plane where the points are more or less condensed. Fig.l reports the attractor in the 107
case of a rubbing rotor condition. It can be seen that the attractor has not a uniform distribution of probability: in warmer color are shown the zones where the density of points per cell is higher. For clearness purpose the grid is not traced.
0.02
0 03
004
0 05
m a) x(»' Fig.4 - Attractor and related grid for n = 2500 rpm (a) no rubbing (b) rubbing condition
b)
Correlation dimension The idea is to center in every point of the attractor a sphere of increasing radius, and to count the average number of other points contained in the spheres. This number increases with the radius of the spheres, and the correlation dimension is a value connected to its rate of increase. The minimum and maximum radii of the spheres, the number of intermediate steps and other parameters are chosen using empirical laws. The number of elementary operations to be done is proportional to the square of the number of points of the series, thus often requiring a long time. Some improvements done in the algorithm have allowed reducing the time needed for the calculation, Pointwise dimension The main difference between a theoretical fractal and a real fractal is that while the former is characterized by the same value of geometrical dimension in any of its parts, the latter has only an average dimension, whose value can vary when considering different zones of the fractal; for this reason the term "multifractals" is often used to identify real fractals. Pointwise dimension allows to calculate the dimension of the attractor in any point, using an algorithm roughly similar to the one of correlation dimension. The software uses the algorithm results to construct a three-dimensional diagram (see fig 5), reporting the attractor on the plane (x, y) and the punctual dimension in each of its points on the z-axis.
Fig.5 - Pointwise dimension, n=2500rpm, a) no rubbing - Dpw=1.12, b) rubbing condition- Dpw=l Relative entropy and K-entropy For complex systems with a high degree of disorder, entropy is high and grows quickly with time. Since disorder and fractal geometry are deeply connected, it is possible to fmd the presence of chaos in a 108
physical system by analyzing the value and the evolution of entropy [11]. Relative entropy is calculated comparing the given attractor to the same system in condition of maximum entropy (i. e.: equal probability of finding the system in any of the possible states on the pseudo-phase plane). K-entropy evaluates the rate of growth of relative entropy during the evolution of the system. Lyapunov exponent Lyapunov exponent is a measure of the sensitivity of the system to changes in initial conditions [9]. A positive exponent means that nearby orbits diverge exponentially (chaotic behavior), while a negative or null exponent means that the orbits diverge linearly or even randomly. This important quantifier allows the recognition of chaotic motion from random noise, thus eliminating one of the most problematic ambiguities in chaotic analysis. To create a program to compute Lyapunov exponents starting from an experimental time history involves many problems, such as the necessity of optimizing a large number of parameters that take part in the calculation. A particularity of the Lyapunov exponents algorithm is that execution time does not grow exponentially with the number of points of the pseudo phase space, but linearly. Fig.6 reports Lyapunov exponents for different rubbing conditions in a rotating shaft. 350 300
^
250
I 200 > 150 o i 100
^ ^ 1
I 2
I
I 3
RL
4
a)
RL
b)
Fig.6 - Lyapunov exponent for different rubbing levels (RL), a) n = 1500 rpm, b) n = 2500 rpm Results obtained with the experimental rotor model with the previous quantifiers in oil whirl conditions at different speed levels are reported in fig.7. The results show the efficiency of the modules for identification and characterization of this nonlinear anomalous conditions: from the diagrams it can be observed that, for rotational speeds higher than 2000 rpm, these indicators increase with the rotational speed, underlining the rise of a chaotic condition due to hydrodynamic instabilities. At 1000 rpm chaos quantifier are high as a strongly irregular condition is present in the rotor. More details on this particular rotor operating condition are given in [5].
CONCLUSION Chaos-based monitoring can add useful diagnostic information for rotating machinery or mechanical systems, supplementing the traditional methods. The results of analyses have made possible to evaluate the efficiency of the modules for identification and characterization of the nonlinear anomalous conditions to the purpose of machines monitoring; in the meanwhile other aspects have been evaluated influencing the application of the modules, first of all the computer time necessary for the execution of the algorithms. The work performed provides a strong motivation for further developments. Since the final goal is a condition monitoring system, which will also collect the information from other different modules, it will be necessary to establish, for the application under examination, the correlations between diagnostic parameters (from the modules) and damage parameters and to formulate, as a synthesis of these correlations, a single function summarizing the state of health of the machine, i.e. the amount of damage and the rate of trend to damage. 109
0
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Fig.7 - Chaos quantifiers in oil whirl condition
ACKNOWLEDGEMENTS The research has been partially supported by MURST.
REFERENCES Moon C. (1987). Chaotic vibrations, John Whiley & Sons, Inc. Adams M.L., Abu-Mahfouz I.A. (1994). Exploratory research on chaos concepts as diagnostic tools for assessing rotating machinery vibration signatures, Case Western Reserve University, Cleveland. Shin K., Hammond J.K., White P.R. (1999). Iterative SVD method for noise reduction of lowdimensional chaotic time series. Mechanical System and Signal Processing 13(l).l 15-124. Fontana M., Lucifredi A., Silvestri P. (2001). Chaos theory provides additional information for machinery and power plants monitoring and diagnostics: an innovative software toolbox, IV Conf. Internat. Acoustical and Vibratory Surveillance Methods and Diagnostic Techniques, University of Technology Compiegne, France. Fontana M., Luciifredi A., Silvestri P. (2001). Un toolbox per la diagnostica ed il monitoraggio delle macchine rotanti basato sulla teoria del caos, XVConf. AIMETA, Taormina, Italy. Ehrichs F.F. (1992). Handbook of rotordynamics, McGraw-Hill, New York. Hillborn R. (1994). Chaos andnon linear dynamics, Oxford University Press, Oxford. Grassberger P., Procaccia I. (1983) Measuring the strangeness of strange attractors, Physica 9D 189-208. 9 Addison P.S. (1997). Fractals and chaos. Institute of Physics Publishing. 10 Higuchi T. (1998). Approach to an irregular time series on the basis of fractal theory; Physica D 31. 11 Ott E., (1994), Chaos in dynamical systems, Cambridge University Press. no
Condition Monitoring and Diagnostic Engineering Management Andrew G. Starr and Raj B.K.N. Rao (Editors) © 2001 Elsevier Science Ltd. All rights reserved.
NOVELTY DETECTION USING MINIMUM VARIANCE FEATURES L. B. Jack and A. K. Nandi Signal Processing and Communications Group, Department of Electrical Engineering and Electronics, University of Liverpool, Brownlow Hill, Liverpool, L69 3GJ, UK
ABSTRACT Reliable novelty detection is one of the most difficult problems to address in the condition monitoring area. Most, if not all, classifiers are based around the assumption that data can be clustered into two or more separate classes. Normalisation is carried out using multiple classes, in an attempt to ensure that the training data provided to the classifier is easy to partition. In condition monitoring however it is often difficult to provide training data that includes all likely fault conditions and this can be a significant problem to overcome. Work previously carried out (Jack, 2000) using feature selection with supervised training of neural networks and support vector machines has indicated that in most cases, the feature selection mechanism chose features with minimum variance within a class, making the data for each class closely clustered together. On this basis, it was decided to examine the impact of using minimum variance features of datasets that contained only normal data, and investigating the impact of this strategy on a simple novelty detection technique using the self organising map (SOM), first proposed by Kohonen. Features are transformed using Principle Components Analysis (PCA), which reduces the dimensionality of data, while reordering the data from maximal to minimal variance. Using the minimum variance dimensions of the transformed data, it may be possible to detect condition changes. This would imply it would be comparatively easy to detect other conditions. Data from two different machines was used; machine A exhibiting rub and misalignment faults, providing 5 different datasets, while machine B exhibits ball bearing faults, from which 6 different statistical and spectral based datasets have been derived. The normal data is then used to train the SOM, and using a simple thresholding algorithm, detection h carried out. Results indicate that minimal variance features can improve detection of fault characteristics, however a hybrid approach using a mixture of maximal and minimal variance feature seems to offer the most robust solution.
KEYWORDS Condition monitoring, Kohonen SOM, variance, novelty detection INTRODUCTION Classifier based solutions for condition monitoring have existed for some time, using a variety of statistical, and artificial intelligence based approaches to discriminate between normal and fault
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conditions, and these have been, for the most part, highly successful. However, in using classifier-based solutions, two main problems exist. These can be summarised as one of training data, where it is necessary to attempt to train a classifier to recognise many different fault types without being able to give sufficient training examples of each type of problem. The other main problem is concerned with selecting a set of features to allow the classifier to discriminate between the classes without becoming "confused" by an excessive amount of data, some of which may be misleading. When multi class data is available, it is comparatively easy to select a good set of features for the classifier that optimise performance on that particular problem. For the case where there is only normal data, the problem is very different, as empirically determining what features are useful at discriminating fault conditions is non longer an option, due to the lack of multiple class data to discriminate between. This then raises the question of how to select a set of features that can discriminate between normal and other conditions without having examples of the different conditions. This paper advances a simple strategy, which seems to work relatively reliably in the two test cases used in the paper. Unfortunately, 100% accuracy of detection is unlikely to be possible in the case of only normal data being available, however, it is still possible to get relatively good results, as this paper shows. THE PROBLEM This paper examines the problem of how best to select features when only normal data is available at the classifier training stage, and examines a number of different strategies for selecting "sensible" features that may maximise the performance of a classifier on data that it has never seen before. The problem is limited to a simple two-class case - normal or fault conditions, with only no fault data being used for both training and normalisation. This superficially creates a number of problems; namely that of choosing a good method of normalisation for the data that will be able tcl accommodate the fault data when it occurs, and also what features to select as inputs to the classifier, th^t will maximise performance of the classifier on the problem in hand. THE KOHONEN SELF-ORGANISING MAP The Self Organising Map (SOM), first proposed by Koh^nen (1982), approximates the mapping of a data set in feature space, by manipulating a mesh, or network of neurons to cover the whole space such that they encapsulate the area covered by the data. The training mechanism is self organising, meaning that no supervised training algorithm or target is required, whilje the network will adapt to fit the data presented. When data is presented to the network, the neuron nearest to the data point will fire. This property can be employed very usefully in the case of a two class problem as this means that the SOM can be trained using only normal data, and then have the network recognise only those examples belonging to the normal class (Taylor, 1998). This can be thought of as aI simple clustering problem. Once the SOM has been trained, a radius finding process is carried out. For each jieuron in the SOM, the training points nearest the neuron are found, and then the greatest distance of any o|f these points is used to determine the "influence" of that neuron. The competitive layer of the network removed and replaced by a simple linear layer. Using the linear layer in place of the competitive layeij means that it is possible to place a quantifiable figure on the distance a given point is from any n in the SOM. This can be used to determine whether the data point lies within the radius of influencje of any of the neurons in the SOM. If this were the case then the point would be classified as normal.
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Radii of influence of different neurons
FIGURE 1: NEURONS AND RADII OF INFLUENCE WITHIN S O M AFTER MODIFICATION
The SOM used in this paper is standardized across all simulations performed. The SOM consisted of a two-dimensional 4x4 array of neurons, which allows the network to cover the input space effectively. As can be seen from the results, this performs reasonably well in the examples given. Unfortunately, due to time constraints and a desire for consistency, it was decided only to test this one size of SOM in this paper. FEATURE SELECTION: MINIMAL VARL\NCE FEATURES When dealing with a two-class case, feature selection is a fairly straightforward process in which features are selected which maximise the separation between the two different categories of data. In many cases however, data from one of the classes only will be available. The question is then how to select a set of features that can reliably detect the onset of a fault, or novel condition. Principle Components Analysis Principle Components Analysis (PCA) is a technique widely used in the pattern recognition community as a method of feature reduction - i.e. maintaining the information content of data while reducing it's dimensionality. This is carried out by performing a rotation of the data in feature space, and creating a new set of axes (dimensions), which maximises the variance in each dimension as much as is possible. After transformation has taken place, the data is ordered, with maximum variance held in the first few dimensions, and decreasing variance as the other dimensions are traversed. The new transformed dimensions are linear combinations of the original dimensions. Further information on PCA can be found in Haykin (1994). During some earlier work carried out using GA based feature selection algorithms (Jack, 2000), it was noted that for multi-class problems, the GA tended to select features with minimal variance - i.e. tightly clustered features, it was decided to examine the impact of using low variance features selected from the low variance end of the transformed data matrix after PCA, in an attempt to see whether the low variance features may be safely used for fault detect in preference to high variance features when normalised on the basis of training data only. Four different scenarios are attempted; using the whole dataset after PCA (i.e. no feature reduction - referred to as "PCA down"), taking features from the bottom end of the PCA transformed matrix (i.e. low variance features - "PCA up"), taking features from the high variance end of the matrix, and finally picking a combination of high and low variance features simultaneously (i.e. picking two highest and two lowest feature from the matrix - "Both Ends"), Figure 2 shows the difference in variance between fault and normal conditions for the High pass filtered dataset of machine B. As can be seen, the variance of the normal datasets, both seen and unseen, is very similar, even in the lower order components of the PCA matrix. The fault condition variance is in all cases significantly higher, and examination suggests that it should be possible to discriminate between the two types of data comparatively easily. 113
High pass filtered stats
FIGURE 2: DETAIL SHOWING VARIANCE OF THE HIGH PASS HLTERED FEATURE SET FROM MACHINE A AFTER P C A , W H E N NORMALISED ON THE BASIS OF NORMAL DATA ONLY.
DATA PREPARATION, TRAINING AND SIMULATION Data Preparation Having sampled the data, pre-processing was begun. A number of different forms of pre-processing were used, and the different calculations made are shown below:. Statistics A number of different statistical features were taken based on the moments and cumulants of the vibration data. Higher order spectra have been found to be useful in the identification of different problems in condition monitoring (McCormick, 1998). A good introduction to higher order statistics is given in Nikias (1993) and Papoulis (1991). For each of the basic pre-processed signals, a set of eighteen different moments and cumulants were calculated. Five different pre-processing techniques are used on the statistical data. The statistics are applied to raw vibration data, high pass filtered data, low pass filtered data (both with a cut off frequency of 129Hz), differentiated and integrated signals. Spectral data A spectrally based feature set was created. For each of the two channels sampled, a 32-point FFT of the raw data was carried out, and 33 values were obtained for each channel. These were then stored as a column vector of 66 values, which was used as the input data set for the given data sample. Data Sets - Machine A For machine A, five data sets were used, consisting of raw statistics, high pass filtered, differentiated and integrated stats and spectral data, each of the statistical sets containing 18 features, while the spectral contained 66. For the machine A datasets, the training set consists of 180 examples (normal only), while the test set contains a further 720 examples (normal and fault conditions). Data Sets - Machine B For machine B, 6 data sets were used. The raw statistical data was used as a stand-alone dataset of 18 features. The high pass and low pass data was combined into one 36 vector feature set, while the same process was also carried out with the differentiated and integrated data sets, giving another 36 vector 114
feature set. All statistical features were combined into one dataset, giving 90 features. This in turn was combined with the spectral dataset to give a total dataset of 156 features. For all datasets of machine B, 320 examples (normal only) are used for training, while a further 960 examples (fault and normal) are used for the testing. PCA transformation The PCA transformation was carried out in such a manner as to leave the transformed data containing 99% of the information content held within the original data. If the number of features extracted was odd, another lower variance feature was added to make the some of the algorithms easier to use. TRAINING Training the SOMs was carried out in batches, trying different numbers of features from 1 up until the maximum number available after transformation had taken place. Each SOM was trained for 100 epochs using the normal data only, and after training had completed, the SOM was tested with a set of unseen data containing both fault and no fault data. This was then used to provide a confusion matrix indicating what proportion of each class was correctly classified. The process was repeated for each of the data sets from machine A and machine B. RESULTS Machine A Figure 3 shows the performance of the different datasets from machine A as the number of features used in the classifier is increased, using the different strategies outlined earlier. As can be seen, the performance achieved using the different strategies varies quite markedly as the number of features used increases. It is interesting to note a number of different facts when looking at the plots; firstly, as four of the five plots show, the "Both Ends" strategy seems to give the best performance fairly consistently, regardless of the number of features used, and appears more robust than the other two approaches, which fluctuate markedly depending on the number of features used, and also the pre-processing method used prior to the PCA transformation taking place. As can be seen, the poorest performance of the five datasets is on the High pass filtered ("Hpf Stats") dataset, in which all three strategies tend to fluctuate as the number of components used is changed. It is also interesting to note, dependent upon the feature set, the contribution of the different approaches - for some datasets, top down gives a good performance, while for others, the bottom up approach does well, sometimes better. The both ends approach seems to be a happy medium however, as it has the advantage of incorporating information from each approach and as a result gaining performance that is an amalgam of both approaches. Table 1 summarises these results as table, comparing the performance of the 3 different approaches with an SOM trained using the whole PCA transformed matrix. As can be seen, in three of the five feature sets the "Both Ends" approach gives the best overall score, and in the remaining two cases is within 1% of the other best scores. This would suggest the approach is comparatively robust.
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Spectral
Int Stats
Diff Stats
4 6 8 No Comp.
4 6 8 No Comp.
4 6 No Comp.
Hpf Stats
Raw Stats
- e - PCA Up PCA Down Both ends 4 6 No Comp.
4 6 8 No Comp.
FIGURE 3: PERFORMANCE OF CLASSIHERS WITH DIFFERENT DATASETS FROM MACHINE A. SHOWS VARIATION OF PERFORMANCE AS NUMBER OF COMPONENTS CHANGES. TABLE 1 CLASSIHCATION PERFORMANCE ON MACHINE A DATASETS USING DIFFERENT STRATEGIES. BEST RESULTS SHOWN IN BOLD. Data Set
Spectral
Diff Stats
Int Stats
Hpf Stats
Raw Stats 1
All PCA
80.8
96.9
92.8
97.8
97.1
PCA Down
91.1
96.9
93.2
91.1
98.5
PCA Up
77.4
96.3
93.1
97.2
96.5
94.4
96.8
97.9
Both Ends
91.9
97.2
Machine B Figure 4 shows details of the performance of the six different data sets with the SOMs, for varying numbers of features. As can be seen, varying the number of features used, and the way in which features are selected had a large impact on the performance of the SOM in classifying correctly. In this particular case, the results are less clear-cut, as no single approach consistently gives the best result. Figure 4 shows that in two cases - that of the differentiated statistics and spectral datasets - the PCA up approach seems to be relatively good, seeming to be more robust as the number of features used changes. This indicates that there is useful information contained in the low end of the PCA matrix, and as a result, the low variance features are of use. Also, in both of these cases, where the both ends approach is using the same features
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sets, the performance is considerably poorer for the same number of features, which perhaps indicates that the high variance features are actually confusing the classifier. Examining Table 2, again is it possible to see the comparison of the best performances for all strategies as the feature sets are varied. Once again, in four of the six feature sets, the both ends approach manages to achieve the highest accuracy, and is within 0.1% of the best accuracy in another. Why this does not happen in the last remaining case (the Hpf/Lpf filtered stats) is difficult to say, although it is interesting to note that another simulation on LPF data from machine A also gave very poor results, and it is possible that this may be caused by the low pass filtering, which may remove a lot of the information content that separates between the normal and fault categories. Consequently, the PCA algorithm finds other maximal components during the transformation process, and the performance is poor as a result. Raw Stats
4 6 8 No Comp.
HP/LP Stats
Djff/lnt stats
4
10
5
6 8 10 12 14 No Comp.
All Stats
1"
Y' - e - PCA Up 1 H— PCA Down - ^ Both ends | J
94 [
g'°
>90 o
> o«
2 D 8 85 /
<
10 15 No Comp.
15
All data
Spectral 95 r
5
10 No Comp.
/ y^
I 88 ^ ^
—
1 "
®
1 5
^
y
80 Cv^—^—^—2_.—1 2 4 6 8 10 12 No Comp.
\
^86
( 84 82 5
10 No Comp.
15
FIGURE 4: PERFORMANCE OF CLASSIHERS WITH DIFFERENT DATASETS FROM MACHINE B . SHOWS VARIATION OF PERFORMANCE AS NUMBER OF COMPONENTS CHANGES.
It is interesting to note that none of these algorithms have achieved totally correct results. This is fundamental to the nature of the problem; using a clustering algorithm (such as the SOM) always has the drawback that any fault data that appears within the centre of a normal cluster will always appear as normal. Using a different ANN approach, such as an MLP might solve this, but still requires target data that discriminates between classes (so as to be able to create a boundary which discriminates between them), and as such cannot be considered for problems of this type.
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TABLE 2 CLASSIFICATION PERFORMANCE ON MACHINE B DATASETS USING THE DIFFERENT STRATEGIES. BEST RESULTS SHOWN IN BOLD.
Data set
Raw Stats
DIff/Int stats
Hpf/Lpf Stats
All Stats Spectral
All data
All PCA
93.3
81.5
88.9
82.5
83.0
82.3
PCA Down
95.3
81.7
93.2
95.9
83.2
94.5
PCA Up
89.8
84.3
93.1
84.6
92.7
85.3
Both Ends
96.8
81.8
95.8
95.8
95.0
95.9
CONCLUSIONS This paper has investigated the impact of using different feature selection approaches that can improve the performance of a novelty detection system without having experience of the fault conditions that the classifier will have to recognise. Three different strategies have been compared, and it was found that for best, or near best performance, an approach that takes both maximal and minimally variant features seems to be able to detect novel conditions relatively reliably. It has been shown that minimal variance features can contribute something constructive to a classifier, and in some cases, can be more useful than maximally variant features for novelty detection. There is considerable scope for further work in this area; a further investigation of the factors that influence whether or not a pre-processing technique will produce useful minimum variance features that reliably allow detection is needed. Furthermore, the problem needs to be tested on other problem types. REFERENCES
Kohonen T. (1982) "A Simple Paradigm for the self organising formation of structured feature maps". Competition and Co-Operation in Neural Nets, Springer Verlag. Jack L. (2000) "Application of Artificial Intelligence in Condition Monitoring", PhD Thesis, University of Liverpool. McCormick A.C., Nandi A.K., and Jack LB. (1998). "Digital Signal Processing Algorithms in Condition Monitoring". International Journal of COMADEM, Vol. 1, No. 3., pp. 5-15. Nikias C. L. and Mendel J.M. (1993), "Signal Processing with Higher Order Spectra". IEEE Signal Processing Magazine: July, pp. 10-37. Papoulis A. (1991). Probability, Random Variables and Stochastic Processes, McGraw Hill Inc., New York. Taylor O. and Maclntyre J. (1998)., "Modified Kohonen Network for Data Fusion and Novelty Detection within Condition Monitoring", Proceedings of EuroFusion 98, pp. 145-154. Haykin S. (1994), Neural Networks: A Comprehensive Foundation. Macmillan College Publishing Company, New York.
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Condition Monitoring and Diagnostic Engineering Management Andrew G. Starr and Raj B.K.N. Rao (Editors) © 2001 Elsevier Science Ltd. All rights reserved.
INTELLIGENT SIGNAL ANALYSIS AND WIRELESS SIGNAL TRANSFER FOR PURPOSES OF CONDITION MONITORING Prof, Sulo Lahdelma' and Prof. Tino Pyssysalo^ ^ Department of Mechanical Engineering, University of Oulu, P.O. Box 4200, Finland ^ Department of Electrical Engineering, University of Oulu, P.O. Box 4500, Finland
ABSTRACT Condition monitoring based on vibrations involves measuring of many individual signals at different frequencies for example at bearing housings of a machine. Although in permanent monitoring system the frequency range may raise up to 10 kHz, typically a range of 1 to 5 000 Hz is sufficient. Accurate enough vibration measurements require at least 16 bits, resulting to a net data stream of 160 kbps according to Nyquist frequency. Continuous transmission of this amount of data is feasible in most wireless networks. In addition, intelligent signal analysis can be done locally in the measurement unit to reduce the amount of transmitted data. In this paper we justify lower and upper limits for frequencies used in condition monitoring. These limits are used to determine requirements of wireless signal transfer. We analyse, how novel wireless cellular networks, such as Bluetooth and GSM evolutions, satisfy these requirements. In the study we take into account both simple cases, such as transmission of peak or rms values of signals, and more complicated cases, where the amount of raw data exceeds the data bandwidth of a wireless network. The goal is to find the limits for both signal processing and transmission capacity of current and upcoming wireless networks.
KEYWORDS Vibration measurement, signal analysis, machine diagnosis, wireless communication, 3G networks, pico-cellular networks.
INTRODUCTION In condition monitoring of machinery standardised Vrms measurements of vibrations in thefrequencyrange from 10 to 1 000 Hz are generally used [3]. However, there are many cases, in which the standardized frequency range is not sufficient. For example in the board machine a dryer cylinder rotates at the frequency of 2-3 Hz only. On the other hand gear mesh frequencies can exceed 1 000 Hz. That is why we
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have chosen a frequency range to be 1-5 000 Hz, which is sufficient in most cases without a very few exceptions. A detailed justification, why this range is sufficient is given in this paper. If an analog signal is sampled at the frequency 5 000 Hz, the sampling rate should be at least the Nyquist frequency, i.e., 10 kHz. To enable an FFT analysis of the signal at least a sampling rate of 2.56 times the upper cut-off frequency is needed. Using a 16-bit A/D-converter would result to a net data stream of 160 kbps (bits per second) with the Nyquist frequency and to 204.8 kbps with the multiplier 2.56. These transmission rates are beyond the capabilities of most current wireless networks. Instead of transmitting the whole net traffic, the signal can be analysed in the transmitting end of the communication link. For example transmission of a peak, or rms value requires naturally less bandwidth than transmission of the whole data. This is a more typical case, because data from the sensors must usually share a common transmission medium. In addition to bit rate, there are two important requirements for wireless data network with respect to condition monitoring. First, the transmission should be reliable. Any bit error or lost packet affects the shape of a signal, which may indicate different types of defects in the machine, although none exists. Reliability means that transmission errors must be detected and erroneous packets either corrected or dropped. Second, the measurement system should use a fixed amplification to be simple enough. This may be difficult to achieve, because signal levels vary a lot at different frequencies. This may require that the frequency range should be divided into two sub-ranges, e.g. one from 1 to 100 Hz and another from 10 to 5000 Hz. In this paper we study, how current and upcoming wireless networks satisfy these requirements of transfer of condition monitoring data. First we study the key characteristics of vibration signals used in condition monitoring and justify frequency ranges, used in the signal transfer analysis. Then we describe novel wireless data communication networks, such as 3G, GSM evolutions, Bluetooth, and Wireless Local Area Network (WLAN). Finally we analyse, how these networks satisfy the requirements of transmitting condition monitoring data.
KEY CHARACTERISTICS OF VIBRATION SIGNALS In condition monitoring standardized Vrms measurements are generally used [3]. In these measurements the frequency range lies between 10-1000 Hz and Vrms is measured in mm/s. In this way information about the unbalance, misalignment, bent shaft, mechanical looseness, and resonance of the machine can be obtained. On the other hand, it is known that there exist machines, in which the rotationfrequencyof some parts can be clearly less than 10 Hz. One example of this is a dryer cylinder of paper and board machines. If the board machine has a speed of 600-900 m/min, the dryer cylinder with the diameter of 1.5 meters rotates only at thefrequency2.12-3.18 Hz, in which case the lower cut-off frequency 10 Hz is too high to detect the unbalance of cylinders. So in practice measurements must be done also in thefrequencyrange of 1-10 Hz. There are also machines, in which vibrations may occur at high frequencies. For example fast rotating gears can easily have a gear mesh frequency over 1 000 Hz. If measurements should also include the harmonics of gear mesh frequency, a suitable upper cut-off frequency could be about 5 000 Hz. In addition, electrical motors may have vibrations, caused by defects in stator coils, in the neighbourhood of 2 500 Hz. Finally if we want to find defects in bearings at the early stage, the frequency range should exceed 1000 Hz. However, it should be noted that in the analysis of bearing defects, excellent results have been achieved, when the upper cut-offfrequencyhas been only a few thousands of hertz, if time derivates the order of which are higher than acceleration have been used [4,5,6,9].
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According to previous analysis, it is obvious that many of the mechanical defects of machines can be detected, if the measurements are done in the right way in thefrequencyrange 1-5 000 Hz. For example for the detection of the unbalance, bent shaft or misalignment velocity measurements can be used in such a way that the spectrum contains at least vibration components atfrequenciesIxf, 2xf, 3xf, and 4xf, where f is the rotation frequency. In the early detection of defects in gears and bearings time domain signals of acceleration or time derivates of x^^^ and x^"^^ can be used. These signals can be used to determine frequency spectra and they can be input quantity for envelope or PeakVue analysis. With respect to wireless data transfer it is important that the signal can be conveyed as error-free as possible. Let us image a situation, in which a sinusoidal signal, caused by the unbalance, would get erroneous during the transfer. This may result into conclusions: the signal is caused by a defect, which is a result of the misalignment or bent shaft. For this reason it is important with respect to the detection of defects that the signal can be transmitted error-free also at lowfrequenciesbetween 1-100 Hz. On the other hand, standardized measurements in thefrequencyrange 10-1 000 Hz, must be reliable, because standards allow only total error of ± 10% [11]. Another property, which should be taken into account with respect to wireless data transmission is that measurements could always be done with fixed amplification and at the same time with a sufficient accuracy. It should be enough in almost all cases, if standardized Vrms measurements could be made in velocity range 0.2-60 mm/s. In that case the most troublesome situation with respect to the acceleration signal from the sensor would be sinusoidal vibration at thefrequencyof 10 Hz, the rms value of the level of which would be 0.2 mm/s. This corresponds with the rms value of the acceleration 0.012566 m/s^ (Table 1). On the other hand the largest acceleration level (376.99 m/s^) would be achieved with the sinusoidal signal at the frequency of 1 000 Hz, when the velocity level is 60 mm/s. All previously mentioned measurements could be made with a 16-bit A/D-converter, because Vr2^^ = 32 768. TABLE 1 RMS VALUES OF VELOCITY AND ACCELERATION WITH CERTAIN FREQUENCY AREAS
Frequency
Velocity
Acceleration
Ratio
IHz 100 Hz
0.1 mm/s 30 mm/s
6.2832-10"* m/s^ 18.850 m/s^
30 001
10 Hz 1000 Hz
0.2 mm/s 60nmi/s
0.012566 m/s^ 376.99 m/s^
30 001
With respect to condition monitoring another feasible frequency range could be 1-100 Hz. In these measurements a resolution of 0.1 mm/s would be enough, in which case a 16-bit A/D-converter could be used to measure rms values, which Ije between 0.1-30 mm/s, as shown in Table 1. When the upper cut-off frequency is 5000 Hz, the time domain signal levels of acceleration measurements can exceed 200 m/s^. This can happen for example in gears and feed-water pumps. If the diagnosis system has been adjusted to measure accelerations of 1000 m/s^, the resolution of a 16-bit A/D-converter is 0.03052 m/sl In this case an acceleration signal, measured in thefrequencyrange of 10-5000 Hz, could be utilized to find such defects, which cause impacts. For example defects in gears and bearings can cause these.
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WIRELESS DATA COMMUNICATION NETWORKS In condition monitoring applications coaxial cables are typically used to convey measurement data from sensors to a diagnosis unit. Coaxial cables have a number of drawbacks, most of which can be solved using wireless transmission methods. Cables may suffer from vibrations or electromagnetic interference, both of which may induce errors into the digital data in the cable. Connectors may wear out and suffer from humidity of the surrounding air. In addition, cabling is extremely expensive for a company. In addition, the lack of standardized and competent enough wireless technology has limited its usage in noisy environments such as paper mills and power plants. A production Hne in the paper mill may contain hundreds of accelerometers. Traditionally all the sensors have had a wired connection to a central unit gathering the data. All these cable should be replaceable with wireless communication methods today or in the near future. There are two interesting trends in the development of wireless communication with respect to wireless condition monitoring [8]. Firstly enormous effort is put on the development of 2.5th generation cellular systems such as GSM evolutions. Currently High-Speed Circuit-Switched Data (HSCSD) and General Packet Radio Service (GPRS) [2] are available. HSCSD enables the transfer of multiple time slots in one connection, allowing a theoretical maximum data rate of 4-14.4 kbps = 57.6 kbps. GPRS offers a packetconnected service instead of the circuit-switched and is thus more suitable for the transfer of condition monitoring data. The air interface in the transmission channel is reserved for the user only when she has data to transmit, while in the circuit-switched service the data channel is reserved for the user as long as she disconnects from the network. The first GPRS systems now in use have a transmission rate of 40-60 kbps. In addition to the packet service, the transmission rates of the GSM will increase as soon as the EDGE (Enhanced Data rates for GSM Evolution) technology is taken into use within a few years. EDGE provides a data rate of 155.2 kbps. The final near future goal to which GPRS and EDGE evolve will be a universal broadband mobile network called UMTS (Universal Mobile Telecommunication System). In the beginning UMTS will be a combination of EDGE and GPRS, achieving a data rate of 384 kbps. The other trend in the development of wireless systems is ubiquitous computing meaning that all possible widgets in our environment will be computer-controllable by a short-range wireless pico-cellular network. Thermometers, coffee makers, elevators, and locks can be used wirelessly with a portable terminal, which most probably will be a mobile phone. Most well-known pico-cellular networks are Bluetooth [1], HomeRF, Piano, and Wireless Local Area Network (WLAN). Wireless Data Communication in the Future The mobile network of the fumre will not be only one network but will consist of several hierarchical networks. Global networks have a global coverage, but they cannot provide high speed data rates. The lower we go in the hierarchy the smaller the network coverage and the bigger the transmission rate. For example in pico-cellular networks the cell coverage may be only 10 meters and the transmission speed 100 Mbit/s. Personal cells or personal surrounding network connects the user to her environment providing wireless access to car doors, videos, televisions, or coffee makers. In addition to reliable data transfer, there are two important issues a mobile network should support. Firstly, ad hoc networking should be supported meaning that nofixedbase stations are required, but rather any mobile station can take a role of a base station and start controlling the network. This reduces network investments, because no fixed infrastructure is required. Second issue is the support of context and location dependent services, which requires that the location of a mobile terminal should be known. In principle the propagation delay of a radio signal can be used in location tracking, but to be accurate clocks of terminals and base station must be synchronized.
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In the first next phase evolutions of the GSM network the maximum data transmission speed will be 384 kbps as previously mentioned. In the upcoming Universal Mobile Telecommunication System (UMTS) a planned maximum supported data rate will be 2 Mbps. WLANs provide locally access to Internet at a rate of 11 Mbit/s and in the near future even up to 100 Mbit/s using highfrequencies.To provide both global access and local high-speed access a multi-mode terminal is required, which increases the price of the terminal. WLAN is an unreliable network. It has a built-in error detection mechanism, but it cannot retransmit erroneous packets. There are at least three competing pico-cellular technologies available: Bluetooth, HomeRF, and Piano. Sometimes also WLAN is classified into pico-cellular network. Each mobile station in a pico-cellular network support ad hoc networking. In Bluetooth there can be currently only 8 mobile users in an ad hoc cell, but in HomeRF there can be as many as 127 users in one ad hoc cell. Bluetooth supports both asynchronous data traffic with the maximum data rate of 721 kbit/s in downlink and 57.6 kbit/s in uplink direction and synchronous speech traffic (64 kbit/s). Bluetooth uses error correction and detection and ARQ protocol (Automatic Repeat Request) to retransmit packets that cannot be corrected. Bluetooth chips will be integrated into a mobile phone to provide short-range access to ubiquitous services. HomeRF is based on two wireless network standards. Voice is transmitted using the DECT standard (Digital European Cordless Telephone) while data is transmitted on the top of IEEE 802.11 WLAN standard so it supports voice and data communication as the Bluetooth does. Although Motorola participates in the development of Bluetooth it has its own Piano platform for picocellular networking. In Piano the network coverage is targeted to 5 meters.
EXPERIMENTAL SYSTEM We studied the transfer of vibration signals using an experimental environment as shown in Fig. 1. Wireless communication is based on Bluetooth, because it is the most obvious technology to be utilised in the transfer of condition monitoring data. GSM and its novel evolutions are too complicated and expensive to be applied in a production plant. WLAN is very promising technology too, but the more inexpensive price and lighter infrastructure of Bluetooth makes it more feasible.
Figure 1: Bluetooth-based experimental measurement system and the bearing test rig
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In the experimental system two Ericsson Bluetooth kits connect two laptops. The transmitted signal is generated with a Hewlett Packard's function generator 33120A. The generated signal is digitised in the first laptop by using the ordinary microphone connector. The signal is transmitted over the Bluetooth network in fixed sized packets and the receiving laptop converts the samples back to analog form, which is analysed with Ono Sokki FFT Analyzer CF-5220. The amplitude of the output signal of the function generator turned out to be too strong for the microphone connector of the laptop and it had to be throttled 40 dB from 2V to 20 mV.
SIGNAL TRANSFER ANALYSIS Based on experience, vibrations with the same rms velocity anywhere in thefrequencyband 10 to 1 000 Hz are generally considered to be of equal severity [3]. This determines the requirement for the minimum data transfer rate, which is the Nyquist frequency of the upper cut-off frequency (1000 Hz) times the number of sampling bits. However, because there are many cases, in which the upper cut-offfrequencyof 1 000 Hz is not sufficient, we have used 5 000 Hz as the upper cut-off frequency in the analysis. To properly digitise the vibration information the sampling rate must be at least 2.56 times the upper frequency bound to enable the FFT calculation. To be safe we use the factor 4 to determine the upper bound for the sampling rate. Finally it must be considered, how many bits should be used in the digitisation. According to [10] 16 bits is usually used for the A/D conversion, but taking into account the whole dynamic range of a piezoelectric accelerometer (140-160 dB), 23-27 bits should be used in the A/D-conversion. To be able to continuously transfer a signal, we would thus need at least 5 000 Hz • 2.56 • 16 b = 204.8 kbps or 5 000 Hz • 4 • 27 b = 540 kbps. A constant net data stream of 540 kbps cannot be properly transmitted in other wireless networks than WLAN. Even in WLAN the undeterministic throughput changes makes it difficult to transmit the stream at constant speed with a small jitter, i.e., variation of latencies [7]. In [10] a wireless interface for condition monitoring has been developed. It uses fourth generation smart accelerometers having a local A/D-converter and built-in intelligence for e.g. FFT analysis. Using the system expensive cabling costs can be reduced but the system is capable of transmitting only 2.4 kbit/s, which is far from our requirement of 204.8 kbps. Increasing the local intelligence of a sensor the transmission rate requirement can be reduced, but this quickly increases the cost of the sensor. Our approach is to try to utilize the increased capabilities of upcoming wireless networks. Results of the Wireless Transfer Experiments The Bluetooth network consists of slaves and masters. Slaves must always be initialised first, which means that they start to listen to the beacon signalfromthe master. If there is no master in the network, one slave can take a role of the master and establish an ad-hoc network infrastructure. Conununication between slaves and masters is problematic with respect to condition monitoring, because bandwidth is highly asymmetric. In the uplink direction, i.e., from a slave to a master, the bandwidth is only 57.6 kbps. A natural network topology is such that wireless transmitters locate near accelerometers, while a terminal in a service person's hand is initiated to a master. In this way the master can poll the slaves to send the required condition data, when the distance between the slave and the master is short enough so that the connection can be established.
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In the analysis we have been especially interested in the lower and upper cut-off frequencies, i.e., 1 and 5000 Hz. At low frequencies 1 and 5 Hz we have used a sampling rate of 8 000 Hz and at the upper cut-off frequency sampling rates of 11 025 and 22 050 Hz. In ail samples we have used 16 bits, which would result with different sampling rates to net data streams of 125,172.3 and 344.5 kbps, respectively. Even the lowest sampling rate leads to a stream, which exceeds the capacity of the Bluetooth uplink radio channel. So data cannot be transmitted in real-time without an aggressive compression. However, the problem of efficient compression algorithms is that they are not loss less. To enable the transfer of condition monitoring data we had to first save a short time slice of a sinusoidal sample to a hard disk and send it with the capacity offered by the Bluetooth. Digitisation succeeded well at both lower and upper cut-off frequencies, although the response of the filter was far from linear in lower cut-off frequenzy. The receiver repeated the signal and a spectrum of the repeated signal was taken with the FFT analyzer. Both at lower and upper cut-off frequencies the spectrum was excellent. The level of the first harmonics was about 1.0 per mil of the level of the repeated signal. On the other hand the throughput in the Bluetooth network was not too high. In the measurement the net throughput of only 5 kbps could be achieved. The reason for this is Bluetooth's extreme sensitivity to interference from other networks at the same frequency range. Because of its limited transmission capacity it is obvious that intelligent analysis should be done near the accelerometer, before the signal is transmitted to the destination. One way to analyse the signal is to take higher order time derivates of it. This does not reduce the amount of information, but emphasises the characteristics of the signal. We used the fourth time derivate calculated by the diagnostic vibration meter MIP 1598 in the analysis of a bearings of a machine. The machine rotated at the frequency 9.73 Hz and had a load of 20 bar. Derivates clearly showed the impacts, caused by defects on the outer race of a spherical roller bearing, as shown in Fig. 2. Although the bare impact information, occurred about once in 11 ms, could have been transmitted, we sent the whole signal digitised with 16-bit samples 11025 times a second. This of course did not reduce required bandwidth of the communication channel.
Figure 2. Impacts caused by defects on the outer race of a spherical roller bearing An efficient way to make a wireless condition monitoring system would be to use Bluetooth transmitters for short-range conmiunication. Data could be collected for example from ten sensors to a local server, acting as a proxy for accelerometers. From the proxy data could be multiplexed and transmitted wirelessly using more efficient wireless network, such as wireless LAN network. WLAN would collect all the data to a central maintenance server, which could then provide different types of access methods for service personnel. This kind of an environment is under further study at the University of Oulu.
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CONCLUSIONS We have studied the wireless transfer of condition monitoring data in a Bluetooth network. The uplink transmission capacity of Bluetooth did not allow its usage for real-time traffic. For condition monitoring purposes a frequency range of 1 to 5 000 Hz is required, which results to at least 160 kbps data stream, if continuous communication is used. This exceeds the capacity of Bluetooth uplink and that is why condition monitoring data could not be transmitted in real-time. For lower and upper cut-off frequencies it is enough to use microphone and loudspeaker connections of an ordinary laptop computer. Due to interference of other wireless network operating at the ISM frequency 2.4 GHz, the throughput of Bluetooth drops easily to a few kbps or even less. That is why analysed data, such as peak values or spectrum, should be transmitted rather than the whole raw vibration data.
REFERENCES 1. Bluetooth SIG, www.bluetooth.com 2. ETSI TS 101 343 (1998) Digital cellular telecommunications system (phase 2+); General Packet Radio Service (GPRS), European Telecommunications Standards Institute 3. ISO 2372 (1974) Mechanical vibration ofmachines with operating speeds from 10 to 200 rev/s - Basis for specifying evaluation standards. International Organization for Standardization 4. Lahdelma S. (1995) On the higher order derivates in the laws of motion and their application to an active force generator and to condition monitoring. University of Oulu, Research report No. 101, Department of Mechanical Engineering, (Academic Dissertation) 5. Lahdelma S., Strackeljan J. and Behr D. (1999) Combination of Higher Order Derivates and a Fuzzy Classifier as a New Approach for Monitoring Rotating Machinery. COMADEM '99, Sunderland. 231241. 6. Lahdelma S. (1997) On the Derivative of Real Number Order and its Application to Condition Monitoring. Kunnossapito 11:4, 25-28. 7. Pradhan P. and Chiueh T.C. (1998) Real-time performance guarantees over wired/wireless LANs. Proc. Fourth IEEE Symposium on Real-Time Technology and Applications. 29-38. 8. Pyssysalo T. (2000) Outlooks of Wireless Data Transfer in the Condition Monitoring. Proc. International Seminar on Maintenance, Condition Monitoring and Diagnostics, Oulu, Finland. 149-158. 9. Strackeljan J., Lahdelma S. and Behr D. (1998) Ein neuerAnsatz zur automatischen Diagnose langsam drehender Wdlzlager, Akida, Aachen. 61-77. 10. Thruston B. (1999) Using smart accelerometers and wireless interfaces for condition monitoring. Machine, Plant & Systems Monitor, May/June 1999,15-18. 11. VDI 2056 (1964) Beurteilungmafistabe fUr mechanische Schwingungen von Maschinen. VDIRichtlinie
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Condition Monitoring and Diagnostic Engineering Management Andrew G. Starr and Raj B.K.N. Rao (Editors) © 2001 Published by Elsevier Science Ltd.
CONDITION MONITORING FOR A CAR ENGINE USING HIGHER ORDER TIME FREQUENCY METHOD Sang-Kwon Lee Department of Mechanical Engineering, Inha University, Incon, 402-751, Korea ABSTRACT In previous work, the SWFOMS (Sliced Wigner Fourth Order Moment Spectra) for multiple signals had problems with its application, which were due to the existence of non-oscillating crossterms not smoothed by conventional methods. In this paper, the y-method is developed to smooth non-oscillation cross-terms. The techniques developed are applied to the diagnosis of valve system faults in an engine. KEYWORDS Fourth Order Moment Spectra, the y-method, Engine Suggest, non-oscillating cross-terms 1. INTRODUCTION The two stage Adaptive Line Enhancer [1] and the sliced Wigner fourth order moment spectra (SWF0MS)[2] have been applied to detect the weak impulsive signals in mechanical systems, such as those caused by early faults. The SWFOMS, in particular, has been inherently more robust in detecting impulsive signals embedded in the presence of random (Gaussian) noise. However, the SWFOMS suffers from non-oscillating cross-terms for multicomponent signals. These cross-terms cannot be smoothed by a conventional kernel function such as an exponential kernel function, which has been used for smoothing of cross-terms in the bilinear time-frequency method [3]. In this paper, in order to smooth the non-oscillating cross-terms, the SWFOMS smoothed by the y-method has been developed and applied to the diagnosis of valve system faults in an automotive engine.
2. REVIEW OF THE WIGNER FOURTH-ORDER MOMENT SPECTRA The general Wigner higher order moment spectra (WHOMS) of order n+1 for signal sit) is defined by Fonollosa and Nikias [4]
127
^
\^ 1=1
w •'• A j
j=i
V
n-t-L
J
where n=l and n=3 are the Wigner-Ville distribution (WVD) and the Wigner fourth order moment spectrum (WFOMS), respectively. Although the WHOMS has significant resolution advantages over other time-frequency methods, its application is obstructed by problems associated with cross/interference terms [2]. In general, the n+l* order Wigner distribution for two-component signals is the sum of 2""*"^ distributions, of which two are auto-terms and 2''^*-2 are cross-terms. To reduce these cross-terms it is conmion to consider a subset of the WHOMS called the principal slice. The principal slice is defined as the only plane in which a single complex exponential appears as a Dirac delta. This plane for the WFOMS is obtained by setting/i = -/2=/3=/and w=3 in equation (1). It is called the SWFOMS (sliced Wigner fourth order moment spectra). This slice generally includes both auto-terms and cross-terms and the number of cross-terms is significantly reduced [2]. The remained oscillating cross-terms are smoothed by the conventional smoothing, which is processed by multiplying the ambiguity function of the SWFOMS by the kernel function [5]. However, the conventional smoothing fails to reduce the non-oscillating cross-terms [2] since these non-oscillatory terms are difficult to distinguish from the auto-terms. 3. SMOOTHING OF NON-OSCn.LATING CROSS-TERMS IN THE SWFOMS In order to smooth effectively the non-oscillating cross-terms in the SWFOMS, consider the frequency version of the WVD,
W(r./) = JS-[/ + i | ] 5(/-^|] e-'^-^d^
(2)
The pseudo Wigner distribution (PWD) [6] can be written by using the frequency domain windowing function H{^) as follows:
(3)
where | = ^2. Using convolution, equation (3) may be written as follows: W^(tJ) = sl<J,2t)*s,{f,2t)
(4)
where Sh(f,t) is the short time Fourier transform (STFT) [6], Therefore, the PWD can be written as,
W^(t,f) = s\{f,2t)*s,{f,2t) ?. = \s^{f,t + T)s,{f,t-r)dt
128
^^^
In order to emphasize the auto-terms of the PWD for a multi-component signal, a window function Y(T) can be incorporated into equation (5) and the smoothed Wigner distribution (SWD) can be developed as follows: ^2.^(^/) = J r ( T K ( / , r + T K ( / , r - r ) j T
(6)
We refer to this window function as the Y method. In equation (6), when Y(T) =1, the SWD becomes the WVD and when Y(T) =5(T) the SWD becomes the spectrogram. Therefore, in order to smooth the cross-terms of the WVD for a multi-component signal, the duration Ty for y{T) needs to be selected in accordance with, T,
(7)
where u and tj are the temporal positions of the signal components and Th is the duration of h{t). The SWD is obtained by convolving two signals Shif^lt) and Sh(f,2t) with respect to time and using the Y - method for smoothing the cross-terms in equation (6). Similarly, a smoothed version of the SWFOMS also can be obtained by the convolution of two SWD W2,sw(f,2t) and W2*^(f,2t) with respect to time as follows: W,,,^f^(tJ) = ^JY(T)W,JfJ^T}w;jfJ-T)dr
(8)
4. NUMERICAL EXAMPLE This theoretical observation is verified via a simulation, the results of which are shown in Figure 1. In Figure 1 (a). The SWFOMS is computed for a 128 point time series at an assumed sampling rate of 100 Hz. The signal contains two components occurring at different times, 0.2 and 1.08 s, but at the same frequency, 12.5 Hz. In this case there are three sets of cross-terms, in contrast to a WVD that would only generate a single set of cross-terms as shown in Figure 1(b). The SWFOMS crossterms appear at quarter intervals between the two components. The off-centre cross-terms at l/4th and 3/4ths of the interval are oscillatory albeit only in the frequency direction. However, the crossterms appearing at the mid point contain both an oscillatory element and a non-oscillating element. Figure 2 shows the results of applying the exponential kernel to the data depicted in Figure 1. The results of this simulation are less satisfying since the exponential kernel is poorly suited to removing cross-terms since it is impossible to smooth non-oscillating cross-terms by the exponential kernel function as shown in figure 2(a). Even the WVD weighted by exponential kernel cannot remove the cross term perfectly as shown in figure 2(b). The effectiveness of smoothing the SWFOMS using the Y-niethod is again demonstrated via a simple simulation. Figure 3 depicts the SWFOMS calculated using the Y-method for the same data set as used to compute figure 1. In this case, the sampling frequency is lOOHz, the number of samples is 512 and U -?;= 0.88 s. The width of window for Y(T) is 0.12 s, and the duration of the sliding window Hi) with a bandwidth of 50Hz is 0.64 s. According to these results, the non-oscillating cross-terms can be eliminated, as shown in
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figure 3. 5.
APPLICATION
The engine used in this test was a 2.0 Htre, in-line 4-cylinder engine. The engine was operating at a nominal idling speed of 800 rpm. One microphone was used to make measurements in the engine compartment. This microphone was calibrated with a B&K pistonphone [94dB(A)]. The measured analogue data were converted to digital form at a sampling rate of lOkHz. In the engine production line, impulsive sounds are introduced by incorrectly setting the lash adjuster screw of valve system in an engine out of range of the design value [7,8]. This wrong setting affects not only the dynamic behaviour of the valve system but also the performance of the engine [9]. These impulsive sounds are generated as the valve head impacts on the valve seat of the cylinder head when the valve opens and closes. However, these impulsive sounds are inmiersed in the high level of background noise, such as integer multiples of fundamental rotation speed and broadband random noises [1]. Figure 4(a) shows the signal measured from the microphone. These results show that the impulsive sounds are immersed in the background noise. In order to enhance these impulsive sounds, the two-stage ALE (Adaptive Line Enhancer) has been employed [1]. Figure 4(b) shows the impulsive sounds enhanced by the two-stage ALE. After the two-stage ALE, the harmonic noises of engine rotation and pure tone noises are nearly cancelled. However, making an objective measurement of impulsive sounds still tends to be difficult because of the high level of broadband random noise. In addition, these impulsive sounds are non-stationary in nature and one cannot readily use the frequency domain representation to identify the temporal location of an event. For the time-frequency analysis of this non-stationary impulsive sound immersed in the broadband random noise, the SWFOMS, which is a more robust time-frequency method in the presence of random (Gaussian) noise, has been employed. Figure 5 shows the SWFOMS analysis using conventional exponential kernel for these non-stationary impulsive sounds immersed in the broadband random noise. The light bar expresses the relative magnitude to maximum magnitude with 64 ranks of grey colour. However, it is difficult to identify the impulsive sounds because of non-oscillating cross-terms among the impulsive sounds. If the SWFOMS smoothed by the y-method is applied, this figure can be clarified as shown in Figure 6. Again, this demonstrates that the first dominant impulsive sound occurs at a crank angle of 445°. This impulsive sound, which has frequency components at 4200Hz and 2800Hz, is due to the closing of an exhaust valve. The second dominant impulsive sound, which occurs at a crank angle of 485°, has frequency components at 4200Hz and is one of the weak impulsive sounds due to other moving components of the test engine, such as piston motion, crankshaft, injector, etc. 6. CONCLUSION The sliced Wigner fourth order moment spectra (SWFOMS) has been employed because of its robustness in the presence of random (Gaussian) noise. In the application of the SWFOMS, the nonoscillation cross-terms between impulsive sounds make it difficult to detect the impulsive sounds.
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The SWFOMS smoothed by y-niethod has been developed in this paper to smooth these nonoscillation cross-terms, and it has been successfully applied to the detection of valve system faults in an engine. REFERENCES 1
S. K. LEE AND R R. WHITE (1998) Journal of Sound and Vibration, "The enhancement of impulsive signals for fault detection," Vol. 210,485-505.
2
S. K. LEE AND P. R. WHITE (1997) Mechanical Systems and Signal Processing "Fault diagnosis of rotating machinery using Wigner Higher Order Moment Spectra," Vol.2, 637-650.
3
L. COHEN (1995) Time-frequency analysis Prentice Hall.
4
J. R. FONOLLOSA AND C. L NiKlAS (1993) IEEE Transactions on Signal Processing, "WignerHigher-Order Moment Spectra Definition, Properties, Computation and Application to Transient Signal Analysis," Vol.41, 245-266.
5
S. K. LEE (1998) Ph.D. thesis, ISVR, The University of Southampton, U.K. Adaptive Signal Processing and Higher Order Time Frequency Analysis and Their Application to Condition Monitoring.
6
A.C. M. CLAASEN AND W F. G. MECKLENBRAUKER (1980) Phil. J. Res., 'The Wigner distribution -A tool for time-frequency signal analysis: part I, n, IE", Vol.32, 217-389.
7
T. Nemura, N. Adachi, and K. Susuki (1991) Society of Automotive Engineering, "Research in regard to sensory characteristics measuring for the impulse noise of the engine valve system", SAE 910620.
8
O. Maeda (1988) Internal Combustion Engines, "Quantitative evaluation of abnormal engine noise," Vol.27, 334-340
9
H. Heisler 1995 Advanced Engine Technology Edward Arnold.
(b)
(a)
Figure 1 Comparison between the SWFOMS and for WVD the two component signals centred at 0.2s and 1.08 s with 12.5Hz. (a) SWFOMS. (b) WVD.
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(b)
(a)
Figure 2 Comparison between the SWFOMS and WVD for the two component signals centered at 0.2s and 1.08 s with 12.5Hz. (a) The SWVD weighted by an exponential kernel, (b) The WVD weighted by an exponential kernel.
800
Figure 4 Results of enhancement for measured data from valve system, (a) Time series data, (b) Enhanced data after two stage ALE.
Figure 3 The SWFOMS smoothed by the Y-method for the two component signals centered at 0.2s and 1.08 s with 12.5Hz.
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700
Exhaust valve closing
Figure 5 Sliced Wigner Fourth Order Moment Spectra smoothed by the exponential kernel for the enhanced signal after two stages ALE.
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Figure 6 Sliced Wigner Fourth Order Moment Spectra smoothed by the ymethod for the enhanced signal after two stages ALE.
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Condition Monitoring and Diagnostic Engineering Management Andrew G. Starr and Raj B.K.N. Rao (Editors) © 2001 Elsevier Science Ltd. All rights reserved.
AN INVESTIGATION INTO THE DEVELOPMENT OF A CONDITION MONITORING/FAULT DIAGNOSTIC SYSTEM FOR LARGE REVERSIBLE FRANCIS TYPE PUMP-TURBINES S. Roberts^ and J. A. Brandon^ ' EME First Hydro Company, Dinorwig Power Station, Llanberis, Gwynedd LL55 4TY, Wales, UK 2 Cardiff School of Engineering, PO Box 685, Cardiff CF2 3TA, Wales, UK E-mail:
[email protected]
ABSTRACT Dinorwig Power Station is one of the world's largest hydro-electric pump storage schemes. The station consists of six 300MW Francis-type Pump-Turbines, with each 520 tonne assembly being suspended via a support bracket and a set of oil lubricated thrust bearing pads. Dinorwig performs a critical role in the UK National Grid system due to its ability to provide almost instantaneous power. Each of the machines must operate under an arduous regime in comparison with thermal base-load units, with each unit performing up to twenty-five mode change sequences per day. With such a regime the reliability of such plant is of critical importance in today's competitive electricity market. After carrying out an initial investigation into what useful information could be obtained from information currently monitored to determine the health of the plant, the authors are to continue a stage further and develop a condition monitoring and fault diagnostic system suitable for the machines at Dinorwig. In conjunction with developing the system, an assessment will be made of the improvements in the general condition of a unit which will have a new magnetic thrust bearing installed this year. The main purpose of the bearing is to reduce the load experienced by the thrust bearing at various power loads. This paper summaries the initial development work being carried out in order to identify the most appropriate monitoring parameters, analysis techniques and data acquisition systems required to achieve an effective condition monitoring and fault diagnostic system. KEYWORDS Condhion Monitoring, Rotating Machinery, Pump-Turbine, Transients, Phase Plane, Nonlinear INTRODUCTION In an increasingly competitive UK energy market, as well as other energy markets around the world, condition monitoring and accurate fault diagnosis is becoming ever more important. By combining such systems with an effective maintenance programme, both the reliability of power generation plant and its availability can be increased. The benefits of condition monitoring systems, either as part of an integrated maintenance system or as a stand-alone system have been well documented over the years by many authors, (e.g. Meher-Homji (1996) and Mueller and Eickhoff (1996)).
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Condition monitoring of rotating machinery comes in many different forms and is often a combination of techniques from various engineeringfields,such as oil/debris analysis and Non-Destructive Testing (NDT). The size and scale of the condition monitoring can various enormously, depending both on the complexity of the plant to be monitored and the number of parameters to be regularly monitored. This becomes more complex if a number of different techniques are utilised and the information must assimilated to produce a condition-based/predictive maintenance system, as opposed to a preventativebased system. In terms of rotating machinery, perhaps the most utilised and successful technique for condition monitoring and fault diagnosis is vibration analysis. After many years of research it is now possible to identify the most likely cause of deterioration in machine performance or of failure through FFT analysis of the signal In many cases this can reduce the investigation time into the causes of the failure and hence reduce the outage time and availability losses. Vibration analysis in the majority of cases is done when the machinery is operating in the steady state condition\ However, in recent years there has been great interest in utilising data obtained from machinery in transient states (Safizadeh et al (2000)). With this in mind the main emphasis of this paper is centred upon the assessment of the current vibration analysis capabilities at Dinorwig Power Station, and whether the analysis of vibration data while the unit is in a transient state^ can provide any 'interesting' information. The paper concludes with a brief description of current work being carried out. DINORWIG POWER STATION AND ITS OPERATING REGIME Each 520 tonne unit assembly (six in total), mainly consist of a 330MVA, 500rpm Generator-Motor which is connected via an intermediate shaft, to a Francis type Pump-Turbine. The assembly shown in Figure 1 is suspended from a thrust collar resting on a set of white metal oil-lubricated pads, which transfer the load to the supporting H-bracket resting on the stator supports. The thrust pads are water cooled and have provision for high pressure jacking oil, which is used both for assisting with pad cooling and for the formation of an oil film at start-up. Generation is achieved by the system rotating in the clockwise direction; Pimiping is achieved by rotating the in the anticlockwise direction. The units at Dinorwig have the ability to produce a combined power output of approximately 1800MW at the touch of a button. This is achieved by having the flexibility of different operating modes, namely: 1. Gen.(up to 300MW of Generation depending on head) 2. Pumping (-298MW nominally) 3. Spin-Gen. 9runner rotates at SOOrpm in air, clockwise direction, and the system is synchronised to the grid) 4. Spin-Pump (runner rotates at SOOrpm in air, anticlockwise direction, and the system synchronised to the grid) Each unit can produce up to 300MW in approximately 15 seconds if it is running in Spin-Gen. mode. To achieve fiill power output from standstill would take approximately 100 seconds. Rotating machinery which have a similar operating regime to Dinorwig will undergo the most stress during transient periods such as run-up, run-down and mode changes, i.e. Spin-Gen. to Gen. and vice versa. Particularly during the Gen. to Spin-Gen. transitional period (as well as during a Pump to Spin-Pump transition), the removal of the hydraulic upthrust provided by water flowing through the runner produces an additional 200 tonnes (20001d^) to the load the thrust bearing experiences during Generation. This is just one example of when the units experience high loads and forces. Another For rotating machinery: constant speed, constant load conditions ^ For rotating machinery: variable speed, transient periods between different loads 136
period is during synchronisation at initial run-up, with traces of the vibration levels monitored at each of the three guide bearings showing a large increase in vibration levels over a very short time period.
Key 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
Rotor Shaft Spider Rotor core assembly Pole face Pole winding Stator core Stator windings Stator frame segments Excitation brush gear Thrust bearing assembly Bottom guide bearing assembl> Top guide bearing assembly Brake assembly Bottom bracket Top bracket Concrete enclosure
17 18 19 20 21 22 23 24 25 26 27 28 29 30 31
Figure 1:
Turbine shaft Pump/Turbine runner Adjustable guide vane Guide bearing Spiral casing Top cover Bottom cover Regulating ring Guide vane servomotor Connecting rod Shaft seal Suction cone Man access Intermediate shaft Intermediate penstock
Generator-Motor and Pump-Turbine Assembly
DATA ACQUISITION AND ANALYSIS TECHNIQUES Data Acquisition The current vibration monitoring system at Dinorwig obtains its information from seven proximity probes; six measuring vibration at each of the three guide bearings (two probes per bearing, 90° apart), and one measuring the shaft Uft at the collar. Speed, power and phase are also measured in addition to the vibration levels^. The system is used mainly in two capacities: 1. General monitoring of the machine vibration levels and; ^ Displacement in Microns (Peak-Peak)
137
2. Balancing - the machine can only be used in long term service if the vibration levels are within specific tolerances'* The system is able to monitor the vibration in a number ways, namely the first 4 orders individually, FFT, overall, true peak-to-peak and DC gap. The system can also display orbit plots in real time. Initially it was thought that the data stored by the system could be analysed further. However the data stored is not the raw signal but an average one divided into 4 orders (first order representing vibration at running speed). With the data in this post-processed form it was impracticable to gain any further information fi-om further processing. Another restriction was the sampling rate of the system, the maximum rate being 1 second, which is too slow to catch transient events. A separate data logging/acquisition system was introduced to monitor the raw signal from a probe at each guide bearing on one unit. With the introduction of a new Magnetic Thrust Bearing in one of the units, installation commencing during Summer 2001, a new acquisition system has been installed to monitor a variety of parameters in tandem with existing monitoring equipment. This data will be used in part to assess any changes/benefits to the imit before an after installation of the bearing. The main focus of the analysis will be on the data taken from the guide bearings and shaft lift probes, particularly during transient periods. Although it is too early to present any results from the pre-outage tests, a brief discussion of possible routes for investigation progression is given at the end of the paper. Analysis techniques Over the course of the initial study carried out a number of different variety techniques were utilised, ranging from 'traditional' statistical analysis techniques such as auto-correlation and power spectral density functions, to 'newer' techniques like phase planes and Poincare maps. Or more correctly, their application to condition monitoring is relatively new, as the techniques themselves have been in existence for many years (Virgin (2000)). Statistical analysis techniques have been used for the analysis of vibration data for many years, with Bendat and Piersol (1971) providing a particularly thorough treatment of this subject area, as does Newland (1993). It is sufficient to state at this point that the auto-correlation is useful for identifying periodicities in the data, particularly if it is hidden within random noise, and the power spectral density comes into its own when analysing the data for its frequency content. It can therefore provide useful information about the dynamic characteristics of a system. With the rising interest in utilising transient periods for condition monitoring/fault diagnostic applications, and acknowledging not only the existence of non-linearities but actively trying to detect and identify various 'types' of non-linear behaviour, the use of non-statistically based techniques has now come to the fore. By identifying non-linear behaviour within systems, it will hopefully provide a greater understanding of the 'actual' dynamic behaviour of a real system as well as its susceptibility to minor changes in operating conditions. This in tum may be utilised to identify fault conditions or even general deterioration in machine health. Perhaps two of more well known techniques which are utilised in the identification of non-linear dynamic behaviour in systems are phase plane and Poincare analysis. Figure 2 shows clearly the relationship between time, phase panes and Poincare maps. The different techniques allow different representations of the data, each representation providing more information about the signal and its behaviour.
'' Transient periods: Y2 guide bearing clearance (300nni, pk-pk, 0); steady-state periods: VA bearing clearance (150fim, pk-pk, 0) 138
Figure 2:
3-D phase portrait of a forced mechanical oscillator, showing 2-D phase projection and Poincare mapping (Thompson and Stewart (1991))
PRELIMINARY RESULTS Data was obtained from Unit No. 5 during a Gen. to Spin-Gen. mode change using the additional data acquisition system to collect guide bearing vibration data from three probes. By setting a sampling rate of 500Hz a considerable amount of data was obtained during the course of the mode change. The analysis was confined to analysing four 'snapshot' records (Files A, D, G and J) taken from one probe, G/M Bottom, South. The choice of probe was due to the stability of the orbits in the phase plane when compared with the other two probes. Various time constraints at the time of the study also restricted the amount of data which could be analysed. A priori knowledge of the typical frequencies observed in Dinorwig guide bearing vibration are low, i.e. below 25Hz. This allowed decimation of the data to be carried out, which produced a clearer picture of the behaviour of the signal but at the expense of frequency range and resolution; the maximum frequency which could have been detected being reduced from 250Hz to 25Hz. Figure 3 shows an example of the time history, auto-correlation and power spectral density plots for File G, which occurs towards the end of the mode change. Throughout the mode change the running frequency of 8.33Hz is dominant as was to be expected. This is clearly shown both in all of the plots in Figure 3, with the spectral density plot identifying the frequency. Earlier in the mode change, the spectral density plots revealed a reasonably dominate peak (excluding running frequency) at 4.16Hz, Vi running frequency. This has reduced significantly by File G and is replaced by a 'new' dominant frequency at approximately 12.5Hz. This frequency is the third harmonic of the V2 running frequency. Also during the mode change there is amplitude modulation occurring to varying degrees and this is clearly visible in the time history of Figure 3. This modulation may be due to the two closely spaced frequencies at about 4Hz. Another noticeable feature in the time history are the 'double' peaks (circled on the time history, Figure 3), at approximately 20.75 seconds into the record. These particular 'features' could indicate the sudden presence of a new frequency for a brief period, as the 'double peaking' is not present throughout the record. This in turn could be and indication of a sudden impact or sudden application of large dynamic forces. 139
Time history of GM Botton DC Gap, File G
10Auto-correlation function, GM Bottom DC Gap, File
-1100
-1350 19
19^
20
20^ 21 Elapsed tine/sec
21^
SO
22
100 150 200 250 Time delay, tau/0.02sec (b)
(«)
300
Spectral density function, File G - log scale
'^
.10"
S
0
Figure 3:
2
4
6
8
0
10 12 14 16 18 20 22 24 Frequency/Hz (c)
2
4
6
8
10 12 14 16 18 20 22 24 Frequency/Hz (d)
Different representations of G/M Bottom, South guide bearing vibration, File G
The variations in the signal form can be considered to be the resuh of various events which occur during a Gen. to Spin-Gen. mode change such as closing of the guide vanes and Main Inlet Valve (pressure in the turbine reduces to approximately 7 bar), and initiation of Slowdown Air at 32 Bar through the runner to push dovm the level of the water in the suction cone. All of this will result in the removal of any hydraulic upthrust that was previously available, and does not happen instantaneously. Without knowing the exact timings of these events throughout the mode change it is impossible to directly attribute the changes in the signal to these events. Figure 4 shows File G represented in the phase plane. It clearly shows that the trajectory orbits a single equilibrium point in the plane, and this applies throughout the mode change. The orbits are also sweeping across the phase plane which is an indication of low frequency transients. It can also be an indication of quasiperiodic or chaotic motion, the former being more likely as further tests are required to establish the presence of chaotic motion (Moon (1987)). The phase planes for the four files shows the move away from Period-1 motion to Period-2, which when compared with the other files, the Period-2 motion is much weaker in File G (Figure 4). This is not surprising when considering that the peak at 4.16Hz is small in the spectral density plot. Superharmonics are present in File G (circled on Figure 4). These can t^ an indication of impact or impulse loading. There are a number of events which may cause impulse type loading, namely the removal of hydraulic up thrust or changes in the electromagnetic forces experienced by the rotor. 140
It can also be seen in Figure 4 that there are occasions where there is a sudden change in velocity without the corresponding increase in displacement. This is indicative that the severity of the vibration at this point has increased. The Poincare map analysis will only reveal additional information if the strobing frequency is correct. The choice of a strobing frequency of 4.16Hz should have revealed two clusters of points indicating Period-2 motion (clustered due to transient conditions). However, this was not the case, with only very weak clustering being present. Phase Plane plot, GM Bottom, Gen. to Spin-Gen. mode change, file G; decimated data.
DC Gap/Mioion
Figure 4:
2-D phase plane of G/M Bottom, South: File G, decimated data
CONCLUSIONS AND FURTHER WORK From the application of some of the non-statistically based analysis techniques, the preliminary investigation has shown that these techniques are suitable for detecting 'novel' features within transient data of a low-speed hydro-electric units; they also complement the more traditional statistically-based analysis techniques. Techniques such as the phase plane are well-suited to transient periods which are more likely to display non-linear dynamic behaviour than during steady-state periods. However, this does not exclude non-linear behaviour from steady-state operating conditions, as non-linearities are inherent in any dynamic system. Taking the units at Dinorwig as an example, some sources of non-linearity which are present no matter what operating conditions are present are: • • •
Electric and magnetic forces - Generator-Motor Fluid-related forces - flow through the Pump-Turbine Large deformations in structural solids such as beams, plates and shells (Moon (1987)) assembly's supporting H-bracket
The above is just a small selection of non-linearities present within the system at Dinorwig. By taking such effects and characteristics into account and analysing them further, only benefits can be derived through a greater understanding of machinery behaviour. This can include a better understanding of how and when damage occurs to machinery, leading to further opportunities of either removing/reducing the damage-causing factor, or detecting and predicting possible future failure. 141
These are just some of the possible future benefits from utilising the greater understanding of nonlinear behaviour in 'real' systems. Currently the development of an effective condition monitoring and fault diagnosis system for the units at Dinorwig Power Station is in its infancy, with collaboration between First Hydro Company and Cardiff University to achieve this goal. The research at this stage is to establish a number of techniques which may be applied to analysing transient operating conditions. Joint Time-Frequencies Analysis (JTFA) techniques appear to be promising candidates for analysing data from these types of operating conditions, and are indeed being applied to condition monitoring and fault diagnosis applications (Andrade et al (1999) and Safizadeh et al (2000)). With the installation of a new Magnetic Thrust Bearing (MTB) in one of the units at Dinorwig this year to assist with reducing the loads experienced by the thrust bearing, the effective analysis of transient events can also be used in part to assess changes to the dynamic behaviour of the system, and any subsequent improvements. A new data acquisition system has been installed to measure and record various parameters, the main emphasis being on vibration data, for gathering pre- and postMTB system data. To date information has been recorded (sampling rate at IkHz) for the pre-MTB system for further analysis. The data will require the appropriate filtering/processing before any analysis can take place. Post-MTB system data will be recorded over the summer for later analysis. The installation of the new bearing may also prove useful in identifying 'novel' features which may be used to detect faults or changes in unit condition.
REFERENCES Bendat, J. S., and Piersol, A. G., (1971), 'Random data: Analysis and measurement procedures', John Wiley & Sons. Meher-Homji, C. B., (1996), 'Condition monitoring of power plants \ Handbook of condition monitoring, Chapter 13, pp. 285-322, Elsevier Advanced Technology. Moon, F. C , (1987), 'Chaotic Vibrations: An Introduction for Applied Scientists and Engineers', John Wiley & Sons, Inc. Mueller, F., and Eickhoff, H., (1996), 'Monitoring and diagnostic system for hydro power plants', Hydrovision conference proceedings. Newland, D. E., (1993), 'An introduction to random vibrations, spectral and wavelet analysis', Longman, 3"* Edition. Safizadeh, M. S., Lakis, A. A., and Thomas, M., (2000), 'Using Short-Time Fourier Transforms In Machinery Fault Diagnosis', COMADEM, Vol. 3, No. 1. Thompson, J. M. T., and Stewart, H. B., (1991), 'Nonlinear dynamics a^dchaos', John Wiley & Sons Ltd, 6* Edition. Virgin, 1. N., (2000), 'Introduction to Experimental Nonlinear Dynamics: A case Study in Mechanical Vibration', Cambridge University Press, 1^ Edition.
142
Condition Monitoring and Diagnostic Engineering Management Andrew G. Starr and Raj B.K.N. Rao (Editors) © 2001 Elsevier Science Ltd. All rights reserved.
APPLICATION OF VIBRATION DIAGNOSTICS AND SUPPRESSION BY USING THE CAMPBELL DIAGRAM K. Takano\ H. Fujiwara*, O. Matushita\ H. Okubo* and Y. Kanemitsu^ ' Department of Mechanical Engineering, National Defense Academy 1-10-20 Hashirimizu, Yokosuka, Kanagawa, Japan 239-8686 ^ Department of Intelligent Machinery and Systems, Graduate School of Engineering, Kyushu University, 6-10-1, Hakozaki, Higashi-ku, Fukuoka, Japan,812 —8581
ABSTRACT The Campbell diagram consists of the relationships of rotating speed, spectrum data and rotational order components of the rotor. Generally, the resonance problems generated on the rotor originate from the rotational order of the rotor. Thus, tracking analysis is significantly practical. However, in the case of a belt driving system, tracking analysis is insufficient because it has two or more rotating components. On the other hand, the analysis by using the Campbell diagram produces the possibility of simultaneous diagnostics, which focuses on both the pulley and the belt rotational order in a single operation. In the case of flow-induced vibration, there are two types of excitation phenomena, the forced vibration and the self-excited vibration. The vortex shedding frequency is in proportion to the flow velocity until the self-excited vibration increases. Subsequently, this frequency is locked to the natural frequency of the structure. The Campbell diagram gives us an effective analysis method for this type of vibration phenomena. In this study, we introduce the applications of the Campbell diagram. In addition, these vibration data are referred to as the control method. As a result, these resonance vibrations are significantly suppressed.
KEYWORDS Campbell diagram, Tracking analysis. Vibration, Rotor, Belt driving system. Flow-induced vibration
INTRODUCTION The resonance problems are the most serious problems for a rotor, a belt driving system and a structure. These vibration problems are usually solved by the following procedure; identifying the natural frequency and the exciting sources, measuring of vibration phenomena, identifying critical speeds, development of solution method and confirmation of control effects. It is necessary for the
143
correct analysis to confirm the actual vibration phenomena including some disturbances. Generally, the vibration phenomena of machinery are expressed as the relationships of the natural frequency, frequency of forced vibration, operational speed, and so on. In the case of diagnostics of machinery, it is convenient that all of the vibration data is expressed together and then the relationships of exciting source and the vibration phenomena are analyzed at the same time. In order to do that, it is suitable to express the operational speed and spectrum records subsequent to an operational speed sweep examination. These analysis data are shown as the waterfall diagram and the Campbell diagram (interference diagram). The Campbell diagram is comprised of the spectrum data and the frequency of forced vibration. It means that this is useful for the identification of resonance points. The Campbell diagram and the tracking diagram are usually used for the diagnostics of a rotor. In the case of resonant state, the vibration is excited by the rotational first order component (O. Matsushita, M. Ida (1984)). Analysis by using the tracking diagram provides significant information for the diagnostics of rotor resonance problems. However, in the case of a belt driving system, which has the two rotational components of a pulley and a belt rotation, then an analysis by using the tracking diagram can not provide enough information for the diagnostics (K. Takano et al. (1999)). On the other hand, an analysis by using the Campbell diagram is suitable because it is possible for the simultaneous analysis to focus on both the pulley and belt rotational order components. In the case of flow-induced vibration, self-excited vibration, which is in turn excited by the Karman vortexes, occurs. This vortex shedding frequency is in proportion to the flow velocity and it is locked in the natural frequency of structure (W. D. Iwan and R. D. Blevins (1974)), The analysis by using Campbell diagram will provide the practical diagnostic even if these vibration phenomena occur. In this report, we introduce some examples of diagnostics of industrial machinery by applying the Campbell diagram. These examples include a rotor equipped with active magnetic bearings, a crawler driving device, an actual tracked vehicle and a cable model, which is influenced by flow force. After the diagnostics, the measured data, such as the resonance points, vibration amplitude and natural frequency etc. are used for a control theory, which is based on the variable structure control method.
SUMMARY OF THE CAMPBELL DIAGRAM The Campbell diagram data consist of the fast Fuorier transform (FFT) data and tracking data filtered by the tracking filter (See Figure 1). The discrete Fourier transform (DFT) is conducted by acquiring the data for each sampling interval. Consequently, the FFT data is shown as the relationships of data location and number of data. Generally, DFT data X{k) are obtained by using Eqn. 1. 1
N-l
-liakj
X(k) = -r=^J^x{i)e ^ =>a,
(/: = 0, 1, 2,
, A^-1)
(1)
where, A^ shows the total number of data, jc(/) is the measured data, j is the imaginary unit. On the other hand, the tracking filter (vector filter) extracts the forced vibration components from the vibration data. Therefore, the band pass filter (BPF) should be synchronized with the forced vibration component of the machinery. These operational speed and vibration amplitude data make the tracking diagram and it is used for the identification of the critical speeds or resonance points. This transformation is almost the same as Eqn. 1; however, the transforming frequency should be kept on frequency of forced vibration.
144
1
A^-1
-Imnioj
X(nQ)) = -j===rTx(i)e
[
N
0) : Forced frequency component^ n : n* order
J
(2)
This frequency (o is calculated from the rotational pulse by the phase locked loop (PLL). The n'** order component is the rotational order, which should be considered. Actually, a real part (cosine wave) and an imaginary part (sine wave) are calculated independently, and then they are averaged. The FFT data include tracking data, so tracking data is subtracted and then both vibration data are located on the suitable position of the Campbell diagram. PLL produces the triangle wave, which is synchronized with a rotational pulse signal. After the phase compensation, tracking filter uses this signal and then the synchronized component is calculated. Of course, this transformation is continuously completed as real time processing. Rotational Pulse
Displacement Data Tracking Filter
ffi
PLL
+ lj
Reconstruction sin, cos wave
Xj„ sin noxdt)
_E
Forced Vibration Amlitude a„ Synchronize with not)
Fluctuation
Figure 1 : Outline of data analysis for the Campbell diagram The measurement system consists of a digital signal processor (DSP) and a host personal computer (Host PC). The DSP calculates all of the transformation and the Host PC displays measured data, input/output data and controls the DSP. The FFT loop and tracking loop is handled simultaneously and both data are forwarded to the dual port memory located on the DSP. The Campbell diagram shows the relationships of rotational speed (horizontal axis), frequency (vertical axis), frequency of forced vibration (proportion to the rotational speed) and vibration amplitude (radius of circles). A large circle shows the resonant state. Consequently, we can estimate the external force, critical speed and resonance amplitude, and then we can diagnose the vibration.
CASE STUDY OF DIAGNOSTICS AMB Rotor Figure 2 shows the rotor equipped with the active magnetic bearings (AMB) (M. Itou et al. (2000)). This rotor has 1310 mm total length and 37 mm diameter. It is supported by the AMB in both the radial and thrust direction, and then it rotates completely without contact. The standard rotational
145
speed is 350 rps and it has bending modes in its rotational speed range, which is then called an elastic rotor. The rotor is located in a casing, which can be realized in a vacuum that prevents a loss of air. All of the AMB is basically controlled by the PID controller. The rotor equipped with the AMB has rigid vibration modes, which originated from the transfer function of the controller. The rotor resonance vibrations are usually synchronized with the rotational orders, and the resonance vibrations are oscillated at the meeting points of the natural frequency and the frequency of forced vibration. This means the rotor does not have any disturbance except eccentricity and unbalances. Motor
Radial AMB
Rotor
Outer casing
Radial AMB
Thrust AMB
Figure 2 : General view of rotor system equipped with AMB Motor slip ^oRDl
\MB
lOOC
I
310/
>. i
/
ISTJ
u
i 20 z lO'
10*
lO'
4000 Rotational speed (rpm)
10*
Spring Constant (N/m)
(b) Campbell diagram (a) Critical map Figure 3 : Campbell diagram and critical map of AMB rotor Figure 3 show the critical map, the Campbell diagram and the tracking diagram. The critical map indicates the relationships of bearing stiffness and natural frequencies, designated by the rigidity of the AMB. We can determine the natural frequencies as 20 Hz and 45 Hz for the first and second rigid mode, 90 Hz, 182 Hz, 310 Hz for the first, second and third bending mode, respectively. The experiments are conducted below 140 rps because of high Q value. We can confirm the resonance points of 1200, 4000 and over 8000 rpm. From the critical map, it is estimated that each resonance point is generated by first, second rigid mode and first bending mode, respectively. In the tracking diagram, the solid line shows the overall amplitude and the dotted line shows the vibration component of first order rotation. From this figure, it is confirmed that both vibrations are almost the same. This means that a rotor vibration is mainly governed by the unbalance of the rotor. Consequently, in the 146
case of diagnostics of the rotor, identification of resonance points, resonance amplitude and Q factor are important. This means that the tracking analysis is usually significant for diagnostics of rotor vibration. However, changes of natural frequency caused by the gyro effect can be identified in the Campbell diagram, so that this type of vibration needs diagnostics by using the Campbell diagram. Tracked Vehicle Figure 4 shows the outline of a tracked vehicle, which is the representative of a belt driving system. Figure 4(a) shows the general view of 4 track system vehicle (TOYOTA LAND CRUISER PRADO Crawler: 4TS) This vehicle flourished in the Nagano Olympic Games as transportation of some apparatus or disabled people. The 4TS is equipped with the crawler units instead of wheels. It can move with high mobility on the off-road terrain fields because of low ground pressure. The maximum velocity of vehicle is over 100 km/h (60 miles/h). This has three wheels covered with rubber, one steel wheel, and one sprocket connected to an axle hub through a connecting adapter. The rotational ratio of the crawler to the sprocket is 0.49, which means the crawler rotates one time during two time rotations of the sprocket. A laser displacement sensor is located on a lower part of span in order to measure transverse vibration of the crawler. In the case of crawler (belt) vibration, a low order mode vibration is very dangerous because of large amplitude. The excitation sources are estimated as 1) eccentricity of the sprocket (pulley), 2) unbalance of the crawler (belt). Consequently, two types of resonance vibrations are considered. However, the sprocket contacts at the top of cog, then only the resonance caused by the unbalance of crawler is expected. Sprocket
[ORD]
Crawler rotation
20
40 60 Vehicle velocity (krtVh)
(b) Campbell diagram (a) General view of 4 track system vehicle (4TS) Figure 4 : Outline of the tracked vehicle Figure 4(b) show the Campbell diagram of the 4TS. The unbalance of the sprocket is not so important because the thick rubber crawler is able to absorb the unbalance excitation. However, in designing the crawler, the rubber crawler is made as an oblong card and they are connected together. In this state, the unbalances of the crawler are made. Figure 4(b) establishes these results. We can confirm only the resonance points oscillated by crawler rotation components. This indicates that the tracking diagram for the sprocket is not significant for the crawler driving systems. However, diagnostics by using the
147
Campbell diagram is very practical because all of the resonance points and frequencies are identified at once. If you use the tracking diagram only, the rotational order of the sprocket (pulley) and the crawler (belt) components should be measured independently. When an unexpected disturbance or rotational component exists, tracking analysis should be conducted repeatedly. Also, it is well known that the natural frequencies of the crawler decrease with accompanying increase of velocity because of the Coriolis effect. In the case of the crawler driving system, the natural frequency is enormously decreased. From this point of view, diagnostics by using the Campbell diagram are also effective.
CASE STUDY OF CONTROL EFFECT BY USING CAMPBELL DATA Many control methods for applying the rotor have already reported (T. Yoshida et al. (1993)) and an effective control method can be realized by using tracking data only. Therefore, the control method for the rotor system is omitted. There are several kinds of control methods for the crawler (belt) or cable, such as the control by optimal boundary damping (S. Y. Lee and C. D. Mote, Jr. (1999)), the wave cancellation method (C. H. Chung and C. A. Tan. (1995)), and so on. Also, the variable structure system (VSS) theory (V. I. Utkin, (1977)), which varies the stiffness of the system, is applicable for these systems. We adapt the bang-bang control, which is one of the VSS control methods without sliding modes. The measured vibration data of the Campbell diagram is used for compensation of the control method. Outline of Bang-bang Control In order to explain the control law briefly, we use Eqn. 3. q + 2^(0„q +fi>^(l+ u)q = Fcosox
(3)
where, co„ is the natural frequency, ^ is the damping ratio, u shows the control input, F is the external force and (O is the frequency of forced vibration. In this study, \ye determine the control input (w) based on the following sign functions and fluctuating the natural frequency (tension for the actual system). The sign[^^] control method switches four times during one cycle of the vibration q. It is shown as u = Asign[^^]
(4).
Eqn. 4 indicates that the tension is switched with each quadrant on the phase plane of q. u has two values, A and -A, and varies drastically. This kind of control method is called the bang-bang control method, as well as sign[4] control. The sign[4] control switches two times during one cycle. u = Asign[^]
(5)
Figure 5 shows the transient phase trajectory of the model of Eqn. 3 applied with the above two control laws. All of these mathematical results are calculated by MATLAB and the circle mark shows the staring point of the control. The condition of calculation is as follows; fi)„=ft^5.0x2;rrad/s, f=0.005, F=cu?xO,01, A=0.4. It is confirmed that the phase trajectory is swelled toward the velocity in the gray areas of each control law with the positive value of A. On the other hand, it is contracted in the white areas with the negative value of A. As a result, the phase trajectories are converged to the 148
origin. In addition, the actual system has a phase lag, the transfer function of the analog/digital translation et al. In order to compensate for the phase lag, we change the coordinate system, which is translating from the sensing signal to the compensated signal. The control phase is designated by reference to this translated data.
-0.5
0.0
0.5
-0.5
Displacement q
0.0
0.5
Displacement q
(a) sign[^^] (b) sign[^] Figure 5 : Outline of control law How to Use of Measured Data In order to apply the bang-bang control method to the real machinery, the measured data is effectively used for compensation of control method. (1) Bang-bang control needs BPF in order to prevent some kind of error. If the different vibration mode is excited simultaneously, it is necessary to shift the BPF for the dominant mode vibration. (2) This bang-bang control method needs a phase compensation for the phase delay of the actual system. This phase compensation also should be shifted with the dominant vibration mode. (3) Economical control law that control gain directly varies with control effect is necessary. The control input should be "0" or vary with measured amplitude in order to realize economical control and to prevent the spill over vibration. The amplitude between n^^ peak and n+1^^ peak is very small. Therefore, the control threshold and variable gain are applied. (4) It is estimated that the high order vibration mode are oscillated at the switching period because of the nonlinear bang-bang control. In order to realize the effective control for the case of a single controller; a control priority should be designated in compliance with vibration magnitude. Crawler Driving System Figure 6(a) shows the crawler driving system. The crawler driving system imitates an actual tracked vehicle and it uses the same crawler and sprocket of actual vehicle. The maximum circular velocity is about 40 km/h and the length of span is 1.15 m. As explained above, two types of resonance vibration are considered, which are synchronized with the sprocket and the crawler rotation. In Figure 6(b), the dotted line shows "Without control" and the solid line shows "With control". The result of "Without control", we can not confirm any resonance points synchronized with rotational order components of sprocket, however, resonance points of crawler rotation components are identified in all of the rotational range. Additionally, it is confirmed that the natural frequencies of crawler decrease accompanying increase of belt velocity as same as the actual vehicles. The control effects provide 30% decrease of resonance amplitude. From the Campbell diagram of "Without 149
control" and '*With control", the natural frequency has no major change. By using the vibration data obtained from the Campbell diagram, the significant control effects are provided. lORD]
o
2.00C+01 Imml
20 30 40 Traveling speed (km/h)
(a) General view of crawler driving system (b) Campbell diagram Figure 6 : Crawler driving system Cable Model / , = 8.0 V
;
T
Air Flow
K ^ ^
'
^ ^
0.5
Power Amp. Magnetic Actuator
1.0 1.5 2.0 Flow velocity V (m/s)
(a) General view of cable model (b) Campbell diagram Figure 7 : Cable model Figure 7(a) shows the cable model. The total length of the cable is 1.2 m. Both the transverse iy) and in-hne {x) displacements are measured by using laser displacement sensors. The experimental model consists of a single wire and one styrene foam circular cylinder located on the middle part of the wire. The Strouhal number (5^) determines the frequency of the vortex (f^), which is in proportion to the flow velocity {U) and inverse proportion to the cylinder diameter (D), which is written as 5, = lU/D^ 0.2 for the airflow (W. D. Iwan and R. D. Blevins (1974)). This vortex shedding frequency is locked in the natural frequency of the structure, if the self-excited vibration is significantly increased. Therefore, forced vibration frequency is nonlinear for the flow velocity, and then an analysis by using the Campbell diagram is suitable for this type of vibration. Figure 7(b) show the Campbell diagram of the cable model. We generalized all of the control
150
Figure 7(b) show the Campbell diagram of the cable model. We generalized all of the control procedures listed above. The first mode vibration can be considered as the transverse in-plane vibration. On the other hand, the second mode vibration consists of both directional motions, that is the out-of-plane transverse vibration. In addition, lock-in phenomena are confirmed, which the vortex shedding frequencies are locked to the natural frequency of structure. In the tracking diagram, the thick lines show the transverse vibration and the thin lines show the in-line vibration. In this experiment, sign[^^] control is applied for the transverse vibration and sign[^] control is applied for the in-line vibration in order to control the out-of-plane transverse vibration. It is easy to confirm that this control method is effective for the cable model because all of the vibrations are well controlled.
CONCLUSION In this report, we introduce some examples of diagnostics of industrial machinery that apply the Campbell diagram. The examples include the rotor equipped with active magnetic bearing, the crawler driving system, the actual tracked vehicles and the cable model. In the case of the rotor vibration, an effective control method can be realized by using only tracking data. Thus, diagnostics by using the tracking diagram is significant. However, in the case of the crawler (belt) driving system or cable model, they have some different vibration characteristics, such as some exciting source, lock-in phenomena and so on. Therefore, it is shown that the diagnostics by using the Campbell diagram is very practical because all of resonance points and frequencies are identified at once. In addition, after the diagnostics, the measured data, such as the resonance points, the vibration amplitude and the natural frequency, are used for the control theory, which is based on the variable structure control method. As a result, the significant control effects are provided by using the vibration data measured from the Campbell diagram. In this report, we adapt only the bang-bang control method, however, this kind of modification will be applicable for other control theories.
REFERENCE [1] C. H. Chung and C. A. Tan. (1995), Active Vibration Control of the Axially Moving String by Wave Cancellation, Journal of Vibration and Acoustics, 117,49-55. [2] K. Takano, O. Matsushita, H. Okubo, K. Watanabe and H. Fujiwara. (1999), Vibration and Control of Belt Driving System, Proceeding oftheAPVC V9, Vol, A, 105-110. [3] K. Takano, O. Matsushita, H. Okubo, H. Fujiwara and Y. Kanemitsu. (2000), Bang-bang Control of Vortex-induced Out of Plane Vibration of a Tensioned Cable, Proceeding ofMOVIC '2000, Vol. 1,387-392. [4] M, Itou, O. Matsushita, H. Okubo and H, Fujiwara (2000), Unbalance Vibration Control for High Order Bending Critical Speeds of Flexible Rotor Supported by Active Magnetic Bearing, The 8* International Symposium on Transport Phenomena and Dynamics of Rotating Machinery, Vol. 2,923-929. [5] O. Matsushita, M. Ida (1984), Analysis Method for the Response of Rotational Order for the Flexible Rotor, JSME Journal ofMechanical Engineering, 50 :452,626-634 (In Japanese). [6] S. Y. Lee and C. D. Mote, Jr. (1999), Wave Characteristics and Vibration Control of Translating Beams by Optimal Boundary Damping, Journal of Vibration and Acoustics, 121, 18-25. [7] T. Yoshida, O. Matsushita, N. Takahashi (1993), Development of Rotor Vibration Simulation Method for Active Magnetic Control, JSME Journal of Mechanical Engineering, 59 : 557, 50-57 (In Japanese). [8] V. I. Utkin, (1977), Variable Stmcture System with Sliding Modes, IEEE Transactions on Automatic Control, 22 :2,212-222. [9] W. D. Iwan and R. D. Blevins (1974), A Model for Vortex Induced Oscillation of Structures, Journal of Applied Mechanics, SEPTEMBER 1974, 581-586. 151
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Condition Monitoring and Diagnostic Engineering Management Andrew G. Starr and Raj B.K.N. Rao (Editors) © 2001 Elsevier Science Ltd. Allrightsreserved.
A NOVEL SIGNAL PROCESSING APPROACH TO EDDY CURRENT FLAW DETECTION BASED ON WAVELET ANALYSIS L.Q. Li, K. Tsukada and K. Hanasaki Department of Earth Resources Engineering, Graduate School of Engineering, Kyoto University, Kyoto, Sakyo Ku Yoshida HonMachi 606-8501, Japan
ABSTRACT The application of wavelet transform in the field of nondestructive testing (NDT) has become attractive in recent years. It has the prevalent ability to analyze the local characteristics of signals. In eddy current testing (ECT), the signals of flaw are usually corrupted by noise and other variables due to conductivity, permeability, structures, and probe lift-off etc. The signal-to-noise ratio (SNR) is lowered and flaw detection and characterization become unreliable and inaccurate. This article presents a novel approach based on discrete wavelet transform (DWT) to ECT flaw detection and characterization. The proposed method mainly consists of three steps: pre-processing, DWT processing and flaw detection and characterization. After appropriate pre-processing, the ECT signals are first decomposed into wavelet domain and then the concerned wavelet coefficients are modified. By reconstruction of these modified coefficients through the inverse wavelet transform, the noise and nondefect signals are suppressed and the defect signals are enhanced prominently. The results of our experiment on one-dimensional ECT signals show the effectiveness.
KEYWORDS Eddy current testing (ECT), nondestructive testing (NDT), flaw detection, wavelet analysis, signal processing.
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INTRODUCTION Eddy current testing (ECT) is one of the most effective nondestructive testing techniques to detect the flaws in conductive materials. It has found its applications to bar, to tube, and to wire testing ^^\ To get the flaw information from ECT signals, some recognition methods can be adopted, such as those using impedance plane diagrams^^^^^\ inverse analysis ^'^^^^\ Fourier descriptors ^^^\ and neural networks ^^\ The ECT signals, however, are usually corrupted by noise and a large number of nondefect signals, including conductivity, permeability, geometry and probe lift-off etc. The lowered signal-to-noise ratio (SNR) makes it difficult to detect and characterize the flaws. In order to enhance the SNR of the ECT signals, some signal processing methods, such as rotating technique ^^^ ^^\ multi-frequency mixing technique ^'^^ and data fusion approach ^''^"^'^^, are employed. In this paper, a hybrid approach based on the discrete wavelet analysis (DWT) to the flaw detection and characterization is addressed. The wavelet transform is a relatively new signal processing technique ^^\ The multi-resolution analysis method underlying wavelet transform allows us to extract simultaneously the frequency and spatial information to get the local characteristics analysis impossible in Fourier domain. This feature is very useful in our case as it permits the determination of particular scales where ECT signals have significant energy. The approach mainly consists of three stages: pre-processing, DWT implementation and flaw detection and characterization. A number of pre-processing steps are required before DWT processing. The ECT signals are first filtered to remove the sparse spikes and then normalized in a proper way. At the DWT stage, decomposition, coefficients modifying and reconstruction are performed. Since the wavelet decomposition provides information on both frequency content and spatial position, noise can be removed without loss of flaw information. The DWT processing enhances the SNR of ECT signals so that the flaws can be detected reliably. The rest of this paper is organized as follows. Section 2 briefly reviews the DWT. The signal-processing algorithm based on DWT is presented in section 3. Experimental results and related discussion can be found in section 4. Finally, a conclusion is summarized in section 5.
DISCRETE WAVELET TRANSFORM DWT analyzes the signal at different frequency bands with different resolutions by decomposing the signal into a coarse approximation and detail information. DWT employs two sets of functions, called scaling functions ^(x) and wavelet functions i//(x), which are associated with 'approximation' and 'detail' parts, respectively. The DWT of a signal f(x) is defined as the projection of the signal on the set of the wavelet functions y/ j,k (x) DWTf{j,k) = \f{x)y/.,{x)dx
(1)
where {^j,k(x)} are generated from the same template function \f/, called the mother wavelet^ using 154
the following formula: y^j,(x) = 42^y/i2-'x-k).
(2)
The DWT coefficients {DWT/jy,^;} reflect the local characteristics off(x) both in frequency and space, depending on the scaling parameter j and the shift parameter k. The signal can be reciprocally reconstructed from these coefficients through the inverse DWT (IDWT) if the i// (x) is chosen appropriately +eo
2'
f(x) = £ Y^DWTfU,k)v^jA^).
(3)
j=-oo k=\
Let us fix7 and sum on L A detail Dj is nothing more than the function Dj(x) = YDWTfU,k)i^j,(x).
(4)
ksZ
If we sum ony, the signal is the sum of all the details:
(5)
/W = I^,jeZ
Associated with the scaling function ^ (x), the approximation of signal at the resolution 2^ can be defined as 4/W=Z4/O-.^)^MW
(6)
AfiJ,k) = lnx)<^j,U,k)dx
(7)
where
and (l>.,(x) = ^(l>{T^x-k).
(8)
Take a reference level J in formula (5). There are two sorts of details. Those associated with indices j<J correspond to the scales between 2^ < l ' which are the fine details. The others, which correspond to j> J, are the coarser details. We group these latter details into A,=Y.Dj
(9)
which defines the approximation of the signal. Then the signal can be summed as f{x) = A,^Y.D..
(10)
From this formula, it is obvious that the approximations are related to one another by: A._,=Aj^Dj
(11)
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SIGNAL PROCESSING ALGORITHM BASED ON DWT The original ECT signa^\f(x) can be regarded as a mixture of the flaw signal s(x) and noise or nondefect signal n(x). This modal is denoted by /(X)=5(JC) + «(X).
(12)
White noise, probe lift-off noise, and the signal of structure are the typical interference in ECT signals and then are taken as the objects to be analyzed in our study. Figure 1 shows the schematic of the proposed signal processing approach. The key issue of this approach is the DWT implementation. Signal Pre-Processing
DWT Decomposition T
Coefficients Modifying T
Flaw Detection
Reconstruction Figure 1: Signal processing flow
To enable the DWT implementation, a number of pre-processing steps are required to remove the sparse spikes from the probe signal and to normalize their magnitudes in a proper way. Then the signal f(x) is decomposed into L frequency levels using the fast wavelet transform (FWT) algorithm ^^^^^^^\ From equation (10), we have fix) = A,(x)+f^Dj{x) j=l
= X A(i,*¥i.* W +tXi5WT/(x)v',.,W. *=I
(13)
j=\ k=\
According to the frequency spectrum, the low-frequency noises, such as the probe lift-off noise, are decomposed into AL while the flaw signal and high-frequency noise are into {Dj}. As is known, wavelet coefficients measure the similarity of the signal and each daughter wavelet. The more the daughter wavelet is similar to the feature component, the larger is the corresponding wavelet coefficients. Usually the flaw signal is a band-pass signal so that it has the larger components in the {Dj} at middle frequency levels. On the other higher-frequency domain, the coefficients are mainly dominated by the high-frequency noise, such as white noise. The decomposed coefficients are then modified in such a way as to remain those featuring the flaw signal and to de-emphasize the others from the noises. Two key techniques, masking and threshold techniques, are employed here. •
Masking operation Because the coefficients in AL are contributed by the low-frequency noise, such as the probe lift-off 156
noise, so the masking operator zeroes out the AL and preserves those on {Dj} without loss of the information of flaw signal. Thus A,(L,k) = 0', A: = 1,2,...,2'. (14) •
Threshold operation In the {Dj}, the coefficients originated from the flaw component usually have larger magnitude than those from higher-frequency do. Thresholing is performed in the {Dj} so as to zero out the small magnitude DWT coefficients and retain the large ones. For a fixed resolution level j a threshold is specialized to be Q. Let DWTf\j,k) denotes the modified coefficients. Written out explicitly this gives \DWTfO\k)
DWTf'0\k): •
where7=7,2
\DWTf(j\k)\>Cj \DWTfU,k)\
(15)
\
L.
The modified coefficients are then used to reconstruct theflawsignal via the reconstruction algorithm: theFWToflDWT (16) j=\ k^l
Finally, we have the noise-suppressed signal s(x) from the original one f(x) resulting a more reliable flaw detection and accurate characterization.
EXPERIMENTAL RESULTS AND DISCUSSION Figure 2 shows the schematic of the specimen in the left half and the ECT experimental setup on the right half The specimen is carbon steel marked with SAPH400. There are two simulated flaws on the plate about at position 150 and 350. The testfrequenciesare 200KHz in thefirstexample and 50KHz in the second. flaws
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Figure 3: Seven-level decomposition of an ECT signal picked up at 200KHz, with flaws at 150 and 350.
Figure 4: Noise suppression of the ECT signal of Figure (3). (a) the original signal, (b) the signal obtained by removing the lift-off noise, and (c) reconstructed "clean" signal.
The one-dimensional ECT signals were picked up from our experiment and then were processed by using the algorithm presented in the previous section. Daubechies wavelets were adopted as the mother 158
wavelet functions denoted "Dn" with its order n. In the masking operation of our algorithm, D18 wavelet is used to remove the low-frequency noise from probe lift-off in that D18 has high regularity ^'^l Figure 2 shows the result of a seven-level decomposition of the ECT signal picked up in 200KHz frequency. Figure 4(a) is the original signal and Figure 4(b) is the one reconstructed by setting the coefficients of A7 to zero. It was again decomposed into seven frequency levels and using D3 to implement threshold. Figure 4(c) gives the final reconstruction of "clean" flaw signal. Another example is presented in Figure 5 where the testing frequency is 50KHz. j
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Figure 5: Result of the ECT signal picked up at frequency 50KHz. (a) the original signal, (b) the signal obtained by removing the lift-off noise, and (c) reconstructed "clean" signal.
CONCLUSION A novel signal processing approach was discussed to eddy current flaw detection based on discrete wavelet transform. The noise and nondefect signals were separated from the flaw signal by decomposing the ECT signal into wavelet coefficients domain. Then threshold and masking operation were applied to retain the flaw information and de-emphasize the others noise from signal. The modified coefficients finally reconstruct the noise-suppressed version of flaw signal by using the IDWT. Its effectiveness can be seen from the one-dimensional ECT signals. It is also a feasible method for two-dimensional ECT flaw detection.
References 1. Anthony TeoHs, (1998), Computational Signal Processing with Wavelet, Birkh • user, USA. 2.
C. V. Dodd, J. R. Pate and W. E. Deeds. (1989). Eddy-current inversion of flaw data from 159
flat-bottomed holes, Review Progress in Quantitative Nondestructive Testing 8A, 305-312. 3.
Chady T., Enokizono M. and Sikora R. (1999). Crack detection and recognition using an eddy
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D. E. Bray and R. K. Stanley. (1989). Nondestructive Evaluation, McGraw-Hill, USA
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F. Lingvall and T. Stepinski. (2000). Automatic detecting and classifying defects during eddy
current differential probe, IEEE Transactions on Magnetics 35:3, 1849-1852.
current inspection of riveted lap-joints, NDT & E International 33:1,47-55. 6.
G. Chen, Y. Toshida, K. Miya and M.Kurokawa. (1994). Reconstruction of defects from distribution of current vector potential T using wavelet. Int. J. Appl Electromagn. Mater, 5:3, 189-199.
7.
H. Hoshikawa and K. Koyama. (1994). Flaw depth classification in eddy current tubing inspection by using neural network. Review Progress in Quantitative Nondestructive Testing 14A, 811-818.
8.
Haller A., Tavrin Y., and Krause H.-J. (1997). Eddy-current nondestructive material evaluation by high-temperature SQUID gradiometer using rotating magnetic fields. Electronics; IEEE Transactions on Applied Superconductivity 7:2, 2874-2877.
9.
Hoshikawa Hiroshi and Koyama Kiyoshi. (1998). New eddy current probe using uniform rotating eddy currents. Materials Evaluation 56:1, 85-89.
10. I. Daubechies, (1992), Ten Lectures on Wavelets, CBMS-NSF Series in Applied Mathematics 11. Liu Z., Tsukada K. and Hanasaki, K. (1998). One-dimensional eddy current multi-fi-equency data fusion: a multi-resolution analysis approach. Insight: Non-Destructive Testing and Condition Monitoring 40:4, 286-289. 12. Liu Z., Tsukada K., Hanasaki K. and Kurisu, M. (1999). Two-dimensional eddy current signal enhancement via multifrequency data fusion. Research in Nondestructive Evaluation 11:3, 165-177. 13. S. S. Udpa and W. Lord. (1984). A Fourier descriptor classification scheme for differential probe signals. Materials Evaluation 47, 1138-1141. 14. S.G. Mallat. (1989). A theory for multiresolution signal decomposition: The wavelet representation, IEEE Transaction on Pattern Analysis and Intelligence 11:7, 674-693. 15. Tomasz Chady and Masato Enokizono. (2000). Multi-fi'equency exciting and spectrogram-based ECT method. Journal of Magnetism and Magnetic Materials 215-216:2, 700-703.
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Condition Monitoring and Diagnostic Engineering Management Andrew G. Starr and Raj B.K.N. Rao (Editors) © 2001 Elsevier Science Ltd. Allrightsreserved.
THE WAVELET ANALYSIS APPLIED FOR FAULT DETECTION OF AN ELECTRO-HYDRAULIC SERVO SYSTEM Zhanqun Shi^'^, Jianmin Wang^ Yali Zhang^ Haiwen Zhao* & Hong Yue* *The School of Mechanical Engineering, Hebei University of Technology, Tianjin, 300130, China ^Manchester School of Engineering, The University of Manchester, Manchester, M13 9PL, UK
ABSTRACT This paper presents the development of a wavelet model-based diagnostic methodology to perform fault detection in Electro-hydraulic servo system. The objective is to introduce the wavelet analysis into the fault detection of control systems and provide an effective approach to detect faults in an electro-hydraulic servo system. After comparing the continuous wavelet transform (CWT) and the discrete wavelet transform (DWT), this paper gives the selection. A Discrete wavelet transform is chosen to analyse the pressure response signal from the electro-hydraulic servo system. Signals from healthy condition, incipient fault condition to failure condition are collected from a test rig with electro-hydraulic position control system. The results show that some problems exist on the wavelet analysis when directly applied for control systems. As a solution, this paper develops a wavelet model -based approach (WMBA). In this approach, a model in DWT form is built for the electro-hydraulic servo system at its healthy condition. This model is used to generate a residual signal by comparing the healthy signal and the signal in the running system. By processing the residual signal, faults from incipient fault to serious fault can be detected effectively. As the conclusion, wavelet analysis can be a powerful tool in the fault diagnosis of control systems. KEYWORDS Wavelet analysis, DWT, wavelet model-based approach, electrohydraulic servo system, fault detection 1 INTRODUCTION In recently years, the wavelet analysis has found a lot of applications in fault diagnosis in various areas. It is proved to be the most advanced fault detection tool. This is especially true in vibration signal based diagnosis. Many literatures such as Lu, CJ (2000), Suh, C. Steve (2000), Yen, Gary Y (1999), Al-khalidy, A (1997), Aretakis, N (1996), worked all on the vibration signal processing and monitoring. The major advantage of the wavelet analysis comes from its perfect characteristic in 161
localisation both in time domain and in frequency domain. However, one can seldom find its application on control systems for either condition monitoring or fault diagnosis until 2000, see Ren (2000). In parallel, the model-based approach is highlighted in fault diagnosis on control systems due to its convenient residual analysis. Several survey papers are made by Gertler, J (1991) Frank P.M (1996), and Isermann R (1997) successively. Unfortunately, the application of this approach is far less than its theoretical researches, Isermann R (1997). Only one can be found in fauh diagnosis on an electrohydraulic servo systems Rainer Oehler (1997). Indeed, it is very difficult to detect faults occurred in electro-hydraulic control systems. High integrity is the main problem, in which the combination of fluid, mechanical and electronic parts makes the control system very complex. Signal changes slowly, and the noise from fluids may submerge the fault information. Although the neural network approach is introduced, Atkinson R.M (1996), Le TT and J Watton (1998), the situation is far from sufficient. This paper tries to answer some questions on this field. Although Ren has proved that the wavelet analysis can be applied to control systems, some questions still exist. Is it effective to extract fault signature with this approach? Can it applied directly or has to be combined to other approaches? From the study on this subject shows some difficulties in this approach. The other question is that does the wavelet analysis suit for the electro-hydraulic servo system, or can it sensitive to incipient faults in the system? The answer is different from Ren, but based on the combination of wavelet transform to the model-based approach. It is referred to as wavelet model-based approach (WMBA). The authors v^sh to acknowledge the Hebei Natural Science Fund of China for its support on this research.
2 THE SELECTION OF THE WAVELET TRANSFORM In most applications, the wavelet transform always takes the form of either the continuous wavelet transform (CWT) or the discrete wavelet transform (DWT). In order to choose an appropriate one and to make use of it on control systems, a brief comparison is made here in this section. 2.1 The continuos wavelet transform (CWT) A Function y/{t) e l} (R) is called a basic wavelet or a mother wavelet, if its Fourier Transform satisfies equation (1)
Where i//\o)) is the Fourier Transform of the function y/(t). With its dilation and translation, we can get equation (2)
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a>0
(2)
This is called the continuous wavelet in regards to the dilation factor a and the translation factor b . The continuous wavelet transform of a function /(/) e l}{R) will be in equation (3) Wf{a,b) =< f,y/^,
>= \a\-y^ [f(t)yf(!—^)dt
(3)
The function can be reconstructed by means of equation (4)
/(O = 7!- ] J-V/ {a,bM~)dadb
(4)
The continuous wavelet transform has a perfect localisation both in time domain and in frequency domain. With \a\ decrease, ^^^(0 focus on the part of high frequency. It means the narrower in time window but higher in resolution. This performance makes CWT an advanced tool on fault detection. 2.2 The discrete wavelet transform When used in practice, especially in computers, the continuous wavelet has been discrete. In fact, the continuous wavelet and a continuous wavelet transform of a signal can be discrete by setting a = al and b-kalbQ j eZ. Equation (2) will take the form of equation (5).
The decomposition and its reconstruction of the function f{t)m the discrete wavelet transform are shown in equation (6) and (7).
Cj,, =< / , Wu >= ]f(0¥~JU)dt
(6)
2.3 Application on control system fault detection Different wavelet transforms may give different analysis resuhs to a system. To most vibration signal, both the CWT and the DWT can be used to detect faults. However, control systems are normally analysed by response performance such as step response. Either time response or frequency response is not the same as a vibration signal but a kind of stable signal. Representation: CWT is described with coloured figure; DWT is described with figures in different levels. DWT allows us to analyse a signal in separate level, and can be reconstructed in any scale. CWT can only give the overall information. Algorithm: DWT is simpler than CWT due to the binary calculation. Therefore, DWT may take a shorter time than CWT in analysing a signal.
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Fault signature: DWT can give clearer fault symptom than CWT does. Furthermore, DWT is more convenient in modelling a signal in resolved form. The comparison above gives us an obvious option that DWT is chosen to fault diagnosis in control systems. 3 THE WAVELET MODEL-BASED APPROACH (WMBA) 3.1 The model based approach on fault diagnosis of control systems For years model-based approach has been proved to be the most effective method in fault detection and fault diagnosis for control systems, see Isermann R (1997). Its significant advantages with respect to other ones are obvious. Each control system has its model even before this system is built up. This model can also be used in monitoring the performance of the system. System models are always objective and not rely on experts experience. This feature makes it possible to deal with new designed devices and systems. In addition, the model-based approach is effective in sensor fault detection, Which in other approaches are always taken granted for healthy. The procedure of the model-based fault diagnosis approach is introduced in several articles, see Ali E and Zhanqun Shi (2001). The model of the system will run in parallel to the actual system. The residual generated from the comparison of the model output and the actual system output. Because the model is built in healthy condition and modified to coincident to the healthy system output, the residual will describe the faulty information within the system. A threshold is pre-defined according to the system condition. If any variable exceeds the threshold then there is a fault occurring in the system. This is referred to as fault detection. Fault location and severity evaluation will follow to carry out fault diagnosis. In practice, different researchers prefer different kinds of system models, such as analytical models, adaptive models and observers as well as Kalman filters, see Zhanqun Shi (2000). Here in this paper, a wavelet model-based approach is developed to detect faults in the electro-hydraulic servo system, which including wavelet modelling, residual generation and fault detection. 3.2 The wavelet model-based approach One property of the wavelet transform is its linearity. If the signal r{t) is combined by two components /, (/) and f^ (t) in equation (8), equation (6) will give the decomposition: K0 = /, ( 0 - / 2 ( 0 00
(8) 00
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= \Mt)V~^)dt- lflt)W~^)dt -00
(9)
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In the wavelet model-based approach, the model is still redundant to the actual system, but the model here is obtained in different way. Generally, the model of a system is built up by means of system analysis and involving parameters. However, one can hardly make an accurate model due to the parameter simplifying. It is even worse for the non-linear control system. Even when an accurate model is built up, the system noise may influence the performance when in rurming. The practical way is modifying the model in the healthy condition. The other way to obtain a system model is by means of data collection. In healthy condition, the output performance of the system is referred to as a proper model output. In order to eliminate the arbitrary of the output, an average is made from several collections. This kind of model is more accurate than the analytical one. In addition, it is very easy to obtain. This model is called a collected system (CS) model. It is used in this paper. The residual generation is much different in the wavelet model based approach. The collected data, /i (/), is analysed with DWT, and the same analysis is made for the model output f^ (0 • The residual r(f) is not generated directlyfromthe comparison of collected data and model output. It is obtained by the reconstruction of the wavelet residual, IDWT. After proper treatment, this residual can be fed into a fault detector. The techniques used in fault detection and fault diagnosis are similar to other kinds of model-based approaches. J
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Figure 1 The schematics of the wavelet model based approach
4 AN APPLICATION ON FAULT DETECTION OF AN EHS SYSTEM 4.1 The system representation In this paper, the wavelet model based approach is applied for an electro-hydraulic servo system. It is a position control system shown in figure 2. The computer takes parts of a control device as well as a monitoring device. A control command is sent from the computer to control the EHS vale after amplified. The EHS vale follows the control signal and sends a hydraulic flow rate to the actuator. The displacement of the actuator is feedback into the computer by means of a displacement transducer. The feedback will compare to the command signal to generate an error signal, which is the next command signal to control the servo valve. 4.2 The data collection in faulty condition Three parameters are collected into the computer. One is from the pressure sensor, the another is from the displacement transducer. At the same time, the control signal is also taken into account. The valve
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used models Q-40. In practice, the stability of this servo vale varies with the oil temperature. This makes it possible to simulated system faults by heating the oil. As well known, control systems are usually analysed in response properties. Therefore, the control signal is the step input. A step response data of pressure is taken and analysed here.
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Figure 2 The EHS system to be monitored Figure 3 shows the raw data with different conditions. Figure 3(a) shows the normal condition when the temperature is 25®C, figure3 (b) shows the condition when the temperature raises to 35°C, figure 3(c) shows the condition when the temperature is 65°C. Although figure 3(c) can be recognised as fault, but no severity evaluation is available. Whereas figure 3(b) is difficult to distinguished from figure 3(a). (a) (a) Nomial signal &KsOWT
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4.3 Wavelet model based analysis When a discrete wavelet transform (WDT) is applied in the analysis of the collected signal, the effect is not as high as what is expected. Figure 4 shows the DWT results, where the wavelet is chosen to be Daubechies 5 and the level is 4. Figure 4(a) is the normal signal or model output signal and its DWT results. Figure 4(b) is the response signal in 65°C and its DWT results. Altiiough it can be found seriously faulty infigure3, one is difficult to observe the difference by comparingfigure4(b) to figure 4(a). It is impossible to give any threshold due to the boundary effects. This tell us that the wavelet analysis alone is not quite suitable for the fault detection in control systems.
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However, if the wavelet model based approach is applied, the effect is much obvious. Figure 5 and figure 6 show the results. In each figure, figure (a) shows the low frequency reconstruction of the residual in level 2, whereas figure (b) shows the high frequency reconstruction of the residual in level 2. In figure(c), the reconstruction of the overall residual is given out. In order to stress on the fault detection, a square signal entitled wavelet-based square (WBS) residual reconstruction is used. Two thresholds are pre-defined. The lower one is warning threshold for recipient detection. The upper one is for fault alarm when the fault goes seriously. (3)
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Figure 6 WBS residual of signal F. 3(c)
When the system goes healthy, the performance v^ll keep normal, and the WBS residual will keep zero (not shown here). As soon as the system performance varies, the WBS residual will give difference information. If the residual does not exceed the lower threshold, the system is referred to as normal. When the residual exceeds the lower threshold but lower than the fault alarm threshold, a warning will be sent out to call operator's attention. Figure 5 shows this case, which shows that an incipient fault may occur and you'd better keep an eye on it. When the WBS residual exceeds the upper threshold, an alarm will be sent out. It means the system is in faulty condition. Figure 6 shows this case, which tell us the condition is serious and a proper measure is necessary. 5 CONCLUSION This paper describes the diagnostic method based on the combination of the wavelet transform and the model based fault diagnosis approach. From the theoretical and practical understanding, the following conclusions can be drown. (1) Discrete wavelet transform is more suitable for the signal processing and fauh detection in control systems; (2) Wavelet analysis alone is less effective when applied for fault detection in control systems; 167
(3) Wavelet-model based approach (WMBA) is developed for the fault detection of an electrohydraulic servo system. It is proved that this approach is sensitive both in incipient faults and serious faults. It is more effective fault diagnostic tool. (4) New concepts have been developed. One is the wavelet based square (WBS) residual, the other is the collected system (CS) model. These new concepts are helpful for the model based fault diagnosis approach. REFERENCS A. El-Shanti, Z. Shi, F. Gu and A. D. Ball (2001). Dispelling the Rumors About Model-Based Diagnostics, Maintenance and Reliability Conference, USA. Al-khalidy, A, et al (1997). Study of health monitoring systems of linear structures using wavelet analysis, ASME, Pressure Vessels and Piping Division (PVP), Vol. 347, 49-58. Aretakis, N, and Mathioudakis, K. (1996). Wavelet analysis for gas turbine fault diagnostics, ASME paper. Atkinson R.M., et al (1996). Fault diagnosis in electro-hydraulic systems using neural networks, 5^^ International Conference on Profitable Condition Monitoring, UK, 275-285. Frank P.M. (1996). Advances in observer-based fauU diagnosis in dynamic systems. Engineering Simulation, Vol. 13, 716-760. Gertler, J.(1991). Analytical redundancy methods in fault detection and isolation, IFAC symposia, 921. Hogan, P. A, et al (1996). Automated fauh tree analysis for hydraulic systems. Trans. ASME, J. of dynamic systems, measurement and control. Vol 118, 279-282. Isermann R and P. Ball (1997) Trends in the application of model-based fauh detection and diagnosis of technical processes. Control Engineering Practice, Vol. 5,709-719. Le TT and J Watton (1998). Fault classification of fluid power systems using a dynamics feature extraction technique and neural networks, Proc. Instn. Meek Engrs, Vol. 212, part I, 87-97. Lu, C.J, Hsu, Y.T. (2000). Application of wavelet transform to structure damage detection. Shock and Vibration Digest, Vol. 32, 50. Rainer Oehler, et al (1997). Online model based fault detection and diagnosis for a smart aircraft actuator, IFAC symposium, 591-596. Ren, Zhang, et al (2000). Fault feature extracting by wavelet transform for control system fault detection and diagnosis. Proceedings ofIEEE Conference on Control Application, Vol. 1, 485-489. Suh, C. Steve, et al (2000). Wavelet-based technique for detection of mechanical chaos. Proceedings ofSPIE'The International Society for Optical Engineering, 267-274. Yen, Gery Y, and Lin, Kuochung (1999). Wavelet package feature extraction for vibration monitoring. Proceedings of the InternationalJoint Conference on Neural networks. Vol. 5, 3365-3370. Zhanqun Shi, et al (2000). Kalman filter and its improvement used in fault signature of the machatronic control systems. Proceedings ofMARCON 2000, 4101-4108.
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Condition Monitoring and Diagnostic Engineering Management Andrew G. Starr and Raj B.K.N. Rao (Editors) © 2001 Elsevier Science Ltd. Allrightsreserved.
ADVANCED FAULT DIAGNOSIS BY VIBRATION AND PROCESS PARAMETER ANALYSIS U. Stidmersen, O. Pietsch, C. Scheer, W. Reimche, Fr.-W. Bach Institute of Materials Science, Department of NDT, Appelstr. 11 A, D-30167 Hannover, Germany
ABSTRACT Due to the liberation of international markets, the economic success of power generation, process and fabrication industries depends on the availability, reliability and the efficiency of related key components. Maintenance can account for an extremely large proportion of the operating costs of machinery. Additionally, machine breakdowns and consequent downtime can severely affect the productivity of factories or the safety of products. Therefore, the manufacturing industries of the 2V^ century are influenced by the following major subjects of increased availability of physical resources, the improvement of product quality, and the improvement of manufacturing methods and techniques at high yearly utilization rate. Base is the effective use of available information contents from the machines in operation, like measured vibration signals and process parameters to extract significant patterns with the aim of early fault detection, the discovery of lack in the organization leadership and/or the operating condition. The presented case studies prove the success of the fault detection combining vibration measurements and process parameter analysis at different turbines. KEYWORDS vibration analysis, fault detection, signal processing, predictive maintenance INTRODUCTION The present state in the development of industrial plants is marked by a steady increase of complexity and increased automation. Contingently through the capital costs, an economic production is to be realized mainly through a high year utilization rate, reached by an aimed permanent supervision of the units in operation. Damages or unfavourable operating conditions have to be diagnosed and located at an early stage. The economic potential of increased availability is stated by a statistical evaluation of 1329 German capital goods industries in the year 1997 by the Institute of System Technique and Innovation. As a result only 50% of them reach a technical availability above 50%, only 5% reach values higher than 98% and the same amount less than 50%. Tofixthe initiating point of investigation a systematical evaluation of industrial life cycle potentials has to be done as by BMW for a car production line, which is characterized by: O the unique investment costs of 25% (planing, design, building-up, start-up, decommissioning), O the planned operation costs of 44% (maintenance, operation, energy, personal), and O the unplanned consecutive costs of about 31% (delays in start-up, unplanned reconditioning, faults). 169
As drawn out here the highest amount of saving can be seen at the unplanned consecutive costs by increasing the system's availability. Due to the interaction of several machine units in modem production lines, this problem becomes quite complex, including start-up, re-start, and normal as well as faulty operation with increased wear. The state of art of investigations for early failure detection emphasizes mainly on single system components of industrial machines, e.g. rolling element bearings, meshing gears, using vibration and acceleration analysis techniques. Actually extensive research work is done on the field of diagnostics of process data and machine control. Using this basic knowledge, actually, the global consideration of process lines has to be looked at, leading to new ways in the field of transient vibration signal analysis. Machine condition, machine faults and on-going damage can be identified in operating machines by fault symptoms, e.g. mechanical vibration, air borne noise, and changes in the process parameters like temperatures and efficiency. DATA ACQUISITION AND PROCESSING To the fulfihnent of the demands on comprehensive vibration analysis, an aimed instrumentation of the unit to be supervised is required whereby displacement, velocity and acceleration pick-ups are used. State of technology in vibration monitoring of rotating machines is related to the calculation of standard deviation and/or maximum values, their comparison with thresholds and their trend behaviour to determine increased wear or changes of the operation conditions. Spectrum analysis with special phase constant averaging routines allows to determine machine specific signatures by magnitude and phase relation. By correlation analysis common information of different vibration signals is evaluated for source localization, S' while cepstrum and hocerence analysis is used to quantify '^ ° °^'4l periodical information of spectral and coherence data.
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The global structure of a generally used monitoring system can be divided in three main parts, the data collection with data reports in digital manner, followed by the acquisition phase calculating the statistical values and functions in time and frequency domain with integrated data 1001J reduction to get fault and operational patterns. The more 5 4 difficult third phase of fault diagnosis is still under development and permanently adapted to the necessities of industrial applications, still mainly dependent on the acting personnel at the monitoring system. Investigations at several test benches and complex machines in operation are concentrating on the determination and separation of fault ^ and operation descriptive values in time and frequency domain. Different faulty operation conditions can show ^,^ ^J' similar changes of statistical vibration values as well as shapes of spectral signatures. Especially for complex machine arrangements with multiple step gears, several rollfrequency [kHz] ing element bearings, it is quite difficult to separate clear- Figure 1: Phase Enhanced Data Processing ly the different speed related excitations from structure or fluid induced components. A common method to identify speed related information is time averaging of the vibration signals using external signal triggering (e.g. by strobe light, laser, decoder). If those trigger units 170
are not installed, in case of multi shaft arrangements, or even for just in time measurements phase-enhanced data processing is applied as shown infigure1 at the example of source localization at the complex rolling element bearing arrangement of a fresh air fan. The amplitude spectrum of a non-averaged time signal includes the amplitude information of all frequency components, while their phase relation is lost. Fault specific patterns as the teeth mesh frequency with modulation, the blade rotation sound, as well as rolling element bearing frequencies are characterized by constant phase relations. Choosing one of these machine specific components as reference, calculating all phase relations relative to this component, the new calculated ''phase enhanced spectrum'' only includes the related phase constantfrequencycomponents, while the others will be damped by the averaging. The upper diagram in figure 1 shows the amplitude spectrum of acceleration measured at the casing of the fresh air fan. The averaged spectrum includes many narrow banded speed related information as well as broad banded system related excitations. To separate all these information becomes quite difficuh. If phase enhanced spectrum calculation is applied using the blade rotation sound (numbers of blades x speed = BRS) all driving speed related information is obtained, just as would be seen by time averaging. Applied here, the signal ground level drops down, while also the narrow banded speed related modulations are reduced, hinting to other main excitation sources than pure speed related ones (upper part spectrum). If one of the bearing characteristic frequencies is chosen, e.g. the ball pass frequency outer race (BPFO) of the cylindrical bearing NU348 similar results are obtained (middle part spectrum). Selecting the BPFO of the rolling element bearing FAG7252B proves it as nearly main excitation source by receiving nearly the original shape of spectrum as above for the "normal" averaged one (lower part spectrum). Additional data reduction by calculating the corresponding cepstra allows to determine excitation patterns. Further acquisition techniques for parallel processing and visualization of multi-plane measurements were developed for failure separation and source localization. Wavelet analysis and neural networks are actually investigated concerning their integration to existing monitoring systems. SUPERIMPOSITION OF MECHANICAL MISALIGNMENT AND THERMAL UNBALANCE The extension of analysis and visualization tools using already installed vibration sensors, additionally implementing time and rotor synchronous multi plane data acquisition and processing, often fit to determine the influences of reconditioning and operational changes to the machine's condition, as demonstrated by the following example of vibration assignment at a 920 MW steam turbine. After reconditioning and changing the LP-parts the vibration measurement system showed increased levels of rotor and bearing vibration with additional load and time dependencies. Related to the requirements of electrical consumption the operation is characterized by a large number of shut-downs and start-ups during which the vibration amplitudes reaches values far above the limitsfixedby the VDI/ISO standards. Single plane balancing was able to reduce the maximum values locally, but the general vibrational behaviour was still poor. The axialfixingkeys of the combined radial/axial bearing had to be changed due to wear after half a year of operation, only. An additional installed multi-channel parallel processing system should determine the complex rotor behaviour, looking for source indications of the machine's extraordinary vibration behaviour. Figure 2 presents the relative rotor displacement and the absolute bearing vibration as orbit plots at each measurement plane for different load conditions. To visualize the dynamical rotor behaviour the kinetic wave paths are drawn as 3D-diagram with an imaginary rotor axis. Connecting the synchronous points of the wave paths the dynamical rotor displacement is obtained, which in combination with the rotor sweep give hints to alignment conditions. The parallel processing allows to describe the phase relation of the rotor excitation and the mechanical bearing response. Directly after start-up and at low loads the rotor displacement in all measurement planes shows elliptical kinetic wave paths, with nearly 90" compounding maximum values at HP, HP-IP, and IP-LPl bearing hinting to some alignment problems. Similar vibration signatures are
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absolute bearing block displacement
obtained at the LP2 and GE bearing, emphasized by counter rotation of the rotor sweep. -^,^1 Increasing the active load the — 2o4smax i4.i| phase angle of the S^-values '" stays stable, while the elliptical kinetic wave paths within the first three bearings collapse with superimposed stochastical excitations in direction of S,^. In addition to alignment problems this signature hints to thermal related compulsive forces inside the first three bearings, which also influence the bearing vibration between the LPl-LP2-part. Significantly at the exciter bearing the shape, the phase angle, and the maximum amplitude change, as well as for the kinetic rotor wave path as for the orbit of bearing vibration hinting to internal thermal influences.
The sensibility and instability of the complex rotor system to small changes of operational -50 0 50 850 MW parameters become certainly hor. [Mm] 210MVAR visible comparing the directions of Stationary rotor dislocation Figure 2: Relative Rotor Displacement and Absolute Bearing Vibration during start-up, stationary load operation, and shut down. The rotor position inside the bearing is determined by the DC-values of the here used eddy current displacement pick-ups, exemplarily drawn in figure 3 for several start-ups, shut-downs, partial and full load operation. As made visible, the rotor inside the HPbearing moves with increased speed to therightside, in the HP-IP-bearing to the lefl-hand side, and inside the IP-LPl-bearing again to theright-handside proving alignment problems within the first three bearings. After five days of operation the stationary rotor displacement during shut-down dropped down to the same level as at start-up. Due to the fact that the absolute position of the rotor inside die bearings cannot be determined exactly using the DC-values of the displacement pick-ups, the measurement must be calibrated by fixing the zero position, e.g. the slow motion during turn drive with a pre-heated turbine. As marked in the figure after a standstill of two days the rotor in the IP-LPl bearing behaves slightly different than in cases of a short time standstill with only low temperature gradients of turbine parts and foundation. Measurements prove a temperature related tipping of the concrete key in axial rotor direction due to thermal radiation of the IP-part. Additional mechanical load is added to the here located turbine's fixing point due to handicapped axial expansion in direction of the HP-part as becomes visible comparing different rotor positions at low active load (250 MW) shortly after start-up and increased temperature conditions at nearly nominal load (850 MW). The misalignment between HP, IP, and LPl seems to increase. By tipping the HP-IP-bearing to reduce the misalignment in combination with increasing the gap of the radial keys at the IP-LPl-bearing the absolute vibration levels in the HP- and HP-IP-bearing dropped down. The sensitivity of the system against alignment changes becomes obvious by the reduction of the maximum LPl-LP2-bearing vibration from 55 to 16 |im at maximum load of 920 MW.
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The complex measurement rehorizontal view sults implicate several excitation sources which influence the actual condition of the machine. The statistical evaluation of vibration signatures and the correlation with related operational parameters like electrical power, exciter current, certain structure temperature conditions, or the steam parameters often hint to excitation sources, too. One example is presented in figure 4, where exemplarily the maximum values of rotor displacement and velocity at the turbine and generator bearings are drawn as function of exciter current. Near to the determination of load dependencies within the vertical view single measurement planes, inFigure 3: Static Rotor Dislocation cluding threshold comparison of the absolute vibration values to the standards, the parabolic behaviour of the maximum rotor displacement in the exciter bearing shows superimposed mechanical and thermal unbalance, proved by the corresponding absolute bearing vibration, and visible as change of the phase angles of the S„,ax-values. 150.0
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Figure 4: Statistical Evaluation of Vibration Amplitudes and Phase Angles Levels of rotor vibration up to 150 |im in the generator bearings are not tolerable for permanent operation. The nearly linear behaviour with only slight dependencies on the exciter current, the constant phase angle and frequency analysis fix the unbalance component as main excitation source concluding the requirement of further generator balancing. An interaction between the rotor vibration of the generator and the thermal unbalance of the exciter machine could be stated. By using the installed vibration pick-ups in combination with advanced data visualization the success of investigations to increase the security and range of efficiency in operation could be tested directly, giving hints to further requirements, too. 173
UNACCEPTABLE VIBRATION OF NEW STEAM REGULATION VALVES The second example demonstrates the facility of vibration analysis for fast vibration source evaluation and the influence of design changes, avoiding damage and/or unplanned plant shut-down. During reconditioning the steam valve design of a 350 MW turbine was changed to allow faster start-up and load regulation, combining left the possibilities of a quick acting HP-valve gate valve and an automatical regFigure 5: Combined Tripping and Regulation Valve ulation valve in one housing. Restarting the turbine with two new valves at the HP-part after only a few weeks of operation valve rod fatigue fracture occurred just behind the regulation head of the left valve. Additionally the carbide-clad of the rod showed surface cracks and excessive wear in the guide sleeve and stuffing box. Shortly after changing the valve spindle the same problem occurred again, confirming exceeded vibration as failure source. Increasing the diameter of the spindle solved the problem of fatigue but not the problem of vibration excitation nor the wear of the hard metal coating. To clarify warranty competence vibration measurements at the valve casing during transient and stationery operation should prove excitation sources, including the interaction of steam flow, pipe arrangement, turbine, foundation, etc. High temperature accelerometers were fixed in three directions at the valve body and the hydraulic drive units. Pressure transducers installed in the steam piping determine pressure fluctuations and allow the correlation between the flow and mechanical induced excitations. 350 MW turbine, start-ups, HP-regulation valves, acceleration vertical reference left valve
frequency [kHz]
frequency [kHz]
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operational parameter
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Figure 6: SFFT-Spectra of Acceleration at Steam Regulation Valves 174
Figure 6 shows the SFFT (short-time-fast-fourier-transformation) amplitude spectra of vertical acceleration on the valve casing as Campbell diagram during start-up for three different operation conditions. To allow the allocation of the different excitation sources the process parameters of valve position (HPr: right valve, HPl: left valve), rotor speed (n) and active power (Pw) are also drawn. With valve aperture ratios less than 20% the spectra of acceleration at both valves show as main information a frequency component at about 130 Hz with harmonics. The duration of excitation at the left valve corresponds to the valve characteristic of aperture ratio. Thefrequencymodulation and the peak form (broad banded base) prove the component as mechanical system resonance. By a simplified substitution beam model the resonance frequency was also estimated mathematically, whileflowinduced vibration of standing pressure waves could not be determined in that frequency range. The narrow banded peaks at 50 and 150 Hz are related to electrical noise of the measurement devices. The visual inspection of both valves proved wear of the coating at both spindles after only some months of operation with weekly start-up and shut-down operation, nearly without partial load. The left valve showed in comparison to the right one exceeded wear of the hard metal coating, as already presumed by the evaluation of the vibration signatures. Additionally the surface of the valve head showed characteristics of striking, explaining the high amount of resonance harmonics in the spectra. To reduce the vibration amplitudes and the related wear of the hard metal coating the gap at the valve head was mottled. The gap reduction causes changes of the boundary condition of the vibrating valve rod, visible in the amplitude spectra of acceleration in the middle part offigure6. The model of the free-supported beam has to be modified by a supported-supported one, as become visible by nearly doubling of the resonance frequency. Again the spectra of acceleration show a high amount of harmonics, pointing to striking of the valve head in the guide sleeve with even higher absolute amplitudes and amount as proved by cepstrum analysis, which summarize the periodical information of spectrum by its gamnitude and quefrency. The result repetitive exhibits, that the problem source is related to the design of the valves and not to the piping system nor to the turbine in operation. 350 MW turbine, start-up, pressure after HP-regulation valves gap reduction at valve head left valve
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Figure 7: SFFT-Spectra of Pressure Fluctuation behind the Steam Regulation Valves Due to the automatic valve regulation, dependent on the steam parameters and limited by the temperature condition of the turbine, during certain start-ups high vibration levels of HP and IP-rotor occurred related to interference of the valve resonance at 250 Hz and the 5^^ harmonic of synchronous speed. As seen in figure 7, the valve vibration is passed as pressurefluctuationto the steam flow. Dependent on the time gradient of aperture ratio during some start-ups the resonance became stationary at exactly 250 Hz and the HP-rotor is excited at one side only by steam pressurefluctuationsequal tofivetimes the rotor speed of 50 Hz. To reduce this probability the valve characteristics were adjusted to equal aperture ratios during operation. As result both valves show now nearly the same time behaviour of resonance excitation with slightly reduced absolute values. Actually the damping characteristics of the hydraulic drive unit, the possibilities of modifying the valve's cross sectional area as well as the influences of the valve head design are investigated to optimize the operation conditions. In this case the vibration measurements do not only prove the excitation source, also they facilitate a fast evaluation of the performed valve design changes, hinting to fiirther required modifications coincidently avoiding the risk of unexpected shut down due to total system faults.
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SUMMARY AND OUTLOOK On-line process monitoring is beneficial for maintaining high quality products at high production rates and low costs fixed by the terms of availability, operational reliability and service life. Therefore the main task of a vibration diagnostic system is the processing of necessary measured data to identify, or at least to limit, the cause of damage and disturbances in rotating machines and production lines, whereby two methods of fulfilment for this task are offered: o the manual diagnosis, where the specialist has to extract significant informationfromthe vibration signal, his knowledge is of main importance for the correct trend setting of the results, conclusions about fault patterns and actions to save the machine's life. O the automatical diagnosis, where the knowledge of the specialist has been successfully transferred into an expert system to realize an automatic interpretation of the measured data. The task of the expert system is not to replace the specialist, but rather to provide support to speed-up his diagnosis. It could help for automatical determination of simple problems displaying possible causes of damage and their probabilities. It can recognize and avoid critical operational conditions when short reaction times are necessary and there is no time to consult experts. And finally, it provides assistance to an expert in solving complex problems. Practice has shown that vibration monitoring is,froman economic point of view, a profitable strategy. The investment costs can be amortized already after the prevention of only one unscheduled downtime of the machine. New technical requirements will grow out of this demand and will lead to further developments in present concepts for hardware and software modules which can be subdivided among others by the following concepts: • improved expert systems with higher level ofdiagnostic authoritativeness and greater diagnostic accuracy, n improved infrastructure by more powerful performance information and control systems to interlink planning, start-up, production, and maintenance, • build up ofcomputerized information centers to interlink the experiences and investigations ofmachine manufacturer, the operator in industrial production, also including the related insurance companies, with the ideal threshold of a planned 100% machine's availability during the fabrication or processing cycle. REFERENCES Barber A. (1992). Handbook ofNoise and Vibration Control, 6* Edition, Elsevier Advanced Technology Publication, UK Runkel, J. (1996). Condition Monitoring of Rotormachinery in Nuclear Power Plants, IAEA Specialists Meeting on Reliability and Safety, Bamwood-Gloucester, UK Yang S.M., Lee G.S. (1997). Vibration Control of Smart Structures by using Neural Neutworks, ASME, Vol 119, US A Hammond L.K. (1998). Signal Processing for Condition Monitoring, CETIM98, Senlis, France Pietsch, 0., et al. (1998). Advanced Vibrational Diagnostic for Failure Source Localization, 5^* IFToMM, Darmstadt, Germany Stidmersen U., et.al.(2000). Failure Root Cause Analysis applying Vibrational Diagnostics, ImechE Vibrations in Rotating Machinery, Nottingham, UK Liu Y., et.al.(2000). Vibrational Diagnostics at Turbines in Operation, an example of failure root cause analysis, ACSIM2000, Najing, China 176
Condition Monitoring and Diagnostic Engineering Management Andrew G. Starr and Raj B.K.N. Rao (Editors) © 2001 Published by Elsevier Science Ltd.
PARTIALLY BLIND SOURCE SEPARATION OF THE DIAGNOSTIC SIGNALS WITH PRIOR KNOWLEDGE Haijun Zhang, Liangsheng Qu, Bingang Xu and Guangrui Wen Xi'an Jiaotong University, School of Mechanical Engineering Research Institute of Diagnostics and Cybernetics 710049, Xi'an, P.R.China [email protected]. lsqu(g>xitu.edu.cn
ABSTRACT Rolling bearing is one of the most important machine elements. Its condition monitoring and fault diagnosis have been addressed for a long time. This paper presents a new signal processing method—Independent Component Analysis (ICA) to detect the faults in rolling bearings. The ICA has been widely adopted for blind source separation without any prior information on the sources and their mixing process. However, some limitations exist in natural signals separation because of the embedded noise signal, convolution, etc. In practice, there should exist some prior knowledge useful for source separation about the collected signals. For example, the knowledge about the structure of the machine under examination and the sensor layout are helpful to identify the source behavior and the number of independent components. Considering these prior knowledge, the source separation process becomes partially blind. Example reveals the advantages of this method. The potential applications of Independent Component Analysis in machine diagnosis are also reviewed. INTRODUCTION The essential objective of an engineering diagnostic system is to detect the potential fauhs existing in a continuously running machine. From this point of view, the engineering diagnosis is just a pattern recognition problem [1]. As shown in Figure 1, there are five factors mostly influencing the fault recognition ability of a diagnostic system: the type of fault in diagnosed machine, the quality of diagnostic information, the accumulated experience, the complexity of diagnosed machine and finally, the design perfectness. Among them, this paper discusses the quality of diagnostic information and recommends an effective method to improve it. Undoubtedly, the signal to noise ratio (SNR) of
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the collected signal is an important criterion to evaluate the quality of diagnostic information. To some extent, the SNR determines whether we can successfully detect the fault. The high SNR is very helpful to identify the fault. Diagnosed object 1. Design Perfectness
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For simplicity, let us observe a simple example: As shown in Figure 2, there are two main parts in the machine, and two sensors are mounted to pick up the dynamic signals. In fact, the signals x,, x^ picked up from sensor / and // are the weighted sum of the sources 5,, s^, which are produced from part / and // respectively. We may express the the relationship between x and s using following equations: k ( 0 = « l l ^ l ( O + ^,2^2(O |X2 (0-^2,5, (0 + ^22-^2(0 Where ci\\,a^2^^2\^^22 denote the weight, 5,, ^2 denote the sources, X,, Xj denote the observed signals. Obviously, if we can get the source signals from the observed signals, the difficulty of fault detection will be greatly reduced. In this case, without any knowledge on the sources and mixtures (except independent assumption), this problem is so-called Blind Source Separation (BSS). Most solutions to the BSS problem adopt ICA. This approach is also employed in this paper. BASIC PRINCIPLE OF ICA ICA was originally developed to deal with the problems that are closely related to the cocktailparty problem [2]. Since the recent progress in ICA, it has become clear that this method will fmd widespread applications as well. If we use vector-matrix to express the equation 1, it may be written as : X=^WS (2) Where X is the observed vector, W is the mixed factor matrix, S is the source vector. Obviously, if we can get the inverse matrix of W, indicated hyW' , we may easily obtain the
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source signal matrix S from the observed signal matrix X, the former 5 may be written as: S^JV'^X (3) ICA can be used to estimate the source signals from the mixtures based on the information of their independence. As we know, independence of two random variable means the joint Probability distribution function (PDF) is equal to the product of individuals. It may be defmited by the equation p{x,,x^)=p,{x^)p,{x2) (4) Usually, for the sake of convenience, we don't use the equation above directly, but use the entropy information to evaluate the independence, such as measures of nongaussianity, minimization of mutual information, and maximum likelihood estimation etc. Basically speaking, ICA is an optimization problem, its objective is to optimize the coefficient matrix W so as to obtain the components S, which are statistically as independent as possible to each other. In this paper, we introduce a efficient method for maximizing the nongaussianity— Fast ICA, which is first presented by Aapo Hyvarinen and Erkki Oja. The Fast ICA Algorithm is based on a fixed-point iteration scheme for finding a maximum of the nongaussianity of JV' X[2]. We may express the algorithm as following. Proprocess the observed signal^ by centering and whitening
Randomly choose an initial weight vector W
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Figure 3 The flowchart of Fast ICA algorithm As an illustration. Fast ICA is used to separate two simulated samples. The original signals are shown in Fig 4. One is a sine wave, the other is a shock wave. We randomly select a mixing matrix to obtain the mixtures of them as shown in Fig 5, the objective is to restore the original signals on the basis of two mixed signals. The recovered signals is got by Fast ICA as shown in Fig 6. It is obvious that we may recover the original signals without any distortion by FastlCA.
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Figure 6 The recovered signals using Fast ICA based on the mixed signals shown in Figure 5 PARTIALLY BLIND SOURCE SEPARATION Despite the high effectiveness of the method on simulated data sets, it is still difficult to apply BSS into practice due to embeded noise, time delay betw^een the sources and echos. In fact, during the course of separation, not all sources are so-called blind. In this paper, source separation is realized with some prior knowledge, and we call this method as Partially Blind Source Separation (PBSS). Its application in fault diagnosis of rolling bearing reveals its effectiveness. The condition monitoring and fault diagnosis of rolling bearing have been addressed for a long time. Many efficient methods have been proposed, such as resonance demodulation and ferrography. Herein, we recognize the bearing fauhs by PBSS. A simple bearing test rig used is shown in Figure 7. In this case, some prior knowledge can be listed as following. Firstly, before signal separation, it is necessary to identify the number of sources. According to the simple structure of the rig, it is easy to presume that there are two mainly sources. One is the
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motor, the other is bearing under detection. In experiment, two Sound level Meters were mounted to pick up the machine sound. One aimed at the motor sound, the other aimed at the bearing sound. Stxnd I evel nat er /
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I
>o4i> Figure 7 The rolling bearing test rig
Secondly, some information concerning the mixing process were available. On one hand, we should determine the type of the mixing process: linear or convoluted. It is well know that the sound speed in air is 340m/s. While the distance between two Sound Level Meters was about 0.8 meter, and the distance between Sound Level Meter and the test rig was about 0.2 meter. Consequently, there existed approximate 1.8 millisecond delay between two sources. It was so short that we can neglect this time lag and simply assume the mixing process to be linear. Quantitatively, let 5, be the source signal from the motor, and 5-2 be the other one from the bearing, x^.Xj be the signals picked up by Sound level Meter / and Sound level Meter // respectively, it is clear that much larger part of component 5, were included in x, than that of 5,, at the same time, much larger part of component ^2 were included in jc, than that of 5,. There exists intrinsic relationship between the mixing weight as follows: ^|, > a,2 and a^^ < 0^2 • Finally, as a machine element, the vibration behavior of bearing faults can be easily identified using following equations [3]: /, = o.5r 1 + —cos a /
Spall in inner raceD
f„ = o.5zf 1 -d_—cos ajf
Spall in outer raceD
A = f / [ ' - ( f ] ^os ^ a j
Spall in ball.
E
E
Where z denotes the ball number of the bearing; d denotes the diameter of the ball; ^denotes the pitch diameter of the rolling path; a denotes the angle of contact; / denotes the rotating frequency. EXAMPLE In order to compress the noise contant in collected bearing signals and increase the SNR before
181
ICA. The method of 'threshold denoising with Continue Wavelet Transform (CWT)' [4] is adopted. The original signals collected are shown in Figure 8, their corresponding contour maps are shown in Figure 9. According to the prior knowledge on the feature frequency of the bearing, we select a scale band from 3 to 35 to reconstruct the collected signals. The reconstructed signals are shown in Figure 10. With the information on source number, mixing process and the feature frequency of bearing faults, the source signals are obtained, which are shown in Figure 12. As shown in Figure 11 and Figure 12, the result based on PBSS is much better than that based on BSS. Furthermore, according to Fig 12, the separated source like white noise is due to the motor, while the impulsive signal with periodic impacts was originated from the spall in the inner race of the tested bearing.
"O
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a) The signal from Sound level Meter I
b) The signal from Sound level Meter II
Figure 9 The contour maps
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,itw**ft^-/f*#|^v 0
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Figure 10 The reconstructed signals based on CWT
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Figure 11 The separated signals based on BSS
Figure 12 The separated signals based on BBSS
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CONCLUDING REMARKS The ICA was first addressed by Herault and Jutten in 1983 [5]. It has been widely applied in different engineering fields. The sound signals are one of the most common studying objectives using this method. The promising applications of ICA are BSS and feature extraction [6]. In this paper, we introduce ICA with prior knowledge to the study of sound signals emitted from faulty bearings, and achieved good result. Further investigations for separating audio signals mixed with convolution utilizing PBSS are envisaged. ACKNOWLEDGEMENT The authors wish to show deep appreciation to Prof Erkki Oja for his valuable suggestion. Besides, the authors would like to thank Prof Tang Yiping for his kind discussion. REFERENCE [1] Liangsheng Qu, Guanghua Xu, The Fault Recognition Problem in Engineering Diagnostics, Insight, Vol 39, No 8, 569-574 (1997). [2] Aapo Hyvarinen, Erkki Oja, Independent Component Analysis: Algorithms and Applications, Neural Networks, Vol 13, 411-430 (2000). [3] Qu Liangsheng, He Zhengjia, Mechanical Fauh Diagnostics, 86-87. (Shanghai Science & Technology press, 1986) [4] Lin Jing, Mechanical Dynamic Signal Processing Based on Wavelet Technology, Doctor dissertation, Xi'an Jiaotong University, (1999). [5] Pierre Comon, Independent Component Analysis, A New Concept?, Signal Processing 36, 287-314(1994). [6] Aapo Hyvarinen, Erkki Oja, Independent Component Analysis by General Nonlinear Hebbian-Like Learning Rules, Signal Processing 64, 301-313 (1998).
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Condition Monitoring and Diagnostic Engineering Management Andrew G. Starr and Raj B.K.N. Rao (Editors) © 2001 Elsevier Science Ltd. All rights reserved.
COMPARISON OF SIMPLE MULTI-ATTRIBUTE RATING TECHNIQUE AND FUZZY LINGUISTIC METHODS IN MULTIATTMBUTE DECISION MAKING Ralph O. Buchal^
Chris K. Mechefske^
^Department of Mechanical and Materials Engineering, The University of Western Ontario, London, ON, Canada, N6A5B9 ^Department of Mechanical Engineering, Queen's University, Kingston, ON, Canada, K7L 3N6
ABSTRACT In this paper, we evaluate three alternative methods for a sample decision problem in maintenance management: Simple Multi-attribute Rating Technique (SMART), SMART with uncertainty analysis using probability fimctions, and a fuzzy linguistic method. The three approaches are assessed based on their ease of use, their ability to handle qualitative inputs, and their effectiveness in assessing or quantifying the degree of confidence that the best-ranked alternative is clearly preferable to the others.
BACKGROUND Engineers and managers arefrequentlyrequired to choose among several alternatives based on a set of criteria. This class of multi-attribute decision-making has been extensively studied, and multi-attribute utility theory (MAUT) has produced many effective and widely used methods, including Simple Multiattribute Rating Technique (SMART), and Analytic Hierarchy Process (AHP). These methods are well documented in many textbooks by Clemen (1996), Goicoechea et al. (1982), Von Winterfeldt and Edwards (1986), and many others. In recent years, alternative decision analysis techniques have been proposed based on fuzzy linguistics and fuzzy set theory. Regardless of the technique chosen, decision makers want to determine tiie best alternative, they want to assess the degree of superiority of one alternative over the others, and they want to assess the impact of imcertainty or imprecision on the decision. Often some or all of the criteria are qualitative ones like "quality", "performance", "effectiveness", etc. The alternatives are rated relative to the criteria using a qualitative scale like "poor to excellent". Furthermore, the criteria themselves are often weighted or ranked based on subjective assessment of relative importance. MULTI-OBJECTIVE UTILITY THEORY (MAUT) The simplest and best-known approach to this kind of problem is the weighted decision matrix, based on multi-objective utility theory (MAUT). Let X = {Xi,X2,...,x„} be a set of alternatives, and let C = (Cj ,C2 ,...,c„} be a set of decision criteria. Each alternative Xj can be rated against each criterion c/
185
by a rating value r^. Further, the relative importance of each criterion can be specified by a weighting m
factor Wi. If all r,y e [0,1] and ^^w^ = 1, then the weights and criteria ratings can be aggregated to give a single rating r, for each alternative as r.-lH-.T-
(1)
All MAUT q)proaches comprise the following generic steps (Von Winterfeld and Edwards 1986, p.273): 1. 2. 3. 4.
Define alternatives and value-relevant criteria; Evaluate each alternative separately on each attribute; Assign relative weights to the attributes; Aggregate the weights of attributes and the single-value evaluations of alternatives to obtain an overall evaluation of the alternatives; 5. Perform sensitivity analysis and make recommendations.
In its simplest form, MAUT involves summing the weighted attribute values to arrive at single values for each alternative, which can then be compared directly. Once the weights and attribute values are established, all MAUT methods result in a single numerical value for each alternative. In cases where the best alternative is clear-cut, no further analysis is required. In many cases, however, two or more alternatives may rank close to each other, requiring sensitivity analysis to determine whether there is a justifiable preference of one over the other. Simple Multi Attribute Rating Technique (SMART) is commonly used in decision-making situations where direct numerical assessment of weights and ratings is possible. Decision makers frequently use a verbal, multi-point scale to make qualitative assessments. There is an extensive literature (e.g. Clemen 1996; Goicoechea et al. 1982; Von Winterfeldt and Edwards 1986) on techniques and methods for establishing suitable scales and determining values. Often a simple linear scale is used, but in many situations a nonlinear scale is more appropriate. Methods have been developed to incorporate nonlinearities in human perception of differences on a qualitative scale. For many problems it is difficult to establish the correct attribute values and weights. The Analytic Hierarchy Process (AHP) is a widely used technique for establishing weights and ratings when direct evaluation is difficult. AHP is based on the observation that decision makers are adept at comparing two criteria or alternatives and deciding if one is more important or better than the other. The decision maker considers a set of pairwise comparisons, which are used to determine a comparison matrix. The principal eigenvalues of the comparison matrix are then used to determine a set of normalized weights or ratings. These weights and ratings are then used in calculating the scores in the standard way. One disadvantage of AHP is that inconsistencies can occur in the pairwise comparisons (e.g. A is better than B, B is better than C, C is better than A). The effect of this can be minimized by measuring the degree of inconsistency and making sure it is below some specified threshold. Another drawback of AHP is that the ranking of the best alternatives can change with the addition of another alternative, even if the new alternative is very poor. SMART does not have this problem - the ratings of alternatives are independent of each other.
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FUZZY LINGUISTIC METHODS Many authors in recent years have proposed fuzzy linguistic methods for decision-making problems of this type. Fuzzy logic is well suited to dealing with qualitative information, where concepts and classifications are vague and imprecise. However, this approach is relatively new and many of the published approaches are somewhat ad hoc. Many authors assert the superiority of this approach, but so far there seems to be little evidence supporting this assertion. A common approach is a simple extension of conventional MAUT using fuzzy numbers instead of crisp values for ratings and weights (Lootsma 1997; Klir and Yuan 1995). Triangular fuzzy sets or numbers can be represented by three parameters, as a = (a/,a„,a„). The parameters fully define the triangular membership function as shown in Figure 1.
Figure 1: Triangular Membership Function of a Fuzzy Number a =(ai,a^,a^) Typically triangular fuzzy numbers 7y =iryi,ry^,ryj and w^ =(w,/,W/;„,w,„) are used for the ratings and weights. These are now aggregated using fuzzy addition and multiplication to get afiizzynumber for the overall rating of each alternative using Eqn. 2, which is the fuzzy equivalent of Eqn.l. m
In order to determine the final score of each alternative, the fuzzy sets must be ranked. Several alternative methods have been proposed (Klir and Yuan 1995), including: 1. The Hamming distance of each pair of alternatives; 2. Methods based on alpha-cuts; 3. Methods based on extension principle. The fuzzy sets can also be defuzzified to yield crisp values that can be compared directly. Several defuzzification schemes are widely used (Nguyen and Walker 2000), including: 1. Centroid defuzzification; 2. Mean-of-maxima defuzzification; 3. Center-of-area defuzzification. More advanced fuzzy decision methods are discussed in several references including Stowinski (1998), and Li and Yen (1995), but most of these references are very advanced and require expert knowledge of fuzzy set theory or diligent and lengthy study to understand the underlying principles and to be able to apply the methods to real problems. These are significant barriers to proper assessment of these methods, and limit wider adoption of them in decision-making practice.
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UNCERTAINTY AND IMPRECISION Uncertainty and imprecision in the weights and attributes produce uncertainty in thefinalratings. It is important for decision makers to assess and quantify the uncertainty in their decisions. For example, if a particular attribute or weight is known only approximately, then the result is also only approximate. If two alternatives have similar evaluation scores, and they are only approximate, then one cannot conclude with confidence that one alternative is better than the other. Probability distributions If we assume that uncertainty can be modeled using probability theory, then the decision maker can estimate a probability distribution to represent the degree of uncertainty about each weight and attribute. Tlie resulting probability distribution of the evaluation score can be found directly or by Monte Carlo simulation. If each alternative evaluation is considered to be a random variable with a known or estimated probability distribution, then well-known statistical methods can be used to compare them. It is now straightforward to set standards for accepting one altemaive as being significantly better than another. A common standard is a 95% confidence level. Fuzzy sets An advantage of fuzzy linguistic methods is that they naturally produce overlapping fuzzy sets for each alternative. Superficially these appear to be similar to probability density functions, but their meaning is quite different. The degree of overlap is a useful indicator of Aether there is a clear preference of one alternative over the other. One way to evaluate a pair of alternatives is to calculate the overlap/underlap ratio of the 50% level sets of their respective fuzzy sets (Lootsma 1997). A value near one indicates the two alternatives cannot be distinguished (they overlap completely), while a value near zero indicates a strong preference (they don't overlap at all). The definition of weak and strong preferences seems to be qualitative and somewhat arbitrary with this method. CASE STUDY Formulation of problem In the following section we consider a simple case study of selecting a maintenance strategy. This example has previously been analysed using fuzzy linguistic methods by Wang and Mechefske (2000). In the previous work, linguistic variables were used to assess weights (importance) and attribute ratings (capability). Importance and capability were represented by three triangular fuzzy sets over the universe of discourse (shown bold in TABLE 1). Several other linguistic values were created by modifying these basic ones. The fuzzy operators modified the upper and lower limits and shape of the fuzzy sets, but not the modal value. For our comparison, we need numeric values, so we reinterpret the results as shown in TABLE 1:
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TABLE 1 NUMERIC SCALE FOR IMPORTANCE AND CAPABILITY Importance
Capability
Indeed critical Critical Very important Important More or less important Unimportant
Indeed superior Superior Above average Average Below average Poor
Numeric value 5 4 (0.7,1,1) 3 (0.1,0.5,0.9) 2 1 (0,0,0.4) 0
Fuzzy set
!
In the cited example, importance and capability were assessed linguistically by a group of experts. The equivalent ratings and weights are shown in TABLE 2. TABLE 2 EQUIVALENT RATINGS AND WEIGHTS "•
—
"
_
_
_
Maintenance goals (Criteria)
_
_
_
Criterion Weight (Importance) Enhanced competitiveness 3 High product quality 5 Low maintenance cost 2 Development of expertise 1 Improved reliability 2 Improved safety 4 Minimum inventories 3 5 Return on investment Acceptance by labour 2 Technological leadship 1
Strategy Rating (Capability! Condition Scheduled Breakdown Based Maintenance Maintenance Maintenance 1 3 3 1 3 5 1 3 4 1 4 4 3 3 3 3 4 4 3 3 3 4 4 5 3 3 3 3 4 3
Analysis using SMART The example was modeled using Simple Multiple Attribute Rating Technique (SMART). The analysis was done using Criterium DecisionPlus software from Infoharvest, Inc. Note that all ratings and weights are automatically normalized. The model hierarchy in Figure 2 shows the criteria and their weights, and the alternatives and theirfinalratings. The results are also tabulated in TABLE 3.
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•109 Enhanced ComD
•182 High product aualltv •073 LowfffWlntonancacoat
~
.036 Deyfl9Pmfm gf f X P f f t l t t Z 11.000 Goal I
.673 Schadulad maintanance
.146 hnprowd aafatv .073 Improvtd ralabWtv .091 Minimum hnvntertoa
~ 1.804 CondHlon-basad malntanancal
•182 RetMrT^ m lOVfrtn^fnt .073 Acceptanca bv labour .036 Tachnotocrical laadarahlp
Figure 2: Model Hierarchy TABLES CALCULATIONS OF FINAL RATINGS ~-^____ Lowest Level Criteria bnhanced Competitiveness tligh product quality Low maintenance cost Return on investment improved reliability Development of expertise Minimum inventories improved safety [Acceptance by labour technological leadership Results
Breakdown maintenance
Strategy Scheduled maintenance
Condition-based maintenance
Model Weights
0.2 0.2 0.2 0.8 0.6 0.2 0.6 0.6 0.6 0.6 0.476
0.6 0.6 0.6 0.8 0.6 0.8 0.6 0.8 0.6 0.6 0.673
0.6 1 0.8 1 0.6 0.8 0.6 0.8 0.6 0.8 0.804
0.109 0.182 0.073 0.182 0.073 0.036 0.091 0.145 0.073 0.036 1 1
1
i
Incorporation of uncertainty using probability distributions To incorporate uncertainty about ratings, all rating values were modeled as probability distributions rather than precise values. The type of distribution, and its properties (mean, variance) can be ^elected to best represent the uncertainty characteristics. Conmionly used distributions include ijniform, triangular, and normal. For this analysis, we modeled the uncertainty using normal distributi0ns with mean equal to the numeric scale value, and standard deviation equal to half the difference between adjacent scale values. Uncertainty could also be applied to the weights, but DecisionPlus 4oes not support this, and it is unnecessary for the purposes of this example. The analysis now produces three overlapping probability functions, rather than three exact values. Standard statistical methods can now be applied to determine confidence mtervals, and to determine confidence levels in the comparisons. The results are shown in TABLE 4. According to this analysis, we are confident at the 99% level that condition-based maintenance is superior to the other two alternatives. The probability distributions are shown graphically in Figure 3, which confirms visually that there is little overiap between the best alternative and the runner up.
190
TABLE 4 UNCERTAINTY STATISTICS ^^^*--->-___ Breakdown Uncertainties maintenance 5 percentile 0.42 itnean 0.48 95 percentile 0.54 <5% bainvise <5% Absolute
Strategy Scheduled maintenance 0.62 0.67 0.73
Condition-based maintenance 0.73 0.77 0.82
<5% <5%
50% 99%
| 1
0.4 0.6 Decision scores
Figure 3: Probability Distributions of the Three Alternatives Analysis usingfuz^ linguistics Wang and Mechefske (2000) analysed this problem using a fairly complex multi-step fuzzy linguistic approach. Their algorithm used the Hamming Distance from the "ideal" case to produce a relative evaluation of the alternatives, giving the results in TABLE 5. TABLE 5 RELATIVE HAMMING DISTANCE Strategy Breakdown maintenance Scheduled maintenance Condition Based maintenance
Relative Hamming distance 0.621 0.256 0.079
Comparison of results Several points should be noted about these results:
191
1. The rankings are the same as for the SMART analysis 2. The relative Hamming distance measure has no intuitive meaning to decision makers 3. The numeric ratings cannot be directly compared to the SMART results because of differences in the inputs and methodology 4. No information is provided about the confidence of the decision. DISCUSSION While many authors have proposed the use of fiizzy linguistic methods for qualitative decisionmaking, the approaches appear to be ad hoc and hard to understand. Fuzzy set theory has become a well-established field, but most of the references are very advanced and complex. As a result, the principles are poorly imderstood by practitioners who want to apply them, and occasionally they are miss^plied. We were unable to find any clearly explained fiizzy decision methods in the literature, and there seems to be no intersection between tfie conventional decision analysis literature and the fuzzy logic literature. The claims by proponents of fuzzy methods that only fiizzy linguistic approaches are suitable for qualitative decision making does not seem to be supported by any evidence. On the contrary, there is a rich body of knowledge in decision analysis using "conventional" methods, which are u s ^ effectively every day for qualitative decision-making. Existing methods like SMART have the additional advantage that they are easy to comprehend and use. Furthermore, there are many commercially available decision support packages based on conventional methods, but we have been unable to find any based on fuzzy linguistics. REFERENCES Clemen, R. T. (1996). Making Hard Decisions: An Introduction to Decision Analysis, Duxbury Press Goicoechea, A.; Hansen, D. R.; and Duckstein, L. (1982). Multiohjective Decision Analysis with Engineering and Business Applications, Wiley Klir, George J.; Yuan, Bo. (1995). Fuzzy Sets and Fuzzy Logic, Prentice Hall Li, H..; Yen, V. C, (1995). Fuzzy Sets and Fuzzy Decision-Making, CRC Press Lootsma, Freerk A. (1997). Fuzzy Logic for Planning and Decision Making, Kluwer Academic Publishers Nguyen, Hung T.; Walker, Elbert A. (2000). A First Course in Fuzzy Logic, Second Edition, Chapman and Hail Slowinski, R. (Ed.). (1998). Ftdzzy Sets in Decision Analysis, Operations Research and Statistics, Kluwer Academic Publishers Von Winerfeldt, D.; Edwards, W. (1986) Decision Analysis and Behavioral Research; Cambridge University Press Wang, Z.; Mechefske, C. K. (2000). Condition Monitoring Technique Selection using Fuzzy Linguistics, InternationalJournal ofCOMADEM, 28:2, 22-30
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Condition Monitoring and Diagnostic Engineering Management Andrew G. Starr and Raj B.K.N. Rao (Editors) © 2001 Elsevier Science Ltd. Allrightsreserved.
REASONING APPROACHES FOR FAULT ISOLATION: A COMPARISON STUDY Andrej Rakar, Dani Juridic Department of Computer Automation and Control Jo:^ef Stefan Institute Jamova 39, 1111 Ljubljana, Slovenia andrej. [email protected]
ABSTRACT Fault isolation is a part of system diagnosis, the role of which is to determine the location of a fault. The isolation performance of model-based diagnostic systems significantly depends on the selected reasoning technique. This paper focuses on comparison of several known reasoning approaches, which have been suggested by different engineering communities. For this purpose, a three-tank benchmark system is chosen. Diagnostic results clearly demonstrate major benefits and drawbacks of each method, of which the transferable belief model seems to be most suitable one for practical applications. KEYWORDS Fault detection, fault isolation, approximate reasoning, transferable belief model, fuzzy logic. INTRODUCTION Tough competition on the market is putting pressure on companies to steadily increase product quality while reducing production costs and adhering to environmental constraints. To accomplish this, modem model-based diagnostic systems can be designed. From available process data they first infer about the presence of a fault in the system {detection), and then they try to determine the type and location of a fault (isolation). Fault isolation is performed on the basis of symptom evaluation and their mutual dependencies by means of logical reasoning. The traditional Boolean reasoning seems to be unsuitable for practical applications (Rakar et al, 1999) due to the problems with diagnostic instability. Therefore, only approximate reasoning approaches will be considered. Bayesian reasoning, fuzzy logic (Zadeh, 1978), confirmation theory (Hempel, 1965) and its derivatives DMP (Diagnostic Model Processor) (Petti et al, 1990) and DMA (Deep Model Algorithm) (Chang et al, 1994) belong to this track. A step fiirther represents TBM (Transferable Belief Model) (Smets & Kennes, 1994), which originates from the Dempster-Shafer's mathematical theory of evidence (Shafer, 1976). The isolation performance is determined by available process data and precision level chosen for symptom evaluation. By increasing the precision, one can considerably improve the isolation
193
capabilities of the diagnostic system, in particular improved diagnostic resolution (with no need to implement additional measurement points in the system). However, higher level of precision demands increased modelling effort. How to integrate this additional knowledge is the essential distinction among various reasoning approaches. Furthermore, some advanced approaches have the ability to deal with inconsistency in data and to express it with a measure for credibility and reliability of diagnostic results. The paper is organised as follows. The second chapter reviews several approximate reasoning approaches. A comparison study undertaken on a laboratory test process is performed in the third chapter. Discussion based on diagnostic results is given next. The paper ends up with main conclusions. APPROXIMATE REASONING APPROACHES A problem with classical Boolean reasoning is that symptoms are allowed to take only two values, i.e. present (1) or not present (0). Unfortunately, small changes in signals due to noise, modelling errors and other disturbances can imply large changes in diagnostic results. This is referred to as diagnostic instability (Kramer, 1987). Rather than assigning binary values to symptoms, the approximate reasoning approaches associate a degree of presence in terms of a number between 0 and 1. Reasoning is therefore based on smooth decision fimctions (Figure 1). In turn, small changes in the signals result in small changes in the degrees of belief associated to the suspected faults. The result of the approximate reasoning is then a ranked list of possible faults with assigned degrees of belief
a)
b)
Figure 1: Common smooth decision functions This is the common idea of approximate reasoning approaches. In the sequel, only the most notable ones will be addressed. Comparison with Bayesian reasoning is not trivial due to conceptual differences and fundamental problems related with interpretation of uncertainty (Rakar, 2000). This topic exceeds the scope of the paper and requires a thorough separate study. Fuzzy logic Fuzzy logic is often used for reasoning in expert systems. It can be used for symptom evaluation where symptoms are represented with smooth decision fbnctions (Figure 1) that act as membership fimctions. This way, the degree of membership of the symptom is represented by an interval [0, 1]. Symptoms that represent deviations of process quantities from the normal values are usually referred to as residuals. Hence, residuals close to zero correspond to normal process operation, while non-zero ones indicate the presence of the fault. This can be represented as 2i fuzzy decision vector:
where //, represents the degree of membership of the residual, 0
P#7 if [ri]=au A [r2]=<Ji2 A ... A /rj=cr/^ thenfni P#2 if [rjj=(72i A [r2]=(J22 A ... A [r,J=(72n, thenfn2 PM
(2)
if [rJ^Gm A [r2]='(JN2 A ... A [rfJ=crNn, thenfnN,
where ni,n2,...,nN€{l, 2, ..., m}, [rj is qualitative value of the symptom r/, which can take a value atj e {1, 0} meaning high (1) or normal (0). Generally, more specific values, i.e. high (1) and low (-1), are also possible, which require an extension of the membership functions to the interval [-1, 1]. Often, the rule base is given in the form of an incidence matrix: (3)
A: K\
^km.
where r± 1 if y - th fault affects the / - th residual '^
[O
if j - th fault doesn' t affect the / - th residual
The belief of fault fnj is given by conjunctive operator over a set of residuals. It is defined as the minimal value of the degrees of membership of the corresponding residuals (Rakar et al, 1999): Kfnj) = minK^,...,//,^,/7^^^j,...,/7^^ }„., where v. G{1,...,W}, V.. ^y,. and /7„ =l-jUy J
7}
(4)
represents a degree of absence of the residual in the 7j
normal case. Beliefs of faults can only be assigned to those faults anticipated in the decision rules. Therefore, if an unknown fault occurs, reasoning based on fuzzy logic in its basic form can produce misleading results. DMP (diagnostic model processor) DMP (diagnostic model processor) is a modification of the confirmation theory, which tries to improve the resolution of fault isolation. For this purpose, Petti et ah (1990) introduce additional sensitivity matrix S [m,n] to incorporate supplementary quantitative knowledge. If residuals are represented in the form:
where i^\fhfz-Jnf can be defined as:
stands for the fauh vector, then the sensitivity of the /-th residual to they-th fault
*'
W
and in the relative form:
''-tv
<"
The elements of the sensitivity matrix Sy are defined relatively to the selected threshold values TU This way, the sensitivity of residuals to faults is higher for residuals with low thresholds, and lower for residuals with high thresholds. Fault isolation is possible by combining sensitivity matrix S with satisfaction factors, defined as:
195
where k is an even number and determines the slope of the sigmoid (Figure 1 b), n is the residual value and Ti its threshold value. Failure likelihood is defined as a w^eighted average:
F.=^
.
(9)
IKI Values ofFj close to 1 or -1 confirm the occurrence of fault jj in positive or negative direction. It is important to note that the sum of failure likelihoods Fj of all faults does not equal 1, moreover, it usually exceeds it. Evidence supporting one fault does not exclude another simultaneous fault. By implementing suitable structural residuals it is generally possible to isolate multiple faults. Due to the additional quantitative knowledge, one would expect better isolation of individual faults. However, it can be shown (Rakar, 2000) that reasoning based on weighted averaging (9) is not appropriate, as it fails already on simple qualitatively isolable faults. DMA (deep model algorithm) To overcome the problems encountered previously, Chang et al (1994) suggested a modification of the DMP. The basic idea of the DMA (deep model algorithm) approach is to define the thresholds for each residual and each fault separately. Threshold values are defined relatively to the sensitivity of residuals to faults:
r,-P,s,l,
(10)
where Sy stands for the sensitivity as defined in (6), f. is the possible fault range and pj percentage of the range that the diagnostic system is required to respond. Satisfaction factors sfy are now calculated for each fault separately. Then, the degree of fault jj is obtained by:
Values of <^ close to 1 or -1 indicate fault jj occurrence in positive or negative direction. This definition is similar to (9), except that the sfy are already weighted by sensitivity factors through relatively defined thresholds (10). Better diagnostic resolution can be achieved by considering the consistency factor, which is given by: c/,=l-
•Vmax,/
'Vmi
(12)
[max(|54j,|5/2.|,...,|5^|)J
where sfmaxj and sfminj stand for maximal and minimal satisfaction factors for fault j^: Sfra^j
= ^^(Sf,j,sf^j,...,sf„j)
^j^^
cfj can take values between -1 and 1, Positive values confirm the consistency between particular sfy, while negative values show possible inconsistency between them. Higher resolution is achieved by making use of pairs:
196
{dj.cfi).
(14)
Then, among faults with similarly high degrees dp the fault with the highest consistency factor cfj is chosen as the most likely candidate. Again, as the algorithm considers different sensitivities of residuals, one would expect to be able to distinguish qualitatively non-isolable faults as well. However, this is not always the case (Rakar, 2000). TBM (transferable belief model) The transferable belief model (TBM) represents an extension of the Dempster-Shafer theory of evidence (Shafer, 1976). Its key feature is the introduction of the concept of the "open-world" (Smets & Kennes, 1994). Unlike other approaches, besides possible and impossible events, it assumes the existence of unknown events as well. Thus, if an imknown fault occurs, misleading and incorrect diagnostic results can be avoided. Reasoning is performed in two steps. In thefirststep, basic belief masses m are assigned to the subsets: ^- -{^j]
'" 5, = { V / , v/„},.- = 1.2,...,^,y = l,2....,M,
(15)
where A^ij are elements of the incidence matrix (eqn. 3), and At and Bt are mutually complementary {m(Bf)=I-m(Ai)). The belief masses are defined as smoothfimctionsof the residuals. In this case the sigmoidal one is employed (Figure 1 b):
a
r.
where a is the belief mass assigned at threshold hi and y is an adjustable smoothing parameter. In the next step, the belief masses for fault candidates and the fault-free case are calculated by using the unnormalised Dempster rule of combination (Smets & Kennes, 1994). In the diagnostic context, with residuals as the single source of evidence, the rule reduces to: m{fj) = {m,^m, '"®m^){fj)^f{mXA,)f[m,{B,), i=l
(17)
i=l
The remaining belief is assigned to the empty set: M
m(^) = SC = 1^2^ifj)
(18)
and is referred to as the strength of conflict (SC), which may be caused by various sources like modelling errors, noise and unknown or unforeseen faults. It can be treated as a measure of confidence in the diagnostic results, which is an interesting feature of this theory. The final diagnostic result can then be represented as a ranked list of fault candidates according to the given belief masses. It is important to note that faulty states are not necessarily single fauhs. In fact multiple faults can be treated. If new evidence becomes available, then the belief originally allocated to more faults is transferred to a more particular one. Hence the name transferable belief model.
197
COMPAMSON OF REASONING APPROACHES The presented reasoning approaches for fault isolation are applied to a three-tank system. For this purpose, a simulator with a detailed semi-physical mathematical model is utilised. The process flowsheet is depicted in Figure 2. A full description is given in (Rakar, 2000).
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Type of fault leakfromRi clog in Vi bias in sensor hi clog in V4 bias in sensor h2 bias in sensor hi clog in V2
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Obviously, faults y} and/j cannot be qualitatively isolated due to identical codes. The same holds forfe and/7. The diagnostic resolution is improved by introducing the sensitivity matrix S=[5,y], which is calculated by (6) and (10):
198
0.3256 -0.2835 -0.0272
0 0
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0
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0
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-0.1602 -0.0920
0
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0
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(19)
DMP and DMA approaches use their own original methods of integration of S (eqn. 9 and 11), while fiizzy logic and IBM approaches make use of additional residuals, which are derived as a linear combination of primary residuals so that the resulting residuals are insensitive to particular faults. When two residuals n and rj are sensitive to faults/^ and/,, the resulting residual defined as:
is insensitive to fdult fu. This way, the sensitivity factors are calculated only when this is necessary for fault isolation. In this example, the following two residuals are introduced: ^4 ~ '^231 ~'^13'2
ni)
Note that additional residuals are used only for discrimination of qualitatively non-isolable faults, as they are unnecessary for other faults. Diagnostic results Results show the comparison of DMP, DMA, fuzzy logic and TBM approaches. The response of different reasoning methods is depicted in Figures 3 to 6. Because of space limitation, only faults/y,/2 and^j are presented. Due to restrictions of the DMP and DMA methods, the fault-fi-ee diagnosis is not shown. Problems encountered by the DMP and DMA approaches show lack of algorithm elaboration, which were obviously developed for solving very specific applications (Petti et al, 1990, Chang et al, 1994). The main deficiency concerns the noise level, which is not properly considered. Naturally, a modification of these algorithms is possible, which was not the purpose of this work. Results obtained by the DMP approach (Figure 3) are often misleading and unreliable. In spite of taking into account different sensitivities of residuals, several faults are assigned similar failure likelihoods (see detail in Figure 3), which makes the isolation impossible. Similar holds for the DMA approach (Figure 4), which obviously has trouble with diagnostic instability as well. This is mainly due to unsuitable definition of threshold values. As noise level is not considered, too low thresholds are assigned to low sensitivities, resulting in diagnostic instability. Fuzzy logic (Figure 5) and TBM (Figure 6) give superior and quite similar diagnostic results. The desired maximal resolution is achieved, as all the faults are clearly isolated. Diagnostic results are accurate, stable and reliable with minor difficuhies only during the transient periods. TBM responds with increased strength of conflict (SC) during these periods, which correctly indicates lower confidence in diagnosis during this time. CONCLUSIONS The aim of this paper is to show the potentials of various approximate reasoning approaches. For this purpose, a simulation study on a laboratory test process was performed. Differences in performance are mainly due to the different combination of additional knowledge from various precision levels to achieve higher diagnostic resolution. Superior results are obtained by using fiizzy logic and TBM. Other approaches could produce comparable results only by introducing extensive modifications in the combination of evidence (proper consideration of noise). Similarly holds for the concept of the "openworld", which is inherent to the TBM approach. The relevance of the latter for practical user is the ability to consciously interpret diagnostic results. This way, an active role of the operator is preserved which enables one to incorporate human experience in the diagnostic process (Rakar, 2000).
199
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Figure 5: Diagnostic results by fuzzy logic REFERENCES
Chang, I.e., C.C. Yu, C.T. Liou (1994). Model-based approach for fault diagnosis. 1. Principles of deep model algorithm. Ind Eng. Chem. Res., 33, 1542-1555. Hempel, C.G. (1965). Studies in the logic of confirmation. Aspects of Scientific Explanation and Other Essays in the Philosophy of Science, Free Press, New York. Kramer, M.A. (1987). Malfunction diagnosis using quantitative models with non-Boolean reasoning in expert systems. AIChE Journal, 33:1, 130-140. Petti, T.F., J. Klein, P.S. Dhurjati (1990). Diagnostic model processor: using deep knowledge for process fault diagnosis. AiChE Journal, 36:4, 565-575. Rakar, A. (2000). Fault diagnosis in technical systems by means of approximate reasoning. PhD thesis, University of Ljubljana. Rakar, A., D. Juridic, P. Dalle (1999). Transferable belief model in fault diagnosis. Engineering Applications of Artificial Intelligence, Pergamon, 12, 555-567. Shafer, G. (1976). A Mathematical Theory of Evidence. Princeton University Press. Smets, P., R. Kennes (1994). The transferable belief model. Artificial Intelligence, 66, 191-234. Zadeh, L.A. (1978). Fuzzy sets as a basis for a theory of possibility. Int. Journ. of Fuzzy Sets and Systems, 1, 3-28.
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Condition Monitoring and Diagnostic Engineering Management Andrew G. Starr and Raj B.K.N. Rao (Editors) © 2001 Elsevier Science Ltd. All rights reserved.
MIGRATION TO ADVANCED MAINTENANCE AND MONITORING TECHNIQUES IN THE PROCESS INDUSTRY* S. M. O. Fabricius^ and E. Badreddin^ and W. Kroger^ ^ Laboratory for Safety Analysis, Institute of Energy Technology, Swiss Federal Institute of Technology Zurich (ETHZ), Weinbergstrasse 11, 8001 Zurich, Switzerland ^ Chair for Process Automation, Institute of Computer Engineering, University of Mannheim, B6, 23-29, Bauteil C, 68131 Mannheim, Germany
ABSTRACT Collaboration with an industry partner provides us with insight in current maintenance and monitoring practice in the process industry. We observe that preventive maintenance is clearly favored over the breakdown strategies of past decades and that condition-based maintenance using off-line inspection methods is quite established. Online, real-time monitoring techniques on the other hand, are still rare and have not extensively spread so far. There seems to be a gap between promising research ideas in the field of process monitoring and their practical application. This text investigates the reasons for rather slow industrial adoption of available monitoring methodology and proposes remedial action. The demand for more flexible, modular, easier-to-implement, maintainable and cost-efficient monitoring schemes is stressed. Especially in process industry, with production facilities often running at various operating points for different products and rather frequent modifications to the plant itself, adaptable and balanced schemes are thought necessary. To support decisions about cost-efficient introduction of monitoring programs, plant modeling can prove useful. A modeling concept is presented which accounts for all important aspects of entrepreneurial systems including material, energy, information and monetary flows. It is intended to aid in mastering the ever increasing complexity of modem technical systems not only on the component, but on system level as well. KEYWORDS Maintenance, maintenance strategy, maintenance management, fault monitoring, system modeling, system optimization, system complexity
* This work is financed through a project together with Vantico, Basel and Monthey, Switzerland 201
INTRODUCTION Companies in the process industries - especially in commodity markets - often face fierce competition and cut-throat cost pressure. During hard times, even in companies with a very innovative and creative culture, it seems to be rather attractive to cut back on maintenance expense, expenditures on capital intensive infrastructure renewals and delaying projects with a longer-term perspective, among other in the domain of process fault monitoring. Such course of action can arguably give away significant strategic potential for creating competitive advantage. At the same time, computing, networking and communication technology keeps advancing paving the way for yet further realization of productivity improvements. Also, research in process automation and fault detection brings new promising solutions to the fore. Yet it appears though, in practice, technical possibilities are not fully taken advantage of. This paper investigates the reasons why this is seemingly the case and promotes concepts for beneficial usage of available or new monitoring methods. The text starts with an overview of basic established maintenance strategies. Then, the perceived practical situation in the process industry is described and current research in the domain of fault monitoring is abstracted briefly. Needs with respect to advanced monitoring programs are postulated and a monitoring concept is illustrated. Finally, attention is directed to system analysis and optimization by the use of respective modeling methodology.
MAINTENANCE: GENERAL REMARKS According to the definitions of German standardization bodies (as in DIN 31051, 1977 and VDI 2895, 1996), main goal of maintenance is to assure a certain required level of technical availability, to optimize entrepreneurial productivity and flexibility as well as the product's quality itself. All maintenance action shall comply with defined strategic company goals and it's organizational scheme. Larger companies usually share maintenance activities between distributed local repair shops and a centralized specialist support center. A newer tendency is to integrate production and maintenance responsibilities, see BIoss (1995) for a detailed discussion of organizational aspects. breakdown IP
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Basic maintenance strategies, schematically depicted in Figure 1 (P: production, F: failure, M: maintenance), differ in the type of action carried out (e.g., inspection, repair, overhaul) as well as in their planning and scheduling. With the breakdown strategy, equipment is operated until a failure leads to an interruption of the production after which repair action is initiated. The periodic preventive variant aims at avoiding failures by carrying out maintenance work at predefined points in time. The condition-based strategy involves regular inspections to determine the actual current state of equipment with succeeding overhaul or repair action if thought necessary. While the first strategy cannot guarantee a certain degree of availability, the condition-based one is suited to reach such predefined requirements. Each strategy has it's merits and respective choice is not a simple issue. A variety of influences in the technical, operational, safety as well as economic domain have to be considered. Overall, production management is confronted with a complex multi-criteria optimization problem balancing maintenance efforts - called "direct costs" 202
(Warnecke, 1981) - against "indirect costs" resulting e.g., from production interruptions, see Figure 2 for a simplified schematic illustration. The quantification of the costs involved is indeed nontrivial. A variety of established systematic methods help analyzing system performance criteria as availability, safety and productivity, namely FMEA (Failure Mode and Effects Analysis) in several flavors, HAZOP (Hazard and Operability Analysis), FTA (Fault Tree A.), ETA (Event Tree A.) and forms of Root Cause Analysis.
MAINTENANCE IN THE PROCESS INDUSTRY Due to a common research project with a Swiss multinational corporation in the field of specialty chemicals, we have gained some insight into established maintenance programs and have reason to believe other companies proceed in a similar way\ Not long ago, maintenance used to rely heavily on corrective action. In recent years, process industry has intensified efforts favoring preventive maintenance techniques aimed at avoiding potentially costly production interruptions. A production site of our industry partner in the U.S. has managed to decrease direct cost of breakdown maintenance from 40% to 7% (of total annual maintenance expense) between 1995 and 1999. Relative cost of preventive action has augmented accordingly while overall absolute maintenance expense in the time period decreased by 14%. Among other, preventive programs have been successfully implemented in the following areas: -
vibration monitoring lubrication sample collection and analysis ultrasonic steam leakage detection non-destructive vessel and pipe thickness measuring thermography (infrared camera)
All of these are carried out in an off-line manner i.e., personnel is manually collecting sensor readings or samples on routes through the plant, scheduled at regular intervals or if special need arises. Figure 3 shows a vibration measurement with a mobile vibration analyzer (intensity levels, frequency spectra). The sensor, in this case, is attached by magnetic force to a predetermined position on the electric motor. The results can be transferred from the device to a desktop PC for reporting and further analysis. The combination of several inspection techniques has proven successful. Experience shows, often, results of lubrication analysis yield first signs of component degradation. Further investigations can then be scheduled gradually and dynamically to determine the degree of wear and the urgency to take responsive measures. Generally, improvements are sought in an ongoing process, continuing on a day-today basis. Problems arising, e.g., components repeatedly failing, are investigated to determine the root causes. In such a way, existing systematic weaknesses can be eliminated and the type of maintenance action that only fights symptoms is minimized over time. Computerized Maintenance Management (CMM) systems handle maintenance work orders and allow to track component history. Statistical evaluation of available data supports detecting and eliminating weak spots as well. Recently, so-called Process Information systems (PI) have been deployed. They allow for easy desktop access to both live and historic plant measurement data and are predominately put to work for the purpose of process optimization in the sense of product quality or safety. But the respective data accessibility opens new opportunities for the domain of fault monitoring and efforts to try to exploit the process data could pay of. One example is to investigate process trajectories after failures had happened to facilitate finding root causes or determine the existence of consequential damages and component failure interdependences. Another prospect is to create computer models of the plant and have them simulated in real-time, with discrepancies of model and real plant trajectories taken as signs for incipient faults. Faults detected and identified in such a manner could help scheduling appropriate action, ^ Our intention is to provide a rough overview of techniques used, not to draw a most appropriate picture of current practice based upon extensive questioning or numerous companies. 203
canying out preventive maintenance or changing operating procedures before the production is interrupted by failures. The operation of a process plant can differ significantly form, e.g., a large power plant. The later is usually run at a steady-state operating point, a process plant on the other hand can have frequent start-up and shutdown sequences for product changes and varying process parameters (temperatures, pressures...) or operate in a batch manner. This can signify greater stress and more abusive conditions for installed components. The characteristics of process plants shall be discussed briefly. A production line (see Figure 4) can be considered to consists of processes "P" changing mainly the physical or chemical properties of the product, containers "T" (tanks, silos) and equipment for transport (conveyor belts, pumps). It can be naturally segmented into sections by storage elements which can have a buffer - and to some degree decoupling - function.
Ti yj ojj Figure 4: Plant structure
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Figure 3: Vibration measurements
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Figure 5: Section product throughput capacities
Every line has a bottle neck, which is located at the component with the smallest throughput capacity (purposely planned or as a result of historical developments). The bottle neck capacity determines the performance of the whole line. In the example above (Figure 5), the botde neck is in section 2. As is easily understood, it can make a great difference - from an operational point of view - weather a component fails in section 1 or in the bottle neck section 2. Apriori, all interruptions in the bottle neck section can never be caught up again. If the interruptions happen in other sections with greater throughput capacity, under certain conditions, depending on the size of the buffer tanks and the tank level control strategy applied, it might be possible to dampen the effect of failures on the productivity of the line. Such aspects of plant structure, operation and dynamic behavior all influence the choice of appropriate maintenance strategy. The above example (Figure 4) is of course only a simple case of a purely serial arrangement, the situation is more complex when parallelism, loops or network structures are involved and more careful analysis is necessary to optimize maintenance procedures. Maintenance strategy is currently chosen relying on experience supported by decision aids as company specific manuals and directives as well as flow charts. To address dynamic behavior more explicitly, modeling and simulation can prove fruitful and will be discussed later.
MAINTENANCE AND MONITOMNG RESEARCH From practice to theory, this section briefly abstracts fault monitoring research. Faults are generally understood as deviations from the normal behavior whereas failures signify complete inoperability of a component or a system. Fault monitoring deals with methods to produce an early diagnosis (detection, isolation and identification) of faults, whilst they are incipient and there is enough time to react 204
appropriately in order to avoid further degradation. Reactions are not limited to maintenance action only, control system reconfiguration or more graceful operation are other options. An inherent problem is to define what "normal" behavior is, it can be a rather simple task for individual process variables monitored, as e.g., the pressure in a vessel not allowed to be above a certain level. The situation is more complex if the process is not abundantly equipped with sensors or if some faults do not have direct correlation with single sensor readings. A method to detect sensor faults is to have a second or third sensor for the measurement of the same physical quantity (physical redundancy). With two sensors, a fault can only be detected, with three sensors and some voting scheme it is possible to identify the faulty one. So-called analytical redundancy methods employ models of the process monitored. Filters and observers (state estimation) and parameter estimation techniques are designed for good fault sensitivity with robustness to noise, disturbances and modeling errors. Many publications have been written about fault detection, a very small selection of books is Himmelblau (1978), Patton et al. (1989), Pouliezos and Stavrakakis (1994) and Gertler (1998). Monitoring and supervision is a special discipline of process automation and many methods developed stem from control theory. As a general impression (referring to the Safeprocess 2000 conference in Budapest), it can be said the theory of fault monitoring is well understood, sophisticated and established, yet often limited to simple or idealized processes, predominately on component level. This is partly due to the limitations of linear theory, which is used for the basic monitoring methods. It seems that current modeling methods do not allow to grasp more complicated, complex and large industrial systems - running at different operating points and showing real-life non-linearities - in an integrated fashion. There is yet a lot of interesting work to be done in order to be able to describe complex dynamic systems with formal models. At the time, attempts to further use nonlinear methods are ongoing and so-called "black-box" model approaches, e.g., Artificial Neural Networks are popular and successful as well.
MIGRATION TO ADVANCED MONITORING PROGRAMS After having described trends in maintenance and monitoring, this section asks questions about why modern fault monitoring schemes have not extensively spread in process industry so far. The demands for successful migration to modem programs are postulated and a concept for integrated monitoring and maintenance management is proposed. Do academic solutions lack practical usability? Are the approaches to complicated or costly? In some cases, it is argumented the initial investments to get a monitoring solution up and running are too high. The benefit is said to be unproven, the risk of investment therefore to high and the perceived return on investment too little or too far out in the future. Moreover, the technology is seen rather new and unproven, the developments necessary to complicated and in-house expertise and know-how not sufficient. Another aspect of concern is maintaining the monitoring system, especially in light of frequent transients in the plant, product changes and modifications, not to mention the fast pace at which new technology may render current one obsolete. Besides, safety is an issue, reluctance of plant operators to experiments with the running plant for testing monitoring schemes is understandable. At the same time, a notion of suspicion that the potential for competitive advantage by modem control and monitoring schemes is underestimated might be in justified. Cost-efficiency and proven or at least foreseeable benefit within a reasonably short time frame is prerequisite to the introduction of modern monitoring solutions. There is a need for clear methods, processes and know-how about deployment of monitoring programs. Methods to reliably quantify cost and benefit in an early project phase are in demand and analysis methods to choose appropriate monitoring solutions considering plant characteristics and individual component behavior are important. Next to technical and economical feasibility, also, organizational, personnel and qualification issues must be considered. To overcome some of the concerns named above, we propose a multitude of ideas and actions. First, we suggest a move from highly tailored and individually engineered monitoring solutions to 205
parameterizable, reusable modules. Some sort of standardization should yield cheaper, adaptable and re-configurable ways to flexibly implement process monitoring. Take an expensive and large steam turbine in a power plant as an example. For such a device, it can be economical to develop customized monitoring requiring several man-years of engineering. In contrast, for smaller components installed in great numbers as e.g., pumps in a process plants, such a procedure is not advisable. One had better be able choosing some intelligent sensor off-the-shelf - with fault monitoring logic attached - and parameterizing it to fit the specific working conditions. Figure 6 shows a combination of distributed networked, component-oriented monitoring devices with a plant level logic (C: component, FM: fault monitoring). On the lower level, e.g., intelligent vibration sensors with fault monitoring capability can send signals about the components current state to a higher hierarchy plant supervisor which integrates component information with knowledge about the plant operational and maintenance schedule. System level hierarchy can account for plant structure and dynamics as well. At the University of Mannheim, Germany, currently, a laboratory test bed is realized in collaboration with industry partners and the Swiss Federal Institute of Technology of Zurich to develop and test such a monitoring scheme. In an interdisciplinary approach, both technical as well as economical aspects are to be investigated.
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Figure 7: Monitoring concept In addition to the integration of component and system level monitoring, it is thought important to allow for a combination with existing off-line inspection methods. Also, from the end-user perspective, maintainability and intuitive ease of use are of great importance. If the solutions involve complicated or complex parts, these must be effectively hidden. Further, plant control and monitoring should be dealt with in an integrated fashion. The idea of using the monitoring devices for component protection comes up. That is, if the operating conditions are out of specification for the component, appropriate action could be taken, either in a closed-loop way (automatic control) or in an open-loop manner (alarms, manual operator interaction). Compare also Hayha and Lautala (1997) and Theilliol et al. (1997) for a hierarchical and maintainable monitoring concept. Figure 7 illustrates a monitoring approach involving a maintenance workshop, plant operation and production management. Care should be taken to interface a CA3M (Computer Aided Maintenance and Monitoring Management) system with existing company software, e.g., with ERP (Enterprise Resource Planning).
SYSTEM ANALYSIS A process plant can be a very complex system. Complex (lat. complectere) means "a whole made up of
206
complicated or interrelated parts" (Websters Collegiate Dictionnary) whereas complicated refers to "what offers great difficulty in understanding, solving or explaining". Complexity - in computer science - is usually measured either by computing time or by storage space needed to process or store a certain problem. Complexity can be mastered by two principles, by abstraction (only consider aspects of importance neglecting all remaining details) and by decomposition (splitting up a system in subsystems or elements which are easier to understand). Modeling methodology supporting these principles is therefore suited to grasp the complexity of technical and organizational systems. But, as Sanz et al. (2000) indicate, there is a lack of versatile modeling and simulation tools to deal with complex plants and in particular, the need for better solutions for tackling problems belonging to the tactical and strategic layers of process control is recognized. Haverkort et al. (1996) write that since the mid 1980's, there has been an increased interest in the integrated modeling of performance and dependability aspects of mainly computer and communication systems. This so-called performability modeling was motivated by the notion that in many modern technical systems, the operation can continue, even in the presence of failures or scheduled maintenance. As examples, fault-tolerant, parallel and distributed computer systems are mentioned. Haverkort further says, the term dependability was originally defined by Laprie (1985) to denote reliability, availability as well as security all-together. In the context of fault monitoring, system modeling can be used with two main purposes in mind. First, modeling to investigate the feasibility of monitoring projects (system analysis and decision support), and second, modeling to serve the fault monitoring task itself (model-based fault detection). Historically, system modeling formalisms have been divided into continuous and discrete-event approaches. In the continuous domain, usually, differential and algebraic equations are used to mathematically describe a system. In the discrete-event domain, a variety of formalism exists, as finite state automata, state charts and Petri nets. Recently, hybrid formalisms try to combine the descriptions in a way to be able to model continuous and discrete-event behavior in a unique and integrated way in one model. At the same time, the object-oriented modeling paradigm is gaining importance to describe system behavior in a declarative way (as opposed to the algorithmic description) in combination with object-oriented technology as know form software engineering (classes, inheritance and class instances). The Modelica (http://www.modelica.org) modeling language specification incorporates these ideas and several simulation tools were developed or extended based on this standard, among them, probably most prominently Dymola (http://www.dvmola.se).
Figure 8: The 4F modeling concept We are currently experimenting with several modeling formalism and tools and use a modeling concept we call the the 4F's (see Figure 8), indicating the four flows of mass, energy, information and money in an entrepreneurial system. Production management is interested in producing as much product as possible with their installed infrastructure (mass flow) selling it on the market realizing sales revenue, deducting all costs incurred during production (various money flows) to achieve income. During business activities, all kinds of information are exchanged in order to keep processes running as wanted (information flows) and energy is consumed in the form of electricity, steam and other variants of energy
207
flows (which in turn incur cost again). Conservation laws govern mass, energy and money flow in every system. Figure 8 illustrates schematically relations between the different flows. The 4F concept can be applied on several hierarchical abstraction layers to single components as well as whole production lines. The concept shall be tested in conjunction with the laboratory pilot plant mentioned earlier. CONCLUSION AND OUTLOOK Current state-of-the-art of maintenance practice in process industry has been illustrated and the characteristics of process plants were briefly discussed with respect to maintenance strategy choice. Experience shows that a combination of various strategies and inspection methods together proves to be efficient. The task of optimizing maintenance procedures is a nontrivial problem. Dynamic plant models, incorporating cost aspects can be useful to decide upon a viable mix of maintenance action. To bridge the perceived gap between fault monitoring research and it's application in industry, a monitoring concept was presented, which focuses on modularity, flexibility and reusability while integrating component and system level views. The 4F modeling approach was introduced and shall demonstrate in the future, how complex systems can be modeled on several abstraction layers, recognizing the need to not only reproduce technical, but also economical behavior. REFERENCES Bloss C. (1995). Organisation der Instandhaltung, Deutscher Universitatsverlag, Wiesbaden, Germany DIN 31051 (since 1977). Instandhaltung, Deutsches Institut fiir Normung, Berlin, Germany Gertler J. (1998). Fault Detection and Diagnosis in Engineering Systems, Marcel Dekker, New York Haverkort B.R, Niemegeers I.G. (1996). Performability modeling tools and techniques. Evaluation 25, 17-40
Performance
Hayha P. and Lautala P. (1997). Hierarchical Model Based Fauh Diagnosis System; Application to Binary Distillation Process. Proceedings of IF AC Symposium on Fault Detection, Supervision and Safety for Technical Processes 1, 26-28. August 1997, Kingston Upon Hull, United Kingdom Himmelblau D.M. (1978). Fault Detection and Diagnosis in Chemical and Petrochemical Processes, Elsevier, Amsterdam Laprie J.C. (1985). Dependable computing and fault-tolerance: Concepts and terminology. Proceedings FTCS15, IEEE Computer Society Press, 2-7 Patton R., Frank P., Clark R. (1989). Fault Diagnosis in Dynamic Systems, Prentice Hall, UK Pouliezos A.D. and Stavrakakis G.S. (1994). Real Time Fault Monitoring of Industrial Processes, Kluwer Academics, Dordrecht, The Netherlands Sanz, R., Segarra, M., de Antonio, A., Alarcon, I., Matia, F., Jimenez, A. (2000). Plantwide Risk Management Using Distributed Objects. Proceedings of IF AC Symposium on Fault Detection, Supervision and Safety for Technical Processes 2, 14-16. June 2000, Budapest, Hungary Theilliol D., Aubrun C , Giraud D., Ghetie M. (1997). Dialogs: A Fault Diagnostic Toolbox for Industrial Processes, Proceedings of IF AC Symposium on Fault Detection, Supervision and Safety for Technical Processes 1,26-28. August 1997, Kingston Upon Hull, United Kingdom VDI 2895 (1996). Organisation der Instandhaltung, Instandhalten als Unternehmens-aufgabe, VDIRichtlinien, Beuth Verlag GMBH, Berlin Wamecke H.J. (1981). Instandhaltung, Verlag TUV, Rheinland
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Condition Monitoring and Diagnostic Engineering Management Andrew G. Starr and Raj B.K.N. Rao (Editors) © 2001 Elsevier Science Ltd. Allrightsreserved.
INTRODUCING VALUE-BASED MAINTENANCE D. Perry and A. G. StanManchester School of Engineering, The University of Manchester, Manchester, M13 9PL, UK
ABSTRACT This paper states a business methodology designed by the author in the area of global asset management. It is a staged management process that has been designed as part of a global consultancy solution for the manufacturing business sector. The actual business process and the requirements of the methodology form the subject of the paper. According to the author, the link between maintenance and business decision making has historically been an area full of contradiction, uncertainty, and confusion. The classic example for this is the unenlightened organisation, which when economic conditions become difficult, dramatically reduce their maintenance budget. The management directly sees that a reduction in maintenance department spending produces an increase in their short-term profit. However, with resultant costs over time not being attributed, overlooked and ignored in all aspects of the business, any benefits of maintenance is unseen. The paper discusses a methodology using a Michael E. Porter manufacturing management model at its core. It provides a clear understanding of the impact of using maintenance in an enterprise and enables a structured decision making platform. This can be used from the board level to technicians as well as supporting the sharing of the business requirements throughout all levels of a firm's hierarchy. The author has titled this methodology as Value-based Maintenance. A summary will be presented to illustrate how the methodology is combined together with a consultancy solution that would provide a manufacturing organisation with a complete and continuous Asset Performance Improvement Process.
KEYWORDS Asset Management, Maintenance Management, Maintenance Effectiveness, Asset Performance.
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Value-based
Maintenance,
INTRODUCTION Maintenance is an important activity for a firm seeking to be more efficient in today's global marketplace. Competitive advantage achieved through maintenance provides firms with vital industrial edge over rivals. However, the repercussions of contemporary maintenance are not just found within the operational area of firms. They can be found in varying degrees throughout firms' activities. Topical examples are found in the supply and storage of spare parts and the delivery of on-site customer service. Therefore, the impact of maintenance decisions on the entire business is the topic of discussion for this paper. Justification to examine the holistic impact of maintenance has been brought about by current trends in business that is for the removal of the maintenance department. Whether this is achieved by integration such as it being amalgamated with production or by being contracted out to a specialist firm. Either approach moves maintenance from being a function of the business to a more general business activity within the operational activity of production. Mitchell (1996) stated thatfirmswho have adopted a cost centre approach to maintenance in their enterprise remove any incentive for improvement. Firms who have set a maintenance budget are unintentionally guilty of adopting this poUcy. Reducing or removing this financial budget brings a direct financial impact on a business that is shown in the improvement of the year end financial company statements. Resulting in the issues of maintenance not being comprehensively addressed, and as previously stated, the decisions being transferred to the operational activity of production because of the removal of the maintenance ftmction. It is therefore important to take the perspective of management on the business performance. First step to achieve this is by producing a business model of the firm. Porter (1985) suggested the Generic Value Chain model that can allow the top management of a firm to understand their business in terms of only the strategically relevant sections. Therefore, allowing them to understand how costs effect the firm and also the potential areas and sources of differentiation. This final point forms the core of the firm achieving competitive advantage over its industrial rivals. This is achieved by the defined activities being performed by thefirmat lower cost and more efficiently than their competitors. This model has become widely excepted throughout industry and manufacturing and taught by academics all over the world. It is not the author's intention to recreate an akeady exhaustive academic and industrial research area, however, it is to apply these existing proven ideas and concepts to enhance and gain the appropriate recognition of the positive impact that maintenance has on a business. The value chain is a systematic approach of examining all the activities that interact and are performed, in order to analyse the sources of competitive advantage. The model works by desegregating a firm into its strategically relevant activities in order to understand the behaviour of costs and the existing and potential sources of differentiation. For an organisation to achieve competitive advantage it needs to perform these strategically important activities more cheaply or better than its competitors.
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Supporting Activities Firm Infrastructure Human Resource Development Technology Development Procuremen t
Inbound Logistics
Operations
Outbound Logistics
Marketing
Service
& Sales
Primary Activities
Figure 1: Porter's Generic Value Model [Porter, (1985)] The concept of the generic value chain is from a competitive viewpoint. Therefore, value is the amount buyers are willing to pay for what the firm is providing. Profitability is only achieved when the value exceeds the costs associated with creating the product. The generic value chain model shows the total value and consists of the value activities and the margin. The value activities are the physically and technologically distinct activities a firm performs, while the margin is the difference between total value and the collective cost of performing the value activities. The value activities of a firm are divided into two categories. These are the primary and supporting activities. The primary activities are divided into five further subcategories for all firms. This means that they are concerned with the physical creation of the product, the sale and transfer to buyer and the after sales assistance. The support activities not only support the primary activities but other support activities as well. The firm infrastructure activity supports the whole chain but does not associate itself with a specific primary activity while, the other support activities of procurement, technology development, and human resource management can all be associated with a primary function as well as the whole chain. These nine value activities Porter describes as the building blocks of competitive advantage. How each individual activity is performed combined with economics will show if a firm is high or low cost relative to competitors. How each is performed will determine its contribution to buyer needs and hence differentiation. After a business has analysed their current position they undertake a complete and continuous asset improvement process. The following methodology uses the Generic Value Chain (shown in Figure 1) that was suggested by Porter, with maintenance as the means to provide the tools and techniques that can aOow it to perform activities at lower cost and more efficiently than its competitors. It combines some basic management principles into a structured consultancy solution to aid manufacturing
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firms in their quest for improved asset performance, providing a means to measure the effectiveness of maintenance decisions in respect to the whole firm.
METHODOLOGY The following structured methodology has been designed to provide firms with the initial elements for an asset improvement process by allowing them to analyse their business into the strategically relevant activities. An external consultant guides the top management team through each step of the process. A sununary of the inputs and outputs of the methodology are outlined in Figure 2. This places the context of the analysis at the business unit level.
Directors vision, time & direction. Knowledge of firm and industry practice.
Value-based Maintenance i Methodology
> Model offirmbased on value activities. > Identified responsibility and how activities can be -> measured. > Developed business challenges that are linked with asset improvement challenges.
Figure 2: Model showing the Inputs and Outputs of the Value-based Maintenance Methodology Step 1 - Get top management backing It is vital to the success of the asset improvement process to get support from the top management team. It is important that even the most unenthusiastic of team members realise that the full potential of the methodology will only occur if they support the process. The election of an individual is required to have responsibility for the process. This team leader should be either the Chief Executive Officer, Managing Director or senior director of the top management team who has the authority to guide and control the process in the firm. It is vital that the terms of reference are stated for the asset improvement process. All team members, the project title, the scope, time frame and the output need to be clearly stated. Also, any plans for the implementation of deliverables.
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Step 2 - Get them to define their Vision, Mission and business objectives It is important that the top team is clear on the direction and future direction of the firm. In many cases the vision, mission and business objectives are produced as part of the marketing departments role. This publicity material may not be a true and honest reflection of the top team thinking. Therefore, this has to be discussed and unanimously agreed before moving onto the next step. Also, they all have to relate and show a natural progression. Once this has been completed the business objectives can be Unked to the businesses prioritised cost drivers. Examples of these include economies of scale, capacity utilisation and location. Step 3' Use the Porter Generic Value Chain model The completed model provides a business with the ability to break up their business activities into nine segmented primary and support activity areas. This provides the basic platform for the physical asset analysis. All segments of the business have physical assets, however, it is important to prioritise segments to gain maximum benefit from the asset improvement process. In addition, it important to consider the possible and potential maintenance requirements in each segment. This is achieved by stating and then combining the percentage operating costs in each segment and the percentage value of assets in each segment. This analysis as shown in Eqn. 1 only provides a rough breakdown of where to target maintenance resources. Proportioned = Size of Maintenance for Segment
Percentage Operating Costs
Percentage Value of Physical Assets
x
(1)
The actual activities of the firm in each of the nine main activity segments have to be stated. For example, the primary activity of operations may include component fabrication, assembly and testing. These are then used in step 4. Step 4 - State who is responsible in top team for each activity It is important to assign responsibility within the top team for each of the identified activities. This may cross-existing management boundaries, so, the team leader will have to decide any disputes amongst team members. It is important that an individual is directly responsible for each activity. This is carried out to provide a clear, undisputed authority to each activity. Step 5 - State how activity should be measured and produce base line The activities and those responsible for them have been defined. It is now possible to decide how the firm should measure the performance of each activity. These measures are specific to the unique identity of the business. It is extremely important that the data to provide this information is readily available and that no duplication of activity measures is undertaken.
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Having finished this step will mean that the firm has produced a fuU set of Key Performance Indices. An initial compilation of these performance measures will provide a base line for future analysis. It should be stated that a timeframe is present in all the Key Performance Indices that are stated as examples in Eqn. 2, 3 and 4. These equations have been adapted from Wireman (1998). Total number of orders completed on demand Total number of orders requested Maintenance labour hours on emergency jobs Total maintenance labour hours Sales volume achieved to new customers Total sales volume
(2) (3) (4)
Step 6' Decide the business challenges Only at this point will the firm be able to decide the new business challenges they face. Using the defined information from the previous five steps, the top team can produce a list of the business challenges that relate to each activity whether supporting or primary activity. The list of challenges produced has again any duplication removed. It is important that time is spent producing a detailed Ust and once completed should be compared to thefirmsstated objectives in step 2. Step 7 - Map the asset improvement challenges to the business challenges Using a structured matrix approach the top team can learn the asset improvement challenges they face. This set of challenges is directly resolved from the business challenges. The example shown in Figure 3 provides a possible example map for the activity of assembly. Business Challenges
Asset Performance Challenges Waste detection systems
Cost of raw material per tonne
Firm Activities
Planned maintenance Spares cost
Assembly
Employee safety
Guarding standards Maintenance training
Product quality Figure 3: Diagram to show Map of Activity of Assembly
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Step 8 - Decide which of the maintenance tools and techniques are going to be used This is the point when the tools and techniques of maintenance can be discussed and shown how to be used and delivered. The ten tools and techniques that the author has categorised maintenance into from information supplied in Kelly (1997) and Wilson (1999) are: > > > > > > > > > >
Organisation and Strategy Condition Based Maintenance Safety & Environment Objectives Review of Equipment Maintenance Training & Team working Systems & Data Management Maintenance Planning Stores & Spares Management Life Cycle Costing
SUMMARY OF COMPLETE SOLUTION The methodology that the author has called value-based maintenance has been integrated into a complete consultancy solution for the manufacturing business sector. This provides a complete and continuous asset performance improvement process. This business product is titled ADV@NCE™ (see acknowledgements). Stage 1 and 2 are the parts that form the focus of this paper. They provide the requirements for the third stage, that of creating the vision for asset improvement in the business. Further, Stage 6 uses the Key Performance Indices that have been defined. The following diagram represents the complete creating the vision and asset performance improvement plan. Stage 1. Top Level Strategy Stage 2. Mapping Business Challenges to Asset Performance Challenges
Stage 6. Cost Benefit Business Impact Forecast
t
i
Stage 5. Asset Performance Improvement Plan
Stage 3. Creating the Vision Stage 4. Asset Performance Improvement Audit
The Six Stage ADV@NCETM process
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CONCLUSIONS The value-based maintenance methodology outlined in this paper provides a clear structured decision making platform for a business that undertakes an asset improvement process. Understanding the impact of using maintenance in an enterprise can be achieved by measuring and analysing the activity based Key Performance Indices. This information can be used from the board level to technicians as well as supporting the sharing of the business requirements throughout all levels of a firm's hierarchy. The integration of an asset improvement consultancy solution with the value-based maintenance methodology provides a business with the ability to care, with the optimum efficiency for their physical assets, in a complete and continuous business process. Using the methodology, costs over time are attributed and not overlooked or ignored in all aspects of the business. This allows for the true benefits of applying maintenance for the business to be made visible, clear and understood. Finally, the approach is currently being used and developed within a selection of UK companies with the goal to investigate the link between business decisions and asset management decisions. Potentially, a computer based business solution will be offered to clients. REFERENCES Kelly A. (1997). Maintenance Strategy Butterworth-Heinemann, Oxford, UK.
Business-centred Maintenance,
Mitchell J. S. (1996). Beyond Maintenance to Value Driven Asset Management, Proceedings of the 5^ Int. Conf. on Profitable Condition Monitoring, December 1996, 37 - 43. Porter M. E. (1985). Competitive Advantage: Creating and sustaining superior performance, The Free Press, New York. Wilson A. (1999). (Ed.). Asset Maintenance Management - A Guide to Developing Strategy and Improving Performance, Conference Communications, Surrey, UK. Wireman T. (1998). Developing Performance Indicators for Managing Maintenance, Industrial Press, Inc., New York. ACKNOWLEDGEMENTS The author would like to thank the support and advice of Wolfson Maintenance in the production of this paper. ADV@NCE™ is a trademark of Wolfson Maintenance and is used by kind permission.
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Condition Monitoring and Diagnostic Engineering Management Andrew G. Starr and Raj B.K.N. Rao (Editors) © 2001 Published by Elsevier Science Ltd.
VIBRATION-BASED MAINTENANCE COSTS, POTENTIAL SAVINGS AND BENEFITS: A CASE STUDY Basim Al-Najjar and Imad Alsyouf Department of Terotechnology - Vaxjo University, Vejdes plats 4, 351 95 Vaxjo, Sweden [email protected]
ABSTRACT Maintenance related expenses have usually been divided into direct and indirect costs. In this paper, maintenance related costs are identified and reclassified to reveal maintenance cost factors and highlight its profit. Maintenance profit is defined as the difference between the minimum savings resulted due to performing maintenance tasks during operational planned stoppages and investments in maintenance. The economic losses accumulated due to maintenance impact on production, quality, etc. are expressed as potential savings that could be recovered when a more effective maintenance policy is used. A model for identifying, monitoring and improving the economic impact of using vibration-based maintenance (VBM) is developed. This model provides additional possibility for identifying where, why and how much capital should be invested in maintenance. Further, the model is utilised to develop and use relevant maintenance performance measures, for monitoring cost factors and detecting deviations. The main results achieved, when the model is tested in a Swedish paper mill during 1997-2000; the average yearly maintenance profit achieved by using VBM was at least 3,58 Millions Swedish Krona (MSEK), and the average potential savings was 30 MSEK. It was not difficult to identify problem areas and know where investments should be allocated to eliminate the basic reasons and increase savings. The major conclusion is the better data coverage and quality the more control of maintenance direct costs, savings and more profits in maintenance. Also, it would be easier and more reliable to detect deviations in the maintenance performance and eliminate their causes at an early stage. KEYWORDS Vibration-based Maintenance, Maintenance Costs, maintenance Profits, Economic Losses, Potential Savings and Total Quality Maintenance (TQMain). INTRODUCTION Maintenance-related costs are usually divided into direct and indirect costs without considering maintenance savings and profits, which in turn implies falsely that it is no more than a cost-centre. Economic benefits that can be gained by more efficient maintenance can be found as savings in the results of other activities. But, the company's budget shows only direct maintenance costs, which constitute part of the operating budget. Mckone and Weiss (1998) cited that the amount of money du 217
Pont spent, in 1991, company-wide on maintenance was roughly equal to its net income. The total indirect maintenance costs such as loss of income due to breakdown stoppages and poor quality is, in many cases, not easy to estimate. In 1991, the direct and indirect Swedish maintenance-related losses were estimated to be about SEK 150-160 billion, where, in most cases the total losses that arise because of maintenance omission or ineffectiveness exceeds the purchase price of the equipment, cited by AlNajjar (1997). About 15-40% (28% on average) of the total costs for the manufactured products can be related to maintenance activities, ibid. The total utilisation of the equipment in Swedish industry is estimated on average about 55-60%, see Ljungberg (1998). So, industry could increase its production capacity without investing in new machinery if it implements an efficient maintenance policy. The most popular maintenance techniques are failure-based maintenance, preventive maintenance, condition-based maintenance, e.g. vibration-based maintenance (VBM), reliability-centred maintenance (RCM) and total productive maintenance (TPM). Nowadays based on experience and using the most efficient maintenance approach, failure can be reduced to approximately zero and the planned maintenance stoppages can be reduced as well by making use of better quality data, Al-Najjar (1997). VBM is becoming more widespread especially when the downtime costs are high. It gives tremendous possibilities to receive indications of changes of the condition of the machine in an early stage, Collacott (1977) and Al-Najjar (1997). These indications can be of great importance also in detecting deviations in the product quality early and before they show on quality control charts, Al-Najjar (1996, 1997, and 2001). The precision of the assessment of the condition of the machine and what actions have to be taken depends upon the technical efficiency and the precision of the condition-based system, Al-Najjar (1998 and 2000A). Better precision means fewer stoppages and lower production looses, i.e. lower costs and higher profits. Life Cycle Cost (LCC) has been widely used at acquisition of the most effective assets in the long term. In this study, we focus more on identifying the cost factors in LCC and on describing their behaviour during equipment life, so that it will be possible to monitor different cost factors and identify problem areas in the process to improve company's profits. Economic benefits gained due to improvements in VBM performance can be found in a wide range of plant activities and disciplines such as production, quality, assurance, and logistics, but it is difficult to specify maintenance impact on these activities. This is, among other reasons, why maintenance is counted as a cost- and not profit-centre especially when maintenance demands investments such as the case with using VBM.
MAINTENANCE COSTS AND POTENTIAL SAVINGS In this study, direct maintenance costs consist of the internal capacity costs needed for the maintenance function to perform its stated objectives such as direct labor, i.e. manpower, direct materials, e.g. spare parts, and overheads such as tools, instruments, training and other expenses. In addition to the external capacity offered by the original equipment manufacturers or others, i.e. outsourcing. Indirect costs are all the costs that may arise due to the planned and unplanned maintenance actions, e.g. lost production during stoppages. Usually it is difficult to estimate all these cost but nowadays with the assistance of the company wide IT systems much of the information needed for this purpose can be found. In general, the majority of these indirect costs (see below) are due to failures and short stoppages as a result of maintenance performance deficiencies, Al-Najjar (2000B): 1. Unavailability cost due to failure, and unplanned-but-before-failures-replacements (UPBFR). 2. Performance inefficiency costs due to idling minor stoppages and reduced speed. 3. Bad quality costs due to maintenance deficiency. 4. Idle fixed cost resources such as idle machines and idle workers costs during breakdowns. 5. Delivery time penalty costs due to unplanned downtime. 6. Warranty claims from dissatisfied customers. Compensation for product liabilities and repair. 7. Customer dissatisfaction costs due to bad quality and reliability, delivery delay or other reasons. 8. Extra energy cost due to disproportional energy consumption. Rao (1993) states that savings of up to 20% on the tremendous bill of the energy consumption in UK could be achieved by employing efficient monitoring and management strategies. 218
9. Accelerated wear due to poor maintenance. It was reported that loss due to corrosion of plants and machinery in UK, USA and other countries are of the order of 3 to 4% of GNP, Rao (1993). 10. Excessive, spare parts, buffer and work-in-progress (WIP) inventory costs. 11. Unnecessarily equipment redundancy costs to avoid waiting time after equipment failure. 12. Extra investments needed to preserve WIP and redundancies in good conditions. 13. Extra costs due to the absence of the professional labour as a result of maintenance-based accidents such as compensation labour costs and costs of using less skilled labour. 14. Environmental and pollution fines. 15. Extra insurance premium costs due to the increased number of accident and its consequences. The importance of these costs may be different for different companies, but they should all be considered when evaluating the maintenance role because they are representing the potential savings.
ASSESSMENT OF SAVINGS DUE TO MAINTENANCE LCC are divided into acquisition cost (AC), operating cost (OC), support cost (SC), unavailability cost (UC), indirect losses (IL), modification cost (MC) and termination cost (TC), see Eqn. 1. LCC = AC + OC + SC + UC + IL + MC + TC
(1)
Considering LCC factors, it is not difficult to realise that they are influenced by maintenance, which in its turn includes several cost factors. Some of maintenance cost factors such as labour and spare part costs can directly be related to maintenance activities. The other indirect cost factors such as maintenance related rejected items, maintenance related losses in market share and reputation, in many cases, hardly to be found in the accountancy system without being confused with other costs. Based on the available databases, not all the indirect cost factors such as losses of production due failures and UPBFR can easily be related to maintenance. To evaluate the economic importance of a particular investment in maintenance, it often demands the assessment of life cycle income (LCI). One way to do it is to assess the savings achieved through: 1. Reducing the downtime due to failures, UPBFR, planned replacements and repair. 2. Reducing the number of rejected items due to lack of maintenance/service. 3. Reducing inventory' capital, e.g. reducing redundancies of spare parts, equipment and personnel. 4. Reducing operating cost, i.e. reducing stand-by equipment and personnel when a high confidence in the used maintenance policy is created. 5. Less assurance premium due to less failure related accidents. 6. Less delay and more accurate delivery schedules. It can be approached by improving machine reliability and overall equipment effectiveness (OEE), i.e. availability, performance efficiency and quality rate, through using an efficient and continuously improved maintenance policy to detect deviations (and eliminate causes) in the machine condition in an early stage. The assessment of savings achieved by more efficient maintenance is easier than to assess LCI, due to the effect of the external factors (see below), e.g. on profit margin and product price: 1. Currency value in the international market is not usually stable rather varies. 2. World wide political crises and wars influence the cost of raw material, machines, etc. 3. New discoveries and products and new competitors. 4. New national or international regulations, e.g. those are related to environment. Talking, only, about maintenance direct and indirect costs is the first step to emphasise the claim that maintenance is more or less a cost centre. During recession, in general, companies cut down maintenance (cost) budget regardless of its generated benefits that have been collected by production, quality, safety, environment, etc. The usual and unrealistic question being asked by chefs is "why are we paying that much for maintenance while the plant does not suffer of so many failures and production disturbances?" without realising the role of more efficient maintenance in achieving these results. In this paper we attempt to introduce a new view of reality in order to place maintenance in its right position among plant activities, see Figure. 1. 219
Fig.l. Maintenance role in reducing production cost and increasing plant profit. TECHNICAL AND ECONOMIC EFFECTIVENESS Plant value-adding activities are usually monitored by technical measures such as overall equipment effectiveness (GEE). Usually the development of GEE and its elements are observed. But when it is considered in conjunction with the total production cost or with plant profits, it gives an impression of how much could the company reduce production cost and still satisfying customers, stakeholders and society in order to increase its sales and market share. To survive the hard competition, companies need to improve their manufacturing processes and profitability continuously. Continuous improvement demands effective tools for measuring and analysing data, results presentation, optimisation (and suboptimisation) and reliable decision-making procedures. In general, quality rate is influenced by many factors, some of them related to the machine design and construction, raw material, cutting tools, environment, quality control system, company culture, etc. The others arise because of the selected maintenance policy, service and maintenance performance quality. In many cases, especially when there are long-term (chronicle) problems, the reasons behind quality problems are particular combinations of some of the above mentioned factors. This means that high quality input elements needed for establishing a manufacturing process should be maintained in order to secure high quality product at a competitive price through high availability and stable product quality (quality variation within narrow limits). These can not be secured without an effective maintenance policy, Al-Najjar (2001 A). Such a policy will be very useful to reduce short stoppages and enhance performance efficiency as well. Less failures and better control of the production plant help minimise pollution generated and fulfil society's demands. In general, these results can be achieved in the following steps: 1. First step; when the customers are satisfied, 2. Second step; the society will gradually (and widely) accept the company and its products, and 3. Third step; stake value increases, which makes stakeholders satisfied and stake-demand increases, which leads in its turn to an additional increase in stake value. Note that along all these steps profit is generated, because the more the customers are satisfied and the company (and its products) being accepted by the society, and the stake value increases the more profit can the company gain, and vice versa. 220
MODEL DESCRIPTION The suggested conceptual model includes five main parts that are shown Figure 2: the maintenance related cost factors (potential savings), the direct maintenance cost, maintenance savings, maintenance profits, and maintenance performance measures. At first, direct maintenance cost and the related maintenance costs factors should be identified (which may not necessarily be the same for different companies). The next very important essential step is to know where to find the required input economic and technical data in the available accountancy system/database and how to calculate or estimate them. Then, the data can be used to calculate/estimate the maintenance related potential savings, i.e. productions losses. The minimum savings that have been achieved by more efficient VBM policy, which is based on utilising operational planned stoppages to perform the necessary maintenance tasks, can be assessed as well. Knowing the maintenance investments, which is part of the direct maintenance cost, and the minimum savings achieved by better maintenance planning enable the user to estimate maintenance profits. Further, based on the maintenance cost factors (potential savings) and other relevant information parameters maintenance performance measures can be identified and calculated. Trends, i.e. rate of change, of the interested maintenance cost factors and performance measures can then easily be obtained from the available data in the model, i.e. past and current data, for the study period. Furthermore, using analysis tools such as Pareto diagram help the decision-maker to identify the problem areas and perform the never-ending improvement process, i.e. KAIZEN, which is based on Deming; cycle Plan-Do-Check-Act. This can be based on doing technical analysis to relate problems and their causes to the expected results that might be achieved by means of new investment, i.e. to describe which economic losses can be eliminated through performing particular improvements in the maintenance policy. For example if the maintenance personnel get more reliable support such as using a new monitoring and diagnosis system that eases detecting and localising of damages, then fewer failures and UPBFR should be expected. Comparison between the expected and achieved results is a reliable indication to check whether the investment was cost-effective or not. This will enable the user to act and localise where the next investment should be, and so on. Where to find the data in the Plant Databases?
Equations to calculate the model's factors.
;;=
2) Direct Maintenance cost (inclusive invetments)
[^^ 3) Savings that could be achieved due to more effective maintenance policy
4) Maintenance profits
Fig.2. Maintenance cost, savings and profits model. In order to assess the savings achieved due to using more efficient maintenance we consider that all the maintenance tasks such as condition-based replacements (CBR) being performed during operational planned stoppages, which are scheduled by production department for doing some other tasks, are avoided failures. CBR, e.g. bearing replacements, are recommended by VBM after detecting damage. When these replacements are done in parallel during planned stoppages to avoid failures and utilise the planned stoppage time. Therefore, all the corresponding costs, i.e. profits losses and unutilised fixed cost expenses during the performed CBR tasks, could be counted as savings. These are considered the 221
minimum savings because if the replaced components failed during the planned operating time, they will probably occur one at a time and the time needed to do the task will be longer with more failure consequences. In general, the direct maintenance cost is required to maintain machine quality and fulfil production requirements. But, deterioration in the essential elements of the machinery is not avoidable forever even if it is possible to be arrested for a while. Therefore, more production losses can be generated, which motivate special investments for reducing these losses. In this case, the reduction in the losses is called saving, i.e. maintenance related recoverable expenses. It equals to the difference in the losses (potential savings) of two following periods if no other investments such as those in quality, production procedure and instructions (in addition to that done in maintenance) have influenced some or all the savings. As long as it is possible to reduce the economic losses by reducing failures, short and planned stoppages and decreasing stoppage times through improving repair, these economic losses, potential savings, i.e. these are recoverable expenses. This means that by means of more efficient maintenance policy some of these production losses can be recovered. The model presented Figure 2 can be utilised to achieve: 1. Keep track of the minimum savings being generated by maintenance due to new investments for improving maintenance performance, which is not possible otherwise. 2. When the minimum savings are classified according to the basic cost and potential saving factors, it will be easier to identify where, how much and why a new investment should be done. 3. The achieved savings can be compared with the investments done for improving maintenance policy to reveal whether the investment was cost effective or not. 4. Develop and use relevant performance measures utilising the available data to detect deviations before changes become unacceptable. 5. Achieve the never-ending improvement process, i.e. KAIZEN. The new in this model can be summarised in the following: 1. Monitoring changes in the relevant maintenance related cost and potential saving factors, which can be identified in (or estimated from) the existing accountancy system of most companies. 2. Direct maintenance cost can not be reduced to zero even if all the losses are eliminated. These costs are required to keep the condition and quality of machinery high enough to fulfil company's requirements. But, the investments in maintenance are for improving maintenance and overall company performance and reducing economic losses. Therefore, the minimum savings would be considered to justify these investments. 3. Using the model, maintenance would be considered as a profit-centre instead of cost-centre as long as the minimum savings can be identified and monitored versus investments. CASE STUDY The case study was conducted at StoraEnso (a paper mill company in Hyltebruk area in the southern part of Sweden). The data collected was delimited to only stoppages of mechanical components, which were (or could be) monitored by vibration signals. The study was conducted at PM2, one of the company's four machines. It was selected due to its valuable database especially during study period (1997-2000). A special data sheet was designed for collecting, manually, technical and economic information parameters from the company databases. The data sheet was adapted to suit the company terminology and context. Technical data included parameters such as planned operating time, planned production rate, time and frequency of planned stoppage in which mechanical tasks, e.g. bearing replacements, were performed as a result of using VBM, unplanned stoppage, i.e. failures and UPBFR, short stoppage, quantity of bad quality products caused by maintenance problems. Economic data such as fixed and variable operating costs, profit margin, net profit, working capital, direct maintenance costs, investments in maintenance, spare parts inventory, etc. were also collected. The conceptual model was validated using the data collected from the case company. The first factor is direct maintenance cost which was almost constant during the study period with an average of about 13 MSEK, see Figure.3. The total maintenance investment in PM2 both in general and training was increasing for the years 1997-1999 with a little bit decrease in year 2000, as shown in Figure 4, on average it was about 0,455 MSEK per 222
year. The total production losses (potential savings) consists of the summation of profit and unutilised costs calculated for unavailability due to failures, UPBFR, and planned stoppage times; short stoppages; bad quality products caused by maintenance problems; and tied up capital due to extra spare parts inventory. On average the total potential saving w^as about 30 MSEK, and it was increasing, as shown in Figure 5. Pareto diagram for the total losses elements are shown in Figure 6. We can see that losses due to short stoppages represent the highest value, then the planned stoppages, and quality problems, after that comes the failures and UPBFR, finally the tied capital due to extra spare parts inventory, which was calculated with respect to year 1997. On average the minimum savings was estimated to be about 4 MSEK, which was increasing especially during the years 1999 and 2000. The last factor is maintenance profit, which represents the difference between the minimum savings and maintenance investments. On average it was about 3,58 MSEK.
Maintenance Mechanical Dmct costs for PM2
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FigJ, Maintenance mechanical dircci costs Total maintenance Inveslments In PM2
X
OJO0 OJO0 0JOO 0.500 0,400 0,300 0.200 0J0O 0,000
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Fig.4 Total investments in maintenance Potential savings (Total losses) zi.%
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Fig. 5. Total potential savings (economical losses).
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Pareto OUigrMii for the Averagt valiits of LotMS aiKi ^i«ir causes
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Fig. 6. Pareto Diagram for the total losses elements
PERFORMANCE MEASURES Twelve maintenance-performance measures were developed and used. The first and second performance measures are the direct maintenance cost (mechanical), and total investments in maintenance, which were shown previously in Figure 3 and Figure 4, respectively. The third, fourth and fifth are the ratio of the direct mechanical maintenance costs to operation cost, running time and accepted product, which showed approximately the same trend (almost constant) during 1997-2000. The sixth measure is the total losses (in profit and resources) per each accepted ton produced, see Figure 7. It shows that on average about 168 SEK were lost for each ton of paper produced, and, also, the trend of losses is increasing. The seventh measure is the minimum savings divided by the accepted tons of paper produced. The eighth measure is the ratio of maintenance investments to potential savings. It was appreciably small and varied between 1-2.2%. The ninth measure is the ratio of minimum savings to maintenance investments, see Figure 8, on average it was about 9,2 which was within the range achieved and published in USA, UK, Russia, and China, i.e. 5-10. The tenth measure is the ratio of lost profit to actual profit. We noticed that on average a value of a bout 3.5% of the actual generated profit could had been gained for lost production during failures, UPBFR, and short stoppages. Note that the un-produced quantities during the planned stoppage time are not included. The eleventh measure is the restricted overall process effectiveness (ROPE). ROPE is equivalent to OEE but restricted to only when the machine is subjected to mechanical faults that can be maintained by VBM. Finally, the last measure, which represents the value of ROPE, divided by the direct mechanical maintenance costs. Ra^ofmifitfntim savings to main^natiea invastmatit
Total fo8aas(profit+fasotireM) per ton of papar producad (SEK/ton)
Fig.7. Total losses to accepted ton produced.
Fig.8. Minimum savings to maintenance investments. 224
CONCLUSIONS Using the model means that LCC factors will be used as monitoring parameters to provide the required information for decision-making and to insure cost-effective actions and enhances never ending improvement efforts. Comparing the minimum savings with the investments done for improving maintenance policy reveals how cost effective the investment in maintenance was and whether it was relevant or not. ACKNOWLEDGEMENT This article was one of the results of a project that was financially supported by the Swedish board for industrial and technical developments, NUTEK, and the Swedish companies StoraEnso Hylte AB, VolvoTruks components AB in Koping, SKF-Condition Monitoring, and ABB Alstom Power AB in Vaxjo. We would like to thank maintenance department staff at Stora Enso Hylte AB and in particular Mirela Jasarevic, Jorgen Blomqvist, Bernt Petersson, and Jan Andersen for their efforts. REFERENCES Al-Najjar B. (1996). Total Qualit>f Maintenance: An approach for continuous reduction in costs of quality products. Journal of Quality in Maintenance Engineering 2:3, 2-20. Al-Najjar B. (1997). Condition- based maintenance: Selection and Improvement of a Cost-effective Vibration-Based Policy in Rolling Element Bearings. Doctoral Thesis, Lund University/LTH, Sweden. Al-Najjar B (1998). Improved Effectiveness of Vibration Monitoring of Rolling Element Bearings in Paper Mills. Journal of Engineering Tribology, ImechE. Proc Instn Mech Engrs, 212: part J, 111-120. Al-Najjar B. (2000A). Accuracy, Effectiveness and improvement of Vibration-based Maintenance in Paper Mills; Case Studies. Journal Jf Sound and Vibration 229:2,389-410. Al-Najjar B. (2000B). Impact of rdal-time measurements of operating conditions on effectiveness and accuracy of vibration-based maintenance policy: A Case Study in Paper Mill. J. Of Quality in Maintenance Engineering 6:4, 275 287. Al-Najjar B. (2001). A Concept for Detecting Quality Deviation Earlier than when using Traditional Diagram in Automotive: A Case situdy. To appear in J. of Quality and Reliability Management 18:8. CoUacott (1977). Mechanical Fault Diagnosis and Condition Monitoring, Chapman and Hall, London, Ljungberg O. (1998). Measurement of Overall Equipment Effectiveness as A Basis For TPM Activities. International Journal of Operations |ind Production Management 18:5,495-507. Mckone K. and Wiess E. (1998). TPM: Planned and Autonomous Maintenance: Bridging the Gap Between Practice and Research. Prdduction and Operations Management 7:4, 335-351. Rao B.K.N. (1993). Profitable Condition Monitoring and Diagnostic Management. Profitable Condition Monitoring, Kulwer Academic Lorjdon, pp.37-44.
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Condition Monitoring and Diagnostic Engineering Management Andrew G. Starr and Raj B.K.N. Rao (Editors) © 2001 Published by Elsevier Science Ltd.
BALANCED SCORECARD CONCEPT ADAPTED TO MEASURE MABSfTENANCE PERFORMANCE: A CASE STUDY Lnad Alsyouf Department of Terotechnology - Vaxjo University, Vejdes plats 4, 351 95 Vaxjo, Sweden E-mail: Imad.alsyouf @ips.vxu.se
ABSTRACT There is a need for having a holistic maintenance performance measurement system that can assess the contribution of the maintenance function to the business strategic objectives. The balanced scorecard (BSC) was adapted to measure maintenance performance and evaluate its impact on other enterprise's activities. It was applied to data collected from a paper-mill machine during 1997-2000. The average value of a new measure named total overall process effectiveness (TOPE) was about 86%. TOPE is the product of availability, performance efficiency, quality rate, and planned operation time indices. The average value of return on investment (ROI), i.e. net profit divided by the working capital, was 16,43%. The economic consequences of the measured TOPE have the potential to improve ROI by 1,47%, which is equivalent to 66,93 millions SEK extra profit. The planned inoperative time represents the highest contributing factor to the ROI potential improvement 43,8%, unavailability is the second 19,2%, then quality losses 18,5%, and finally short-stoppages 18,4%. At least 14,1% potential improvement of the ROI is related directly to maintenance function as lost profit due to unplanned stoppages and bad quality caused by maintenance related problems. This percentage will increase according to how the maintenance actions are linked to the causes of the unutilised time such as short stoppage time, planned stoppage time, and planned inoperative time. Using the modified BSC provides a framework to translate the company strategy into operational terms and follow up the cause and effect linkages of using an efficient maintenance strategy.
KEYWORDS Maintenance Performance, Balanced Scorecard (BSC), Key Performance Indicators (KPI), Overall Equipment Effectiveness (OEE), Total Overall Process Effectiveness (TOPE), and Paper Mill.
INTRODUCTION Nowadays maintenance is considered as one of the most important elements in process and chemical industries where the downtime cost is very high, e.g. paper industry. Traditionally, it has been considered as a necessary evil, but it could be a profitable internal business, rather than just unpredictable and unavoidable expense. A strategic approach to maintenance management has become essential especially 227
in capital-intensive industries. The impact of maintenance actions can not be viewed only from their effect on the maintenance department, since the consequences of maintenance actions may seriously affect other units of the organisation. Looking only at the direct maintenance costs can not appraise the complete impact of maintenance. The direct maintenance costs represents, on average 55% of the total maintenance costs, see Ahlmann (1998). Actually, there is a need for a holistic performance measurement system that can: 1) Assess the contribution of the maintenance function to the business strategic objectives. 2) Identify the weakness and strengths of the implemented maintenance management system. 3) Establish a sound foundation of quantitative and qualitative data for a comprehensive maintenance improvement strategy. 4) Clear rules for benchmarking maintenance practice and performance with best practice within and outside the same branch of industry. In this paper, we discuss the general framework of business processes, performance measurement systems, i.e. the traditional performance measurement systems and the BSC concept, and then we introduce a BSC model adapted to measure maintenance performance. Next, we illustrate the impact of the maintenance function on the elements of business performance according to the suggested BSC. Finally we apply the suggested BSC in a Swedish Paper Mill and draw some conclusions.
BUSINESS PROCESSES The general framework of business processes, its elements and the flows of materials and information within the extended enterprise are illustrated in Figure 1. The upstream organisation consists of the internal and/or external suppliers, designers of all materials, e.g. equipment, tools, etc. needed for the processes. Furthermore, universities and research centres that provide the firms with qualified employees, collaborators and the latest innovations and developments are considered a very important part of the upstream organisation. The inputs consist of all the resources necessary to produce the goods and/or services such as people, materials, capital, energy, data/information, methods, skills, etc. The value adding processes consists of all the utilities, i.e. values and usefulness that the firm creates in its products or services for fulfilling a want or need, see Lambert et al. (1998). There are various types of utilities such as form, time, and place utility, etc. The form utility is the process of creating the good or service, e.g. product manufacturing. The time utility is the value added by having an item or service when it is needed. This applies to both internal customers and end customers. The place utility means having the item or service available where it is needed, ibid. The output of the value adding processes is the products and or services that should satisfy the customer's needs and requirements at minimum costs, i.e. best value and minimum cost. The quality, efficiency, and effectiveness of the adding value processes affect the downstream elements, i.e. the customers, the shareholders (owners), and the society.
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Materials flow Figure 1: The framework of business processes. 228
PERFORMANCE MEASUREMENT With the advent of total quality management emphasise has shifted towards customer satisfaction, and providing quality products and services as a means of maintaining competitive advantages, i.e. value and cost advantages. Therefore, it became necessary to use a performance measurement system that provides feedback information about all relevant areas of business operations for the success of the continuous improvement efforts (Plan-Do-Check-Act). Hence, the traditional performance system that is based only on financial measures became inadequate. In the beginning of the 1980s, academics and practitioners questioned this one-sided model. Since then, several performance models were introduced: for example, performance measurement matrix; the results and determinants model; the Institute of Chartered Accountants of Scotland (ICAS) model; Brown's model; performance pyramid; the Business Excellence Model etc, see Neely et al. (1995 and 2000). One of the key problems with performance measurement systems is that they have traditionally adopted a narrow or Uni-dimensional focus. The Balanced Scorecard (BSC) Kaplan and Norton (1996) showed that most of performance measurement systems are based on measures that are used only for feedback and control of short-term operations. Therefore, they suggested the BSC as a new framework to translate a strategy into operational terms, where measures of past performance are enhanced with measures of the drivers of future performance. The objectives and measures of the scorecard are derived from an organisation's vision and strategy based on four different perspectives; financial, customer, internal business processes, and innovation and growth. The different measures across the four perspectives can be linked in a series of cause and effect relationships to illustrate, for example, how investments in employee training, would improve future financial performance. They presented the BSC concept in early 1992. Since then, many companies have adopted it as a base for their strategic management systems. Kald and Nilsson (2000) showed that the design and use of BSC are widely discussed in Nordic management literature, many well known companies have applied it like ABB, SAS, SKF, Volvo, etc. But, many researchers such as Neely et al. (1995), Bontis et al. (1999), and Mooraj et al. (1999) have criticised the BSC concept. They showed that it is failing to; 1) Identify performance measurements as a two-way process, it focuses only on top-down performance measurement. 2) Unable to answer what are competitors doing. 3) Being rigid and limited to customers, ignoring other parts such as suppliers, alliance partners local community and final consumers, i.e. it does not consider the extended value chain. In fact, the BSC concept can be used also to measure, evaluate and guide activities that take place in certain functional areas of a business. Martinsons et al. (1999) have developed a BSC for information systems (IS). Tsang (1998) presented a strategic approach for managing maintenance performance based on the four perspectives of the BSC concept as suggested by Kaplan and Norton. But we believe that there is a need to adapt BSC concept to suit the maintenance context as a support function taking into account the criticism mentioned above. Modified Balanced Scorecard We suggest a modified BSC's model that could be structured into three main parts, as can be seen in Figure 2. Part one (Downstream elements) includes: consumer, society, corporate business, i.e. financial, perspectives. Part two (The operations elements) it consists of production's perspective, and productionsupport activity's perspectives, e.g. maintenance, quality, logistics, etc. Finally, the (Upstream elements): which may include human resources perceptive, and the suppliers and designers perspectives. In each perspective of the BSC there should be a set of strategic objectives that are derived from the vision and mission of the organisation. In addition to well-chosen key performance indicators KPI, i.e. measures that are identified in a two-way process (top-down and down-up). The KPI are used to control the performance of the enterprise and benchmark the business performance practice with best practice within the same branch or other branches, and provides the feedback information, which is necessarily for the never-ending improvements (plan-do-check-act) efforts. 229
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Figure 2: The suggested perspectives of the modified BSC MAINTENANCE IMPACT ACCORDING TO THE SUGGESTED BSC PERSPECTIVES Maintenance function is one of the production support activities that affects the business performance through its impact on the other units of the enterprise, e.g. production capacity, quality, environmental and employee safety, costs, etc. In the following, we present and discuss a model for the cause and effect of maintenance impact on the enterprise according to the suggested BSC perspectives, see Figure 3. Upstream: Innovation and Growth Perspective Most of the operational and maintenance costs of physical assets are linked to decisions taken at an early stage of the machine design. Therefore, it is easier to avoid a significant part of the maintenance costs at the design stage rather than at the operational stage, see Al-Najjar (1997) and Ahlmann (1998). This is because the reliability and maintainability of the asset are determined at the design stage, which could be improved depending on the ability of the implemented maintenance policy to provide feedback information about the maintenance and operational experience of the previous generation of evolutionary designs. On the other hand, increasing the co-operation and mutual research projects with research centres and universities could enhance implementing new effective maintenance methods and innovations. This would help, also, in developing the competence of firms' own professional and technical employees, which is considered as one of the important factors that affect the number and duration of production disturbances. It can be described as a combination of knowledge, skills, ability, willingness, interest and personal characteristics. Anon (2001) reported that a paper mill machine was damaged by fire in year 2000 due to faulty electrical wiring. It took several months to repair and restart the machine, i.e. very high losses. To develop competence more investment should be pumped into the maintenance department because it would be returned quickly as savings, e.g. less production losses. The Operations Perspectives This can be divided into two parts: the production operations and the support functions operations. The support operations such as maintenance, quality, logistics etc. could vary depending on which activity is under focus. Here we focus on the role of maintenance as a support function. 230
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Figure 3. The cause and effect of maintenance impact on the business performance The Operations: Maintenance Perspective Company performance could be affected by the disturbances that occur in the manufacturing plant. These disturbances are highly affected by maintenance efficiency. To assess the efficiency of a maintenance policy two measures (effectiveness and accuracy) are suggested in Al-Najjar (1991 and 1997). Maintenance efficiency is affected by the ability (effectiveness) of the maintenance function to avoid failures and unplanned-but-before-failure-replacements (UPBFR), i.e. near-failures. Maintenance accuracy depends on the ability of the maintenance function to make use of as much as possible of the equipment/component effective useful life by performing the replacement just before failure. To be more effective the duration or number of failures and UPBFR should be minimised, while to be more accurate the number and duration of planned stoppages should be optimised. To achieve these results it is necessary to select and implement the most cost-effective maintenance policy, see Al-Najjar (1991) and Al-Najjar and Alsyouf (2000). On the other hand, accurate maintenance improves the frequency and duration of short stoppages, product quality, the number of accidents and the average spare parts inventory level. Maintenance function efficiency is hard to evaluate in absolute terms. There are two categories of ratios, which are used as performance indicators: economical measures and technical measures see De Groote (1995), Al-Najjar (1997). The Operations: Production Perspective The maintenance function affects the technical performance and cost effectiveness of the production department. Technical performance of the production function can be assessed by a modified version of OEE, i.e. the Overall Process Effectiveness (OPE), see Al-Najjar (1997). OPE is calculated by multiplying the availability, performance efficiency, and quality rate. A very important factor which is not considered when calculating the OPE is the major planned stoppage time and the yearly vacations which are not considered when assessing the availability. In this paper, we modify the OPE by multiplying it by a new index called the planned operation index, hence the new measure is called Total Overall Process Effectiveness (TOPE). Using the operative time index and its economic consequences provide the management with more information about the cost effectiveness of doing investment or/and 231
making decisions, e.g. to work during the inoperative time. The planned operation index is calculated as {the theoretical production time, e.g. 1 year minus (the planned vacation and major planned stoppage time)} divided by the theoretical production time. On the other hand, the capability of the machine to produce quality products, e.g. products that satisfy the customer requirements, is highly affected by the maintenance efficiency. Ollila and Malmipuro (1999) studied the impact of maintenance on quality, in few Finish industries. They found that in mechanical wood processing maintenance was the third most important reason, in paper machine operations it was the second most important reason, and in power production it was the first. Also, maintenance affects the production cost effectiveness. It has an effect on consumption of the various resources used in the company's operations, see Al-Najjar (2000). For example, the labour allocated for a production line could be idle during the stoppage time, and extra overtime hours could be needed to compensate the production lost during the stoppage time. Moreover, the labour safety could be affected by the accidents that occur due to maintenance causes. The amount of raw materials used in production is influenced by the number of breakdowns because of bad product quality caused by machine condition deterioration, and start-up losses. Hence, the consumption of materials will increase due to scrap and rework. On the other hand, spare parts inventory and consumption will be affected by the implemented maintenance approach, e.g. a large inventory of spare parts must be held in store when using failure based maintenance because consumption of spares is then unpredictable. Capital investments in the plant is influenced through factors such as equipment/component useful life, equipment redundancy, buffer inventory, extra investment in facilities for buffer and equipment redundancy, damage in equipment due to breakdown. Energy consumed can be affected by the condition of the plant, e.g. a worn bearing means more friction between the rotating parts and results in more energy consumption. Therefore, efficient maintenance contributes by adding value through better resource utilisation (higher output), enhanced product quality, e.g. reduced rework and scrap (lower input production costs). In addition, it avoids the need for additional investment in capital and people due to expanding the capacity of existing resources. Downstream The impact of maintenance on the downstream elements can be traced by finding its effects on the customers, society, and shareholders. As regards customer perspective: customer satisfaction is the main point of the TQM philosophy. Dearden et al. (1999) showed that firms try to capture new customers, satisfy them and retain existing customers by promising assurance of supply on time, which in its turn depends on adequate production capacity with minimum disturbances, and high quality products. Flynn (1994) among others showed that demand is usually driven by market price, general economic conditions, in addition to other factors such as high quality and on time delivery. Dearden et al. (1999) cited that a chance of 40% of change of suppliers following a missed delivery is reported! The base of success in any competitive context is either having cost advantage or value advantage such as image or reputation, or a combination of both advantages. Concerning society perspective: the impact of maintenance on society can be traced through its effects on safety, environment and ecology. The environment is affected by accidents and pollution, which occur due to the (improper) use, disposal or bad maintenance of an asset. Rao (1993) indicates that environmental pollution due to various unnatural causes is very high, at the same time losses due to industrial fire damage is running to millions of pounds in the UK. These could have been avoided or reduced by effective maintenance. Finally regarding financial perspective: the shareholders, usually, are interested in achieving profit. The impact of maintenance on shareholders can be found by analysing the effect of maintenance on the generated profit, which is usually measured by indexes such as Return on Investment (ROI).
CASE STUDY The modified BSC was applied at a Swedish paper-mill company, StoraEnso Hylte AB. Technical and economic data was collected about one of the manufacturing machines, i.e. PM2, during the period 232
1997-2000. Technical data included parameters such as planned operating time; planned production rate; planned stoppage time; unplanned stoppage time, i.e. failures and UPBFR; short stoppage time; bad quality products. The collected parameters covered all types of stoppages, e.g. mechanical, electrical, hydraulic, instruments, etc. Economic data such as fixed and variable operating costs, profit margin, net profit, working capital, maintenance costs, investments in maintenance, spare parts inventory, etc. were collected. It was found that, according to the innovation and growth perspective, the company is implementing vibration-based maintenance (VBM). The estimated direct maintenance cost was on average about 14,11% of the machine operational cost. 0,65% of the direct maintenance cost was invested in maintenance. The company has a highly skilled maintenance staff, e.g. more than 50% of the investment in maintenance was spent on training. On the other hand, a good level of co-operation with research centres and universities and original machine manufacturer were noticeable. As regards the maintenance perspective, we found that for 5,8% of the planned working time the machine was stopped. The total stoppage time consists of short stoppages 48,8%, unplanned stoppages 33,6%, and regular planned stoppages 17.6%. The reasons of the unplanned stoppages and the percentage of each reason with respect to the total unplanned stoppages were distributed as follow: Electrical 13%, lining change 11,4%, Mechanical 10,8%, cleaning 9,5 %, hydraulic 1,6%, instruments 0,8%, others 52,9%. The bad quality products due to maintenance causes were estimated to be about 7,5% of the total bad quality produced. No accident was reported during the study period. On average, there was an increase of about 18,8% in the spare parts inventory level during the three years 1998-2000 with respect to the year 1997. Concerning production perspective, the average value of the overall process effectiveness OPE, i.e. without considering the operation time index, is 91,4%. When considering the operative time index the total overall process effectiveness (TOPE) becomes 85,6%. On the other hand, we found that the company did not face problems with late delivery to its customers, or with injury due to accidents related to maintenance problems. But the capital tied up in spare parts inventory was increasing. When considering the downstream elements we can say that the ability of the company to provide its customers with their orders on time was high, we also found that no environmental penalties were paid, e.g. pollution fees. Finally, when considering the financial perspective, we found that the average value of the return on investment (ROI) during the years 1997-2000, i.e. net profit divided by the capital invested, was 16,43%. The economic consequences of the measured TOPE have the potential to improve ROI by 1,47%, which is equivalent to 66,93 millions SEK extra profit. The planned inoperative time represents the highest contributing factor to the ROI potential improvement 43,8%, unavailability is the second 19,2%, then quality losses 18,5%, and finally short-stoppages 18,4%. At least 14,1% potential improvement of the ROI is related directly to the maintenance function as lost profit due to unplanned stoppages and bad quality caused by maintenance related problems. This percentage will increase according to how the maintenance actions are linked to the causes of the unutilised time such as short stoppage time, planned stoppage time, and planned inoperative time. In addition, it was found that the direct maintenance costs, in pulp and paper industry, represent about 63% of the total maintenancerelated costs, i.e. direct costs, lost profit, and other indirect maintenance costs such as unutilised resources and extra tied capital. The lost profit was about 25,9% of the total maintenance-related costs, while the indirect maintenance costs was about 11,1%. More improvements in the value of ROI could be achieved when increasing the maintenance efficiency due to the increase in profit margin and decrease in operating costs, e.g. less tied-up capital because of decreasing the spare parts inventory level. CONCLUSIONS Using the suggested BSC provides a framework to translate the company strategy into operational terms and measure the impact of support functions, e.g. maintenance, on the overall business performance. It makes it possible to select the suitable KPI in each area of the suggested perspectives. In particular, it can be used, strategically, to compare the global maintenance performance with best practice, tactically, to control the performance of the firm by identifying the strengths and improvement opportunities. Also, it establishes a sound foundation of quantitative and qualitative data for a comprehensive maintenance 233
improvement strategy. But we believe that applying the suggested model has some pre-requisites such as having a well designed IT system which provides the required data that are currently missing. ACKNOWLEDGEMENTS This article was one of the results of a project that was financed by the Swedish board for industrial and technical developments, NUTEK, and the Swedish companies StoraEnso Hylte AB, VolvoTruks components AB in Koping, SKF-Condition Monitoring, and ABB Alstom Power AB in Vaxjo. I would like to thank the maintenance staff at StoraEnso; in particular Mirela Jasarevic, Jorgen Blomqvist, Bemt Petersson, and Jan Andersen for their efforts. Finally I would like to thank my main supervisor Assoc. Prof Basim Al-Najjar for his helpful and constructive comments, suggestions, discussion, and continuos support. Prof David Sherwin for his useful comments, and Kenneth Faaborg for his valuable discussion. REFERENCES Ahlmann H. (1998). The Economic Significance of Maintenance in Industrial Enterprises. Lund University, Lund Institute of Technology, Sweden. Al-Najjar B. (1991). On the selection of condifion based maintenance for mechanical systems. Operational Reliability and Systematic Maintenance. Elsevier Applied Science, London, 153-175. Al-Najjar B. (1997). Condition-based Maintenance: Selection and Improvement of a Cost-effective Vibration-Based Policy in Rolling Element Bearings. Doctoral Thesis, Lund University/LTH, Sweden. Al-Najjar B. (2000). Impact of Integrated Vibration-Based Maintenance on Plant LCC: A Case Study. The 3^^^ Inter. Conference Quality, Reliability and maintenance. University of Oxford, UK, 105-111. Al-Najjar, B. and Alsyouf, I. (2000). Selection The Most Efficient Maintenance-Approach Using Fuzzy Multiple Criteria Decision-Making. Conf COMADEM, 3-8 Dec, Houston, Texas, USA. Anon (2001), Intermills restart PM, Pulp & Paper Industry PPI This Week, 6:5, February 2001. Bontis N, Dragonetti N., Jacobsen K., and Roos G. (1999). The Knowledge Toolbox: A Review of the Tools Available to Measure and Manage Intangible Resources. Euro. Management. J. 17: 4, 391-402. Dearden J., Gary L. Lilien and Eunsang Y (1999). Marketing and Production Capacity Strategy for NonDifferentiated Products, International Journal of Research in Marketing 16: 1, 57-74. De Groote P. (1995). Maintenance Performance Analysis: A Practical Approach. Journal of Quality In Maintenance Engineering 1:2, 4-24. Flynn B.(1994). The Relationship between Quality Management Practices, Infrastructure and Fast Product Innovation. Benchmarking for Quality Management & Technology 01:1, 48-64. Kaplan R. and Norton D. (1996). The Balanced Scorecard: Translating Strategy into Actions. Boston, Mass: Harvard Business School Press. Kald M. & Nilsson (2000). Performance measurement at Nordic companies. Eu. Mang. J. 18:1, 113-127. Lambert D., Stock J., Ellram L. (1998), Fundamentals Of Logistics Management, McGraw-Hill. Martinsons M., Davison R., And Tse D. (1999). The BSC: A Foundation for Strategic Management of Information Systems. Decision Support Systems 25:1, 71-88. Mooraj S., Oyon D., and Hostettler D. (1999). The BSC: A Necessary Good or An Unnecessary Evil? European Management Journal 17:5,481-491. Neely A., Gregory M., and Platts K. (1995). Performance Measurement System Design: A Literature Review Band Research Agenda. International J. of Operations & Production Management 15: 4, 80-116. Neely A., Bourne M., and Kennerley M. (2000). Performance Measurement System Design Development and Testing A Process-Based Approach. International Journal of Operations & Production Management 20:10, 1119-1145. Ollila A. and Malmipuro (1999). Maintenance Has A Role in Quality. The TQM magazine 11:1,17-21. Rao B.K.N. (1993). Profitable Condition Monitoring and Diagnostic Management. Profitable Condition Monitoring, Kulwer Academic, London, pp.37-44. Tsang A. (1998). A Strategic Approach To Managing Maintenance Performance. Journal of Quality in Maintenance Engineering 4:2, 87-94. 234
Condition Monitoring and Diagnostic Engineering Management Andrew G. Starr and Raj B.K.N. Rao (Editors) © 2001 Published by Elsevier Science Ltd.
DESIGN, DEVELOPMENT AND ASSESSMENT OF MAINTENANCE SYSTEM FOR BUILDING INDUSTRY IN DEVELOPING COUNTRIES Funso Falade Department of Civil Engineering University of Lagos Akoka, Lagos E-mail: ffalade(a).hotmaiLcom
ABSTRACT This paper examines the problems of maintenance in developing countries. It acknowledges poor maintenance culture as a worldwide problem but notes that the situation is at an alarming rate in the developing countries. Usually building projects are packaged without provision for the future maintenance of the buildings during their service periods. Therefore, when the facilities are being used and deterioration sets in, it takes sometime to put an arrangement in place to correct the defect(s). Most maintenance operations fail qualitatively and quantitatively due to improper design, inappropriate maintenance approach, inadequate planning and inappropriate maintenance method and atimes lack of maintenance policies. The author indicates that the use of labour-based method for maintenance operations in building industry would be more appropriate in developing countries as opposed to equipment-based or labour intensive approach. Labour-based method is considered to be more economical than either equipment based or labour intensive method. An operational concept for effective design and management of maintenance work is presented. The concept provides procedures for carrying out maintenance works, evaluating resources for maintenance operations and assessing the performance of maintenance works. The performance is assessed by cost control (cost indexes and performance checks through the use of man-hours per unit of work done. Good management in maintenance work would furnish such indexes as are necessary to permit evaluation of the performance of the organisation internally and provide top management with information they need to assess the performance of the maintenance.
KEYWORDS: Building Industry, Design, Development, Maintenance System, Maintenance Methods, Key Performance Indicators.
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INTRODUCTION British Standard Institution (1) defines maintenance as the work necessary to keep or restore a facility that is, every part of a site, building and content, to an acceptable standard including minor improvements. With insufficient or lack of maintenance, building deteriorates. Building maintenance possesses little glamour, it does not attract very much attention and is frequently regarded as unproductive, although many of the managing and technical problems are more demanding of ingenuity and skill than those of new works. Lack of maintenance affects us all, since we depend on the state of our homes, offices, factories etc not only for comfort, but more importantly for our economic survival. There is no doubt that dilapidated and unhealthy buildings in a decaying environment depress the quality of life and contribute in some measure to antisocial behaviour. In any building project, the beginning of maintenance takes place the day the contractor leaves site. The usual maintenance free period included in the conditions of contract does not imply lack of the need to maintain the building but that the contractor shall be responsible for the maintenance during the stipulated period of 3-6 months. An unanticipated maintenance jnay even start before the completion of the project due to poor construction practice and use of low quality materials as observed in the case study presented in this paper. It has been reported by Seeley (2) that 40% of the building labour force in Britain were engaged in building maintenance. In most developing countries where there is lack of data on this subject, it can be assumed that less than 5% of the construction labour force is involved in maintenance. Beheshti and Jonker (3) reported that social housing sector in the Netherlands is facing an increasing difficulty in maintaining housing stock to an acceptable level considering the increasing costs of maintenance. Imbert and Ali (4) indicated that the level of maintenance of many public buildings in Trinidad is unsatisfactory. Akagu (5) showed that building maintenance is one of the most neglected areas of the economy in Nigeria. He further noted that there is no existing national policy on building maintenance. The author believes that the situation is not different in other developing countries. There are basically two types of maintenance approaches: Reactive and Proactive. It is reactive when maintenance is carried out in response to unplanned repair, usually as a result of imminent failure. Proactive maintenance may be either preventive or predictive. Preventive maintenance is usually carried out periodically during which a well-defined set of tasks, such as inspection and repair are performed. Preventive maintenance is important in the reduction of maintenance cost and improvement of buildings. Predictive maintenance, estimates through diagnostic tools and measurements, when a part of a structure is near failure and should be repaired thereby eliminating more costly unscheduled maintenance operations. Planned continuous inspection of buildings particularly public ones, will enable the authority to initiate corrective actions that will bring these buildings up to an acceptable level of maintenance. The objectives of this study are to: (i) examine the problems of maintenance (ii) evaluate the existing maintenance methods (iii) develop an operational concept for the design and management of maintenance work using appropriate technology.
PROBLEMS OF MAINTENANCE Design Error Usually, at the design stage, the scope of a building project is defined by what a client can afford except for buildings of commercial nature where funds can be sourced from financial institutions and a payback period is agreed upon based on good feasibility report. At this stage, no consideration is given to the future maintenance of the building. This, more often than not, is responsible for the design and
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construction of massive buildings without putting in place any maintenance system that enables the building retain its initial outlook. This may be due to lack of adequate advice to the building owner from the consultants or inability of the client to accept the advice of the consultants on the need to cut down the scope of work to pave way for good maintainability during the service life of the building. Inadequate Design Information Sometimes, building owners do not give to the Structural Engineer the actual purpose for which they want to build. After the building has been designed and approved by the Town Planning Authority, the building owners commence construction of what they want with modifications on the already designed work on the site without reference to the consultants again. Such situation may give rise to over stressing of the structural members causing early deterioration. Also, in most cases, private building owners believe that it is wasteful to carry out soil test, therefore, designs of the substructural part of a building is based on an assumed bearing capacity of the soil, such design is supposed to be adjusted to the site condition when construction work starts, but such opportunity may not come if the building owner has dispensed with the service of the consultant at the design stage which often does happen. Ignorance Building maintenance is not attractive, even among the affluents. They cannot see the rational behind repairing or face lifting the existing structure while such funds could be invested elsewhere to bring in more money. This is responsible for the unsatisfactory state of privately owned and public estates. Unhealthy environment affects human heahh negatively and it adversely affects the productivity of labour in a working environment. Some building owners ignore the participation of consultants in their projects from inception on the grounds that their services are expensive and not required. Some of those who engage professionals do ignore their advice on the need to procure quality materials for their projects, simply because such materials are more expensive than the low quality ones. Before the completion of such buildings deterioration features of varying magnitudes set in resulting in early need for maintenance work. Lack of Political Will In Government quarters, there is complete absence of continuity in Government programmes. Succeeding one abandons the projects embarked upon by preceding governments. Political consideration takes preeminence over what are technical. Consideration is not given to our level of technology, cultural background and environment. Attitude Lack of maintenance culture is seen in our daily life, this is extended to such facilities as private residential and public buildings. The attitude of the populace to tenants occupier and public buildings is negative. There is a general non-chalant attitude towards their upkeep. Funding Generally, inadequate budgeting is made for maintenance works. Where adequate provision is made, the maintenance budget that eventually gets approved and the funds that get released are small fractions of the initial budget made. This scenario is always attributed to the fact that there are other competing areas of the economy like Health and Education. Lack of Adequate Policy on Maintenance of Infrastructure In most countries there is no government policy that defines the proportion of its fund that must be assigned to maintenance work. Each level of government defmes its areas of priority and more often than not maintenance work is not as attractive as initiating new projects.
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Research and Development Little or no research work is carried out on maintenance of buildings. There are some deterioration features, which are expected to be diagnosed, causes identified and curative measures proffered through appropriate investigation but studies are not done because of lack of fund. Effective research on maintenance work in research institutions will be good for practical use in the field. Some of such research works are supposed to be funded by the industries (the end-users of the results) but there is lack of effective University-industry partnership (Falade & Ibidapo-Obe, 6) Maintenance Management Where fund is made available for maintenance work, the observed continued deterioration of buildings is attributable to poor management of maintenance resources (human and non-human). Inappropriate analysis and management of these resources resuh in failure of maintenance operations qualitatively and quantitatively. Maintenance Methods The existing maintenance methods adopt equipment-based and labour intensive technologies. Equipment-Based Method Reliance on equipment-based method evolved for a number of reasons: (i) The desire of the developing countries to emulate the more advanced ones. (ii) The tendency among intemational consultants and contractors to favour construction methods with which they are familiar. (iii) The basic tied-aid of the foreign consultants/contractors to help exports from their own countries. A particularly important factor is the educational background of the technical leadership in most developing countries. Equipment-based methods are perceived to have production, costs and performance that are Predictable, they are associated with high quality results and they are surrounded by an aura of technology progress. However, the equipment-based method has its shortcomings: (i) Equipment-based operations entail heavy expenditure of foreign exchange such costs might be an unavoidable burden for urgently needed high technology projects considering the dwindling exchange rates in most developing countries. (ii) The method employs few skilled labours with limited number of unskilled labour. The economic situations in developing countries calls for more realistic and sustainable methods of construction/maintenance, which must utilize all the available local resources particularly labour. Labour Intensive Method In most building projects, both construction and maintenance are labour intensive. Labourers are used for operations as much as possible without consideration for their cost effectiveness. Emphasis is not laid on adequate resources analysis. The appropriate method for developing countries for maintenance operations in buildings is labourbased method. The method is both efficient and less costly. It is less sophisticated and can easily be transferred to the rural and unskilled labour. Labour-Based Method The labour-based method describes an approach where the bulk of the activities are undertaken by properly trained, organized and supervised work force with equipment introduced in a step-wise manner from the very light to heavy if this is necessary to obtain good quality output or maximize costeffectiveness objectives. Often, mention is made of labour-based/light equipment-supported, and it is
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also possible to have labour-based/animal-supported where animal power is employed usually for haulage operations. But in the context of this paper, labour-based is restricted to labour-based/light equipment supported. Construction equipment is considered to be light when it has an output, which is compatible to the output of workers. It should be easy to operate, repair and maintain, and it should have relatively low initial cost. The method is also used when the key constraints to growth include limited import capacity and high rates of unemployment and under-employment, which generate a substantial labour surplus. Labour surplus is the component of the overall labour supply of a particular area whose contribution to total output is negligible and can therefore be withdrawn without a reduction in output. In labour-based method, labour costs account for a large proportion of the total costs of the infrastructural works since it is a deliberate effort to give priority to local labour inputs supported when necessary by light equipment. Labour-based method can be considered an appropriate technology for the maintenance of infrastructure given the situation "as exists" in developing countries. Labour-based technologies are postulated to make minimal use of unskilled labour and minimum use of capital equipment to build/maintain works at a speed, quality and cost comparable with those of any other method. The methods are efficient for maintenance of engineering facilities when the defects are of a manageable magnitude. Labour-based method is extensively used for road maintenance and rural road development (Watermeyer, 7 & 8; Clifton, 9; Toumee, 10; Yanney, 11;) If appropriately managed, the method will reduce maintenance cost and reduce unemployment, which is prevalent in developing countries.
INTEGRATION OF MAINTENANCE WITH PROJECT PLANNING Figure 1 shows the integration of maintenance with overall project conceptualization through design and construction.
Conceptualization of a project Feasibility study I Design for construction 1r — Construction
Design for maintenance
Quality control measures
B
i Human Poor
o
Non-human
Good
n
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c
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L Fig 1: Integration of Maintenance with Key Stages of a Project and Factors Influencing Maintenance Time
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In Fig. 1 the activities at design and construction stages of a project determine the level of durability of the structure and therefore its resistance to the applied load and the environment. The human measure includes supervision of all tasks of a project while the non-human comprises materials and environment. Poor quality control measures will necessitate early deterioration of building and unexpected high maintenance cost particularly if the defects are associated with the use of poor quality materials and poor construction practice. But when good quality materials are used with adequate supervision and construction method, minor maintenance operations would be required when the building is being used and such minor work will be required after a long period of use assuming that adequate precautions have been taken against the environmental conditions.
CONCEPT FOR THE DESIGN AND MANAGEMENT OF MAINTENANCE WORK For effective maintenance operations, a system is required that will identify the sequence of operations that are necessary for maintenance work. The use of labour-based calls for consistent appraisal of economy of labour to determine when its use seizes to be cost effective and therefore the need to introduce light-equipment in a step-wise manner for effective management of maintenance operations. Fig. 2 shows an operational concept for maintenance work using labour-based method. The concept entails a structured maintenance system with a maintenance manager as the head of the department. Down the maintenance team are the inspection team, supervisory team and materials procurement unit. The inspection team is responsible for the diagnosis of the type of deterioration in a building and proffer appropriate technical solution. After the inspection, a decision is taken as to whether to demolish the building and replace it with a new one or to improve or adopt it to make it more suitable for either present use or new use. The volume of work is determined while the required resources are analysed. The resources can be categorised into human and non-human; the human can be grouped into Internal (within the maintenance organisation) and External (outside the organisation). The external is majorly at operative level. The labour force required is estimated based on the volume of work and the appropriate productivity of labour for each task and the number of hours worked per day. The non-human resource comprises materials, light equipment and money. The volume of work determines the quantity of materials while the required number of equipment is determined based on the real output of the item of equipment selected for the work. For effective combination of resources, requirement schedules (labour, equipment, materials, suppliers, sub-contractors and information) are prepared to guide the movement of resources to and from site to avoid congestion on site. Also cash flow analysis is prepared to ensure steady flow of cash to ensure continuity in site operations.
PERFORMANCE INDICATORS Performance indicators are essential tools for the assessment of the effectiveness of maintenance work. A good management in maintenance department should provide such indicators that permit evaluation of the performance of the department and furnish top management with information that it needs to assess maintenance performance. Some of the indicators that are commonly used are: Time Usually when the volume of the work is determined, duration is allocated to each task based on the method of executing the task. The total of all the separate durations for all the tasks give the contract duration. The performance of the maintenance can be measured via the ratio of time allocated to the project and the actual time spent in carrying out the entire work. When the ratio is less than 1 it indicates good
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management of resources but when higher than 1 it indicates that the performance is bad. When the ratio is unity it shows that the performance is as per schedule. Maintenance Manager Maintenance Team Supervisory Team
Inspection Team Diagnosis of Observations
Technical Solution Determination of Volume of Work
Labour Requirement
1
Materials Procurement Unit
Cash Flow Analysis
Equipment Requirement
Requirement Schedules
Materials
Selection of Maintenance Method
Suppliers
Information
Analysis of L | Resources Human
Internal
External
Non-Human
5
Materials
Light Equipment
Money
Fig 2: Operational Concept for Effective Design and Management of Maintenance Work Cost The performance of the maintenance can be evaluated based on the cost over-run expressed as the ratio of the estimated cost to the actual maintenance cost. When the ratio is less than 1 it shows inappropriate analysis of maintenance resources or their inadequate management towards the realization of maintenance objectives. When the ratio is higher than 1 it shows good management of resources but if the ratio is unity it indicates that the estimated cost equals the amount expended. Quality This ensures that the specifications are adequately adhered to during the implementation of maintenance operations. This calls for effective total quality management of the maintenance operations. It indicates good supervision and workmanship.
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Income If buildings are given a face-lift, they command higher rental values. The increase in value depends on the location of the building. If the building is used for sales of goods it generates better income because higher prices are charged on goods and services. Some of the benefits of maintenance operations are intangible and not appaient but exist within the work culture. For example, in residential buildings maintenance results in good health while in offices it results in higher productivity of labour and good health.
CONCLUSIONS From the foregoing, the following conclusions are made: (i) Lack of maintenance is due to some factors such as design error, funding etc. (ii) Labour-based method is more appropriate to developing countries because of availability of labour. (iii) Integration of maintenance into project from conceptualization stage will improve maintenance culture. (iv) Effective quality control of project resources at construction stage results in minor maintenance at later stage during the service life of the building whereas poor quality control and poor construction practice result in early major maintenance work. (v) A good maintenance system ensures result-oriented maintenance operations (vi) The performance of a maintenance system can be assessed by some performance indicators.
REFERENCES 1. 2. 3. 4.
5.
6.
7.
8. 9. 10.
11.
British Standard BS 3811 (1974), 'Glossary of General Terms Used in Maintenance Organisation', British Standard Institution, London I.V. Seeley (1976), 'Building Maintenance' Macmillian Press Limited, London R. Beheshti and J.Jonker (1991), 'Maintenance of Social Housing Estates: A Participatory View' Procs, lAHS World Congress on Housing, Ales-France, Sept. 23-27, 1991, pp 618-624 C.I. Imbert and Z. Ali (1990), 'Maintenance Management in the Public Sector: Some Reasons for the Current Unsatisfactory Physical Condition of Many Public Buildings in Trinidad', West Indian Journal of Engineering, Vol. 15, No. 1, pp 37-48 C. Akagu (1996), 'Towards a National Maintenance Management Policy for Public and Institutional Buildings', Journal of the Association of Architectural Educators in Nigeria, Vol. l,No. 3,pp59-61 F. Falade and O. Ibidapo-Obe (1998), 'University-Industry Partnership: The State of Development in Nigeria', Procs of Global Congress on Engineering Education, Cracow, Poland 6-11 September, 1998, pp 515-518. R.B. Watermeyer (1995), 'Evaluating the Benefits of Implementing Labour-based Construction in an Urban Community', Paper Presented at Regional Seminar on Labour-based Roadworks, University of Witwaterstrand, Johannesburg, South Africa, 16-20 January, 1995. R.B. Watermeyer (1995), 'Labour-based Construction and Development of Emerging Contractors in South Africa', 7Ibid. J. Clifton (1995), 'Counterpart and Technology Transfer', 7Ibid. J. Toumee and J. Omwanza (1995), 'Alternative Strategies for the Provision of Infrastructure in Urban Unplanned Settlement Areas: Are these Strategies Effective and How Can They be Supported and Developed? 7Ibid. B.K. Yanney (1995), 'Management of Some Selected Labour-based Highway Construction Activities', 7Ibid.
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Condition Monitoring and Diagnostic Engineering Management Andrew G. Starr and Raj B.K.N. Rao (Editors) © 2001 Elsevier Science Ltd. All rights reserved.
USING MODELING TO PREDICT VIBRATION FROM A SHAFT CRACK J. Howard Maxwell ^ and Darryl A. Rosario ^ ^ Palo Verde Nuclear Generating Station, Arizona Public Service, Tonopah, AZ, 85354-7529 ^ Structural Integrity Associates, 3315 Almaden Expressway, Suite 24 San Jose, CA 95118-1557
ABSTRACT In January of 2001, vibration indications on one of the station's reactor coolant pumps began to show evidence of a possible cracked shaft. In 1996 the station had suffered a guillotine type shaft crack just above the impeller, and the data from this event was used to forecast the probable future trend of the vibration. After a few days of monitoring, it became apparent that the trend of the vibration was considerably slower than the original event. The current pump shaft had been rolled to induce compressive stresses in the surface of the shaft and it was suggested that this was slowing the crack growth rate. This hypothesis was tested by developing a model to predict the vibration caused by a shaft crack, that combined a crack propagation model, and a rotor dynamics model, calibrated by the actual vibration data from the first event. An inspection of the pump rotor during replacement revealed a completely different kind of crack, not previously seen on this type of pump. This paper describes the vibration data, the modelling performed that indicated that the surface rolling would slow crack growth, and a description of the actual crack found.
KEYWORDS Shaft Vibration, Crack Propagation, Compressive Stresses, Rotor Dynamics
HISTORY OF RCP SHAFT CRACKING AT PALO VERDE In 1987, Palo Verde Nuclear Generating Station (PVNGS) became aware that several European reactor coolant pump (RCP) shafts, similar to the PVNGS shafts, had exhibhed shaft cracking. As a result, PVNGS implemented an inspection program, which revealed cracks of varying depths and lengths in their RCP shafts. None of the cracks had progressed to a point where vibration would have indicated a problem. A comprehensive root cause assessment was implemented, as well as an augmented vibration monitoring program, described in Maxwell, J.H., 1988.
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After several years all the shafts were changed out to redesigned or modified versions. During this period no failures occurred and no crack became large enough to cause a vibration indication. In March of 1996, vibration indications on one of the pumps in Unit 1 begin to show evidence of a probable cracked shaft. After 18 days of monitoring the vibration and preparing for a rotor replacement, the Unit was shutdown and the pump rotor replaced. Subsequent inspections showed a guillotine crack approximately 60% of the shaft diameter, at the upper part of the impeller fit area, which was the same location as previously seen on this type of pump. The vibration indications as described in Maxwell (1997), were those of a classic, almost textbook, shaft crack, including amplitude and phase shifting of both the 1 times running speed and 2 times rurming speed vibration components.
SHAFT CRACK INDICATIONS IN 2001 On January 4,2001 during a routine vibration check, the vibration on the Unit 3 IB RCP showed changes that indicated a possible cracked shaft. An intensive monitoring and analysis program was implemented, and after about 4 days, we decided that we probably had a shaft crack. The usual indicators for a cracked shaft are changing amplitudes and phases of both the synchronous vibration and the twice synchronous vibration, and these symptoms were seen on both vibration sensors. There were, however, several features in the data that are not normally symptoms of a crack, and so the initial diagnosis was more difficult than in the case of the Unit 1 cracked shaft event in 1996. Because of these non-crack-like vibration symptoms we considered that there was some possibility that the diagnosis of a crack was incorrect, however we believed that the probability of a crack was high. Crack Symptoms Observed on both the Unit 1 RCP (1996) and the Unit 3 RCP (Dec. 2000 - Feb. 2001) • IxRPM vibration increasing in an exponential fashion. Later in the Unit 3 event the upward trend was more linear, and then the upward trend stopped, or became very slow. • IxRPM vibration phase shifting • Vibration showing on both sensors • 2xRPM vibration amplitude changing and the phase is shifting Non-Crack-Like Symptoms Observed on Unit 3 RCP (Dec. 2000 - Feb. 2001) • A step change (down) in vibration on 11/12/00 about 6 weeks before the upward trend started on about 12/27/00. • An approximately 90 degree change in the vector trend on about 12/27/00 • The slope of the IxRPM vibration amplitude became less exponential Immediately following these initial symptoms of a probable cracked shaft, a number of analyses >and consultations were initiated. After about a week, it became clear that the vibration was trending much slower than was seen in the 1996 Unit 1 event. One theory proposed was that the cause of the slower trend was due to the compressive stresses in these new rolled shafts. Surface compressive stresses due to rolling would probably lower the stress intensity factors at/near the shaft OD, thereby reducing crack propagation rates at these near-surface locations, resulting in a propagating crackfi-ontthat was more elliptical in shape compared to the circular crack fronts seen in the Unit 1 event. To investigate whether these surface stresses could slow crack propagation rates and consequently result in a slower trend of the vibration, PVNGS requested Structural Integrity Associates (SI), to develop a fracture-mechanics-based model for the Unit I cracking, and then modify the model to account for the surface compressive stresses in the newer rolled shafts. PVNGS would then take the data from this crack propagation model and modify the existing rotor dynamics model to investigate the effect of various crack propagation front profiles and rates on the resonant frequencies of the rotor, and on the vibration. Using the actual Unit 1 crack and vibration data, 244
the model would be calibrated to the actual data. This model would then be used to forecast the vibration on the Unit 3 pump to determine when the machine should be shutdown. Before this model could be completed, Station Management decided to take the Unit out of service and replace the pump rotor. On February 17, 2001, 44 days after the vibration indications were detected, the pump was shut down, and on February 26, 2001, the Unit was started up with a new rotor. The new rotor did not show any of the symptoms seen on the original rotor. After the Unit was restarted, a carefully planned disassembly and root cause investigation was begun to determine the cause of the vibration. The results of this investigation will be discussed later. After the pump rotor replacement, the analysis and model development continued, although not in the same "crisis" environment.
CRACK MODEL Reduction in Shaft Stiffness with Cracking A three-dimensional finite element (FE) model of the impeller shaft with circular and elliptical cracks of various sizes subjected to bending loads, was developed and was analysed to determine the change in shaft bending stiffness vsdth cracking. A plot of the FE model with a summary of end-deflection results is shown in Figure 1. 13?^
Total Deflection vs. Crack Size for Pressure Load 0.00040 n1 1 r • Circular 0.00035 |> B -EllpDcalj
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Figure 1: 3D FE Model and End Deflection vs. Crack Size Results The circular crack fronts were based on the 1996 Unit 1 failed shaft fractography results and the elliptic crack fronts for the replacement rolled shafts were based on crack propagation simulations, which are summarized in the section that follows. The end-deflection results shown in Figure 1 were used to calculate the reduction in bending stiffness with cracking. These results were further processed to yield equivalent "reduced" shaft diameters vs. crack size which were then fitted to a polynomial for input to the rotor dynamics model. Crack Propagation A fracture mechanics model was developed to predict the growth of cracks in the shaft. The model was considerably more sophisticated than the model previously used by the pump manufacturer to evaluate cracking in the previous shafts, which did not include near-surface stress gradients, mean stress effects and the change in crack aspect ratio (depth: length) with propagation.
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Crack tip stress intensity factor solutions from the open literature, Forman (1986) and Raju (1986), were corrected using an influence-function approach, Tada (1973) to account for near-surface stress gradients due to rolling and changing crack aspect (depth:length) ratios. Crack tip stress intensity factors due to pressure loading on the crack surface and the effects of axial thrust loads on the shaft were also included. Fatigue crack growth data for the shaft material (SA-182 F6NM German grade martensitic stainless steel) was obtained from crack growth testing conducted by the pump manufacturer and compared with data in the open literature for similar grade steels, Barsom (1987). Except for the once-per-revolution lateral thrust load which is primary crack driving cyclic loading, all the other constant loadings on the shaft collectively determine the mean stress intensity factors at the crack front. In the crack growth model this mean stress effect was incorporated using an "R-ratio" term which is the ratio of the minimum to the maximum computed crack tip stress intensity factors or Km.n/Kmax. For negative values of R (down to approximately R = -2 with K^in positive) crack growth is retarded; Barsom (1987). Theoretically, there should be negligible crack growth if K^ax is negative. However, since the stress intensity factor of a crack in a shaft will vary along the crack front (from deepest point to surface point) there may be some growth at the surface (where highly compressive stresses and negative stress intensity factors exist). This effect was simulated in the fracture mechanics model by basing the analysis on an assumed minimum predicted value of the R-ratio. For the original shafts, which did not have compressive stresses due to rolling, the crack growth model produced results similar to those of pump manufacturer and the time for growth from 20 percent to 60 percent of shaft diameter was comparable to that observed in the 1996 shaft cracking event. When applied to the surface-rolled shaft, the time to grow the crack from 20 percent to 60 percent was increased by about a factor of 3. Figure 2 shows analytically predicted crack growth versus time histories for the original and rolled shafts. 1 100
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Figure 2: Typical Crack Growth History for (a) Original Shaft and (b) Rolled Shaft. The above fracture mechanics calculations, which simulate crack growth from a small initial size of 3 mm to failure, cover a fairly significant fraction of shaft life. From a vibration monitoring and alarm standpoint, however, only a relatively short period of time just preceding failure is of interest. A curve fit of the fracture-mechanics-based crack growth history, in the 400 days before a 60% crack was performed for input to the rotor dynamics model simulations. The form of the fitted curve, shown in Figure 3, which yields an almost-perfect fit, is of some interest and is given in Eqn 1. The majority of the fit is done in the natural log portion of the equation. Several other curve types did a better job of fitting the data alone, including a third order polynomial fit, but all these others had difficulty in matching the curve in the high slope region preceding failure. The polynomial in the equation is used to smooth out about 7.5% of the variations in the early and middle portions of the curve.
246
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A similar analysis was performed on the rolled shaft fracture mechanics model. A comparison of the fitted crack growth curves for the original and rolled shafts is shown in Figure 3. Note that Figure 3 is a linearlinear graph rather than the typical log-log graph to emphasize the near failure portion of the curve. The benefit of compressive stresses due to rolling results in a slower rate of crack propagation in the rolled shaft compared with the original shaft. Starting with a 10% crack, the original shaft would reach 60% in 110 days, while the rolled shaft requires 220 days.
80 ^ 70 ° 60 £50 » 40 t 30 « 20 " 10 n1 U 150
250
350
'
450
550
Days Original Shaft
-Rolled Shaft
Figure 3: Curve Fits of Crack Growth Histories for the Original and Rolled Shafts.
ROTOR DYNAMICS MODEL We had an existing rotor dynamics model following the method of Gunter (1993). The reduction in shaft stiffness due to cracking was then applied to the rotor dynamics model, and the resulting amplitude response is shown in Figure 4a, which shows that, as expected, a reduced shaft stiffness due to a crack lowers the resonant frequency. The X and Y directions shown in this plot are in line with the discharge pipe of the pump and at 90 degrees. The response at the angle of the X and Y proximity vibration sensors was then calculated. The pump has a diffuser with a single discharge line which causes a hydraulic force acting on the impeller. This hydraulic force loads the water lubricated bearing, which is very sensitive to this load and to any internal misalignment. The bearing stiffness was calculated for several loads. High loads can result in a first critical speed as high as 2200 RPM or more. A lower bearing load of 2000 Ibf was used in this analysis. Figure 4a also shows the effect of the bearing non-symetrical stiffness in the uncracked Y-Amplitude curve which dips down near 1500 RPM. Figure 4a is a typical output from a rotor dynamics analysis, but for the purposes of this analysis, we are only interested in the pump running speed of 1185 RPM. 247
The rotor dynamics model was run for a number of shaft stiffnesses for cracks up to 60% by changing the shaft diameter and computing the response at 1185 RPM. This data is shown in Figure 4b. Note that the vibration in the Y direction decreases near a crack size of 50%.
0
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60% Y-Amplitude
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60% Crack Rotated X-Amplitude
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Figure 4: The Unbalance Response as a ftinction of (a) RPM, and (b) crack size.
COMBINED MODEL The response curve shown in Figure 4b was then combined with the fracture-mechanics-based crack propagation versus time calculations to yield a model for vibration amplitude as a function of time, which is shown in Figure 5a. Note that at a crack size of about 20% the change in vibration is very small, but it is detectable.
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400
DAYS
DAYS
I
2
-CrackDepth|
- Unit 1 Actual
Figure 5. Predicted vibration amplitude with (a) Crack Depth, and (b) 1996 actual.
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420
The amplitude of vibration as shown in Figure 5a is proportional to the amount of unbalance, which was arbitrarily selected for the model. The time scale is proportional to the actual crack propagation in the shaft, which must also be adjusted for each situation. The combined model shown in Figure 5a was then calibrated with the actual vibration data from the Unit 1 1996 event. The result, shown in Figure 5b, is an almost perfect fit. The adjustments in the model required to match the actual vibration were: • The time scale was increased by 2.5; i.e. the crack grew 2.5 times slower than the initial un-calibrated fracture mechanics model prediction. This is well within the normal range. • The unbalance was adjusted to 12.5 times the arbitrary value used in the rotor dynamics model. • The predicted vibration was adjusted by 1 mil to account for non-unbalance related vibration.
ROLLED SHAFT CRACK PREDICTION
4 3.5 3 • a 2.5
^1.5
•••'"rT
0.5 300
350
400
450
DAYS - Unit 3 Actual
Figure 6: Predicted vibration of rolled shaft and (a) original shaft, and (b) actual Unit 3 vibration. The calibrated model was then used to predict the vibration trend for a crack in a rolled shaft. This vibration trend is shown in Figure 6a compared to the original shaft prediction. The original goal of this analysis was to compare the actual vibration to the predicted vibration to verify that the vibration was due to a shaft crack similar to the 1996 event, and to select an optimum time to remove the Unit from electrical production to replace the pump rotor. However, as discussed above, the machine was shutdown before the analysis was completed, and so the analysis became academic. Figure 6b shows that the vibration prediction for the rolled shaft does not match the actual Unit 3 vibration trend, which indicates that the cause of the vibration is quite likely due to cracking at a location different from that observed in the 1996 Unit 1 event.
AS FOUND ROTOR CONDITION Upon disassembly we found that the journal bearing shaft protection sleeve had developed an axial crack. At the water lubricated bearing, just above the impeller, the shaft itself does not serve as the journal, rather a sleeve is installed on the shaft. A simplification of the sleeve is shown in Figure 7. The sleeve is shrunk on the shaft at the top, and has a close fit at the bottom. This kind of failure had never occurred at PVNGS or at any other plants of which we were aware, and so was not considered in our analysis. The crack location and approximate size is indicated in Figure 7.
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Figure 7. Shaft Protection Sleeve with crack. CONCLUSIONS This analysis has demonstrated to us that a crack propagation model can be combined with a rotor dynamics model to produce useftil results. This combined model can be successfully calibrated with measured vibration data and used both as a vibration diagnostic and prognostic tool. Because the most recent pump shaft cracking did not reoccur at the same location as past failures, we were unable to calibrate analytically predicted reductions in crack propagation rates due to induced surface compressive stresses with measured vibration data. Despite the many complexities associated with determination of crack tip stress intensity factors due to crack shape, stress profiles/loading, and the dependence of crack propagation rate on these variables, the un-calibrated crack propagation model yielded results which were conservative by about a factor of 2 when compared with actual/measured vibration trends. REFERENCES Barsom, J. M., and Rolfe, S. T. (1987) Fracture and Fatigue Control in Structures, 2nd Edition, Prentice-Hall, Inc., Englewood Cliffs, NJ Forman, R. G., and Shivakumar, V. (1986). Growth Behaviour of Surface Cracks in the Circumferential Plane of Solid and Hollow Cylinders. ASTM STP 905, pp59-74. Gunter, E.J., O'Brien, J.T., and O'Brien, J.T., Jr.. (1993) Experimental and Analytical Investigation of a Main Coolant Pump. Sound and Vibration, pp 12-24. Maxwell, J.H. (1988). Detecting Cracked Reactor Coolant Pump Shafts with Vibration Monitoring. ASME 88-PVP-ll Maxwell, J.H. (1997). Palo Verde Nuclear Generating Station Cracked Reactor Coolant Pump Shaft Event. Proceedings of the 21^^ Annual Meeting of the Vibration Institute Raju, I. S., and Newman, J. C. (1986). Stress-Intensity Factors for Circumferential Surface Cracks in Pipes and Rods under Tension and Bending Loads. ASTM STP 905, pp789-805. Tada, H., Paris, P. C, and Irwin, G. R.. (1973). The Stress Analysis of Cracks Handbook. Del Research Corporation
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Condition Monitoring and Diagnostic Engineering Management Andrew G. Starr and Raj B.K.N. Rao (Editors) © 2001 Elsevier Science Ltd. All rights reserved.
AN INVESTIGATION OF ABNORMAL HIGH PITCH NOISE IN THE TRAIN 2 COMPRESSOR MOTOR A G A Rahman\ S M al-Attas^ and R Ramli^ Department of Mechanical Engineering, University of Malaya, Jalan Lembah Pantai, 50603 Kuala Lumpur, Malaysia/'^' E-mail: agar(a).fk.um.edu.mv\ mahathir(S)ik.um.edu.mv^. rahizar(a).fk.um.edu.mv
ABSTRACT Continuous high pitch noise of the compressor motor of Train 2, at a petrochemical plant was investigated. Techniques implemented for the investigation includes Sound Intensity Analysis, Envelope Analysis and machine maintenance history and trending. The objective of the investigation was to determine the source of noise and advise the operation personnel whether the compressor would be able to continue operation until the planned maintenance shutdown in 6 months time or to stop immediately for repair. Any unexpected break down prior to the planned Turn-Around period will cost the plant a considerable amount of losses. It was concluded that, high intensity spots occurred at two diagonal ends of the rotor-stator interface. The highest intensity of the two spots is recorded on plane B. presence of a dominant 2x line frequency components of lOOHz combined with the 2490Hz component, suggested that the problem associated with stator fault rather than rotor related defect i.e. shorted stator turns (current variations). There was no apparent sign of rub between the rotor and stator and no eccentric gap. It implied that the vibration most likely to be due to magneto-motive force (MMF), electrical related defect. Spectral trending of the vibration level at 2490Hz component for the last 5 months showed gradual increase on its amplitude. It was recommended that the plant should continue the operation of the compressor and closely monitors the trending pattern for 2490Hz, lOOHz, overall vibration level and noise spectrum level at 2490Hz using sound level meter at the high sound intensity spot. If rapid change in the gradient of the trend curve for both vibration and noise is observed, the motor should be stopped.
KEYWORDS: Envelope Analysis, Sound Intensity Analysis and Compressor Motor
INTRODUCTION Two units of 3-phase induction motor with output power of 3 760kW were used in the petrochemical plant for chemical processing namely Train 1 and Train 2. For the last 5 months, Train 2 of the compressor motor experiencing continuous high pitch noise suspected to be due to rotor-stator windings fauh. The rotor is a squirrel cage type with 50 rotor bars placed in box-type casing (Figure I).
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Visual inspection on the gap in between the winding showed that the gap is still within the allowable tolerance. Vibration trending showed that there is a gradual increase in the overall vibration level. Any unexpected breakdown will result in huge amount of downtime loss. Advice from the mechanical maintenance section to the plant manager is to continue operation. However, the electrical maintenance section and the compressor motor manufacturer's advice was to stop fearing catastrophic damage would occur if it continue running. The findings will be very crucial before any decision were to be made whether the compressor will be able to continue operation until the planned Turn-Around or it has to be immediately shut-down for rectification. Therefore, the management had decided to call for third party opinion on the matter. The objective of the investigation is to identify the source of the high pitch noise, hence determining the root cause of the problem. Upon determining the root cause of the problem and the conclusion of the investigation will be the basis of any decision to be made by the top management on the compressor motor, whether to be shutdown or continue operation.
Figure 1: Sketch showing the layout of the Compressor Motor, rotor-stator position and drive-end bearing. INSTRUMENTATION Sound Intensity Measurement v^ HP3569A Real Time Frequency Analyser ^ ACO Pacific Sound Intensity Probe (face-to-face configuration) with phase-match microphones and preamplifiers. y RION Sound Level Calibrator NC-73 (IkHz / 94dB). Vibration Signal Analysis (Envelope Analysis) ^ ^ ^ ^
SPS390 Dual Channel FFT Analyser WR Signal Conditioner Model P702B Vibromax Bearing Analyser WR ICP Accelerometers Model 786C
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METHODOLOGY The entire project was done on-site at the petrochemical plant while the compressor motor is in its normal operation mode. Performing investigation in the neighbourhood of high voltage power lines requires special considerations for reeisons of safety. Some of the safety procedures required by the plant Operation Safety and Hazard Department forced us to compromise on the usual sound intensity measurement practice. The practice of moving the sound probe in a region that is very close to high voltage lines is, needless to say, an unsafe procedure. Example given by [1], high voltage terminals extending or protruding out would prevent from measuring the sound energy flowing through this region. Failure to close the path of integration over the region however is not serious. Assuming that the sound power flowing out through the region would continue flowing out into free space. This contribution of the sound power most likely does not re-enter the integration surface from reflection. Since the whole objective is to locate the noise source and not to quantify the sound power, the approach can be applied in this investigation. Analysis implemented for the investigation includes the following: Noise source surveys Two methods of noise surveys were performed to identify i. Dominant frequency component ii. The region having high positive or negative intensity and if exist any localized noise flow [2]. The first method was done by sweeping the probe in the region of the source system and by displaying the results in Narrowband FFT spectra (Pa). The latter method conducted also by sweeping through the entire surface area of the source system at a distance of about 100 mm with the axis of the probe (sideby-side configuration) directed parallel to the source of the surface. The results are displayed in 1/3 octave, real-time intensity mode where regions of high positive intensity, together with any negative intensity regions are noted. Directing the probe perpendicular to the source surface identifies a localized region in the noise flow. Sudden varying of positive and negative signs indicates the localized region. Discrete-point Sound Intensity Measurement The use of this technique is to give the overall sound intensity contour around the compressor motor. It is useful, as it would provide a more detailed picture of the sound field generated by the source. Several sources and/or sinks can then be identified with better accuracy. A grid is set-up to define the surface and measurements normal to the surface are made from a number of equally spaced points on the surface. Post-processing of the results are extracted based on the results obtained from the noise surveys and contour mapping, were done at single frequency 1/3 octave band. Envelope Analysis Envelope detection is a method for intensifying the repetitive components of a dynamic signal to provide an early warning of deteriorating mechanical condition [3]. A vibration signal from a defective bearing is made up of low frequency signals from rotational components, defect impulse signals and machine noise. Often fault signals are of very short duration that translates in the frequency domain as small harmonic amplitudes spread over a wide frequency range and buried in machine noise. Machine
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noise masks the early stages of bearing faults making spectrum analysis alone a difficult diagnostic tool. Envelope analysis will first filters out the low frequency rotational components from the complex signal. The high frequency repetitive components are enhanced and converted down to the bearing spectrum range while machine noise is reduced by a significant signal-to-noise factor. If vibration amplitude appear in the envelope spectrum that is related to bearing defect frequencies it c
RESULTS AND DISCUSSIONS Noise Spectrum Analysis and Sound Intensity Analysis, Noise Spectrum analysis had determined the dominating high pitch frequency at 2490Hz (see Figure 2a and 2b). The result shown is in linear scale (Pa) as well as in dB level is to have a clearer identification of the dominating noise component. Investigation using Sound Intensity analysis had determined the noise source location of the compressor motor as shown in the high-intensity region (see Figure 3a and 3b). With the running speed of 49.8Hz and there are 50 rotor bars, therefore, it is suspected that the noise is coming from the rotor-stator inductive force during operation. The highest intensity of the two is located on plane B (facing south). Side bands of lOOHz around 2490 Hz components in the noise spectrum are also being detected. This indicated the modulation of the meshing frequency at 100 Hz implying development of electrical defects. Sound Intensity mapping for lOOHz component is shown in Figure 4a and 4b.
Envelope Analysis Envelope spectrum analysis at the drive-end bearing and drive-end body surface plane had recorded a dominant impulsive force of 100 Hz and 2490 Hz component respectively (see Figure 5a & 5b).
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SPECTRUM IN clBA
2490Hz
u
£0 .seekHz
Figure 2a: Noise FFT Spectrum in dBA (0 - 20kHz)
NOISE FEE S PECTRE'M N LINEAR SCAEIi (Pascal)
^ 1 2490 Hz
"
i
0
J
M
UL
M \a\ii
e . e e e HZ
Figure 2b: Noise FFT Spectrum in Linear Scale (0 - 20kHz)
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.bUiikHx
2490Hz with lOOHz sidebands
th
Figure 2c: Noise FFT Spectrum in dBA (0 - 5kHz) showing sidebands of lOOHz components.
Figure 3a: Normal Intensity Contour Plot of the Compressor Motor at 2500Hz 1/3 Octave Band of Plane A (facing north)
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High Intensity Region at the Rotor-Stator interface
Figure 3b: Normal Intensity Contour Plot of the Compressor Motor at 2500Hz 1/3 Octave Band of Plane B (facing south)
Figure 4a: Normal Intensity Contour Plot of the Compressor Motor at lOOHz 1/3 Octave Band of Plane A (facing north)
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High Intensity Region at the Drive-end Bearing
Figure 4b: Normal Intensity Contour Plot of the Compressor Motor at lOOHz 1/3 Octave Band of Plane B (facing south)
1 N \ l . l , ( ) 'i: si'i c I R l i M I)RI\'I. 1 NI) Bl ARINCJ
40 r m 10 OHz
0 t wLiWi*«._ c1
.-II
Hz
Figure 5a: Envelope Spectrum showing the lOOHz components
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4k
M DRIVE END ON BODY SURFACE OFPEANEB
10 m
lOOHz
2490Hz
^ ^ " ^-^
- - ^ ^ ^ - j j l j j n ^-.- •
Hz
4k
Figure 5b: Envelope Spectrum showing the lOOHz & 2490Hz components
TRENDING ANALYSIS Based on the Maintenance Department database obtained using PRISM4 (Figure 6), there is a gradual increase in the overall level taken over the last five months exceeding the alert level set by the maintenance for this compressor motor, which is at 0.1 Gs. Overall vibration determines whether a machine is vibrating more than usual. However, it does not accurately measure low frequency vibration signals in "noisy" conditions and does not indicate the cause of excessive vibration.
Figure 6: Trending analysis result of the compressor motor for the past 5 months showing gradual increase in its amplitude. 259
DISCUSSION Troubleshooting performed by Maintenance personnel indicated that there was no apparent rub between the rotor-stator interfaces and there was no eccentric gap and the dominating component of lOOHz and 2490Hz dies off as they observed the waterfall spectrum analysis during shutdown. This suggested that the vibration was due to magneto-motive force MMF implying an electrical problem(s) rather than mechanical problem. There is a gradual increase in the vibration overall level taken over the last five months (Figure 6). Running speed 49.8Hz meshing against the 50 rotor bars rotating in the compressor motor gave a dominant 2490Hz high pitch frequency noise with sidebands of lOOHz (Figure 2c). Due to the very strong 2x line frequency components of lOOHz present combined with the 2490Hz component, the problem would not lie in the rotor but rather the possible cause lies in the stator winding faults e.g. Shorted stator turns (current variations). Small eccentric air gaps due to weakness of stator support should not be ruled out as small eccentricity can cause large magneto-motive force. Sound Intensity analysis had determined the noise source location for 2490Hz component of the compressor motor. High intensity spots occurred at two diagonal ends of the rotor-stator interface. The highest intensity of the two spots is located on plane B - facing south (Figure 3b). High intensity region for the 100 Hz components is located close to the drive end journal bearing on Plane B (Figure 4b). It is highly recommended that Maintenance personnel shall continue and increase the monitoring frequency from once a month to once a week. It is also recommended to monitor the 2490 Hz noise level at the high sound intensity spots and perform trending. This should be done using sound level meter with its analogue output cormected to the FFT analyser and extract the 2490Hz components only. Positioning of the microphone must be consistent in every measurement. Maintenance personnel shall provide the trend report to the respective authority after each data collection routine. If rapid change in the gradient of the trending curve for both vibration and sound level is observed as shown in Figure 7, the compressor motor should then be stopped.
ii
Trending curve
Amplitude Stop Motor
V. Time
Figure 7: A rapid increase in trending curve indicates a rapid deterioration of the machinery.
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CONCLUSIONS In conclusion, the root cause of the high pitch noise of the compressor motor is due to an electrical fault rather than mechanical fault, which means it is a non-contact, air-borne noise problem (rapid pressure fluctuation, 2490Hz component) from magneto-motive force at the rotor-stator interface. It is recommended that the plant should continue running the compressor motor. At the same time, the Maintenance personnel shall continue to monitor the vibration amplitudes of the overall and 2490 Hz component. It is also recommended that the monitoring rate of the compressor motor be increased at the rate of once a week. Maintenance personnel should also monitor the 2490 Hz noise level at the high intensity spots at the same monitoring rate and perform trending. Additional to the existing parameters, the Maintenance personnel are recommended to monitor the 2490Hz noise components. Positioning of the microphone must be consistent throughout the monitoring period. Maintenance personnel must provide the trending report to the respective authority after each data collection and if rapid change in the gradient of the trend curve for both vibration and 2490Hz noise component is observed, the motor should be stopped. The top management had agreed to decide that the compressor motor should continue operation until the plant planned Turn-Around. With proper implementation of Condition Monitoring procedure and recommendation outlined to the Maintenance personnel, the compressor motor had continued running smoothly without any unexpected breakdown until the planned Turn-Around, which was scheduled 6 month later. The estimated total cost savings of unplanned 2 weeks production downtime loss plus maintenance cost for this compressor motor is in the region of USD 3 million. REFERENCES 1. Kendig R. P. and Wright S. E. (October 1991), "Validation of Acoustic Intensity Measurement for Power Transformer", IEEE Transaction on Power Delivery", Vol. 6, No.4. 2. Fahy F.J (1995), "Sound Intensity", E & FN SPON, London, 3. Application note CM3014-US (1-92), 1992 SKF Condition Monitoring Inc.
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Condition Monitoring and Diagnostic Engineering Management Andrew G. Starr and Raj B.K.N. Rao (Editors) © 2001 Published by Elsevier Science Ltd.
AN APPROACH TO THE DEVELOPMENT OF CONDITION MONITORING FORANEW MACHINE BYEXAMPLE B S Payne^ A D Ball\ M Husband^ B Simmers^ and F Gu^ ' Maintenance Engineering, School of Engineering, University of Manchester, Ml 4 9PL. [email protected], www.maintenance.org.uk ^ Rolls-Royce pic, PO Box 31, Derby, DE24 8BJ. [email protected], www.rolls-royce.com
ABSTRACT A high power electric motor, significantly different in design compared with any conventional machine, is currently being developed by Rolls-Royce for naval propulsion. In order to aid the development stages of this motor through machine prototypes and to work towards satisfying the end-user requirements, a heavy emphasis is being placed on implementing well thought through condition monitoring. Approaching the development of condition monitoring for a completely new machine is an extreme case as the introduction of such novel equipment in industry is relatively rare. However it is felt that a detailed examination of this approach is highly relevant to asset managers and maintenance engineers alike. There are several reasons for this relevance. Firstly, in practice, very few pieces of off-the-shelf equipment are used in a standard application (with different applications having different monitoring requirements). Secondly, many pieces of equipment are tailored and modified for a specific application, this may be done at the design level by the manufacturer but more often than not will be a change made on an ad hoc basis by the user. The effect of this change on condition monitoring can only be understood by knowledge of the theory behind the approach taken. Furthermore there are the more general issues such as the haphazard implementation of monitoring without really considering for what reasons and how often. Additionally, without a real understanding of the approach required for monitoring many have formed alliances with one supplier irrespective of whether the technology is useful or not and have relied too heavily on ambitious marketing claims. This paper assumes that for a piece of equipment in question, the best and most relevant maintenance strategy for a piece of equipment is condition-based maintenance (CBM) either in whole or in part. (If the reader needs to justify this prior condition then it is recommended that the well-established works by either Kelly [1], Moubray [2] or Tavner [3] are examined.) A systematic series of sequential steps, equal in importance, for implementing an approach to maintenance based on the condition of a piece of equipment is then outlined. Firstly this is provided in general terms and then each step is exemplified with reference to Rolls-Royce's novel motor. KEYWORDS Condition Monitoring, Maintenance Strategy, Novel Machines, Transverse Flux Motor
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INTRODUCTION Maintenance, once relegated to the least visited comers of industry, is now recognised as a key element to increase the competitiveness of almost every aspect of Britain's industry. It has emerged as a sophisticated discipline, which embraces management techniques, organisation, planning and the application of substantial electronic, engineering and analytical know-how to manufacturing processes, transport and power generation. Over the last decade there has been increased commitment from all industrial sectors (along with the defence sector) towards the subset of maintenance management that is condition monitoring. The motivation has been the achievement of increased system reliability and equipment availability, and better planning and efficiency improvements in the maintenance function. Also important is the fact companies are becoming aware of the after-market and the support function that was previously unexploited is seen as potentially very lucrative. Effective condition monitoring has been facilitated by cheaper and more robust transducers and hand-held devices, improved asset management and integrated plant monitoring software packages, and perhaps most importantly, improved training of staff in this area. Having established that condition-based maintenance (CBM) is the best strategy to employ, the specific approach to be taken for an item of equipment is for many an area of concern. Unfortunately this has led to poor implementation in many companies resuhing in misfocus of maintenance efforts, recording and storing of gigabytes of data for no objective reason and surprisingly often even without analysis. At worst, the net result has been absolutely no prior fault detection, diagnosis or discrimination before failure and therefore costly unplanned plant downtime. Also, ineffective monitoring has given rise to unreliable fault detection and diagnosis so that users either stop the plant more fi*equently than necessary to investigate/repair or simply ignore any warning provided by the system. In other cases the lack of confidence in the systems has meant that preventative maintenance strategies are performed as rigorously as before implementation of condition-based monitoring. In summary, where such an Identify objectives employing condition monitoring and the resources available. approach has been implemented without understanding or by following a logical thought procedure, in many csises the Target and assess a select number of significant capabilities and benefits of possible or known failure modes. condition monitoring have been dismissed by management and the maintenance Identify symptoms exhibited by the function itself targeted failure modes.
I I
This paper therefore attempts to make a contribution to eliminating the pitfalls outlined above by providing and describing a series of logical steps to successftil implementation. These steps are summarised in Figure 1. The flow chart has previously been seen in many guises but this paper explicitly analyses each step and what it is trying to achieve, and then makes reference to an example.
Consider issues related to transducers and measurement.
Interpret and analyse measured data.
Revise results and feedback changes where necessary.
Figure 1 - Implementation of CBM
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GENERIC APPROACH TO SUCCESSFUL CONDITION MONITORING i. Objectives and Available Resources The knowledge of user requirements from a piece of equipment is vital in the development of an associated maintenance strategy. The requirements include the expected operational conditions (such as load and speed) under which the equipment is expected to function. Specifically the maximum and minimum boundaries and rates of change should be known (dictating whether speed compensation in data analysis is required for example). It should also be determined whether overload is anticipated, as this will normally make equipment more susceptible to development of fault conditions because it is forced to operate outside of its design limits. The working environment of the equipment will also have an effect on the approach to monitoring. Environment issues include expected humidity levels, maximum and minimum temperatures, and the amount of grime and dirt. Finally the duty cycle should be considered, to find out whether there is an annual shutdown, if the equipment has an expected lifetime or if it is in operation 24 hours a day, 365 days a year (the shift patterns will of course, also be of interest). The available personnel support and expertise for maintenance purposes will also have an effect on a condition monitoring strategy. If there are many critical machines on a plant it will be expensive to instrument each with hard-wired transducers. Therefore on a cost basis alone management may prefer to direct more personnel to maintenance so that data from each machine may be regularly collected using hand-held devices. Other issues to be considered include the degree of training required to use a particular hand-held device, how to ensure a measurement tour is completed and that data is downloaded to a host machine where required. The use of hand-held data collectors has also proved useful in ensuring a duty-ofcare. For example the authors have heard of one case study in which a plant operating an informal system by which personnel in maintenance and engineering had responsibility to examine equipment had to report any abnormalities. However to improve availability levels of only 50%, external consuhants introduced a formalised duty-of-care in which personnel followed a prescribed tour with hand-held data collectors and answered basic questions using drop-down menus. This alone led to a 20% increase in availability. 2. Targeting Failure Modes To implement effective condition monitoring it is important to have a comprehensive grasp of machine failure modes and dynamics. This should be achieved through relevant training, knowledge of past experience, case studies and industry surveys (such as those produced by the Electric Power Research Institute (EPRI) on motor failures for example). The identified failure modes can then be ranked (using pareto analysis/engineering nous and based on cost to repair, effect of unplanned downtime, safety and product quality control) so that 6-8 failure modes may be concentrated on for detection and diagnosis [2]. This approach is deemed to be more beneficial than trying to detect and diagnose all possible failure modes but only monitoring them half as well [2]. However the extent to which this is done will depend upon the criticality of the equipment in the plant. 3, Identification and Monitoring of Fault Symptoms The type of parameter (eg vibration, temperature, speed) to be measured from a piece of equipment will depend directly on those failure modes that have been targeted for monitoring. The key here is in identifying the symptoms that are likely to be exhibited by these failure modes. For this to be achieved most effectively a fundamental engineering
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understanding of the machine dynamics is required along with the ability to reason theoretical changes as a result of a particular failure mode. For example a bending fatigue crack in a helical gear will not produce wear debris, whereas a theoretical understanding from basic principles could be used to explain the effect of an imbalance fault on vibration. The next step is to pattern-match the types of measurement parameters to the symptoms that will be exhibited (for example rotor imbalance will exhibit itself in abnormal machine vibration, therefore measure vibration at a convenient point). In addition to a fundamental understanding of a particular machine and its failure modes there are also some widely accepted "rules-of-thumb". For example vibration is suitable for rotating and reciprocating plant [1], current for electrically powered plant [4] and temperature monitoring (or thermography) for plants that generate heat (either electrically or mechanically). Furthermore lubrication debris analysis may be used for contacting frictional surfaces that wear. Also proving successful in recent years is measurement of airbome acoustics [5] and (quite separately) acoustic emission [6]. Airbome acoustics yields similar information content to vibration but has the advantage of providing global information content compared with the use of accelerometers, which are both location and orientation dependent [5]. For example an accelerometer mounted on one end of a gearbox may not detect changes in vibration at the other end. Acoustic emission is based on the detection of the high frequency component of structure borne sounds which are naturally generated by all machines. It directly detects physical processes such as friction, impacts and metal removal that occur when machinery degrades. Additionally parameters routinely measured for control purposes may also be valuable for monitoring, one such example is that of instantaneous speed [7]. The advantage of these control parameters is that no additional instrumentation is required. 4, Transducer Issues In addition to choosing transducers simply to measure parameters associated with fauh symptoms, other issues to be considered are transducer cost, reliability and the physical access possibilities that the machine and its environment allow. For example piezo-electric accelerometers vary in cost from £100-£1500. The more expensive ones allow tri-axial vibration measurement, have a large bandwidth, are well calibrated and allow extremely repeatable measurement. Therefore the user should consider the frequency range required and whether tri-axial measurement is required (this may alternatively be achieved by using three separate uni-axial devices orientated in three directions perpendicular to each other). Physical access requirements are also a significant issue. Intrusive measurements theoretically provide the best indication of parameter change, as such transducers are closer to the operating dynamics of the machine (the protective casing acts a damper to temperature and vibration characteristics for non-intrusive measurement). However, generally nonintrusive transducers are preferred to avoid dismantle of a machine and any problems associated with dismounting after reassembly. Externally mounted and contacting transducers are frequently employed (such as accelerometers or encoders) but sometimes physical attachment and also overheating may be a problem. For the purposes of easy data interpretation and to reduce the computational resources required in data analysis the number of transducers fitted to a machine should be minimised. However in order to achieve accurate fault detection and diagnosis, the maintenance engineer should never rely upon a single transducer (ideally three channels of data measuring at least two types of parameter should monitored). Although the measurement of data from more transducers does to some extent imply the ability to gain more reliable information output,
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many practitioners take this to extreme and at the compromise of sufficient data analysis. In fact the Maintenance Engineering Research Group beheves that in general industry measures more than twice as many parameters required but only does half the amount of analysis that should be carried out. The excessive use of measurement transducers is of particular concern when it is considered that sensor systems are inherently unreliable (a 1999 Trade and Industry survey estimates that 40% of unscheduled plant stoppages are due to sensor systems failure). 5. Measurement Frequency and Monitoring Practice The frequency of monitoring will depend on the rate of deterioration (or Mean Time Between Failure, MTBF [8]) of perceived failure modes and the importance of detecting a failure mode at incipient stages of development. In practice, if single-value trending (for example RMS vibration level) is carried out, at least three trending points are required. Therefore if a perceived mode is bearing failure it is known from experience and past cases studies that it may take approximately 12 months from initialisation to failure. In this case (vibration) measurements should be acquired at intervals of no longer than 4 months. However if the failure mode (such as imbalance) was due to build up of dirt on a fan, this may take only 6 weeks from being clean to very dirty and therefore measurements at intervals of no more than 2 weeks should be taken. Additionally the frequency of monitoring will also depend on the availability of spare parts. For example if the availability of a damaged component is 10 weeks, then fault detection and diagnosis should be achieved at least 10 weeks prior to failure. If diagnosis is later than this then there will be plant downtime much longer than that required solely for repair and this would counteract the principles behind condition monitoring. If a more detailed and analytical approach to monitoring is carried out (using vibration spectral analysis instead of RMS values for example) then an important issue is the sampling frequency at which data should be collected during a single acquisition. This will depend upon the frequency range of interest and the resolution required to distinguish between frequency components, harmonics and sidebands. For example, the frequency range of interest for an induction motor is up to its running speed (24 Hz) multiplied by the number of rotor bars (36), ie 846 Hz (the passing frequency). In order to compensate for aliasing [9], the sampling frequency should be at least 2.5 times this range (2.5 x 846 Hz) ie 2115 Hz (if raw data, time analysis is required it is recommended that the sampling is 10 times the frequency range of interest, ie 8460 Hz). However the sampling frequency will also depend on that allowable by the data acquisition system, as usually a choice may only be made from a discrete number of possibilities. Concerning frequency resolution, if the fundamental component due to rotational speed of an induction motor is for example 24 Hz, breakage of a rotor bar will lead to sideband modulation of twice the slip frequency (2 x ((50 Hz mains supply/2) - 24 Hz) = 2 Hz) [4, 5]. Therefore the frequency resolution will subsequently have to be at least 2 Hz in order to distinguish between the fundamental component and its sidebands. 6. Data Analysis Another extremely important consideration to the approach of monitoring is the method by which data will be analysed. This is potentially a minefield for anyone establishing an approach to machine monitoring for the first time. The key advice is not to jump into an advanced processing technique before understanding the basics. The basic level incorporates raw data averaging, RMS and peak-to-peak calculations, and trending over time for example. This will subsequently allow the setting of warning and alarm levels to detect the presence of
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an abnormality (but will not be able to provide any fault diagnosis). The next levels include visual interpretation of a raw time waveform (if an underlying signal may be clearly identified from underlying noise) and interpretation in the frequency domain, both of which may be employed to attempt to identify why a particular anomaly has been detected by basic trending (ie fault diagnosis). Alternatively particular frequency components may themselves be trended over time. Higher levels of analysis include the application of time-frequency analysis [11], Higher Order Statistics (HOS) [10], Principle Component Analysis (PCA) [11] and also neural networks [11]. 7. Review and Revise This is a critical step in effective implementation of condition monitoring but is, in too many cases, often forgotten or ignored. The relevance of CBM to a particular piece of equipment should be reviewed along with the robustness of measurements and the subsequent interpretation (for example is the system providing too many false alarms, or is fault detection and diagnosis provided early enough?). The closing of the loop in monitoring is important to reassess a number of issues such as symptom warning and alarm limits, what parameters are monitored, how and where the parameters are measured, and also the frequency of measurement. THE TRANSVERSE FLUX MOTOR It is unusual for a maintenance engineer to be in a position of developing condition monitoring technology for a completely novel machine. However the development of the 20MW Transverse Flux Motor (TFM) by Rolls-Royce is one such example of a novel machine requiring condition monitoring to be developed from basic principles. This therefore provides an ideal platform on which to illustrate the previously outlined approach to monitoring. The novelty of the TFM has the implication that conventional tools for motor monitoring are less valid in this new application but at the same time its significantly different design opens up new opportunities for monitoring previously unmeasured parameters The TFM will potentially provide naval propulsion for a range of vessels including the Royal Navy Future Escort, Future Carrier (CV(F)), Future Surface Combatant (FSC) and Future Attack Submarine (FASM). In addition, other potential applications also exist in locomotive drive and automotive industries. Major advantages of such electric propulsion include low noise, reduced operating costs, increased flexibility of operation and improved possibilities for naval architecture [13]. The TFM is, at a basic level, based on the Permanent Magnet Synchronous Motor (PMSM) where there are no exciting coils on the rotor and the magnetic field is provided only by permanent magnets. However, several features distinguish this machine from more conventional PMSMs [14]: 1. The phases are entirely separated from each other and hence do not couple electromagnetically with the other phases. 2. The stationary armature coils are simple solenoids. 3. The overall flux path is three-dimensional (the flux in the rotor is radial/circumferential whilst that in the stator is predominantly radial/axial). 4. Substantial freedom of design is achieved by virtue of being able to vary the magnetic flux geometry and the coil section without compromising the dimensions of either.
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Although one of the benefits of the TFM is its potential for design flexibility [13], its most simple configuration consists of a single rotor disc to which two opposite rotor rims are attached (Figure 2). The (active) rotor comprises of alternate soft iron laminated pole pieces and permanent magnets (Figure 3). The magnetisation of the magnets is in the circumferential direction and aligned so that the pole pieces form alternate (N) and south (S) poles. The stator consists of a number of C-shaped stator cores (constructed by stacked laminates) and a single solenoid armature coil that passes within them (Figure 2). MONITORING OF THE TFM The requirements for condition monitoring of the TFM are significantly different to those for most conventional machines. Explicitly the machine is very different in design compared with any other currently on the market and it is being employed in an application previously fulfilled by reciprocating diesel engines or gas turbines. In addition the TFM will primarily fulfil a military application in which both reliability and availability are paramount.
. Inner Root
. Outer Root
Flux Path
Rotor Rim
Figure 2 - Most Simple TFM Configuration
Figure 3 - Rotor Section and Flux Paths (View A from Fig 1)
7. Objectives and Available Resources The TFM will provide propulsion to vessels that will travel the world through arctic and tropical environments - therefore monitoring must also be able to function reliably in these regimes. The motor will also be operated under varying speeds and in both forward and reverse modes, thus monitoring will ideally be speed compensating and accommodate for bidirectional operation. The design life of the TFM has been set by the MoD at 30 years, of which 25 years are operational (during the other five years the navy vessel is dry-docked for routine maintenance lasting 6 months at a time). During its operational lifetime it must be 100% available so that the vessel can deal with any international requirement immediately. Monitoring should therefore be able to detect any incipient faults at the earliest stages of development (before they become critical to machine availability), provide a diagnosis of the fault (so that the
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order of spare parts may be initialised) and discriminate the severity of the identified fauh (so that future availability may be estimated and a maintenance window planned). If the Navy can reduce its manpower requirements it can reduce the space required for accommodation and therefore the size of a vessel. This can be bring about significant cost savings and is therefore actively being persued by MoD (in fact the Navy is currently looking to achieve a 20% cost reduction). For example the old Lander Class frigate required a crew of 20 for maintaining machinery, however for the Type 23 this number is only 3. The maintenance crew has a formalised watch-keeping system during which a prescribed tornmust be carried out and data collected using hand-held devices at half-hourly intervals. However this crew has lots of experience of maintenance^ operations but not expertise. Therefore, as the Royal Navy has a fleet of vessels that at any one time are distributed all over the world, the trend is to consolidate a group of experts at one location. Condition monitoring data is then transferred to them electronically for detailed off-ship analysis and interpretation when automated or manual on-board analysis flags an anomaly. 2. Targeting Failure Modes As the TFM is a completely novel machine its failure modes have not previously been experienced and nor are there any case studies or industry surveys that may be referred to (such as [15] and [16]). However, given that it is part of the subset of machines that is electric motors, it is known that both mechanical and electrical failure modes are of importance. Through discussions with the designers and as a result of experience of the TFM prototype manufacturing and development, the following possible failure modes have been identified: 1. Stator core circumferential misalignment (due to movement in the stator slot), 2. Stator core outer and inner tip creep (occurring if the magnetic forces become greater than the stiffness of a stator core itself, with the tips being attracted to the rotor rim), 3. Lamination shorting (caused by manufacturing defects, voids in the adhesive or overloading by the excitation supply), 4. Stator core overheating (as a result of increased losses due to shorting for example), 5. Stator core delamination (due to manufacturing defects, voids in the adhesive or overheating), 6. Locally and globally distributed demagnetisation (due to overheating or material flaws), 7. Rotor misalignment (as a consequence of shock loads on the propeller for example), 8. Insulation deterioration (due to high switching frequencies, ageing and defects [18]), 9. Rotor pole piece shorting (possibly due to breakdown of the insulating material), 10. Mechanical and electrical misalignment (resulting from problems with the inverter or the shaft encoder). 3, Identification and Monitoring of Fault Symptoms Magnetic flux is an important operational parameter in any electrical machine and this parameter will be directly affected (although in different ways) by any of the failure modes listed above. It may be monitored by winding coils of copper wire around the magnetic material (ie the iron), with the resulting voltage pick-up being directly proportional to the normal rate of change of flux density through the iron. In an ideal TFM (ie with all poles identical and with no degradation during the lifetime of the machine) the search coil flux should vary periodically with a wavelength equal to the magnetic pitch of the machine [17]. Flux measurements from the rotor search coils will detect differences (as a result of incipient fault conditions) in the excitation components of the flux output of a stator core. On the other
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hand, stator core search coils are capable of detecting both the magnetic and excitation components of the flux in the rotor rim. The precise way in which particular faults manifest themselves in the flux profiles may be examined by theoretical analysis and some degree of simulation [17]. Additionally many of the failure modes are likely to give rise to changes in temperature of different machine components. Therefore hard-wired thermocouples have been installed in the TFM. Some of the failure modes (eg rotor misalignment and demagnetisation) are also likely to exhibit symptoms in vibration. However, although the use of accelerometers was considered, as the bearings are set within the frame a suitable mounting position could not be found. 4, Transducers Issues In the TFM at least one magnetic flux search coil is required on the rotor and another on the stator, this would allow determination of corresponding stator and rotor faults respectively and whether they are locally or globally distributed. However in order to satisfy the requirements ouflined in the "generic approach" section of this paper at least two search coils should be employed to detect and diagnose a particular fault condition. A third sensor measuring a completely different parameter would be that of a thermocouple. In the case of stator core laminate shorting, a rotor search coil would detect a reduction in flux every time it passed that core. However a stator coil would only detect a reduction in flux if it was located on that faulty core. Thermocouple output would also provide support for this type of fault in detecting a temperature rise, but only if the thermocouple was located on the faulted core. Therefore assuming a minimum number of transducers and detection of a locally distributed stator anomaly by a rotor search coil alone then it would be difficult to accurately diagnose the fault condition (it could potentially be due to stator core circumferential misalignment, de-lamination or laminate shorting for example). For the TFM accurate and definitive fault diagnosis and localisation can only be achieved by instrumentation (with search coils and thermocouples costing in the order of only pence per device) of all the stator cores. However, clearly this is impractical and therefore the degree of instrumentation is governed by a subjective assessment, constant review and limits set be both the machine (for example the number of rotor search coils is restricted by the slip-ring unit) and the data acquisition system (16 channel data acquisition is currently the maximum available). It should be observed that both types of sensor (flux search coils and thermocouples) must be intrusive for successful application (in fact the rotor search coils are actually embedded within the rotor rim itself). Although this would usually raise concerns to the maintenance engineer, this is less of a problem in this unique situation as condition monitoring is being proactively developed alongside the development stages of the TFM. However mounting has been considered to be a significant problem in the attempt to monitor stator core vibration by physical attachment of a transducer. If an accelerometer was to become detached this could potentially lead to catastrophic failure of the machine - making a whole Navy vessel unavailable for operations. Therefore although such measurement is seen as potentially beneficial this has been outweighed by practical implementation concerns. 5. Measurement Frequency The TFM is unique in that its failure modes are previously unseen. In addition many aspects of its manufacture (including that of the rotor rim, C-shaped stator cores and solenoid) are significantly different to prevent similarities being drawn with other conventional machines or parts of machines. For these reasons it is anticipated that the monitoring will initially be
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carried out online for a production version of the TFM. In this naval propulsion application a cost-benefit analysis has justified the extra expense and knowledge resources of such an approach. Presently a special type of unsupervised neural network (Componential Coding) is one of the techniques being developed for online monitoring purposes [12]. 6. Data Analysis During the development stages of the TFM and its prototypes, flux waveform analysis is being performed manually in both the time and frequency domains. In addition arbitrairily set warning and alarm levels have been implemented for temperature monitoring. This hands-on experience in analysis of the data is allowing for the development of higher level and more automated interpretation (such as the application of Higher Order Statistics [10] and Componential Coding [12]). 7. Review and Revise A serious commitment is actively being pursued to the review of condition monitoring for the TFM. This is particularly necessary given that the TFM is a novel machine that requires parameters (such as magnetic flux) to be measured in a way not routinely done in any other application. Other key motivations during the development stages are to identify the minimum number of transducers realistically required for the purposes of monitoring, determine how reliable they are during machine operation and also evaluate the ability of currently measured parameters to identify and diagnose incipient faults and anomalies. CONCLUSIONS Without a detailed understanding of the specific monitoring objectives and failure modes to be targeted, maintenance efforts can easily be misfocused. In addition it is important to appreciate that every machine will likely have slightly different requirements whether it is completely novel, or simply a conventional off-the-shelf machine being used in a new application or having undergone some degree of modification. However effective condition monitoring of unfamiliar plant with previously unseen but anticipated failure modes may be successfully achieved by adopting a structured andrigorousapproach such as that outlined in this paper. ACKNOWLEDGEMENTS Rolls-Royce pic are thanked for the support and funding of condition monitoring technology development for electric machines in the Maintenance Engineering Research Group, University of Manchester. REFERENCES [1] [2] [3] [4]
A Kelly and M J Harris, Management of Industrial Maintenance, NewnesButterworths Management Library, ISBN: 0-408-00297-2,1979. J Moubray, Reliability Centred Maintenance, Butterworth-Heinemanne, Oxford, 2"^ Edition, 1997. P J Tavner, Condition Monitoring - The Way Ahead for Large Electrical Machines, IEEE Electrical Machines and Drives Conference, pg 159-162,1989. B Liang, B S Payne, A Ball, Detection and Diagnosis of Faults in Induction Motors Using Vibration and Phase Current Analysis, Proceedings of the 1^^ International
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[5]
[6] [7]
[8]
[9] [10]
[11]
[12]
[13] [14]
[15]
[16]
[17]
[18]
Conference on the Integration of Dynamics, Monitoring and Control (DYMAC '99), Manchester, UK, pg 337-341, September 1999. B S Payne, A Ball, F Gu, W Li, A Head-to-head Assessment of the Relative Fault Detection and Diagnosis Capabilities of Conventional Vibration and Airborne Acoustic Monitoring, Proceedings of the 13^*^ International Congress on Condition Monitoring and Diagnostic Engineering Management (COMADEM 2000), Texas, USA, pg 233-242, December 2000. Holroyd Instruments, MHC - Memo Maximises Plant Availability at Cleveland Potash Ltd, Application Note, 1997. A Ben Sasi, B S Payne, A York, F Gu and A D Ball, Condition Monitoring of Electric Motors Using Instantaneous Angular Speed, Proceedings of the 5^^ Annual Maintenance and Reliability Conference (MARCON 2001), Gatlinburg, Tennessee, USA, May 2001. P J Tavner, J P Hasson, Predicting the Design Life of High Integrity Rotating Electrical Machines, Proceeding of the 9^*^ International Conferences on Electrical Drives and Machines, lEE, pg 286-290, 1999. R B Randall, Frequency Analysis, 2"^ Edition, ISBN: 87-87355-14-0, September 1977. B S Payne, L Bo, F Gu, A D Ball, The Detection of Faults in Induction Motors Using Higher Order Statistics, Proceedings of the 5^ Annual Maintenance and Reliability Conference (MARCON 2001), Gatlinburg, Tennessee, USA, May 2001. N Baydar, B S Payne and A D Ball, Detection of Incipient Gear Failures by Statistical Technique, to be published as a special issue in the IMA Journal of Management Mathematics, April 2001. B S Payne, F Gu, C J S Webber and A D Ball, Application of Componential Coding in Fault Detection and Diagnosis of Rotating Plant, to be published in the Proceedings of the 14^ International Congress on Condition Monitoring and Diagnostic Engineering Management (COMADEM 2001), Manchester, UK, September 2001. A J Mitcham, Transverse Flux Motors for Electric Propulsion of Ships, lEE Colloquium, pg 3/1-3/6, 1997. Myers, T M S, Mitcham, A J, Wilkin, G A, Grime, C J, Prothero, D H, Rickman, J, Preliminary 20MW Propulsion Motor Design for Rolls-Royce and Associates, RollsRoyce Electrical Machines Group, 10/1998. B S Payne, B Liang, A Ball, Modem Condition Monitoring Techniques for Electric Machines, Proceedings of the 1^* International Conference on the Integration of Dynamics, Monitoring and Control (DYMAC '99), Manchester, UK, pg 325-330, September 1999. A Starr and R Wynne, A Review of Condition Based Maintenance for Electrical Machines, Chapter in Handbook of Condition Monitoring. B K N Rao publication, Elsevier, ISBN 1 85617 2341, 1996. B S Payne, M Husband, B Simmers, F Gu and A D Ball, The Development of Flux Monitoring for a Novel Electric Motor, to be published in the Proceedings of the 14 International Congress on Condition Monitoring and Diagnostic Engineering Management (COMADEM 2001), Manchester, UK, September 2001. B S Payne, A D Ball and F Gu, An Investigation into the Ways and Means of Detecting, Locating and Assessing the Severity of Incipient Tum-to-tum Stator Shorting Faults in 3-phase Induction Motors, Proceedings of the 13*"^ International Congress on Condition Monitoring and Diagnostic Engineering Management (COMADEM 2000), Texas, USA, pg 195-202, December 2000.
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Condition Monitoring and Diagnostic Engineering Management Andrew G. Starr and Raj B.K.N. Rao (Editors) © 2001 Elsevier Science Ltd. All rights reserved.
CONDITION MONITORING AND DIAGNOSTIC ENGINEERING - A DATA FUSION APPROACH Paul Hannah^ Andrew Starr^ Peter Bryanston-Cross^ ^ University of Manchester, UK ^ University of Warwick, UK
ABSTRACT This paper aims to review and consolidate the potential benefits of data fusion implementation in a multi-sensor environment to condition monitoring and diagnostic engineering. Sponsored by the Faraday Intersect scheme and the EPSRC, this works main theme focuses on the development of an intelligent multi-sensored engine. Thus, the partners involved in this research effort aim to develop a robust methodology for the sensing-analysis under harsh environments, stressing its application to the fields of combustion and fault diagnostics analysis. In addition, the work here presented introduces a new framework for the application of data fusion solutions to the analysis of engineering problems. A thorough review of frameworks used in data fusion applications is presented, along with important factors to consider in the lay out of a robust process model, to host a coherent and effective data fusion problem-solving strategy. The proposed process model will be used to facilitate the implementation of a common strategy to tackle the aforementioned combustion and fault diagnostics problems. KEYWORDS Data Fusion, Multi Sensor, Condition Monitoring, Combustion, Application, Strategy, Framework, Faraday, EPSRC. INTRODUCTION Why do we need data fusion? The applications are found in areas where a required parameter cannot be measured directly. These include: • •
Machine condition monitoring and diagnosis, e.g. vibration analysis in helicopter gearboxes. Non-invasive medicine, e.g. the combination of X-ray and Magnetic Resonance Imaging (MRI). • Remote sensing, e.g. target identification and tracking. • Non-Destructive Testing (NDT), e.g. material property testing and weld quality analysis. • Redundant sensor arrays and sensor validation.
In a wide range of engineering applications, vast amounts of measurements are generated from multiple sensors, containing similar or different class of data, which need to be handled in a robust and logical 275
manner. The demand for extracting meaningful information from the recorded data exposed the engineering community to the use of data management techniques. The fundamental integration of data, recorded from a multiple sensor system, to obtain a more precise perception of the physical phenomena under analysis is known as data fusion. Several authors have proposed definitions of data fusion. Fusion is defined materially as a process of blending, usually with the application of heat to melt constituents together (OED), but in data processing the more abstract form of union or blending together is meant. The 'heat' is applied with a series of algorithms, which, depending on the technique used, give a more or less abstract relationship between the constituents and the finished output. A 'fused' definition, which fits many examples in engineering, identifies data fusion as the process of combining data and knowledge from different sources with the aim of maximising the useful information content, for improved reliability or discriminant capability, whilst minimising the quantity of data ultimately retained. The sensor and signal processing communities have been using fusion to synthesise the results of two or more sensors for some years. This simple step recognises the limitations of a single sensor but exploits the capability of another similar or dissimilar sensor to calibrate, add dimensionality or simply to increase statistical significance or robustness to cope with sensor uncertainty. In many such applications the fusion process is necessary to gain sufficient detail in the required domain. STRUCTURES IN DATA FUSION Several architectures, as structures are commonly called in the data fusion community, has been proposed in the literature. The lay out of these architectures varies in relation to the field of application. In 1984 the US Department of Defence established the Sub-Panel for Data Fusion Joint Directors of Laboratories (JDL) in an effort to consolidate this analytical field among researchers. The architecture developed by JDL [1] assumes a level distribution for the fusion process, characterising the data from the source signal level to a refinement level, where the fusion of information takes place in terms of data association, state estimation or object classification. Situation assessment could then proceed, at a higher level of inference, to fuse the object representations provided by the refinement and draw a course of action. The reader could refer to Figure 1 to depict the general JDL model. Without lost of generdity, it is obvious that the aforesaid architecture can be adapted to accommodate the problem at hand.
Level 2
Sensors
•
Preliminary filtering
— •
• • • •
Data alignment Association Tracking Identification
Situation assessment
Threat assessment
Level 1 Level 3
Figure 1 : JDL data fusion architecture. The strategy to implement data fusion varies from one application to the next, but three stages can commonly be identified. Depending on the problem, it is not always necessary to apply all the stages:
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•
Pre-processing: i.e. reduction of the quantity of data whilst retaining useful information and improving its quality, with minimal loss of detail. The pre-processing may include feature extraction and sensor validation. Some of the techniques used include dimension reduction, gating for association, thresholding, Fourier transform, averaging, and image processing.
•
Data alignment: where the techniques must fuse the results of multiple independent sensors, or possibly features already extracted in pre-processing. These include association metrics, batch and sequential estimation processes, grouping techniques, and model-based methods.
• Post-processing: combining the mathematical data with knowledge, and decision-making. Techniques could be classified as knowledge-based, cognitive-based, heuristic, and statistical. Figure 2 shows an overview of the aforementioned techniques, which characterise data fusion applications, dividing the domain into three overlapping regions. Pre-processing
Data alignment
Rect. and elliptical gating Linear and Non-liiKar PCA
FFT, Cepstrum, Envelq)ing, Thresholdii^.
Post-processing
Eucledian, Mnkowsky, Manh^tan, NMialanobis distance metrics
i ! i
Correlation metrics
Bayesian, Den^CT-Shafe-, 1 Generalized Evicfeice.
Classical Inference: Maximum apostoriori, Neyman-Pears(»i, Mmimax, Baye's cost
Figure of Merit Voting, Consensus, Scoring.
Wavelets Least square, Msan square error, Maximum likelihood. Image processing
Fuzzy l(^c. Logical tenplates, EjqDcrt systans.
Kalman filtmng
Parametric tonplates, Clustering, Neural Networks, Voting, Bitropy, Image Algehu
Figure 2: A method map in data fusion. Many researchers have focused on specific methods applied to particular problems, or particular aspects of the architecture. Examples include architectural issues dealing with the problem of multiple sensors in similar or dissimilar domains [2-4]. Extended Kalman filtering [5], model based approaches [6-8], wavelet decomposition [9], Artificial Neural Networks [10,11] and Fuzzy Logic [12]. The National Physical Laboratory has provided a review of data fusion to the INTErSECT community [13]. FRAMEWORK FOR DATA FUSION IMPLEMENTATION An extensive number of models that can facilitate the mapping of the potentially most advantageous data fusion algorithms into the phenomenon characterisation problem for solution implementation can be found in the literature. Theseframeworkshave been adapted to accommodate for the application at hand, and they are in charge of integrating all aspects of the multiple sensor data fusion process. In the early years of data fusion, advances were driven by military applications. The US Department of Defence, in an effort to unify the terminology and methods applied in data fusion, established the JDL Data Fusion sub-panel. For the sake of brevity, the work provided by the JDL research group in
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addressing the data fusion process model issue is the only one presented in this paper in the form of a review. They began identifying the need to combine the data at three levels of processing [27]: •
•
•
Level I, object refinement, attempts to locate and identify objects. For this purpose a global picture of the situation is reported by fusing the attributes of an object from multiple sources. The steps included at this stage are: Data alignment, prediction of entity's attributes (i.e. position, speed, type of damage, alert status, etc.), association of data to entities, and refinement of entity's identity. Level 2, situation assessment, attempts to construct a picture from incomplete information provided by level 1. That is, relate the reconstructed entity with an observed event (e.g. aircraft flying over hostile territory or a pipe liking). Level i , threat assessment, interprets the results from level 2 in terms of the possible opportunities for operation. It analyses the advantages and disadvantages of taking one course of action over the other.
A process refinement, sometimes referred to as level four, runs along these three levels to monitor performance, identify potential sources of information enhancement, and optimise allocation of sensors. Other Ancillary Support Systems include a Data Management System for storage and retrieval of pre-processed data and Human-Computer Interaction. THE CONSTRUCTION OF A MODEL PROCESS Before a robust data fusion strategy can be legitimately submitted, there is a need to underline some of the difficulties arising with the application of data fiision, as well as other features that could be incorporated into the proposed model process. Some of the difficulties arising in muhi-sensor data fusion could be summarised as follows: • • •
• • •
Diversity of sensors used: Nature, synchronisation, location, sensor outputs. Diversity of data representation: Imagery, spatial, statistical, and textual. Registration: The information sensored refers to the same entity. There is a need to check the consistency of the sensor measurements [28]. This can be improved by objectively eliminating fallacious data sets. Calibration of the sensors when errors in the system operation occur. Limitations in the operability of the sensors. Deficiencies in the statistical models of the sensors and limitations in the algorithm development.
This is, by no means an exhaustive list of problems that could arise in the implementation of multisensor data fusion, and the reader should be aware of the inherent difficulties arising in any data acquisition and data analysis tasks. Performance assessment is another factor that needs especial consideration. Oxenham et al. described a measure of the quality of the data fusion process based on the correlated enhancement of the output information [30]. This sort of metric is determined by the uncertainty in the system: A decrease in uncertainty yields an increase in information delivered. They developed an expert system based on the "generalised modus ponens" to perform approximate reasoning given a type of uncertainty (the reader should refer to Oxenham for a thorough discussion on this method). Kewley gives another measure of uncertainty, provided by the system, in terms of ambiguity and vagueness [31]. APPLICATIONS OF DATA FUSION
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Practical applications of data fusion have necessarily been those areas in which the required output of an analysis may not be measured directly. This is particularly important in medical imaging [6], nondestructive testing [14] and remote sensing, such as target identification and tracking [3,8,10]. The methods are particularly popular in Condition Monitoring, where the purpose is to detect faults and the degradation of machine health [5,7,9,11,12,15,16]. Work at Manchester has pursued a number of methods under the data fusion umbrella. A variety of novel measurement, advanced signal processing and feature extraction techniques are being used in the detection, location, severity assessment and diagnosis of faults: • • • • • •
Modelling and parameter estimation have been used to analyse diesel fuel injectors, characterising the measured data with wavelet transforms [17]; Three dimensional measurements are fused from stereoscopic image data for the measurement of robot repeatability, using robust pixel interpolation with the Hough transform [18]; Gear faults are diagnosed and located using classical vibration analysis, cepstrum and wavelet transforms [19]; Neural networks have been applied to a variety of applications including diesel cylinder pressure reconstruction [20]; Linear and non-linear System Identification is extensively used in structural analysis for aerospace applications [21]; Optimisation in control and aerospace applications have utilised parameter estimation, fuzzy logic, neural networks and statistical methods [22-24].
This range of applications has led to a deep understanding of particular techniques but moreover a comprehension of the differing architectures of problem solution configurations and their unique characteristics. A CONDITION MONITORING PERSPECTIVE Data Fusion has rooted applications in the field of Condition Monitoring due to the fact that large amount of data should be processed if proper assessment of the machine's health is to be ensured. The inspection of the machine could be performed on-line, in a continuous fashion, or off-line, on a scheduled basis. The data would then be processed in a sequential or in a batch manner, respectively. The data arriving to the fusion centre contain vibration, temperature, pressure, oil analysis, and other measurements that encapsulate the parametric properties of the system and can aid in its condition assessment. An important aspect of Condition Monitoring is the fidelity of information received by the sensor units. The data acquired must be consistent and as free from noise as possible. One should also be concerned with sensor complementarities, rather than emphasising on sensor redundancy. These aspects should be considered at the source level to alleviate the pre-processing of the information. On the other hand, the sample cycle should be small enough to be contained within the time over which faults in the machine develops, and input frequencies should be carefully selected to achieve the desired monitoring capabilities. After the data has been acquired at the source level, it passes through to the pre-processing unit for digital conversion and proper manipulation. At this stage spectral analysis, correlation, image processing, time averaging, thresholding, and dimension reduction techniques are implemented based on the data at hand. The processed data is then pushed through to the fusion centre and routed according to the level of fusion sought, i.e. raw data, feature, or decision level fuMon. Thus, the data will reach the data alignment stage or the post-processing stage accordingly.
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No single ftision technique has been proposed, selection must be made depending upon the application. The information available and the level of inference sought would clearly determine the 'most likely to work' method. The fusion process could be applied considering a unique Condition Monitoring system, combining different sets of data, or considering several Condition Monitoring systems, combining different measurements. For condition monitoring purposes, the output from the fusion centre should contain explicit information that can lead towards the health assessment of the machine. A faulty/not-faulty type signal, with a range of in-betweens, can certainly aid in the decision making of the plant supervisor. This sort of information can be derived from the best estimate, based on decision logic, in the form of a probability measure. CHARTING THE WATERS Data fusion has been used in many disparate fields, and must be regarded as a superset of data processing algorithms, parts of which are well-classified and documented for particular fields and applications. The difficult part is to generalise the strategy. A necessary prerequisite for an engineering solution is a full statement of the problem: its definition and classification. This enables a specification to be drawn up and a solution devised. Indeed, it is often agreed that the problem definition is half the battle. In data fusion, individual problems have received thorough treatments. It is not easy, however, to approach a new data fusion problem. Even relatively experienced users of data processing methods have difficulty selecting the best approaches, and the expertise used in such selection is far from uniform. These issues have been characterised in the 'application pull' from industrialists in the Faraday INTErSECT programme. The limitations lie in the focus, to date, on particular advanced applications and specific techniques, combined with the understandable reluctance to report negative results. A generic fi*amework is required which allows selection of best practice methods based on problem and required solution characteristics. Work is to begin shortly at Manchester to characterise the range of known problem definitions with known good solutions, using the literature, in-house expertise, and case studies conducted by the project partners (Rolls-Royce, CORUS, DERA, NPL, Wolfson, BIRAL, AGS Technology, Marcha, and Sensor Technology). The aim is to provide a simple methodology for problem definition and classification and an application route map. Armed with known problem characteristics, the intrepid problem solver will then be able to steer a clear course towards the desired solution, while avoiding the muddy waters of trial-and-error and poor solutions which: • • •
Do not reveal as much information as they might. Are unreliable or not robust. Retain excessive raw or processed data in an uninformative state.
The knowledge of a range of academic and industrial practitioners will be mapped and best practices recorded as case studies. Strengths of particular techniques and architectures will be cross-referenced to problem characteristics, particularly in the field of Condition Monitoring. As and adjunct, known hazards will also be clearly mapped. An underlying and fundamentally generic approach will be derived.
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CONCLUSIONS Data fusion is widely used by scientists of many disciplines. There are many individual examples of successful application. There is a need, however, for a clear overall strategy with which to define and classify the problem and hence select fusion techniques. A picture is emerging of a flexible strategy, which incorporates a number of steps. This needs to be refined by encapsulating expertise, from a variety of sources, which defines the patterns and interconnection between solution steps, and matches the solution to the previously defined problem characteristics. REFERENCES [I] Waltz E L, and Llinas J (1990), Multisensor data fusion, Norwood MA, Artech. [2] Park S, Lee C S G (1993), Fusion-based sensor fault detection, Proc. 1993 IEEE Int. Symp. Intelligent Control, pp.156-161, pub. IEEE, Piscataway, NJ, USA, ISBN: 0-78-031207-4 [3] Grime S, Durrant-Whyte H F (1994), Data fusion in decentralized sensor networks, Control Engineering Practice, Vol.2, No.5, pp.849-863, pub. Pergamon Press Ltd, Oxford, UK, ISSN: 09670661 [4] Roy J, et al (1996), Quantitative comparison of sensor fusion architectural approaches in an algorithm-level test bed, Proc. SPIE, Vol.2759, pp.373-384, ISSN: 0277-786X, 0-81-942140-5 [5] Ganser T, et al (1996), Realization of a multi-sensor data fusion algorithm for spark ignition engine control, Proc. SPIE Vol.2784, pp.74-79, ISSN: 0277-786X, 0819421707. [6] Boyd J E, Little J J (1994), Complementary data fusion for limited-angle tomography, Proc. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp.288-294, pub. IEEE, Los Alamitos, CA, USA, ISSN: 1063-6919, 0-81-865827-4 [7] Sun H, et al (1994), Study on an algorithm of multisensor data fusion, Proc. IEEE Proceedings of the National Aerospace and Electronics Conference, Vol.l, pp.239-245, pub. IEEE, Piscataway, NJ, USA [8] Korona Z, Kokar M M (1996), Model-based fusion for multisensor target recognition, Proc. SPIE, Vol.2755, pp.178-189, ISSN: 0277-786X, 0-81-942136-7 [9] Thomas J H, Dubuisson B (1996), Diagnostic method using wavelets networks application to engine knock detection, Proc. IEEE International Conference on Systems, Man and Cybernetics, Vol.l, pp.244-249, pub. IEEE, Piscataway, NJ, USA, ISSN: 0884-3627 [10] Sun C, et al. (1996), Application of multisensor fusion technology in diesel engine oil analysis, Proc. 3rd International Conference on Signal Processing ICSP, Vol.2, pp. 1695-1698, pub. IEEE, Piscataway, NJ, USA [II] Lou K N, Lin C J (1997), Intelligent sensor fusion system for tool monitoring on a machining centre. International Journal of Advanced Manufacturing Technology, Vol.13, No.8, pp.556-565, pub. Springer, ISSN: 0268-3768 [12] Naish M D, Croft E A (1997), Data representation and organisation for an industrial multisensor integration architecture, Proc. IEEE International Conference on Systems, Man and Cybernetics, Vol.l, pp.821-826, pub. IEEE, Piscataway, NJ, USA, ISSN: 0884-3627 [13] National Physical Laboratory (1997), Initial report on Data Fusion, Ref THIS/0010 [14] Edwards I, et al (1993), Fusion ofNDTdata, British Journal of Non-Destructive Testing, Vol.35, No.l2,pp.710-713, ISSN: 0007-1137 [15] Stevens P W, et al (1996), Multidisciplinary research approach to rotor craft health and usage monitoring, Proc. Annual Forum, American Helicopter Society, Vol.2, pp. 1732-1751, pub. American Helicopter Soc, Alexandria, VA, USA, ISSN: 0733-4249 [16] Kandebo S W, Domheim M A (1995), 747-100 engine test bed provides new options. Aviation Week and Space Technology, Vol.142, No.l3, pp.50-52, pub. McGraw-Hill Inc., New York, NY, USA, ISSN 0005-2175 281
[17] Gu F, Ball A D (1996), Diesel injector dynamic modelling and estimation of injection parameters from impact response. Part I; modelling and analysis of injector impacts. Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering 1996, Vol.210, No.4, pp.293- 302, ISSN: 0954-4070 [18] Edwards C, Starr A G (1997), A review of current research in 3D machine vision and robot accuracy, 3rd International Conference on Laser Metrology and Machine Performance (LAMDAMAP), pp 423-430, ISBN 1-85312-536-9. [19] Yesilyurt I (1997), Diagnosis and location of gear damage, PhD Thesis, University of Manchester [20] Gu F, Jacob P J, Ball A D (1996), RBF neural network model for cylinder pressure reconstruction in internal combustion engines. Proceedings lEE Colloquium on Modelling and Signal Processing for Fault Diagnosis, London, UK, Sep 18 1996, (Conf. code 47877) No.260, pp.4/1-4/11, ISSN: 0963-3308 [21] Cooper J E, Emmett P R (1994), An Instrumental Variables Approach to Non - Parametric Estimation of Frequency Response Functions, Int. J. of Analytical and Experimental Modal Analysis [22] Cooper J E (1997), On-line version of the Eigensystem Realization Algorithm using data correlations. Journal of Guidance, Control, and Dynamics, Jan-Feb. 1997, Vol.20, No.l, pp.137-142, pub. AIAA, Reston, VA, USA ISSN: 0731-5090 [23] Chen Q, Emmett P R, Sandoz D J, Wynne R J (1998), The Application of Density Estimates to Condition Monitoring for Process Industries, American Control Conference, Philadelphia, USA, June 1998 [24] Cooper J E, Emmett P R, Wright J R (1992), A Statistical Confidence Factor for Modal Parameter Estimation, Proceedings of the 17th International Seminar on Modal Analysis, September 1992, pp. 1611-1625. [25] Bryanston-Cross P J et al (1997), Whole field visualisation and velocity measurement of an instantaneous transonic turbine flow. International Congress on Instrumentation in Aerospace Simulation Facilities 97CH36121, pp.278-286 [26] Bryanston-Cross P J et al (1997), Transonic PIV (particle image velocimetry) measurements made in the stator trailing edge and rotor region of the ILPF (Isentropic Light Piston Facility) at Pyestock Farnborough, Proceedings SPIE, No. 3172.90, pp.561-575 [27] Llinas, J. & Hall, D. L. (1998), An introduction to multi-sensor data fusion. Proc. IEEE intemationsl Symposium on Circuits and Systems 1998, 6: 537-540. [28] Hackett, J. K. & Shah, M. Multi-sensor fusion: a perspective. IEEE CH2876-1/90: 1324-1330. [29] Varshney, P. K. Multisensor data fusion. Electronics and Communication Engineering Journal Dec. 1997, 9(6): 245-253. [30] Oxenham, M. G., Kewley, D. J. & Nelson, M. J. (1996), Performance assessment of data fusion systems. Proc. Of the Australian Data Fusion Symp. 1996: 36-41. [31] Kewley, D. J. A model for evaluating data fusion systems. IEEE 1058-6393/93: 273-277.
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Condition Monitoring and Diagnostic Engineering Management Andrew G. Starr and Raj B.K.N. Rao (Editors) © 2001 Elsevier Science Ltd. All rights reserved.
TEACfflNG THE CONDITION MONITORING OF MACHINES BY UNDERSTANDING Walter Bartelmus Wroclaw University of Technology Machinery Systems Division Wroclaw Poland
ABSTRACT
This paper is devoted to the book presentation Condition monitoring of open cast mining machinery published in Poland and now it is being prepared by COMADEM in English. The paper gives examples of the ways of material presentations in the book. Condition monitoring inference is based on different factors. The factors are divided into four groups: design factors, production technology factors, operational factors and change of condition factors. Taking these factors into account: Design, Production Technology, Operation and Condition Change factors leads to DPTOCC inferring diagnostic information of the machine system condition. The book illustrates diagnostic inferring and teaching based DPTOCC and mathematical modelling with computer simulation. The DPTOCC support ways for improving understanding of condition monitoring of machines and a diagnostic inferring. KEYWORDS
Teaching, condition monitoring, diagnostics, mathematical modelling, computer simulation, vibration analysis, particle analysis INTRODUCTION
The use of condition monitoring in the operation of machines is an essential element of their rational maintenance. And yet in some industrial practice condition monitoring, machinery engineering diagnostics is applied only to a small extent. The reason is lack of proper knowledge of diagnostic methods on the part of both engineering and maintenance personnel responsible for the operation of such machines. This makes the spread of diagnostic methods impossible. For improving the situation faculty graduates need some basic knowledge of diagnostics. This basic knowledge is the subject of the book by Walter Bartelmus: Condition monitoring of open cast mining machinery (English version is being prepared by COMADEM). The book is mainly devoted to the open cast mining machinery but the subject is generally presented so the material given in the book may be used for teaching condition monitoring of any machinery. In the book is given: Role of engineering diagnostics and testing in maintenance of surface mining machines. Basic terms in machine diagnostics. Machine maintenance procedures. Introduction to mechanical vibration (vibration terms, basic physical values in machine equation of motion, vibration damping, introduction to machine equation of motion, dynamic analysis of simple machine system and so on). Gearing dynamic modelling (velocity distribution at a contact point of gear teeth, forces and moments of friction in gearing, mathematical 283
description of: gearing stiffness, friction moments, errors, inter teeth backlash, inter teeth forces). Kinematics and dynamic modelling of rolling bearings. Machine components wear forms. The listed content of the book gives opportunity to understand the role of influence of design, production technology, operation and change of condition factors to condition monitoring signals. It leads to "Design, Production Technology, Operation, Condition Change, factors based diagnostics" (DPTOCC factors based diagnostics). Inferring is supported by computer simulation. This introduction is further developed in chapters: Accompanying machine processes description as information source of machine condition (Vibroacoustical signal description, Vibroacustical signal estimators and their use in diagnostic^ wear particles as machine condition signal, thermal phenomenon as machine condition signal, ultrasounds and acoustic emission). Diagnostic inferring from vibroacustical signal. Machine condition classification and standardisation. Gearboxes and bearings condition monitoring is considered. This general consideration gives background for understanding use of condition monitoring to open cast machinery: belt conveyor diagnostic, diagnostic of a bucket wheel excavator gearbox diagnostic, hoist ropes diagnostic, hydraulic system diagnostic, sluing ball bearing diagnostic. The book has about 400 figures in about 264 pages. Understanding the vibration as a signal for condition monitoring For understanding mechanical vibration many computer simulation results are presented in the book. As an example mechanical system properties are investigated using a simple mechanical system given in Figure 1.
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fTIg
Figure 1: Simple mechanical system For the system given in Figure 1 an equation of motion is given and a solution is obtained by computer simulation. The results are presented in Figures 2 to 7.
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Figure 3: System response to abrubt exitation
Figure 2: Plot of gavity forces
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i
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Figure 4: System response to abrupt excitation at critical damping: time scale in Figure b) extented relative to that in Figure a) 284
•• m
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Figure 5: System response at over critical Damping
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Figure 6: System response at no damping (un-damped free vibration)
The reader of the book in Figures 1 to 6 is aquatinted with phenomena of abrupt excitation which occur in mechanical systems when some faults exist. The reader is also aquatinted with the system with a dry-friction damping when response of the system for different friction forces is given in Figure 7. •i*m •am
Figure 7: Response of the system with dry-friction For the diagnostics supported by vibration analysis understanding of influence design, production technology, operation, condition change factors are very impotent. The book gives students the background for proper reasoning linking mentioned DPTOCC factors with diagnostic signals. The link is given by analysis of dynamics of a simple machine system as is given in Figure 8.
Figure 8: Elementary physical model of machine Many properties of the machine system may be investigated using the simple model given in Figure 8. An example of the system properties is given in Figure 9.
285
Figure 9: a) fragment of external load plot, b) fragment of motor angular velocity plot. MODELLING OF POWER TRANSMISSION SYSTEM DYNAMICS
A physical model of a one-step power transmission system is shown in Figure 10. The system in Figure 10 consists of an inertia which represents motor rotor inertia /y, a toothed gear consisting of two gear wheels represented by two bodies with inertia hp and hp and a working machine represented by reduced inertia /;„. The motor's characteristic is M^(0j) where (p^ is the motor's angular velocity. The principle of determining the inter-tooth force is illustrated in Fig. 11 where meshing is represented by only one parameter - stiffness. If it is assumed that the meshing has stiffness k^, the inter-tooth forces is F^k^Xi-X2) (1) where: k^ - meshing stiffness, N/m; xuX2- tooth deflections under moments Mj and M2, m. Since the deformation of the teeth is slight in comparison with radii rj and r2, the following relations can be written xj = rj(pj
(2)
^2 = r2(p2
(3)
/V;*M
Ms
9i
Figure 10: Model of power transmission system
Figure 11: Gear model for determining elestic force
Using mathematical modelling and computer simulations we can understand the relation between inter-tooth forces and relative acceleration of gear wheels. It is presented in Figure 12: to 15. In Figure 12 and 13 one can recognise characteristic periods of time: 1 - system running with increase of rotation from 0 to about lOOOrpm; 2 - free running of the system with rotation lOOOrpm; 3 - running with load increase from 0 to a rated load; 4 - system running under a rated load. Figures 12 to 15 give relation between inter-tooth force and acceleration - diagnostic signal. The book gives detailed consideration of relation between inter-tooth forces an acceleration and relation between acceleration and gearing condition.
286
2^.M.
^:
15Q0..m^^..
QQ s r^ .150p s 5,0 Figure 13: Plot of acceleration of gear wheel Figure 12: Plot of inter-tooth forces: 1- 4 gear meshing periods torsional vibration ?5Kd
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Figure 14: Zoom plot of inter-tooth forces
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Figure 15: Zoom plot of acceleration of gear wheel
Forms of wear of machine elements Changes in the condition of machines are due to the wear of their elements which make up kinematic pairs or joints. The following forms of wear of machine elements can be distinguished: abrasive wear, adhesion, pitting, seizing, corrosion, fretting and erosion. The forms of wear vary depending on the loading, the kind of kinematic pair and the environment in which the machine operates. Condition of machine element surface is given in Figure 16.
Figure 16: Surface of gear wheel tooth damaged by micro-pitting
Figure 17: Micro-pitting particles in form of flakes 287
Figure 19: Image of gearing damage (scuffing) particles
Figure 18: Sheared products of wear of mating surfaces
Different wear products particles generated by machinery during its live are given in Figures 17-19. For detail analysing of wear products particles an analytical ferrography is used, forms of particles are classified as is given in Figure 20 and for particle detecting many other methods are used given in Figure 21.
IT~] \
~2] ^
Ui
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1
4 1 ^
51
3 1
6,1
i 5,2
6,3
Figure 20: Forms of particles (Zu, K, Za): 1 - normal wear particles: flat, smooth surface, dimensions 0.5-5 ^un; 2 - abraded particles: chips, dimensions 25-f-lOO jim; 3 - spherical particles: dimensions 1+5 ^m; 4 - surface fatigue (pitting) particles: rough surfaces and edges, dimensions > 20 ^m; 5 - interference particles: rough surfaces (surface irregularities running in parallel), dimensions > 20 ^ ; 6 - other particles: 6.1, - sand, 6.2 - plastic, 6.3 - rust.
288
Cj
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Figure 21: Methods for particle detecting Vibroacustical signal presentation Relations between time signal and frequency domain spectrum Figure 22 and quifrency domain cepstrum Figure 23 are presented in the book.
ifi
^4
DUL^y 4—hr Figure 22: Examples of poly-harmonic signals and their spectra: a - signal in time domain, b - signal in frequency domain. The impotence of vibration signal presentation in the form of a spectrum and spectrum component relation between the components and faults are given. Mathematical statements for finding frequencies of these components are given. For example meshing frequencies for planetary gearboxes are given.
289
Sk Hz
4k
Figure 23: Spectrum of signals generated by gears and their cepstrum for two signal reception points, compare a) and b). Figure 23 shows the importance of signal presentation in the form of cepstrum and its independence of a signal reception point. CONCLUSIONS
The brief look into the book contents shows importance of graphic illustrations in understanding the relation between a diagnostic signal and the machine condition. The fact is that in 264 pages there are 400 figures. The importance of mathematical relation is not neglected, there are given about 370 mathematical statements. The book illustrates the dynamic properties of machines using computer simulation for generation synthetic vibration signals. Students studding condition monitoring and diagnostics of machines have to know the relations coming from design, production technology, operation, condition change factors on diagnostic signals. The DPTOCC supports ways for improving understanding of condition monitoring of machines and a diagnostic inferring. The student must be acquainted with forms of machine degradation, especially forms of surface degradation, forms of particles generated by machine's wear.
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Condition Monitoring and Diagnostic Engineering Management Andrew G. Starr and Raj B.K.N. Rao (Editors) © 2001 Elsevier Science Ltd. All rights reserved.
A SUCCESSFUL MODEL FOR ACADEMIA'S SUPPORT OF INDUSTRY'S MAINTENANCE AND RELIABILITY NEEDS Thomas V. Byerley, P.E. Maintenance and Reliability Center "where industry andacademia meet" University of Tennessee 505 East Stadium Hall Knoxviile, TN 37996-0750 USA [email protected] (865) 974-4749
ABSTRACT U.S. universities are not meeting the needs of industry and business in the fields of maintenance and reliability. The resultant lack of modern maintenance and reliability tools and practices is growing and will gradually prevent companies from competing at levels necessary for prosperity, or perhaps survival. The University of Tennessee's (U.T.'s) College of Engineering has developed a program to address this current deficiency in academia. This program, residing in U.T.'s Maintenance and Reliability Center (MRC), is pointed directly at the manufacturing and service sectors of industry and is dedicated to utilizing advanced technologies and management principles in the maintenance and reliability fields. The MRC is "where industry and academia meet", and as this paper will discuss, can serve as a successful model for other academic institutions to emulate. KEY WORDS: Education, Certificate, Masters Degree, Academia INTRODUCTION Traditionally, U.S. universities have shied away from education, research, and outreach in the area of industrial maintenance and reliability. While developing excellent capability in reliability statistics, probabilities and other mathematical approaches, they have largely ignored the critical needs in the area of industrial reliability and maintenance, much to the detriment of the manufacturing and service sectors. Whether maintenance and reliability was deemed "unworthy" of consideration by university faculties or was simply overlooked in the constant review of which important subject matters to include in a typical educational curriculum, academia generally has missed the emerging opportunities to deliver in the area of good maintenance and reliability know-how and education.
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Various unofficial studies indicate that U.S. industry spends between 300 and 500 Billion dollars annually on direct maintenance related issues, not including losses of production throughput and resultant losses of both profit and business. Industry practitioners suggest that at least 1/3 of those direct costs is completely wasted due to improper maintenance. Amazingly, that percentage of wasted effort has not changed significantly over the last several years, despite advances made both in production methodologies and in advanced maintenance tools and technologies. Obviously, the advances are only being utilized in small amount and/or in narrowly focused applications. Generally, industry's view of maintenance has remained as one of "a necessary evil" or "a necessary cost drain". This attitude reinforces the common wisdom that cultures are difficult to change. Today's engineers, business leaders, and other decision-makers have learned their view of maintenance and reliability from a culture that did not value it. Another contributing factor to the inertia holding back the maintenance and reliability advances is the lack of appropriately educated engineers, business majors and others entering the workforce. Typically, U.S. engineers and business majors have little contact with maintenance and reliability concepts during their undergraduate years. Certainly engineers will see a bit of reliability statistics and probabilities in one or two of their courses, but it is normally delivered in the very theoretical format that leaves students wondering why they are even studying it. Business majors may be taught that maintenance is considered a "controllable cost", but hear little or nothing else about it. Often, the new graduate's first introduction to the real world of maintenance and reliability is during a problem solving activity in a plant, led by and reinforced by the old guard's philosophy of "maintenance is a necessary evil". Thus the opportunity to utilize their creative and energetic minds is immediately diminished by their environment. Universities must recognize that industry needs qualified graduates to bring about change in the workplace. Universities must recognize that improved reliability and maintenance is absolutely key to the long-term survival and health of our industrial and services base. Universities must accept their responsibility to deliver graduates who can enter today's business world and make a difference - and that includes modernizing and optimizing the approach to maintenance and reliability. Concepts, systems, and tools already exist to significantly improve present maintenance practices. Concepts, systems and tools already exist to radically improve the reliability of equipment and processes. What doesn't exist is a large-enough pool of graduates educated in these areas to make the critical difference. Universities also need to recognize and seize upon the opportunities that exist for doing research and technology assessment in these fields. Many of today's advances have been brought about by practitioners in industry applying concepts and tools developed in other fields for other applications. While great strides have been made, the speed and amplitude of progress could be greatly increased by appropriate research and technology development directed toward maintenance and reliability. Understanding the ftindamentals of vibration analysis, eddy current testing, signal conditioning, etc. could lead to development of tools and systems directly applicable to industry.
THE MODEL Recognizing this deficiency. The College of Engineering at The University of Tennessee has developed and implemented a multi-faceted program to help address this situation. This program should serve as a model for other academic institutions to follow in addressing current and future needs of the manufacturing and service sectors. It consists of four elements: education, research and technology assessment, informafion exchange, and business support and alliances.
292
This program resides in U.T.'s Maintenance and Reliability Center (MRC), an industry sponsored center under the oversight of the Dean of Engineering. The MRC is self-supporting through annual membership dues and various for-fee programs. Membership is open to any company and the present membership includes a diverse group of end-users, suppliers, and integrators/consultants. In other words, all facets of the business are included.
EDUCATION The MRC has developed several programs to deliver maintenance and reliability education to a variety of recipients. One such program is the on-campus MRE Certificate Program. This program offers the Maintenance and Reliability Engineering Certificate to those students enrolled with the MRC. This certificate is in addition to their standard BS, MS or PhD degree in one of the traditional engineering disciplines. Thus, the graduating student must complete the work required to graduate within the normal accredited discipline program and should be able to perform the duties normally associated with the traditional field. In addition to those requirements, the students in the MRE Certificate program must complete the following: Maintain a 3.0 out of a possible 4.0 Grade Point Average Complete at least two summer internships with a MRC member company 1 week - on-campus introductory training period 12 weeks - on-the-job-site working experience Successfully pass two for-credit courses Introduction to Maintenance. Introduction to Reliability Note: The two required courses for the certificate are cross-listed in all seven of the engineering departments and can be taken as technical electives within each discipline or as additional courses beyond the required coursework. We have found that this blend of high academic performance, introductory coursework, focused training and significant work experience, combined with a traditional engineering degree, results in graduates who are well prepared to enter the manufacturing and service sectors and make immediate positive impacts on both day-to-day business decisions and longer term culture changes. Additionally we have found that students participating in the program have good internship experiences, have above average starting salary offers, and bring other students into the program. The program has grown each year since its inception as shown in Figure 1.
1997 1998 1999 2000 2001 Figure 1: MRCINTERNS Another program the U.T. MRC offers is off-campus distance education in Maintenance Management and Reliability Engineering. This program is in partnership with Monash University at Gippsland, 293
Victoria, Australia. Monash has a long-established program that closely fits the needs of today's industrial practitioners. U.T. MRC offers the Monash program in North America, including a oneweek residential school on U.T.'s Knoxville campus each year. This program offers several pathways through a wide variety and mixture of courses leading to Certificates in Maintenance Management or Certificates in Reliability Engineering, Graduate Diplomas in Maintenance Management, and/or Masters Degrees in Maintenance Management and Reliability Engineering. Although only into its second year through the MRC, participation in the program has doubled from the first year. As this program matures, U.T. and Monash faculty will work together to exchange content, information, and course delivery techniques to continually improve the quality of this program. A newer program that The U.T. College of Engineering offers is the Graduate Certificate of Credit in Maintenance and Reliability. Three departments - Mechanical Engineering, Nuclear Engineering, and Industrial Engineering - presently offer this certificate. Participation in the program requires enrolling in and being accepted in the university's regular graduate school, and successfully completing four pre-approved courses, all of which could lead to a full MS in the particular department. All of these pre-approved courses are being offered by distance education delivery systems, thus eliminating the normal requirement for a participant to be resident on campus. One additional program in this area is the MS in Industrial Engineering with a Maintenance and Reliability concentration. This program follows the traditional MS path with the added benefit that the maintenance and reliability courses can be taken through distance education delivery. Knowing that to be ultimately successful, changes must have Top-Down Leadership, Middle Management Buy-in, & Bottom-up Understanding, UT also offers executive seminars directed at top and middle management decision-makers. These two-day sessions stress the "WHY" of maintenance and reliability from a business and financial perspective and include faculty leadership from the Business College and industry as well as from the Engineering College and the MRC.
RESEARCH AND TECHNOLOGY ASSESSMENT In addition to delivering knowledgeable and experienced graduates, and education to practitioners and executives, universities normally have tremendous opportunities to produce research and technology assessment. Experienced faculty, energetic graduate students, laboratory facilities, and a general expectation to do research combine to provide an excellent environment. Additionally, maintenance and reliability research has not seen a significant push previously, so the area is fertile for those who wish to move into this area of research. The U.T. MRC manages research projects sponsored by member companies or by other institutions such as National Science Foundation, National Institute of Standards and Technology, Department of Energy, etc. Interested and appropriate university faculty members are matched up with the various projects based on the scope of the project and the credentials, interest, and available time of the faculty member. Examples of MRC projects done to date are: Motor Shaft Alignment & Impact on Energy Costs Motor Shaft Alignment & Impact on Coupling and Bearing Life Motor Aging & Fault Detection Asset Continuous Improvement & Economic Evaluation Remote Sensor Calibration and Validation Impending Seal Failure Detection - Centrifugal Pumps Impending Arc Detection in DC Motors A Process for Optimizing Internal M&R Processes
294
All these research and technology assessment projects have resulted in detailed reports being prepared for the company sponsors. In addition, with the sponsors' approval, several of the results have been reported in public forums such as conference proceedings and periodicals. Much of the knowledge garnered from the research work has also been integrated into classroom material by participating faculty members. Research and technology assessment activities not only serve to move the state of knowledge forward, they enhance the experience and capability of the faculty members, who in turn pass their knowledge and experience on to the university students, both on-campus and off-campus.
INFORMATION EXCHANGE Universities stand as major information repositories and exchange centers. Information is created, collected, interpreted, distributed, shared, and stored within the university. However, information bottled up inside a university does no one any good. Thus, one of the U.T. MRC's principal missions is to help interpret and pass along information about maintenance and reliability. This is accomplished in several ways. A maintenance and reliability conference is held each year, open to all. Here, some 70 to 90 papers are presented, normally in one of three separate tracks - asset management, best practices/case studies, and new technology/academics/research. The papers are published in a set of Proceedings and made available to both attendees and other interested parties. Additionally, the U.T. MRC sponsors semi-annual meetings of its member companies and other prospective members where in-depth best practice presentations are shared among those present. Copies of this material are made available to all MRC members whether attending or not. Finally, the MRC serves as a clearinghouse for questions, answers, and other information that flows among MRC members, as well as non-members. Often this informal information flow leads to more formal presentation and exchange as ideas take form and turn into concrete activities.
BUSINESS SUPPORT AND ALLIANCES An integral part of the U.T. MRC mission is to serve industry and business. They are the ultimate customers and recipients - of students, of knowledge, of research results, of information. The MRC seeks to support industry and business by providing programs, graduates, and services that truly add value. Perhaps the largest single support service is the networking opportunities provided by and encouraged by the MRC. Time is set aside at meetings and conferences to allow networking. MRC staff members serve as facilitators, bringing interested parties together. Joint research projects encourage the close linking of various MRC members.
CONCLUSION A huge gap exists today between universities' offerings and industry's maintenance and reliability needs. This is due both to universities generally shunning this area of educational responsibility and to the general reluctance of industry to address the opportunities in this area. Only a small percentage of companies have truly tried to deal with maintenance and reliability as an investment opportunity. However, the demands of today's world of manufacturing and services dictate that maintenance and reliability must be treated differently. This is an almost-unparalleled opportunity for universities to move forward and fill the gap through the right combination of education, research, information exchange, and business support. 295
The University of Tennessee and its Maintenance and Reliability Center is a clear example of a university rising to the challenge. This program is dedicated to utilizing advanced technologies and management principles in the maintenance and reliability fields. While still somewhat in the embryonic stage of development, it has laid a solid foimdation and pointed the direction that universities can and should go in order to respond to the industrial and business needs. The significant growth of the MRC programs suggests that there is indeed a demand for this type approach by universities. We believe that the MRC is "where industry and academia meet" and, as such, is truly a successful model for academia's support of industry's maintenance and reliability needs.
RELATED READINGS Hines, J.W. and Shannon, T.E., "The Future of Maintenance and Reliability Education", published in Proceedings of COMADEM 2000, pp. 189-193. Kerlin, T.W., and others, "A Program in Maintenance and Reliability Engineering", published in Proceedings of MARCON 99, pp. 52.01-52.07. Kerlin, T.W. and Shannon, T.E., "Maintenance and Reliability Engineering", published in Proceedings of MARCON 97, pp 100.01-100.05
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Condition Monitoring and Diagnostic Engineering Management Andrew G. Starr and Raj B.K.N. Rao (Editors) © 2001 P.W. ffiUs and J. Thompson. Published by Elsevier Science Ltd.
CERTIFICATION IN CONDITION MONITORING DEVELOPMENT OF AN INTERNATIONAL FCN SCHEME FOR CM PERSONNEL Peter W. Hills ^ John Thompson^ ^ Rockwell Automation - Entek, ^ British Institute of NDT ABSTRACT To date there is no recognised training or certification scheme for personnel engaged in condition monitoring. This shortcoming has been recognised for many years with the result that personnel engaged in condition monitoring have not been fully recognised for their contribution to effective Plant Asset Management. The International Standards Organisation (ISO Technical Committee 108, Sub-committee 5) is now finalising a draft international standard that will have significant consequences for industry applying condition monitoring strategies. The British Institute of Non-Destructive Testing, which owns and operates the PCN Scheme on behalf of industry, is developing new PCN examinations through its COMADIT Group Executive Committee to satisfy the requirements of the draft standards. These examinations will lead to the issue of PCN certificates of competence for various providers and vendors of condition monitoring technologies. This paper gives some background to the development of the PCN Scheme, discusses the draft standards and describes developments in response to the new draft condition monitoring standards.
INTRODUCTION Techniques collectively referred to as Condition Monitoring (CM) have a common objective of indicating the early signs deterioration or malfunction in plant and machinery. The term Condition Management suitably raises the status of CM and combines the range of many specialist techniques as a strategic tool for the management decision process. Condition Management properly applied to process and manufacturing industries is a proven strategy for maximising plant availability and safety. The technique involves a programme of plant and machinery measurements such as vibration, oil debris and thermographic heat pictures etc that will detect the onset of a fault. Subsequent analysis will indicate the
297
appropriate production and maintenance strategy needed long before catastrophic failure and plant outage. The diagnostician is able to identify specific machinery faults inter alia such as unbalance, misalignment, mechanical looseness, bad bearings, resonance etc. The techniques can be applied to most continuous rotating machines across all process, manufacturing, marine and building industries. The current success of CM is largely attributed to the skills of a few dedicated individuals. These * specialists' are highly knowledgeable with expertise that has been acquired by self development and with the assistance of equipment vendor's courses. The continuity of a condition monitoring system is often frustrated by: •
Offshore industry personnel change about every three weeks and if not properly planned can disrupt the CM programme.
•
In the marine industry ships crews will change after a voyage and the fresh crew needs to be able to continue the CM programme.
•
Contract labour, is common in places such as the Middle East where they have to prove their skills. In the case of CM, this can be difficult to verify.
•
Companies offering CM contract services need a benchmark to be judged by and to prove their competence.
•
In the Defense services short tours of duty mean that staff must undergo continuous training programmes in CM.
•
A Company setting up an in-house CM service will require its operatives to be adequately trained before the programme becomes effective.
•
Skilled CM contract personnel chase the money and move around a lot. Hence staff turnover can often disrupt a programme due to lack of skills.
•
Vendors of CM training have independent in house standards and individual approaches to condition monitoring, there is no common standard of excellence.
In 1996 the UK Department of Trade and Industry (DTI) undertook a survey of maintenance practices and reported that from 403 UK factories 27% gave 'lack of staff ability' as one factor causing dissatisfaction in maintenance (see Table 1). Table 1 - Factors that cause dissatisfaction with maintenance: Planned maintenance
50%
Slow response
23%
Lack of spares
26%
Lack of staff ability
27%
Lack of management ability
21%
Lack of staff
34%
Plant complexity
27%
Excessive costs
24%
Lack of information
16%
The contribution that condition monitoring can make to reduce dissatisfaction applies to most of the above listed factors to extend the maintenance cycle.
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The acceptance of Condition Management throughout in industry is unfortunately far from fiilly penetrated but it is exploited by the more enlightened and successful industries. To assist CM becoming an institutionalised practice, a nationally recognised process of training and certification is seen as the prime route for guaranteeing the financial benefits and improved plant OEE rating. At the post graduate level some universities offer courses in Condition with specialised units focussing on maintenance and reliability but such expertise is scarce. However, industry is increasingly moving towards cross and muhi skilling. Maintenance Technicians and Operators have become more involved in the overall operations of the production process and are also expected to apply CM techniques. Often this results in the CM system failing due to incorrect application and analysis. Also as industry makes increasing use of labour outsourcing and contract services there is considerable uncertainty as to the capabilities of the service providers who fill the gap. Product vendors and consultants generally provide training to this group and while such courses fulfill a clear need they have limited recognition. There is no standard approach by vendors and the quality of CM training must therefor vary considerably. In 1996 Hills ^^^ and 1997 Rao ^^^ reported that there is an urgent need for a nationally recognised standard for training and certification in condition management. The equivalent of the British Institute of NDT's PCN scheme and/or NVQ's was seen as a basic requirement to enable skills across the engineering spectrum to be measured and recognised. Over the past three years COMADIT has been actively working towards establishing a condition monitoring certification scheme. COMADIT (Condition Monitoring and Diagnostic Technology) is a specialist group operating within the British Institute of NDT (BINDT) who are an independent non profit organisation. COMADIT exists to further the research, development and use of techniques for prediction and prevention of unwanted and hazardous malfunction of vehicles, machinery and structures. Amongst the group's enabling objectives is a remit to "encourage training in the proper use and application of condition monitoring and diagnosdc technology". As the first step COMADIT prepared a draft 'Requirements for the Training and Examination of Personnel Engaged in Condition Monitoring for Total Plant Maintenance' in 1998. Recognising the wide scope of such an undertaking COMADIT then conducted an industry wide survey in March 2000 to evaluate the specific needs and priorities for training and certification standards in Condition Momtoring. The outcome of this broad survey indicated that there was an overwhelming need for an independent certification scheme. During this time work was progressing at the International Standards Organisation on a draft international standard applying condition monitoring strategies. This paper now looks at the progress of the ISO activities and how the BINDT is preparing to provide a Nationally recognised accreditation service as an extension to its existing PCN (certification scheme in personnel quality management systems). SYNOPSIS OF THE HISTORY THE PCN SCHEME The need to establish a national NDT personnel certification scheme offering examinations covering all test methods and industry sectors was perceived in the late 1970's. The British National Committee for NDT (BNC for NDT) and the British Institute of NDT (BINDT) entered into discussions as to how best to achieve the desired goal. In 1980, British industry was invited to participate and, in 1981, a Central Certification Board (CCB), representative of major users and specifiers of NDT, was formed. The first examinations (aerospace eddy current) were conducted in August 1984.
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During 1988, the pace of development of documentation and availability of PCN examinations increased substantially and, as a result, the confidence of major industrial participants in the future of the scheme improved. Subsequently the MOD agreed to a transitional arrangement for its AQD scheme, and was joined in this by the CAA. In 1992, The CCB decided to seek accreditation against European standard EN45013 (General criteria for certification bodies operating certification of personnel). In order to comply with the requirements of this standard, it was necessary to reorganise the structure and status of PCN. PCN limited, as a wholly owned subsidiary of the BESIDT, came into existence in 1992 with articles of association which guaranteed the status and powers of the Governing Board. The Scheme Manager developed and documented a quality system and, following NACCB assessment in 1992; accreditation was awarded by the DTI in 1993. With the anticipated publication of European standard EN473, PCN documentation was revised and re-issued and, on 1 April 1993, examinations were offered in the new format required by the European standard. In 1994, an extension to the scope of accreditation was granted which placed PCN in compliance with international standard ISO 9712. BACKGROUND TO IS09712 ISO 9712 was developed during the late 1980s in order to provide a common minimum standard for central certification of the competence of NDT personnel. The demand for such a standard arose for a number of reasons, amongst which the primary driving force was a lack of confidence in employer based certification systems, necessitating regular and expensive audits of suppliers' in-house certification procedures which are subject to abuse arising from commercial pressures. SCOPE OF 9712 (NDT - QUALIFICATION AND CERTIFICATION OF PERSONNEL) The International Standard established a system for the qualification and 3'^ party certification of personnel to perform industrial Non-Destructive testing (NDT). When certification of NDT personnel is defined in product standards, regulations, codes or specifications, it is recommended to certify the personnel in accordance with this standard which covers the following methods: eddy current testing (ET) magnetic particle testing (MT) ultrasonic testing (UT) acoustic emission testing (AT)
liquid penetrant testing (PT) radiographic testing (RT) leak testing (LT) visual testing (VT)
The system described in the Intemational Standard was also intended to apply to neutron radiography (NT), ahemating current field measurement (ACFM), infrared thermography (IRT), strain measurement and other methods where independent certification programmes existed. The examination system within ISO 9712 comprises three main parts: i)
a written general examination to assess the candidate's knowledge of the principles of the test method.
300
ii)
a specific written examination to assess the candidate's knowledge of the apphcation of the test method to product or industry sectors (including an understanding of the applicable codes, standards and specifications), and
iii)
a specific practical examination to test the competence of the candidate in the application ofthe test method.
REASONS FOR ISO CD 18436 ISO 9712 was never applied to condition monitoring technologies within the PCN Scheme because of the difficulty of setting up practical examinations in line with the requirements of the standard, even though it included some condition monitoring methods (AT and IRT) within its scope. ISO/TCI08/SC5/WG7 is the committee responsible for the development of the draft international standard ISO CD 18436, and it is suspected that the main reason for the development of the draft was the potential difficulty of applying ISO 9712 to condition monitoring technologies. TIME SCALE AND PROCESS FOR ISO STANDARDS DEVELOPMENT At this point it is considered necessary to provide information on the process and timescale for the development of an ISO international standard. At the Nanjing meeting during September 2000, it was agreed that the revised parts 1 and 2 would be circulated to the ISO/TC 108/SC5/WG7 (Convenor: R. Eshleman, USA) for comment by 31 December 2000, with the intention of circulating the documents as DIS following the March 2001 meeting of ISO/TC 108/SC5. Furthermore, at the Nanjing meeting, resolutions were agreed concerning the development of parts 3 to 7 inclusive (see references). In a document prepared for the 26''' March 2001 Plenary meeting of ISO/TC 108/SC5 in Vienna, the Secretariat reported that, at the September 2000 Nanjing meeting, ISO 18436 Parts 3 and 4 would be circulated as a first CD (committee draft). At the time of writing, only the draft of part 3 has been made available to BSI/GME/23/7. Within ISO the main stages for standards development and approval are as follows: i)
New work item proposal
ii)
Committee draft (CD) developed within allocated group
iii)
CD circulated for approval to be registered as a Draft International Standard (DIS). At this point we (BSI) usually circulate the draft for public comment within the UK, as the main technical work should be completed by the end of this stage.
iv)
The comments received are discussed within the ISO group and a new draft prepared for circulation as a DIS.
v)
DIS circulated (5 months). Normally no new technical comments should be submitted at this stage. At the end ofthe voting period, if the voting approves the draft, the Secretary and Chairman ofthe ISO group can resolve comments themselves.
vi)
The draft is circulated for a final (FDIS) vote (2 months). The draft is then published!
301
SCOPE OF 18436 (CONDITION MONITORING AND DIAGNOSTICS OF MACHINES - ACCREDITATION OF ORGANISATIONS, TRAINING AND CERTIFICATION OF SPECIALISTS) ISO/TCI08/SC5/WG7 is the committee responsible for the development of the international standard, which specifies the procedures for accreditation of nationaPqualifying bodies and training organizations by a national agency, which will qualify and certificate personnel to perform machinery condition monitoring, identify machine faults, and recommend corrective action. Certification to this standard will provide recognition of the qualifications and, competence of individuals to perform measurements and analysis using portable and permanently installed sensors and equipment to assess machinery condition using the following technologies and fault correction processes: Vibration infrared thermography lubrication analysis alignment balancing failure analysis electrical current analysis DECISION ON DOMESTIC DEVELOPMENT IN ADVANCE OF THE PUBLICATION OF ISO Following the outcome of the COMADIT survey of the industry on Condition Monitoring training (results of the survey are given in Annex A), the emergence of the ISO CD 18436 added impetus to the activities of COMADIT. The committee then recommended to the Council of BINDT to incorporate the CM scheme within the internationally recognised PCN Scheme. This would confer the benefits of working within an established accredited framework, providing wide recognition and international acceptance of certificates. It was also considered that there was sufficient detail in the ISO Committee Drafts on which to base the system and so progress its implementation without further delay. It was however recognised that, if there were significant changes in the drafts prior to publication as an ISO standard, it would be easier to amend drafted PCN documents than to begin drafting anew. It is intended therefore to shadow the development of the suite of international standards with the development of PCN documents defining the system for qualification and certification of condition monitoring personnel. STATUS OF COMADIT WITHIN BINDT Membership of the COMADIT Group, which is open to anyone who is a full member of the British Institute of NDT who has an interest in condition monitoring or diagnostic technology, is free of any additional charge. COMADIT operates according to the Institute's Memorandum and Articles of Association and Bye-Laws and under Terms of Reference approved by the Institute Council. Executive Committee meetings are held at quarterly intervals and at other times as directed by the Chairman.
302
The COMADIT Group reports through its Executive Committee to the Technical Committee of the Institute. PROPOSED ROLE OF COMADIT WITHIN THE PCN SCHEME The Executive Committee of the BINDT COMADIT Group has provisionally agreed to accept special technical responsibility for those aspects of the PCN examinations relating to the assessment of competence of personnel engaged in the various disciplines within Condition Monitoring. In this context and for the purpose of working within the quality management system of the Institute's Certification Services Division, it is to be referred to as the Condition Monitoring Technical Corrimittee (CMTC) It is intended that membership of the CMTC shall be by the acceptance of nominations from organisations and major industrial companies with specific interests in the certification of competence of CM personnel. The CMTC may temporarily co-opt non-member specialist in order to assist in development projects. The CMTC provides expertise for the purposes of: • drafting examination and certification eligibility criteria and examination/training syllabi to address existing and forecast needs of industry, • drafting specifications for examination questions, and assisting in the development of a national question bank, • ensuring that, when drafting documentation, or through revision and amendment of existing documentation, the certification available is compatible with the international standard covering certification of CM personnel, • ensuring that scientific and technical developments in the field of CM and new materials are adequately catered for in the certification examinations available, • undertaking regular review of technical documentation and making recommendations for redrafting or amendment of existing documents and the need to draft additional documents. The CMTC shall ordinarily progress work through properly constituted Working Groups (WG), to which it has the power to delegate the responsibility for any of the tasks detailed at clause 3 above, and to which it will allocate tasks and deadlines for achievement through documented terms of reference which it shall cause to be drafted and approve. Ordinarily, the method of working of WG will be as follows: • The group will meet as often as is required to accomplish the task within the allocated deadline, • Meetings may be held at BINDT headquarters or at any location convenient to the members. • The work of the group may be conducted by correspondence but there must be an initial meeting to assess the task, and a wind-up meeting to ensure completion and reporting of the task.
303
PCN CONDITION MONITORING DEVELOPMENT PROJECT PLAN It is proposed to develop a certification process for CM personnel in disciplines, which satisfies the provisions of ISO/CD 18436. It is anticipated that a fully accredited certification scheme, with scope limited by the state of development of ISO CD 18436, could be in place by December 2001 if work proceeds without delay. The following actions have been identified as key to this project development plan: 1.
COMADIT to fulfil the role of Condition Monitoring Technical Committee (CMTC) within PCN Scheme (see organisation chart at Annex B).
2.
CMTC to nominate representatives to: a. b.
3.
BINDT CSD to draft with the co-operation of CMTC for approval by CMC and CAB: a. b. c.
4.
General Requirements and Scheme Description satisfying ISO/CD 18436 part 1 Specific requirements for the certification of CM personnel in each CM discipline in accordance with relevant ISO/CD 18436 parts Supporting procedures (some of which are currently available as PCN documents)
CMTC to nominate Specialist Working Group conveners for: a. b. c. d.
5.
BINDT Certification Management Committee (CMC) PCN Certification Advisory Board (CAB)
Acoustic Emission Infra-red Thermography Wear Debris Analysis Vibration Analysis
CM Specialist Working Groups will: a. b.
Develop question banks at all levels for each CM discipline Identify and appoint examiners for each CM discipline
ACHIEVEMENTS TO DATE Two main documents have been drafted to date: i)
General requirements for qualification and certification of condition monitoring personnel
ii)
Specific requirements for qualification and certification of vibration analysis condition monitoring personnel
The above draft documents are available for public information and comment. Further drafts, addressing other parts of ISO 18436 as they emerge, will be completed for distribution and comment in due course. Since the first ISO draft standard concerns vibration based condition monitoring it was decided to progress this technology as a first step. Contact has been made with the major vendors of vibration monitoring instrumentation systems to form the working group and many have agreed. The first meeting is to be set in motion at the time of going to press where the listed objectives will be addressed. Full participation by the vendors will be fundamental for early implementation of the scheme.
304
CRITERIA FOR QUALIFICATION AND CERTIFICATION Levels of certification Within the PCN Scheme there are three levels of certification, level 3 being the highest. Eligibility - general Candidates shall have a combination of education, training and experience relevant to the level of certification sought to ensure that they understand the principles and procedures applicable to machinery condition monitoring and analysis technology in which they have chosen to qualify. Training To be eligible for Level 1 or Level 2 examination, candidates must have successfully completed, prior to making application for examination, a BINDT validated course of training, at an Accredited Training Establishment, which covers the relevant PCN Machinery Condition Monitoring Technology syllabus. Taking into account the scientific and technical potential of candidates for Level 3 certification, it is considered that preparation for qualification can be achieved in various ways; by taking training courses, attending conferences or seminars and by studying books, periodicals or other specialised material. The minimum duration of training required for certification in the vibration analysis technology is as defined in Table 2 below: Table 2. Minimum Duration of Training (hours) Technician
Level 1
Level 2
Level 3
16
56
96
140
Note: The hours shovm represent cumulative totals of training hours for each classification.
Experience Industrial experience may be acquired either prior to or following success in the qualification examination. In the event that the experience is sought following successful examination, the results of the examination shall remain valid for up to one year. Documentary evidence (in a form acceptable to BINDT) of experience satisfying the requirements shall be confirmed by the employer and submitted to BINDT prior to the award of PCN certification. The minimum duration of experience required for certification in the vibration analysis technology is as defined in Table 3 below: Table 3. Minimum Experience Requirements (months) Technician
Level 1
Level 2
Level 3
12
24
36
60
Note: The months shovm represent cumulative totals of months of experience for each classification. Work experience in months is based on a nominal 40 Week (175 h/month).
305
Examination content For each certification level, the candidates shall be required to answer the fixed number of multiple choice questions in specified time duration as indicated by Table 4 below. Table 4 - qualification examination content Levels
Number of
Time (hours)
Passing Grade
Question s Level 1
100
3
75
Level \2
100
4
75
Level 3
60
5
75
The questions, covering the subjects in each Appendix to this specification, will be selected from a data base of questions existing at the time of the examination. Questions will be of a practical nature, yet test the candidate on concepts and principles required to conduct machinery condition evaluations. The questions may involve the interpretation of charts and plots. Simple mathematical calculations using a basic scientific calculator may be required; a summary of common formulae will be provided along with the examination questions. Grading To be eligible for certification, the candidate shall obtain a minimum grade of 75% in the written examination Certification Issue of certification, in respect of a successful candidate, normally takes place within 21 days of BINDT receiving the results notice from the appointed examiner. However, where a candidate for certification hzis achieved a pass in all relevant examination parts, but has not yet satisfied the pre-requisite experience requirements, the issue of certification may be deferred for up to one year from the date of success in the PCN examination. The period of validity of the certification is of five years from the date of certification indicated on the certificate. Certification will be invalid: • in any machinery Condition Monitoring Technology which is not covered by the certificate; • at the option of BINDT after reviewing evidence of unethical behaviour; • if a significant interruption takes place in the method for which the individual is certified; • from the date of issue of notification of failure in a PCN examination for recertification.
306
Renewal and Recertification Certification may be renewed for a new period of similar duration provided that the individuals supply documented evidence of satisfactory work activity without Significant Interruption, and satisfactory completion of continuation training as defined in the relevant Appendices to this specification. If the criterion for renewal is not met, the individuals may apply for re-certification following the procedures for new candidates.
CONCLUDING REMARKS The certification of Condition Monitoring operatives is long over due, as some industries have not enjoyed the promised benefits. Poorly supported CM programmes are seldom part of an enterprise's strategic plan but operated as a small interest group. As a result the capital investment is wasted largely due to insufficiently trained and qualified CM personnel. The advent of the PCN scheme within BINDT in accordance with the pending condition monitoring ISO standard will be a major boost to Industry. The certification scheme will position the UK as a centre for global accreditation of a technology that is seen by industry as fundamental to effective Plant Asset Management. With full participation from vendors of condition monitoring products and services, and support from industry the benefits will be: 1. Institutionalising of CM 2. Improved productivity 3. And a more professional based technology 4. Enabling a better return on investment.
ACKNOWLEDGEMENTS The contribution to the evolvement of the PCN scheme for condition monitoring by the BINDT COMADIT committee over the past several years is acknowledged. All the preparatory work has been voluntary and current progress would not have been possible without this contribution. The support by product vendors is now expected to increase dramatically as the actual exam question data base is developed. Without the vendors support the PCN for CM will not come to early fruition. The staff of the British Institute of Non Destructive Testing is thanks for their commitment and the authors thank their respective employers for allowing them to publish this paper. REFERENCES 1. Hills P. W.: Vibration Based Condition Monitoring - The Learning Issue, INSIGHT Vol. 38 No 8 pp576-579, August 1996, British Institute of Non Destructive Testing 2. Rao B. K. N.: The Need for a National Vocational Qualification (NVQ) in Condition based maintenance technology management - INSIGHT Vol. 39 No 8 PP 548-552, August 1997. British Institute of Non Destructive Testing 3. N 142 Training & certification of personnel for condition monitoring
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4. N 148 - First ISO CD 18436 - Condition monitoring and diagnostics of machines Accreditation of organisations and training and certification of specialists - Part 1: General requirements 5. N 150 - First ISO CD 18436 - Condition monitoring and diagnostics of machines Accreditation of organisations and training and certification of specialists - Part 2: Vibration analysis 6. ISO/TCI08/SC5/WG7 resolutions concerning the following related parts have been approved: 7. Resolution No. 3: ISO 18436, Condition Monitoring and Diagnostics of Machines Accreditation of Organisations and Training and Certification of Specialists. Part 3: Accreditation of Certification Bodies would be prepared as a New Work Item Proposal (NWIP). 8. Resolution No. 4: ISO 18436, Condition Monitoring and Diagnostics of Machines Accreditation of Organisations and Training and Certification of Specialists. Part 4: Lubrication Management and Analysis would be prepared as a NWIP. 9. Resolution No. 5: ISO 18436, Condition Monitoring and Diagnostics of Machines Accreditation of Organisations and Training and Certification of Specialists. Part 5: Thermography would be prepared as a NWIP. 10. Resolution No. 6: ISO 18436, Condition Monitoring and Diagnostics of Machines Accreditation of Organisations and Training and Certification of Specialists. Part 6: Diagnostics and Prognostics would be prepared as a NWIP. 11. Resolution No. 7: ISO 18436, Condition Monitoring and Diagnostics of Machines Accreditation of Organisations and Training and Certification of Specialists. Part 7: Condition Monitoring Specialists would be prepared as a NWIP. 12. ISO/TC 108/SC5/WG7 met on 27* March 2001 in Vienna, and it is anticipated that further information on progress may be available for information during the COM ADIT conference on 27* June 2001. ACKNOWLEDGEMENTS This paper first appeared in the Proceedings of CM2001, Oxford, UK and is reproduced with permission of Coxmoor Publishing Ltd.
308
ANNEX A Results of the BINDT-COMADIT Industry Survey into Training and Certification for Condition Monitoring - March 2000
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Condition Monitoring and Diagnostic Engineering Management Andrew G. Starr and Raj B.K.N. Rao (Editors) © 2001 Elsevier Science Ltd. All rights reserved.
THE EXPLOITATION OF INSTANTANEOUS ANGULAR SPEED FOR CONDITION MONITORING OF ELECTRIC MOTORS Ahmed Ben Sasi, Bradley Payne, Fengshou Gu, Andrew Ball Maintenance Engineering Research Group The University of Manchester Manchester, Oxford Road, United Kingdom, Ml3 9PL Email: [email protected] Phone: +44 (0)161 275 4407 Web: www.maintenanceengineering.com COMADEM2001,UK
ABSTRACT Recently, an instantaneous angular speed approach has been successfully used to detect changes in operational condition and reconstruct performance measures in diesel engines. This paper exploits condition monitoring of commonly encountered 3-phase induction motors. The paper conmiences with an overview of the angular speed concept. Implementation of signal processing required for this technique is also introduced. Then, fault detection and discrimination experimental results are presented. These experiments were conducted using an induction motor test rig operating under healthy and phase supply imbalance conditions. Finally, the capability of this approach is compared to vibration spectra technique. KEYWORDS Instantaneous angular speed, condition monitoring, induction motor, fast fourier transform, phase demodulation.
311
ABBREVIATIONS IAS: Instantaneous Angular Speed, CM: Condition Monitoring, FFT: Fast Fourier Transform, RMS: Root Mean Square, and IFFT: Inverse Fast Fourier Transform, rpm: Revolutions per minute. INTRODUCTION There has been extensive research work in the field of process condition monitoring of electric motors over recent years. 3-phase induction motors are one of the most common industrial drives and are often critical pieces of plant. Therefore, they deserve careful condition monitoring, Sharif & Grosvenor (1998). However, the high level of reliability of these devices has made it difficult to develop condition monitoring techniques on them. On the other hand, development of more reliable and cost effective methods of condition monitoring for electric motors has clear benefits such as, early detection of faults results in a longer machine life, reduces the need for visual inspections and reduces the frequency of emergency shutdowns. Importantly, effective application of CM leads to maintenance being carried out based on machine condition, Bradley Payne, Bo Liang, and Andrew Ball (1999). This research aims at developing a more applicable method of monitoring electric motors for industrial applications. In addition, exploiting the possibility of identifying and detecting some failure modes imposed on an induction motor based on changes in instantaneous angular speed. Angular speed is defined as the angular distance travelled by a rotating element divided by time taken for rotation. If the length of time over which the speed calculated is reduced, then the average speed measured will be more representative of any speedfluctuationsof the rotor. If the time is reduced still further, approaching zero, allfluctuationsin the speed will be represented and this leads to the concept of IAS. By reducing the time between 2 successive measurements the resolution of angular speed is increased. In practice tfie number of discrete points on the rotor that may be identified and the sampling capability limits the resolution. Therefore pulses per revolution (PPR) defmes encoder resolution. MOTOR TEST RIG FACILITY The test rig available (as shown in Figure 1) in the Condition Monitoring (CM) Laboratory, Manchester School of Engineering was used to carry out IAS measurements and tests. The electric motor test rig accommodates induction motors of power rating 3kW. These motors are relatively cheap, easy to dismantle and also allow fault conditions to be induced, see Table 1 for motor specification. Table 1 Motor Specification Classification 1 Nimiber of Phases 1 Number of Poles Power (kW) 1 Horsepower (hp) 1 Rated Speed 1 Winding 1 Number of Stator Slots
312
Induction 3 4 3 4 1410 rpm Star-woimd 1 36 1
Loading Bank TrI-Axial Acceierometers
Encoder
Figure 1: Motor Test Facility This experimental 3-phase electrical induction motor (illustrated in Figure 1) is coupled with a loading generator. The generator is connected to an electrical loading bank. This unit contains resistors so that tiie value of the resistance to the electrical output from the generator can be varied (the electrical energy is dissipated as heat). To apply Ml load to the experimental induction motor, the resistance must be lov^ to maximise the current flowing from the generator. To simulate low load the resistance must be high to minimise the flow of current from the generator. This relationship is best explained by considering the following mathematical relationship: P = V x I = V x (V / R) = V^ / R, where V is the power supply voltage (constant). An incremental optical encoder type is attached to the non-drive end and is connected directly to the rotor. It has its own power supply and two transducer outputs: an encoder, and a marker. This device provides an electrical pulse train, and the time interval between successive pulses is inversely proportional to the average speed of the encoder shaft over each time interval. The encoder takes 360 readings during every revolution, and the marker records every complete revolution of the rotor, Ahmed Ben Sasi, Bradley Payne, Andrew York, Fengshou Gu, and Andrew Ball (2001). Fauh conditions can be simulated in the motor test rig. However, the importance of obtaining baseline data from a healthy motor cannot be over-stressed, hence during any test procedure this data should be acquired first. Phase imbalance of power supply faults can be physically seeded using two switches on the top of the control box, as shown in Figure 1. Uneven use of a single phase (e.g. lighting all from one phase) or loose electrical connections typically cause this kind of fault. Phase imbalance of 20V and 40V can be achieved by dropping the voltage on one phase with a transformer. This arrangement is particularly usefiil as these changes can be made online without stopping the motor.
IMPLEMENTATION OF IAS CM ALGORITHM There are a variety of techniques to extract the angular speed signal from the raw encoder data. Fast Fourier Transform (FFT) analysis and phase demodulation techniques are used in this work. Sweeny & Randall, (1996). The whole process starts when a trigger generated from the marker is sent to the ADC board used by the condition monitoring computer to commence sampling of the pulse train yielded from
313
the encoder. The raw datafromthe encoder is sampled at a high sampling rate (^ IMHz) to obtain a welldefined square wave from the encoder output and a higher resolution is needed as well. The sampled signal is then converted into thefrequencydomain using a FFT algorithm and the carrierfrequencyis determined. The carrierfrequencyis approximately: 23.5Hz (the running speed) times 360 (the number of encoder transparent segments) » 8.5 kHz. The ideal rotor running speed should be 25 Hz according to Eq.l. Supply frequency 50 (1) = — = 25 Hz Pole pairs 2 However, at ideal speed, the rotor would appear stationary to the air gapfieldbetween the rotor and stator and no current would be induced in the rotor bars. Therefore, the rotor speed would settle to a value slightly lower than of the ideal case and this difference in speed of the induction motor is about 6% as shown in Eq.2. ^irlpal
s=
^ideal
^rotor
"~
25-23.5 = 6%, where S^^^ is the rotor speed, and s is the slip in speed 25
(2)
Phase demodulation is applied to extract the instantaneous angular positions of the shaft. Sweeny & Randall (1996). In this process, the modulated carrier frequency significant range is extracted and rearranged into a new vector. IFFT is performed on the resulting frequency vector and angular information is calculated. The phase information is "unwrapped" so that the phase increases continuously with time (rather than being restricted between ± 7i). Removing the carrierfrequencyphase gradient from the obtained total phase yields the change in angular rotor positions. Finally, those error angular positions are differentiated to get the change in the instantaneous angular speed in rad/sec. FAULT DETECTION AND EXPERIMENT RESULTS The most useful condition monitoring data is usually collected at higher loads. This would increase the effect of fauh condition in terms of motor overheating. Importantly, this data was recorded at only 0% load. To interpret the instantaneous angular speed results, methods such asfindingthe Root Mean Square (RMS) of the waveforms was applied. The RMS value was calculated for the angular data (over 360°) to provide a single numerical indicator for fault detection. Accordingly, 3 qualitative condition bands were set, based on experimental trials: (i) Level 1: Healthy (RMS<0.8), (ii) Level 2: (0.8<=RMS<1.1), (iii) Level 3: (RMS >= 1.1). Figure 2 shows sampled encoder data (500 samples for display purposes) in the normal operating condition of the induction motor. The instantaneous angular speed waveform appears almost flat with little variations over the nominal speed of RMS=0.5371 (as shown in Figure 3). 500 SamplMl Encodm- P U I M Train of HMlthy Opwatlon •10%
i Coileded encoder pul$e$ 1 after sampling.
100
ISO
200
250
300
350
400
Figure 2: Sampled Encoder Pulses
314
Instantaneous Angular Speed of Normal Operation at 0% Load
IAS response Is aimost fiat > iittte variation over nomSnal speed.
i
0 Wrn-, ^ ^"^V-Vwwv
«AA -V
,„jiX/^'v4^
A/^IV^UAXA*
N•>-•V"
'
yv^
I] W
J6
^
OneRevolulion
.10 0
50
100
150
200
250
300
350
Rotor Angle (degrees)
Figure 3: IAS at Healthy Operation In introducing a phase imbalance of 20V in the power supply to the induction motor, the resulting IAS signal seems to fluctuate with RMS= 0.9843, as shown in Figure 4. Remarkably, the IAS is modulated by 4*running speed (i.e. 4 times 23.5 Hz = 94 Hz). This is due to a reduction in the magnetic field strength at each of the 4 poles (i.e. every 90 angular degrees). Instantaneous Angular Speed of 20V Phase Imbalance at 0% Load
IAS is modulated by 4*funnin9 speed. 1
m^
m
^Arw>
0
50
':.^v:'--rr^ ' ^X"( ^ --,ir«" W^ %
100
150
y
200
250
N,.-l
300
VV\J/
]"f
350
Rotor Angle (degrees)
Figure 4: IAS at 20V Phase Imbalance Applying a further 20V drop on the phase, the IAS waveform fluctuates with a higher speed change amplitude (RMS=1.32), as shown in Figure 5. Therefore, severity assessments can be regulated according to those RMS values to classify the errors in the induction motor rig as healthy, abnormal, or faulty. Notably, the amplitude of modulation has increased as a result of further decrease in the strength of each magnetic pole.
315
Instantaneous Angular Speed of 40V Ptiase Imbalance at 0% Load
1 An^Mliicle of modulation haB Nicraased stM fiuither.
-yr^
[y^
Y
pTTl-
A
"I
—7 *^'\
v^
100
150 200 Rotor An(^a(<
Figure 5 Phase Imbalance of 40V More IAS measurements were performed with dijfferent load values (25%, 50%, 75%, and 100%) for normal operation, 20V, and 40V phase imbalance faults. Those experiments were repeated 6 times in different days to make sure of consistency. As can be deduced from Figure 6, the 0% load IAS RMS classification works satisfactorily between the normal operation of the machine and the 20V phase imbalance regions since they are separable (i.e. RMS<0.8). However, there is an overlap of RMS values between 20V and 40V phase imbalance regions. RMS threshold is not adequate to classify those regions. Therefore, another method is developed taking into account the rotor speed (rpm) as a second parameter which is described in the linear discriminate function Eq.3, Eq.4, and Eq.5 below: RMS-0.8=0 RMS-0.0169*rpm+21.7650=0 RMS+0.0135*rpm-20.3375=0
(3) (4) (5)
Characteristics of F e a t u r e P a r a m e t e r s
1
1 1
* Nomial Operation 0 20V Phase imbalance * 40V Phase ImbalanGe
^^ dtf^
A
""^r^
^ A
"r-
% 1350
1360
* 4
• ••
• •• 1370
1380 1390 1400 1410 Average Shaft Speed (rpm)
4
•
1420
••
1430
Figure 6: New Classification Method
316
1440
VIBRATION SPECTRA RESULTS Various asymmetry faults can be detected in induction motors by monitoring the stator core vibration using IAS techniques. If there is an unbalanced power supply in the induction motor, the vibration signal will contain a significant component with twice the supply frequency (50Hz). For phase imbalance the fault symptom is a significant increase in the 100 Hz component (compared with baseline data), which more or less equivalent to the result obtained by the IAS technique («94 Hz) due to the previously mentioned slip in motor speed phenomenon. However, in vibration spectra technique, this symptom would not be obvious unless at high load rates, say 75%, as shovm in Figures 7 & 8, whereas previous resuhs were all obtained at 0% load for the IAS approach.
40 V Phase Imbaiance
Healthy Operation
50
100
100
Frequency (Hz)
Frequency (Hz)
Figure 7: Vibration Spectrum at 75% Load
40 V Phase Imbalance
Healthy Operation
50
100
150
0
50
100
Frequency (Hz)
Frequency (Hz)
Figure 8: Vibration Spectrum at 0% Load
317
160
CONCLUSIONS Phase asymmetry faults can be detected in motors by monitoring the angular rotor vibration with assessment of fauh severity being another possibility. Modulation by the number of poles provides the key to diagnosis of this type of fault. This could be achieved at 0% operational load, which would pronounce the sensitivity of the IAS method compared to the vibration spectra technique. Promising results suggest similar applications may be made to the detection of other motor faults and other plants (e.g. compressors, pumps). The IAS approach outlined in this paper is not just dedicated to monitoring electric motors, but may also be applied to any rotating machinery. However, the IAS RMS thresholds used appear to be insufficient to distinguish between classes at high loads. Therefore, a set of linear discriminate functions was developed using the average shaft speed (rpm) in addition to the IAS RMS values to classify the overlapped regions. References Ahmed Ben Sasi, Bradley Payne, Andrew York, Fengshou Gu, Andrew Ball. (2001). Condition Monitoring of Electric Motors using Instantaneous Angular Speed, MARCON 2001, Gatlinburg, Tennessee, USA. Bradley Payne, Bo Liang, Andrew Ball. (1999). Modern Condition Monitoring Techniques for Electric Machines, Proceedings of the First International Conference on the Integration of Dynamics, Monitoring and Control (DYMAC '99), Manchester, UK, 325-330. Sharif M A, and Grosvenor R I. (1998). Process Plant Condition Monitoring and Fault Diagnosis, Proc. Instn. Mech. Engineers, 212: E, 13-30. Sweeny P J, and Randall R B. (1996). Gear Transmission Error Measurement Using Phase Demodulation, Proc. Instn. Mech. Engineers, 210,201-213.
318
Condition Monitoring and Diagnostic Engineering Management Andrew G. Starr and Raj B.K.N. Rao (Editors) © 2001 Elsevier Science Ltd. All rights reserved.
DISCRIMINATING BETWEEN ROTOR ASYMMETRIES AND TIME-VARYING LOADS IN THREE-PHASE INDUCTION MOTORS S. M. A. Cruz and A. J. Marques Cardoso Universidade de Coimbra, Departamento de Engenharia Electrotecnica Polo n - Pinhal de Marrocos, P-3030-290 Coimbra, Portugal Email: [email protected], [email protected]
ABSTRACT This paper deals with the use of the synchronous reference frame current Park's Vector in the diagnosis of rotor faults in three-phase induction motors, under the presence of time-varying loads. The effects produced by different kinds of time-varying loads are evaluated, stressing its implications in the diagnosis of rotor faults. It is shown that it is possible to discriminate the effects produced by a motor load torque oscillation from the ones introduced by the presence of a rotor asymmetry. Simulation results show that when the motor has a rotor fault and is coupled to a time-varying load, it is still possible to detect the fault. On-site tests conducted in a cement-mill corroborate the results obtained by simulation.
KEYWORDS Park's Vector Approach, rotor asymmetries, time-varying loads, diagnostics, induction motors.
INTRODUCTION In the past decades, great research efforts have been focused on the development of adequate strategies for the diagnosis of rotor cage faults in operating three-phase induction motors, Schoen & Habetler (1995), (1997a), (1997b). Several of the developed techniques are settled on the analysis of the stator current spectrum and assume that the motor load torque is constant. However, it is known that some kinds of mechanical loads, such as compressors, impose oscillations on the motor load torque, at frequencies multiples of the rotational speed of the rotor. Additionally, several industrial processes are time-dependent, thus leading to load torque changes, which will introduce some additional components in the motor supply current spectrum, which can overlap or mitigate the effects produced by an eventual rotor fault. In fact, if the frequency of the load torque variation is similar to the double rotor slip frequency, it is extremely difficult to distinguish this condition from the one in which the motor has developed a rotor asymmetry. In these circumstances, the diagnosis based solely in the analysis of a single phase current is seriously compromised, Thomson (1992), Filippetti et al. (1998), Salles et al.
319
(2000), Cruz & Cardoso (2001b). In this context, a new approach has been developed in order to improve the diagnosis of the occurrence of rotor cage faults in operating three-phase induction motors, Cruz (1999). This new diagnostic technique, the so called synchronous reference frame current Park's Vector Approach, allows the detection and quantification of this fault by the definition of an adequate severity factor, which is almost independent of the motor parameters, Cruz & Cardoso (2000). Although this technique was originally introduced for the diagnosis of this kind of fault when the motor is coupled to a constant load, recent works have demonstrated its ability to deal with time-varying loads, Cruz & Cardoso (2001b), (2001c). This paper is intended to complement these recent works, in order to fully exploit the capabilities of the synchronous reference frame current Park's Vector in the diagnosis of the occurrence of rotor asymmetries in three-phase induction motors under the presence of time-varying loads.
SUDDEN LOAD CHANGES In order to have a better insight into the effects produced by a time-varying load, an analytical study was conducted, based on the use of the well-known d-q model of the induction motor. The state-variable form of the stator and rotor voltage equations of the induction motor, with the flux linkages as state variables, in the general reference frame, are given by, Vas (1992):
dt
"^ r/'"
"''•'
t
'^^u,s-(o,\i/^-Y^^^^^'^'
^^^
d¥c- = i:^¥u.s-ipVdr'^{co,-Q)„)\i/^,
dt
Z
^y.r_K^.,
dt
Z
,.,
.., X,.,
1
= ^¥,.s-i0)a-0)„)w^--rl/^^, T/"' ^ ° """'^ T
where ¥ds (¥qs) ¥dr i¥qr) ^ds (\s) k^ (k^) T^ (r/) 0)^ co„
stator flux linkage along the ^-axis (^-axis) - Wb ^otor flux linkage along the J-axis (^-axis) - Wb stator voltage along the J-axis (^-axis) - V stator (rotor) coupling factor stator (rotor) transient time constant - s angular speed of the general reference frame - rad.s'^ electrical rotor angular speed - rad.s*
The stator and rotor time constants are defined as:
320
(3)
(4)
r.=i
(5)
r;.i
(6)
where 4 and I^ represent the stator and rotor transient inductances, respectively. Although this formulation of the motor equations is particularly suitable for the inclusion of the magnetic saturation, the study here conducted assumes a linear magnetic circuit. Additionally, although any reference frame could have been chosen in the simulation study, a synchronous reference frame {Q)„ =2;rX) with the ^-axis aligned with the motor supply voltage was used because in this way, in stationary conditions, all the quantities involved in these equations appear as constants. Equations 1-4, combined with the equation of motion, given by:
can be solved by any numerical integration method, thus obtaining the stator and rotor flux linkages. Using the relations between the flux linkages and the stator current:
ks^T{^,s-K¥,r)
(9)
it is possible to plot the current Park's Vector in a synchronous reference frame - /^, =f{ij, ). In the absence of faults, this representation becomes a single point, whose distance to the origin of the coordinates corresponds to the amplitude of the fundamental supply current. This representation is the characteristic one of an induction motor, operating with a constant load and fed by a perfectly symmetrical and sinusoidal voltage supply system. For different load levels, the aforementioned point will assume different locations that, when grouped together, define part of the classic circle diagram, Cruz & Cardoso (2001b). When the motor has an oscillating load torque, the current Park's Vector pattern differs from the one characteristic of the normal behaviour. For the purposes of analysis, let us first consider the case when the motor load torque has a sinusoidal variation, superimposed to a mean load torque. The details of the induction motor used in the simulation study can be found in Appendix. Figure 1 shows the obtained results for the case of the induction motor coupled to a time-varying load for different frequencies of the load torque oscillation. As can be seen, when the frequency of the load torque oscillation is relatively small, the locus of the current Park's Vector, is represented along the circle diagram, which represents the locus of the current Park's Vector in steady-state conditions for different load levels (Figure 1(a)). For this kind of loads the motor is in a quasi steady-state condition because the stator and rotor electric transients are negligible thus justifying the observed behaviour. In these conditions, the variations of the current d-q axis components, imposed by the sinusoidal variation of the load torque, are almost in phase and have the same frequency as the load torque oscillation (Figure 2(a)).
321
When the frequency of the load torque oscillation is increased, the obtained current Park's Vector pattern assumes the shape of an elliptic figure (Figures 1(b)-1(d)), This is due to the fact that in these conditions the current Park's Vector components are not anymore in phase (Figure 2(b)). Additionally, as the frequency of the load torque oscillation increases, the amplitude of the current Park's Vector components decreases because the higher frequency components of the load torque are more heavily damped by the combined rotor-load inertia.
(a)
(b)
40
30
< 3-
1
20
10
20
30
40
50
ids (A)
(c)
(d)
Figure 1: Synchronous reference frame current Park's Vector pattern for the following cases: (a) ^w=100 + 40sin(2jTxU) Nm; (b) 7)^ =100 + 40sin(2;rx3/) Nm; (c) 7;^^ =100 + 40sin(2;rx7.50 Nm; (d) T,^j =100 + 40sin(2;rxl5/) Nm. So far, the discussion has been focused on the study of sinusoidal variations of the motor load torque. However, it is known that mechanical loads such as compressors do not impose this kind of oscillations. In fact, mechanical loads such as piston compressors, used for example in chemical plants, demand a torque with a periodicity equal to the rotational frequency of the rotor of the motor. In these cases, the load torque imposed to the shaft of the motor oscillates between a maximum and a minimum
322
(a)
(b)
Figure 2: Variation of the current Park's Vector components, for the following cases: (a) T,oaj =100+40sin(2;rxl/) Nm; (b) T;^^ =100 + 40sin(2;rx7.5r) Nm. value. Far from having a sinusoidal variation, the load torque encloses several harmonics with high amplitudes. In order to model this kind of mechanical load, it will be assumed here that it can be represented by a square-wave, with a duty-cycle of 50%, superimposed to a mean load torque. For comparison purposes, the values of the mean load torque and of the oscillating component will be considered equal to the ones established before. Figure 3 shows the obtained results for the case of the induction motor coupled to a mechanical load with a square-wave load torque oscillation. Comparing the obtained results for this kind of load with the ones earlier presented for the case of the mechanical load with the sinusoidal variation, it is possible to see that they are somewhat similar. The
(a)
(b)
Figure 3: Synchronous reference frame current Park's Vector pattern for the following cases: (a) Tf^^j =100+40square(2;rxl/) Nm; (b) 7;,^^ =100 + 40square(2;TXl5/) Nm.
323
main difference lies in the fact that for the case of the square-wave load torque oscillation, even for frequencies of oscillation as low as 0.1 Hz, the obtained current Park's Vector pattern is always an elliptic figure. Several simulated results have shown that this result holds true for loads with a dutycycle different from 50%. This is due to the step change of the load torque that these mechanical loads impose to the motor, thus giving rise to electric transients, particularly in the rotor of the motor. Another interesting remark is related with the fact that for the higher frequencies, the current Park's Vector pattern obtained for both kinds of load torque oscillations is almost the same (Figures 3(b), 1(d)).
ROTOR ASYMMETRIES AND TIME-VARYING LOADS So far, the presented results are related with the effects produced by a time-varying load coupled to an healthy motor. At this point, attention will be given to the diagnosis of the occurrence of rotor asymmetries based on the use of the synchronous reference frame current Park's Vector either when the motor is coupled to a constant load or to a time-varying one. As it was reported by the authors in previous works, Cruz & Cardoso (2000), (2001b), (2001c), the representation of the current Park's Vector, in a synchonous reference frame, corresponding to an healthy motor, is a single point. In the presence of a rotor fault, the current Park's Vector representation will be an elliptic figure, whose major radius is equal to the sum of the current spectral components at frequencies of ( 1 1 2 5 ) / , where f^ and s are the fundamental supply frequency and rotor slip, respectively. This elliptic figure is centered at a point of coordinates {i^sm
324
and an end-ring of the rotor cage. The equations thus obtained were complemented with the usual equation of motion. Figure 4(a) shows the obtained results for the case of the motor running with three adjacent broken rotor bars and coupled to a constant load. As it was previously mentioned, the current Park*s Vector pattern is an elliptic figure, whose major axis always lies in the second quadrant of the axis of coordinates. It should be emphasized that while the length of this axis is almost independent of the value of the combined rotor-load inertia, its slope depends on this quantity.
(a)
(b)
Figure 4: Synchronous reference frame current Park's Vector pattern for the case of the motor running with three adjacent broken rotor bars under the following load conditions: (a) T,^^^ = 22.5 Nm; (b) r,^^ =22.5 +7.5sin (2;rx 0.4/) Nm.
When the motor is coupled to a time-varying load (Figure 4(b)), it is possible to see that the pattern previously obtained suffers deviations along a direction that resemble the trajectory of a circle diagram. In fact, it was shown in Cruz & Cardoso (2001c) that even when the motor has several broken rotor bars, the behaviour of the fundamental component of the motor supply current can be predicted by the use of circle diagrams, one for each extension of the fault. The main difference between these circle diagrams and the one characteristic of the healthy motor lies in the fact that their diameter decreases with the increase of the extension of the rotor fault.
ON-SITE TESTS In order to validate the results obtained by simulation, several experimental tests were carried out in motors running in the real industrial environment. Figure 5 presents the obtained results for two medium-voltage induction motors. The first motor, whose obtained results are presented in Figure 5(a), has no rotor asymmetry but is coupled to a timevarying load. In this case, the obtained current Park's Vector pattern is very like the results obtained by simulation for the case of an oscillating load torque of low frequency. Figure 5(b) shows the current Park's Vector pattern obtained for the second motor, coupled to a time-varying load but with an asymmetry in the rotor circuit (slip-ring induction motor). Similarly to the results obtained by simulation, it is possible to identify an elliptic figure oriented along the second quadrant of the axis of coordinates, which suffers deviations mainly along the ^-axis, as the motor load level changes.
325
The presented results show that the rotor asymmetry can be detected by the use of the synchronous reference frame current Park's Vector, even when the motor is coupled to loads that exhibit changes in time. Although the detection of the fault can be made in these working conditions, its quantification still requires some additional research work.
Figure 5: Experimental results concerning the synchronous reference frame current Park's Vector pattern for the following cases: for the following cases: (a) 6000 V, 6-pole, 1850 kW induction motor, coupled to a time-varying load; (b) 6000 V, 6-pole, 1850 kW induction motor (cement-mill), with a rotor asymmetry.
CONCLUSIONS This paper addresses the problem of time-varying loads and its implications in the diagnosis of rotor faults in operating three-phase induction motors. A simulation study was presented, which shows that by the use of the synchronous reference frame current Park's Vector it is possible to distinguish the presence of a time-varying load from the existence of a rotor fault. It is shown that when the motor load torque as an oscillating component of low frequency, the current Park's Vector pattern is an elliptic figure oriented along the first quadrant of the axis of coordinates. The presence of a rotor fault manifests itself in the current Park's Vector pattern by the appearance of an elliptic figure whose major axis is oriented along the second quadrant of the axis of coordinates. The orientation of the obtained representation thus allows to distinguish the presence of time-varying loads from the truly existence of a rotor fault. When a rotor asymmetry and a time-varying load are present, it is still possible to detect the fault although the evaluation of its extension in these adverse conditions still is an open problem. In these circumstances, the synchronous reference frame current Park's Vector Approach can give an indication about the rotor asymmetry, even if the motor load is varying, although not allowing for an exact evaluation of its severity.
APPENDIX Some parameters of the induction motor used in the d-q model: ?„ = 25 kW; C/ = 380 V; / = 50 Hz; 4 pole; J = 0,5 kgm'
326
R^ =0.37 Q; R^ =0.71 Q\ 4 =86.88 mH; L, =87.48 mH; Z,^ =84.18 mH
REFERENCES Cruz S. M. A. (1999). Rotor cage fault diagnosis in three-phase induction motors, by the Park's Vector Approach - Definition of adequate severity criteria (In Portuguese). M.Sc. thesis, University of Coimbra, Portugal. Cruz S. M. A. and Cardoso A. J. M. (2000). Rotor cage fault diagnosis in three-phase induction motors, by the synchronous reference frame current Park's Vector Approach. Proceedings of the International Conference on Electrical Machines, Espoo, Finland, vol. n, 776-780. Cruz S. M. A. and Cardoso A. J. M. (2001a). Diagnosis of the multiple induction motor faults using Extended Park's Vector Approach. InternationalJournal of COMADEM 4:1, 19-25. Cruz S. M. A. and Cardoso A. J. M. (2001b). Rotor cage fault diagnosis in operating three-phase induction motors, under the presence of time-varying loads. 9th European Conference on Power Electronics and Applications, Graz, Austria. Cruz S. M. A. and Cardoso A. J. M. (2001c), Further Developments on the use of the synchronous reference frame current Park's Vector Approach. Conference Record of the 2001 IEEE International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives, Grado, Italy. Filippetti F., Franceschini G., Tassoni C. and Vas P. (1998). AI techniques in induction machines diagnosis including the speed ripple effect. IEEE Trans. Ind AppL 34:1, 98-108. Salles G. et al. (2000). Monitoring of induction motor load by neural network techniques. IEEE Trans, Power Elect 15:4, 762-768. Schoen R, R. and Habetler T. G. (1995). Effects of time-varying loads on rotor fault detection in induction machines. IEEE Trans. Ind. Appl 31:4, 900-906. Schoen R. R. and Habetler T. G. (1997a). A new method of current-based condition monitoring in induction machines operating under arbitrary load conditions. Electric Machines and Power Systems 25:3, 141-152. Schoen R. R. and Habetler T. G. (1997b). Evaluation and implementation of a system to eliminate arbitrary load effects in current-based monitoring of induction machines. IEEE Trans. Ind. Appl. 33:6, 1571-1577. Thomson W. T. (1992). On-line current monitoring - The influence of mechanical loads or a unique rotor design on the diagnosis of broken rotor bars in induction machines. Proceedings of the International Conference on Electrical Machines, Manchester, U. K., 1236-1240. Vas P. (1992). Electrical machines and drives - A space-vector theory approach. Clarendon Press, Oxford, UK.
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Condition Monitoring and Diagnostic Engineering Management Andrew G. Starr and Raj B.K.N. Rao (Editors) © 2001 Elsevier Science Ltd. Allrightsreserved.
ASYMMETRICAL STATOR AND ROTOR FAULT DETECTION USING VIBRATION, PER-PHASE CURRENT AND TRANSIENT SPEED ANALYSIS B. Liang ^ A.D. Ball ^ and S. Iwnicki ^ ' Department of Engineering and Technology, Manchester Metropolitan University, Manchester, Ml 5GD, UK Department of Mechanical Engineering, University of Manchester, Manchester M13 9PL, UK
ABSTRACT Condition monitoring of electrical machines has received considerable attention in recent years. Many monitoring techniques have been proposed for electrical machine fault detection and localisation. This paper presents theoretical and experimental analysis of asymmetrical stator and rotor faults in threephase induction motors by using vibration, phase current and transient rotor speed analysis. The performance and sensitivity of the above three techniques are given. KEYWORDS Condition monitoring, fault diagnosis, induction motors, vibration, stator phase current, transient rotor speed. 1. INTRODUCTION Although induction motors are trad^'tionally thought to be reliable and robust, the possibility of faults is unavoidable. Early fault detection allows preventative maintenance to be scheduled for induction motors that might not ordinarily be due for service. It may also prevent extended periods of down time (or even catastrophic consequences) caused by the failure of a motor. For example failure of an emergency motor in a nuclear power station cannot be allowed. Therefore recently the condition monitoring and fault diagnosis of induction motors has received considerable attention and techniques have been proposed and applied to fault detection in three-phase induction motors, Robert et al showed that mechanical defects are detectable through variations in the vibration of the stator core [1-2]. It has also been suggested that phase current monitoring can provide similar indications [2-3]. Another possible way for induction motor fault diagnosis is the analysis of transient rotor speed since the pulsating torque due to stator or rotor motor faults will alter or modulate rotor speed and can therefore be detected using transient rotor speed analysis [5]. This paper presents theoretical and experimental analysis of asymmetrical stator and rotor faults in three-phase induction motors by using vibration, 329
phase current and transient rotor speed analysis. The performance and sensitivity of the above three techniques are given.
2. THEORETICAL DEVELOPMENT 2,1 Induction Motor Operation with Asymmetric Stator Faults The stator currents in vohage fed induction motors are often not symmetrical. There are several causes of asymmetry such as uneven winding placement, winding short circuits and asymmetry of the supply voltages etc. The asymmetry of the currents and/or windings is reflected in the amplitude and/or phase asymmetry of the magnetic motive forces (mmfs) created by each phase. Independent of its origin, every asymmetry in an induction motor creates negative sequence mmfs. It is known that for a symmetrical three-phase winding fed by a set of symmetrical currents only the positive sequence components of mmfs are left and the negative sequence components are summed to zero [4]. If there is an asymmetric stator or rotor winding, the negative sequence components of the mmfs will no longer be summed to zero. This will cause some disturbance in the machine's operation. Negative sequence mmfs tend to deteriorate machine performance and are therefore minimised or eliminated whenever possible. In general the instantaneous electromagnetic torque of an electrical machine can be obtained by the following form [4]: T^^lpiP^xJ^
(1)
which is the vector product of stator flux linkage and stator current. The voltage equation of the stator in the arbitrary frame can be expressed as : K , = V . + ^
(2)
The corresponding stator voltage K,, stator current 7, and stator flux linkage ip^ can be expressed as a positive sequence (subscript s\) and a negative sequence (subscript 52) component. K>=vy'',K2=K2e~""'
(3)
l,=l,e"'^-,'l,^^l,e'""'
(4)
r„=r„.^'"',r>.=v^,2^"^"
(5)
Substitution of Eqns.2-5 into Eqn.l yields: t'
average
pulsating
^ ^
where T^^^^^^^^ is the electromagnetic torque, which is constant in time and contains two components (the positive and negative torque sequences - T, and Tj respectively) ^average
330
~ ^\
~ ^2
V' )
where T, =(3P/26;i)(|F,,||/,,|cosa, -|7,,f ^.,)
(8)
T^ =:(3P/2a>,){|F,.2||7,2|cosa,2 -|7,2|'/?,)
(10)
The most important feature obtained from Eqn.6 is the pulsating torque component r^„/va/,»K which can be expressed as follows: 7'M™.« =(3^/2«,)Re[(F„7,, -Vjje'"""}
(11)
It can be seen that this torque component pulsates at an angular frequency of 2 ^,. The pulsating torque is caused by both the interaction of the positive sequence stator current and negative sequence stator flux linkages and also by the interaction of the negative sequence stator currents and positive sequence stator flux linkages. Therefore, the 2co^ double frequency time varying pulsating torque component is the symptom of an asymmetrical stator fault. This frequency does not exist when an induction motor operates under symmetrical stator conditions.
2.2 Induction Motor Operation with Asymmetric Rotor Faults Similarly, the effects of asymmetric rotor faults (for example, due to unbalanced internal resistance through a broken rotor bar) can be deduced. In the steady state and by referring to Eqns. 1 -5 7:,=|p(^,.x7,.)
(12)
The electromagnetic torque T^ under asymmetrical rotor conditions is gained as follows: f
average
pulsating
^
•'
where L.e.ag. = -
^
2sco^
f
J | 7 , , | c O S a , , -j7^,|'i^^. - | F , , | | 7 , , | c O S a , , - | 7 , , f 7 ? J I
II
I
i
I
I
II
T^,....,=j^R4{VA^-vJ,y'''''']
1
(14)
I I
(15)
From Eqn.15, it can be seen that the biggest difference with Eqn.l 1 is that the frequency of oscillation is now 2s0)^. The difference between Eqn.l 1 and Eqn. 15 is caused by the stator and rotor asymmetries respectively. Generally, the symmetrical stator windings produce a rotating field at frequency co^ when symmetrical supply currents are applied to the stator windings. This rotating field induces an electromagnetic force in the rotor bars at frequency sco^. Since the rotor bars (with end rings) can be considered as phase windings of the rotor, the induced alternating currents will flow through the rotor bars. Thus the rotor phase windings with the induced alternating currents of the rotor will produce only a positive sequence fundamental component of mmfs of the rotor (which rotate at frequency sco^ if the rotor is symmetrical). If a bar is broken or if there is some other rotor fault, an asymmetry is created.
331
The induced currents of the rotor will then cause a negative as well as positive sequence component in the stator windings because the resultant negative sequence component is no longer zero. The angular speed of the positive and negative sequence mmfs are co^+sco^ = co^ and co,.-sco^ = (1 - 2s)o)^ with respect to the stator. Therefore the angular frequencies of the positive and negative sequence stator currents are co^ and (2^ -1)6>, respectively. It can be seen that the stator currents now consist of the normal supply frequency component o)^, together with a component (1 - 2s)cO]. The variable frequency component has the effect of modulating the supply frequency component at twice the slip frequency. Therefore, the 2so)^ frequency pulsating torque and its harmonic components are the symptoms of an asymmetrical rotor fault. These frequencies do not exist when an induction motor operates under symmetrical rotor conditions.
3. EXPERIMENTAL RESULTS 3J Vibration Analysis for Induction Motor Faults An induction motor test facility was designed, built and refmed. The test rig consists of a 3 kW induction motor, a 5 kW DC motor used to absorb the power, a resistor bank used for putting load on the motor under test and corresponding control boxes and instrumentation. The instrumentation consists of accelerometers, Hall effect current transducers, a high resolution speed encoder (360 pulses per cycle), amplifiers, filters and a DIFA 210 data acquisition system. Figures 1-4 present stator vibration spectra (on a logarithmic scale) under 0%, 25%, 50% and 75% operational loads. Comparison is made between a healthy induction motor and one with a broken rotor bar (that creates rotor asymmetry). For the asymmetrical rotor fault, the 2sco^ components are expected. In Figures 2(b)-4(b), illustrating results for the motor with a broken rotor bar and above 25% load, the sidebands can be seen around the fundamental rotor frequency. These frequency components are slip dependent and shift outwards from 0.6Hz, 1.60Hz, 2.34Hz and 3.12Hz when the loads are increased. When the motor is healthy, no sidebands are visible (even as loads increase from 0%)-75%). With the motor operating under no load (Figure 1), sidebands are not detectable in the vibration spectra whether a rotor bar is broken not. The reason for this is because of the slip being too small. As stated previously, asymmetrical stator faults (caused by stator winding faults or asymmetrical phase supply voltages) are also common in induction motors. An asymmetrical stator system fault was physically seeded in the test rig by decreasing one of the three phase supply voltages. Figure 5-6 shows the vibration spectra (on a logarithmic and a linear scale) from a test motor. A comparison is made between healthy operation and 20V and 40V drops in one phase of voltage supply (creating an stator asymmetry). As expected, the faulty characteristic frequency for this fault condition, 2co^ = 2 x 5 0 = 100Hz, increases when the asymmetrical stator content is introduced and increased. When the same spectra are displayed on a linear scale (Figure 6), the 100 Hz component is seen to substantially increased when an asymmetrical stator fault is introduced.
3,2 Stator Phase Current Analysis for Induction Motor Faults Figures 7-10 show phase current spectra under different load conditions for a healthy motor and for a motor with one broken rotor bar respectively. These zoom spectra are centred on the fundamental supply frequency (50Hz), with a frequency span of 100 Hz (+50 Hz and -50 Hz). It can be seen that very similar phenomena to that seen in vibration spectra occur in this phase-current spectra. The sideband locations are related to load and subsequently slip. The slip and sidebands (0.6 Hz, 1.6 Hz,
332
2.34 Hz and 4.6 Hz) are associated with a range of loads (0%, 25%, 50% and 100%). As with the vibration spectra, when the loads are below 25%, the sidebands (due to the motor with a broken rotor bar) are not visible because of the small amount of slip. When the loads are increased above 25%, a series of sidebands appear in the expected locations. These sidebands in the phase current spectra are much clearer than those in the vibration spectra. Figures 11-13 show the three-phase current spectra when Phase-A was forced to drop by OV, 20V and 40V from the original phase voltage. It can be seen that the current spectra gradually show sideband presence around the harmonics of supply frequency (indicating stator current modulation) as the extent of asymmetry is increased. However the variation of the two times supply frequency (at 100 Hz) is hardly visible in phase current spectra. This is expected because the dominant odd harmonics and the arrangement of stator windings are contributed to minimise the even harmonics in phase currents. This effect does not affect the sensitivity of phase current analysis for detection of asymmetrical supply faults as this fault can be easily detected by observing the difference between the three-phase stator currents.
3J Transient Speed Analysis for Induction Motor Faults From theoretical analysis, it is known that for asymmetric stator or rotor faults, the corresponding fault symptoms are sidebands spaced at ISCD^ and 2o)^. These components give rise to torque ripples at frequencies of 2sco^ and 2co^ respectively, which produce speed ripples at differing amplitudes. Therefore monitoring rotor speed fluctuations via a high resolution encoder and performing additional signal processing also provides information on the motor condition. Figure 14 presents the speed variation of a healthy motor during start-up. After the speed reaches a steady state condition the speed curve is reasonably flat. No fluctuations of speed caused by 2sco^ and 2co^ (100 Hz) can be seen and this is further proved by a corresponding spectrum (Figure 18 (a)). This indicates that the motor is running under normal, healthy conditions. Figure 15 shows the speed curves for the motor with one broken rotor bar and two broken rotor bars. It can be seen that the average speed decreases when the number of broken rotor bars is increased. The speed spectrum in Figure 18 (b) indicates that there is a very small 2sco^ (4.6 Hz) component and the 2co^ (lOOHz) component slightly increases compared with speed spectrum for a healthy motor. Figures 16-17 illustrate the speed variation caused by an asymmetric stator fault. A large speed ripple can be observed at 2ty, = lOOHz (Figure 18 (c) and (d)). This suggests that the pulsing torque caused by the asymmetrical supply voltages give rise to speed fluctuations. When the extent of asymmetry in the stator increases, the speed ripple increases proportionally. This is clearly seen in both the speed time curve and spectrum. However the speed spectrum of a motor with a broken rotor bar (Figure 18 (b)) does not show any sidebands around the fundamental rotor speed. This is because the expected speed ripple modulation caused by the broken rotor bar fault could be absorbed by the inertia and load of the rotor [5].
4. CONCLUSIONS Three methods (vibration, per-phase current and transient speed analysis) have all been assessed on their ability to detect induction motor faults. It was found that they all possess their own advantages and disadvantages. Vibration analysis was found to be sensitive to both asymmetrical rotor and stator faults. However the main drawback of this approach was the requirement for detailed information on motor design characteristics such as knowledge on the frequency response functions (FRFs). FRFs were required because mechanical and electrical responses will vary at different accelerometer positions making quantification of fault conditions. Per-phase current analysis was found to be very sensitive to asymmetrical rotor faults (such as broken rotor bars). However, because of the design of induction motors, the variation of the 2x frequency 100 Hz is difficult to detect in phase current spectra. This does not affect the sensitivity of the per-phase current analysis for detection of 333
asymmetrical stator supply faults as this fault can be easily detected by observing the difference between the three-phase stator currents. Transient speed analysis provides a good indication of asymmetric stator supply because the faulty pulsing torque created by the asymmetric stator and rotor faults causes speed ripples at frequencies of 2sco^ and Ico^. These components can easily be detected when compared with the speed spectra for healthy motor operation. However the sensitivity of the technique depends on the external load and rotor inertia as these influences are capable of filtering out the symptomatic speed modulation around the fundamental rotor speed.
REFERENCES [1] Robert R. G. (1984). "Computer techniques applied to the routine analysis of rundown vibration data for condition monitoring of turbine-alternators". Proceedings of International Conference on Condition Monitoring. Swansea. UK. 229-242. [2] Cameron J. R., Thompson W. T. and Row A. B. (1982). "Vibration and current monitoring for detection air gap eccentricity in large induction motors". Proceedings of International Conference on Electrical Machines. London. 173-179. [3] Thomson W.T. and Chalmers S.J. (1987). " An on-line, computer based current monitoring system for fault diagnosis in 3-phase induction motors". Proceedings of the Third Turbo-machinery Maintenance Congress. London. Vol. 1.1 687-693. [4] Vas, P. (1992). ''Electrical machines and drives, A space-vector theory approach'', Oxford University Press [5] Filippetti, F., Franceschini, G. and Tassoni, C. (1998). "AI techniques in induction machines diagnosis including the speed ripple effect", IEEE Transactions on Industry Applications, Vol.34, No.l
NOMENCLATURE I ^, I^
Stator & rotor phase currents
Ta.ara^.' Tpuisaun^ Average & pulsating torque
/ , , , /,2 Stator positive & negative sequence currents
T^
Electromagnetic torque Stator phase voltage
7^1, fj
Rotor positive & negative sequence currents
V^
P
Number of poles
^vi' ^v2 Stator positive & negative sequence voltages
R^
Stator resistance
R,.
Rotor resistance
S
Motor slip
F,.,, V,.2 Rotor positive & negative sequence voltages CO^, CO^ Rotor speed & Supply frequency if/^, ij/^. Stator & rotor flux linkages
Tj, 7^2 Positive & negative sequence torques
334
(a) A healthy motor under 0% load
,,i||iilW^^ Frequency(Hz) (b) A Motor with one broken rotor bar under 0% load
Frequency(Hz)
Figure 1 Vibration Spectra under 0% Operational Load (a) A healthy motor under 25% load
Figure 2 Vibration Spectra under 25% Operational Load
335
(a) A healthy motor under 50% load
Frequency(H2) (b) A motor with one broken rotor bar under 50% load
Frec|uency(Hz)
Figure 3 Vibration Spectra under 50% Operational Load (a) A healthy motor under 75% load
Frequency(Hz) (b) A motor with one broken rotor bar under 75% load
2.
Figure 4 Vibration Spectra under 75% Operational Load
336
(a) A healthy motor with symm^rical supply voltages
°- -100
80 Frequency(H2)
100
Figure 5 Vibration spectra for Asymmetrical stator faults (Logarithmic scale) (a) A healthy motor with symmetrical supply voltages
E S o
'•
r
-
1
100 Hz
.^J .
,
50 100 (b) One phase supply voltage with 20 volts drop /
50 100 (c) One phase supply voltage with 40 volts drop E
50
100 Frequency(H2)
Figure 6 Vibration spectra for Asymmetrical stator faults (Linear scale)
337
(a) A healthy motor under 0% load
40
50 60 Frequency(Hz)
(b) A motor with one broken rotor bar urKfer 0% load
40
50 60 Frequency(Hz)
Figure 7 Phase Current Spectra under 0% Operational Load (a) A healthy motor under 26% load
40
50 60 Frequency(Hz)
(b) A motor with one broken rotor bar under 25% load
Figure 8 Phase Current Spectra under 25% Operational Load
338
(a) A healthy motor under 5 0 % load
Wv^WuJiALAAL^
.ft/Vv\J 40
50 60 Frequency(H2)
(b) A motor with one broken rotor bar under 50% load
40
50 60 Frequency(Hz)
Figure 9 Phase Current Spectra under 50% Operational Load (a) A healthy motor under 100% load
Figure 10 Phase Current Spectra under 100% Operational Load
339
90
100
(a) A healthy motor with symmetrical voltages
50 100
i I
NwAlAiAivJW*"*^ 50 100 (c) One phase supply voltage with 40 volts drop
• -100
Frequency(H2)
Figure 11 Phase-A Current Spectra (OV, 20V & 40V Drops) (a) A healthy motor with symmetrical voltages
50 100 (c) One phase supply voKage with 40 volts drop
Frequency(Hz)
Figure 12 Phase-B Current Spectra (OV, 20V & 40V Drops)
340
(a) A healthy motor with symmetrical voltages
50 100 (b) One phase supply voltage with 20 volts drop
150
Figure 13 Phase-C Current Spectra (OV, 20V & 40V Drops) Transient Speed (Normal condition)
1600
1200
1000 E B
400
0.15
0.2 Time (s)
Figure 14 Rotor Speed during Start-up for Healthy Operation
341
0.35
Transient Speed (Broken one and two rotor bars)
E
e800
600
Broken one rotor bar Broken two rotor bars
0.1
0.2
0.15
0.3
Time (s)
Figure 15 Rotor Speed during Start-up for 1 & 2 broken rotor bars Transient Speed (20v drop of one phase)
S 800
400
0.15
0.2 Time (s)
Figure 16 Rotor Speed during Start-up for 20V Drop in One Phase
342
0.35
Transient Speed (40v drop of one phase)
1600
1400
1200
1000 E 800
400
200
0.2
0.15 Time (s)
Figure 17 Rotor Speed during Start-up for 40V Drop in One Phase (c) 20v drop of one phase voltage
(a) Normal condition 1
1
0.8
\
;o,6
;o.6
i''rotor Speed
> !
0.8
1-rotor speed
>
5 0.4
10.4
2"'* rotor speed
• 0.2 0
'0.2 0
300
(
(b) Broken one rotor bar
1
\\
100 Hz
\r-
100
(d) 40v drop of one phase voltage 1
0.8
> 0.8
1 *' rotor speed
0.6
1^-rotor speed
; 0.6 !• J
!o.4
0.4
100 Hz
4.6 Hz 0.2
|^„--^
n JS[.
.
100 Hz A*""^ 100 200 Frequency (Hz)
OL
300
L ...h . . , .
-J
100 200 Frequency (Hz)
Figure 18 The Spectra of Transient Rotor Speed with 100% Load
343
•
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Condition Monitoring and Diagnostic Engineering Management Andrew G. Starr and Raj B.K.N. Rao (Editors) © 2001 Elsevier Science Ltd. Allrightsreserved.
NEW METHODS FOR ESTIMATING THE EXCITATION FORCE OF ELECTRIC MOTORS IN OPERATION Hiroki OTA^ Taichi SATCP, Masayuki TAGUCHI^ Joji OKAMOTO*, Makoto NAGAI^ and Katsuaki NAGAHASHI^ 1 Applied Systems Engineering, Graduate School of Science and Engineering, Tokyo Denki University, Ishizaka, Hatoyama, Hiki-Gun, Saitama 350-0394, JAPAN ^Depgutment of Intelligent Mechanical Engineering, Tokyo Denki University, JAPAN ^Toppan Printing Company Limited, JAPAN ^Hitachi Air Conditioning Systems Company Limited, JAPAN
ABSTRACT We developed two methods for accurate estimation of the excitation force of a motor. One is a hybrid method combining an experiment and afinite-elementanalysis. The other is a calculation method that uses both the equivalent circuit andfinite-elementmodel of a motor. The motor excitation force determined by the two methods was compared with the result obtained through direct measurements by using a load cell. The acceleration response of a thin plate excited by a motor was also calculated and the measured and calcdated accelerations of the thin plate were compared and analyzed in terms of accuracy. KEYWORDS Motor, excitation force, phase angle,finite-elementmodel, torque pulsation, vibration response INTRODUCTION Electric motors are used as actuators in many different machines. Vibratory motions and noise in machines arise when the excitation forces of their electric motors act on their elastic parts. An increasing demand for low-vibration and low-noise machines has accelerated the development of new methods for estimating the excitation forces of electric motors. Several studies have focused on vibration and noise caused by electric motors (Timar P. L. (1992), Craggs J. L. (1993), Matsubara K. et al (1996), and Noda S. et al (1996)), and a number of methods for measuring excitation forces have been developed (Mugikura (1989), Hiramatsu T. (1989), Yamagami H. (1994), and Sato T.etal (1999)). 345
The excitation force of an induction motor is composed of many peakfrequencycomponents. The highest peak of the force appears at afiiequencyof 100 Hz, which is twice the power-supply frequency. We called this frequency component a **2f component", and analyzed the force of this 2f component. When the motor is in operation, the torque of the motor fluctuates at twice the power-supply frequency. This torque pulsation is the cause of the excitation force of the 2f component. Based on the mechanism of excitation-force generation, we developed two methods for estimating the excitation force of a motor. One is a hybrid method that combines an experiment and a finite-element (F-E) analysis. In the experiment, the circumferential acceleration of a motor housing was measured when the motor was in operation. Additionally, a F-E model was created for the motor, and the circumferential acceleration response of this model was calculated. The measured and calculated accelerations were then used to estimate the motor excitation force. The results obtained by this hybrid method agreed well with the results of the excitation force measured by using a load cell. The other method is a calculation method. Using this method, the excitation force of the motor was estimatedfromboth the equivalent circuit and F-E model of the motor. To obtain the torque pulsation of the motor, we used a two-phase equivalent electric circuit corresponding to that of the motor. We then calculated the vibration response of the F-E model wiien the torque pulsation acted ontibemotor housing. Through this calculation process, the excitation force of the motor was estimated. We found that the calculation results were close to the measured ones. CHARACTERISTICS OF MOTOR EXCITATION FORCE This study investigates the excitation force of a motor as shown in Table 1. Figure 1 shows the frequency response of the force produced by the testing system. The testing system, based on the direct method, consisted of a motor, a block with high rigidity, and a load cell. A number of peakfrequencycomponents are shown in the figure. Among these peaks,tiiehighest peak is at 100 Hz. This highest peak of the force is afrequencycomponent that is twice the power-supplyfrequency.We called thisfrequencycomponent a "2f component" and analyzed the force of this 2f component. In this study, "motor excitation force" refers specifically to the force of the 2f component. The motor was supported by four feet Therefore, in addition to the amplitude of the excitation force, the phase angle of the force for each foot was also calculated. TABLE 1 MOTOR SPEOnCAnONS Induction motor Motor type 30W Rated power Power supply fi'eaueiicy 50 Hz Power supply voltace 200 V 6 Pole 24 Stator-slot 34 Rotor-slot 0.04 (»960 rpm; without load) Slip
HYBRID METHOD To simplify the measurements, we developed a hybrid method for estimating the motor excitation force without using a load cell. This method combines an experiment and a F-E analysis. In this hybrid method, an accelerometer was used instead of the load cell to estimate the excitation force of a motor. The circumferential acceleration. A, was measured by the accelerometer attached to the motor housing. A F-E model 346
A : Whirling motion B:2f component C : Slot ripple component
100
ISO
200
250
300
3S0
400
4S0
500
Frequency Hz
ra) Frequency response of the excitation force of motoi (b) Experimental device (direct method) Figure 1: Motor excitation force and experimental device
for the motor was also created as shown in Figure 2. The circumferential acceleration, A\ and the motor excitation force, F\ of this model were then calculated using the F-E program MSC/NASTRAN. If the actual motor excitation force is represented by F, then^* and F* have the following relationship F F_ A^ A
(1)
Because the values of ^, F\ andv4^ were determined by an experiment and FEM calculations, actual motor excitation force F can be obtained from Eqn. 1 as follows.
Footl:F,' Foot2:F
Foot4:F/
Figure 2: F-E model of the motor The calculation results of the motor excitation force and those obtained experimentally without using a load are compared in Figure 3(a). When the power-supply voltage increased to 100,140, and 200 V, the motor excitation force estimated by both the calculations and experiment also increased and the experimental results were in good agreement with the calculation results. At the same time, the phase angle of the motor excitation force was calculated for each supporting foot. The experimental results for the phase angle were in good agreement with those obtamed by the calculations as shown in Figure 3(b). 347
lOOV
140V
200V
(b) Phase angle of excitation force of 2f component for results each supporting foot
(a) Amplitudes of motor excitation force
Figure 3: Hybrid method
CALCULATION METHOD We developed another method for estimating the motor excitation force without direct measurements. It is based on using the equivalent electric circuit of a motor to obtain the torque pulsation. The torque pulsation acts on the motor housing as a reaction force, and the motor housing vibrates in the circumferential direction. If we estimate the circumferential acceleration of the motor housing as was described above, the motor excitation force can be calculated by using the F-E model of the motor. Torque pulsation The torque pulsation of the motor can be calculated by using a two-phase equivalent electric circuit (Morrill W. J. (1929)). The equivalent electric circuit is shown in Figure 4. The 2f component of the torque pulsation, Tv, can be expressed by the following equation: • {(«/ -^hih-^1]
^ fu\ + '''l^f + 2a|/;|x|7,|cos(0-^) <[fLf+«'|^f+2^/;|x|7,|cos(0 + ^)jp
wiiere. Rj^ the q>parent resistance to the M-phase forward field li!^= the apparent resistance to the M-phase backward field A.= the apparent reactance to the M-phase forward field X^ = the apparent reactance to the M-phase backward field |^w[ = the effective value of the M-phase current = the effective value of the S-phase current fl = the ratio
^^P<^^°^^Q^ conductors on S phase Fundamental conductors on M phase
0 = the spatial angle between the M and S phases ^ =" the angle by which the S-phase current leads the M-phase current
348
(2)
Main-phase side Sub-phase side Figure 4: Two-phase equivalent circuit To verify the calculation results of the torque pulsation, an experimental device was constructed as shown in Figure 5. The experimental device consisted of a motor, four wings, a high-stifihess block, a load cell, and two accelerometers. A wing was installed in the motor shaft, which enabled changing the rotation speed according to the size of the wing. Two accelerometers were used to measure the circumferential acceleration. The signals of the two accelerometers were differentially amplified, which allowed us to obtain only the circumferential component of the acceleration. After the circumferential acceleration was converted into angular acceleration a, torque pulsation Tv was given by Ty'-Ja
(3)
where, / is the mass moment of inertia of the rotor of the motor. Accelerometer
Slip ring
Load cell
Wing High-stiffness block
Figure 5: Experimental device The calculation results of the torque pulsation are sht)wn in Figure 6 as afimctionof a slip corresponding to the rotational speed for several power-supply voltages. The dots refer to the experimental results. As can be seen in thefigure,the experimental result^ agree well with the calculation results in the low-speed range and low-power-supply-voltage range. However, in the high-speed range and high-power-supplyvoltage range, the experimental results are different from those obtained by the calculations. We thus thought that the calculation method had to befiirtherimproved.
349
''•'-<mlomla±Um (XOO • ) * SiVMiMut (100 • ) (1*0 T) n • T i » « r t — » (140 • ) (aoo • ) O • gTT'rt—> (200 V)
I
^
1
I 0
500 Speed rpm
1000
Rgure 6: Speed-torque pulsation characteristics Calculation ofmotor excitation force The circumferential force acted on the motor housing as a reaction force of torque pulsation. The circumferential force contributed to the deformation of the end plate of the motor. We thus calculated the motor excitation forces generated at the motor supporting feet by using the F-E model shown in Figure 2. The calculation results of the motor excitation force and those obtained experimentally without a load are shown in Figure 7. When the power-supply voltage increased to 100,140, and 200 V, both the calculated and the experimentally measured motor excitation forces increased. The results for the phase angle were the same as those obtained using the hybrid method (Figure 3(b)), because of the use of the same F-E model in both the hybrid and calculation methods.
100 V
140 V
200V
Figure 7: Calculation results of the motor excitation force VIBRATION RESPONSE ANALYSIS Experimental device and F-E modeling We used the excitation force previously measured and calculated to analyze the vibration response of a thin plate structure. The thin plate structure was a 800*600*3 mm steel plate with a motor located in its center as shown in Figure 8. We created an F-E model for the experimental device (Sato T. et al (1999)). In this F-E model, the steel plate was composed of a shell element, and the motor was composed of a concentration mass and several beam elements. The motor excitation force was inputted in four places to be equivalent to the motor foot, and the vibration response was calculated. 350
Thin plate (800*600*3 mm) •~™
(gi
jp
(Qi
1~
700
Figure 8: Thin plate and motor Vibration response The acceleration response of the thin plate excited by the motor was determined by using the F-E model. The amplitudes and phase angles of the motor excitation force used in the vibration-response analysis were classified into three different categories depending on how the motor excitation force was obtained. Each of the three categories corresponded to the motor excitation force that was, respectively, (a) measured directly, (b) obtained by the hybrid method, and (c)calculated using the equivalent electric circuit (calculation method). Additionally, we assumed that the excitation force on each foot of the motor was in phase. Figure 9 shows the calculated and measured responses of the 2f component. The parallelogram represents 1/4 of the thin plate. The acceleration amplitudes at each point of the plate are shown in the bar charts. The gray bars represent the calculated responses, and the slanting^line bars represent the measured responses. In the direct measurements (Figure 9(a)), the calculation results agreed well with the experimental results for each point with the exception of a small area in the plate. In the hybrid method (Figure 9(b)), the calculation results represented the characteristics of the acceleration distribution quite well. However, the responses calculated for each point were a little smaller than the ones measured experimentally. The phase angle is the main cause of the difference between the calculated and measured results. In the calculation method (Figure 9(c)), it is clear that both the gray and slanting-line bars agree well with one another for each point. However, the responses calculated in phase as shown in Figure 9(d) were much larger than the measured ones. In otiier words, the vibration response largely depends on the phase angle. Therefore, we were able to confirm that an accurate calculation of the phase angle of the motor excitation force'\^important for an accurate calculation of the vibration response.
CONCLUSIONS We developed a hybrid method for estimating the motor excitation force that combines an experiment and a F-E analysis. We also developed a calculation method that uses an equivalent electrical circuit and an F-E model of a motor. Using these two methods, the vibration response of a thin plate structure was accurately estimated.
351
f
B Csldzlation
19 CMlculAtion
6
(b) Hybrid method
(a) Direct measurements
MCalculation HJbyrii—nt
?
r <
H Calculation (3gig>
(d) In phase
(c) Calculation method Figure 9: Vibration response
References Craggs J. L. (1993). Specifying and measuring the noise level of electric motors in operation. IEEE Trans, onind Appl 29(3), 611-615. Hiramatsu T. (1989). Measurement of vibromotive force generated by machinery and equipment (part 2, substitution method). Architectural Acoustics and Noise Controlfin Japanese) 67,14-18. Matsubara K. et al.. (1996). Study on vibration and noise reduction for motors (avoiding first mode resonance in rigid-body-vibration of rotors). Prepr ofjpn. Soc. Meek Eng. 96-51(A), 137-140. Morrill W.J. (1929). The revolving field theory of the cqjacitor motor. AJ.KRTrans. 48,614-632. Mugikura (1989). Measurement of exciting force of building equipment (part 1, methods for measuring exciting force and practical use of direct method). Architectural Acoustics and Noise Control (in Japanese) 67y7A3, Noda S. et a l . (1996). Frequency response analysis of stator core in induction. Prepr. ofJpn. Soc. Mech. Eng. 96-51(B), 229-232. Sato T. et al.. (1999). Methods for measuring excitation force of an electric motor in operation. DYMAC PP, 331-336. Timar P. L. (1992). Noise test on rotating electrical motors. Electric Machines and Power Systems 20, 339-353. Yamagami H. (1994). The exciting force of air conditioning machine and example of vibration proof Noise Control (in Japanese) 18(4), 194-197. 352
Condition Monitoring and Diagnostic Engineering Management Andrew G. Starr and Raj B.K.N. Rao (Editors) © 2001 Published by Elsevier Science Ltd.
THE DEVELOPMENT OF FLUX MONITORING FOR A NOVEL ELECTRIC MOTOR B Payne^ M Husband^ B Simmers^ F Gu^ and A Ball^ ^ Maintenance Engineering, Manchester School of Engineering, University of Manchester, Ml 4 9PL, UK, www.maintenance.org.uk ^ Rolls-Royce pic, PO Box 31, Derby, DE24 8BJ, UK, www.rolis-royce.com
ABSTRACT Rolls-Royce (RR) are currently developing a novel electric motor for the purposes of naval propulsion. This machine is referred to as a Transverse Flux Motor (TFM) because of the unique three-dimensional flux paths created within it. This paper considers the development of flux-based condition monitoring for this new machine. Although condition monitoring based on vibration and current analysis has proved valuable to conventional induction motor fault detection and diagnosis [1-5], the design configuration of the TFM is significantly different to require new techniques to be developed. One such technique that is currently being researched and proving particularly useful is that of flux monitoring. In this paper an outline of the novel motor will be provided and then focus will be concentrated on the use of magnetic flux-based monitoring using copper search coils permanently fixed around the machine. A theoretical understanding on the use of flux monitoring is then discussed, including the transformation from the measurement to flux domains. Real data will be presented and the identification and diagnosis of a failure mode will also be examined and validated. KEYWORDS Transverse Flux Motor, Maintenance Strategy, Flux Monitoring, Flux Simulation. INTRODUCTION TO THE TFM AND MONITORING REQUIREMENTS A 20MW novel electric motor is currently being developed to potentially provide propulsion for a range of vessels including the Royal Navy Future Escort, Carrier (CV(F)), Future Surface Combatant (FSC) and Future Attack Submarine (FASM). Major advantages of such electric propulsion include low noise, reduced operating costs, increased flexibility of operation and improved possibilities for naval architecture [6]. The motivation behind developing condition monitoring technology for this machine is primarily to provide an integrated on-line condition-based maintenance package for the customer. In addition, machine monitoring is also aiding maximisation of design during the development stages of the TFM.
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TFM CONSTRUCTION One of the benefits of the TFM is its potential for design flexibility [6]. However, its most simple configuration consists of a single rotor disc to which two opposite rotor rims are attached (Figure 1). The rotor comprises of alternate soft iron laminated pole pieces and permanent magnets. The magnetisation of the magnets is in the circumferential direction and aligned so that the pole pieces form alternate (N) and south (S) poles (Figure 3). The stator consists of a number of C-shaped stator cores (constructed by stacked laminates) and a single solenoid armature coil that passes within them (Figure 1).
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MONITORING OF THE TFM
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Figure 1 - Most Simple TFM Configuration
Prior to commissioning the TFM prototypes much consideration was given to monitoring. It was chosen to instrument the machines with thermocouples to detect changes in temperature and copper search coils to detect changes in magnetic flux. These transducers were located at a variety of positions on the stator and on the rotor (with connections being brought out by a slip-ring unit). The use of accelerometers to measure vibration was considered but as the bearings are set within the frame, a suitable mounting could not be found. Furthermore vibration signatures could not be compared between machine prototypes because of their fundamental design differences (such as number of rotating discs, cancellation parameters and core types). For similar reasons the use of acoustic monitoring was deemed unpractical. In this paper the capability of magnetic flux monitoring will be considered and illustrated. MAGNETIC FLUX IN ELECTRICAL MACHINES Magnetic flux is a measure of the strength of a magnetic field over a given area, equal to the product of the area and the magnetic flux density through it [7]. Magnetic flux is an important operational parameter in any electrical machine and it may be monitored by winding coils of copper wire around the magnetic material (ie the iron). The voltage pick-up of such coils is directly proportional to the normal rate of change of flux density through the iron. APPLICATION OF FLUX MONITORING TO CONVENTIONAL MACHINES Limited application of flux monitoring has been made to monitoring of conventional machines. Two main approaches have been considered: firstly the placement of search coils in the stator slots to measure air gap flux [8, 9] and secondly, placement of a coil around the motor shaft to measure axial flux [10]. However, from a practical viewpoint, these
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approaches have been difficult to implement. Firstly, the insertion of a series of search coils in the stator slots is not realistic for motors in situ. Secondly, the design of a conventional motor and its enclosure means that it is often not possible to install an axially-sensing coil in the correct position to ensure a reliable signal. Although both of these problems could be resolved during design and construction of conventional machines, manufactures are reluctant to make such changes because of the subsequent implications concerning safety legislation and industrial insurance [11]. Therefore although such approaches to condition monitoring based on flux are legitimate, these techniques have, to date, not been widely applied. However the commitment to provide a maintenance strategy along with a functional product from the very outset of development is enabling such flux-based approaches to be reexamined in application to the TFM. APPLICATION OF FLUX MONITORING TO THE TFM The progressive TFM prototypes, built as part of RR development work, incorporate search coils wound at the interface between pole pieces and an adjacent magnet, and around the stator C-cores (Figure 2). This magnetic flux measurement technique provides a possibility for condition monitoring (CM). Additionally this approach to monitoring is TFM prototype invariant.
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In an ideal TFM (ie with all poles identical and with no degradation during the lifetime of the machine) the search coil flux should vary periodically with a wavelength equal to Figure 2 - Search Coil Positions on Stator Core the magnetic pitch of the machine. Furthermore, this flux variation should be the same for all poles of the machine and it would not change during the lifetime of the machine. In a real machine, however, several predicted failure modes may occur. Flux measurements from the rotor search coils will detect differences in the excitation components of the flux output of a stator core. This difference may be the result of: 1. 2. 3. 4. 5. 6.
Physical creep of the core tips, Stator core de-lamination, Core overheating (due to increased losses), Lamination insulation breakdown resulting in shorting, Core vibration (axially, radially and circumferentially), Core displacement.
The stator core search coils are capable of detecting both the magnetic and excitation components of the flux in the rotor rim. These changes may be the result of: 1. A decrease in magnetic strength due to overheating, 2. Eccentricity of the rotor, 3. Increased rotor losses (eg as a resuh of shorting),
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4. Changes in the effectiveness of flux Unkage (due to displacement of the coil, insulation breakdown in the coil, vibration of the coil, or problems with the inverter for example). 5. Mechanical and electrical misalignment (due to problems with the encoder for example) leading not only to reduced performance, but also to increased stresses (the key to current excitation and magnetic alignment is that for optimum motor performance they are both zero at the same point). All of the above would affect the output from the search coils but in different ways. For example: 1. A fault in one magnet would appear in the search coil adjacent to that magnet and not, to any great extent, in the other search coils. 2. A stator core fault would appear as a perturbation in a magnet search coil only when the magnet passed that core. Therefore different search coils would show the same perturbation but would be out of phase with each other. 3. Rotor/stator misalignment would be apparent in all search coil traces, with an appropriate phase difference but the relevant period would be the circumference of the rotor. The above discussion shows that there is considerable potential in using the search coil outputs as a diagnostic tool for identifying and monitoring different faults in the TFM. FLUX SIMULATION For the purposes of understanding the magnetic flux characteristics it is necessary to undertake a degree of simulation. This process allows for an explanation of features observed in real data. The flux paths within the TFM may be simulated by a paper-based theoretical approach. This is achieved by drawing sections of the rotor and stator and then overlaying the theoretical flux paths by consideration of a few set rules. These rules are those given by Maxwell' s equations (relating flux, current and directional force), the fact that flux always enters a surface at 90° to it and that flux lines always form closed loops, do not cross and when parallel repel each other.
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In the TFM the resultant flux path is composed of two components. One is the flux path created by the excitation of the solenoid. The other is the flux path created by the permanent magnets. Additionally these components may further be separated into short and long flux paths. The short path flux passes through a single stator core and rotor section whereas the long path flux passes through every stator core. The relationship between the excitation and magnetic flux components is dependent on the relative stator and rotor positions (the resultant flux paths are illustrated in Figure 3). A theoretical understanding of the flux inferred from any of the search coils may also be derived by consideration of the two different flux components. The main considerations are that excitation flux leads magnetic flux by 90° electrical and also that the peak excitation flux has a magnitude 150% greater than the magnetic flux. A Finite Element (FE) analysis also predicts the resultant flux to vary sinusoidally with time (up to motor speeds of 80%). A search coil provides a voltage output and in order to obtain this expected output the theoretically predicted flux profile should differentiated as in Figure 4. Visa-versa, in able to infer flux from a real search coil measurement, the voltage output must be integrated. Aligned (0° Electrical)
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Figure 4 - Theoretical Flux Measured by a Stator Core Pole Face Magnetic Search Coil
REAL FLUX WAVEFORMS, FAULT DETECTION AND DIAGNOSIS Figures 5 & 6 are examples of raw voltage output (in the time and frequency domains respectively) from a search coil on the inner pole face of a stator core (see Figure 2) and at 50% speed. It may be observed that the voltage output is more triangular than the predictions from the FE analysis and this is confirmed by the series of odd harmonics in the corresponding spectrum. The reason for this shape is related to the saturation characteristics of the iron (which is particularly sensitive to design changes). There is also strong sideband presence spaced at the rotational speed. The fundamental frequency component is at 97.8Hz.
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This correspon(is well to 50% of the maximum rated speed of 308q)m for this prototype (((308rpm/2)/60sec) x 38 pole pairs = 97.5Hz). In addition it can be seen that, unusually for condition monitoring data, there is little noise in the signal recorded. In order to illustrate the condition monitoring potential of flux monitoring, consider Figures 7 and 8. Figure 7 represents healthy operation of the TFM through one complete revolution of the shaft (consisting of 38 periods resulting from the passing of 38 pole pairs). This waveform was recorded before a pole-tip failure in which part of a stator core came away from its many body. Following the subsequent dismantle and repair, Figure 8 illustrates a flux waveform from the same search coil as in Figure 7. It should be observed that this second plot shows a reduction in amplitude (of approximately 15%) over one flux period. Cham^iJ-J: Flux over One Mechanical Revolution [deml.bin]
Chann^iJ.J: Flux over One Mechanical Revolution [dem5.bin]
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Three possible explanations for this feature were: 1. Local demagnetisation due to intense local heating, 2. Eddy current losses due to magnet compression or cracking, 3. Shorting of laminations (due to rubbing with the detached pole piece) within a single pole piece and subsequent eddy losses. In order to identify the exact cause of the anomaly, when the machine was next dismantled a Gauss meter was used to check the magnetic strength of the pole pieces and a DVM was used to check for laminate shorting. No pole piece shorting was detected but the Gauss meter clearly determined the location of demagnetisation of one rotor magnet between pole pieces
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23 and 24 (Fig 9). The difference between the average of all pole pieces (not including 23 and 24) and the average of 23 and 24 was 6.1%. This leads to an expected reduction of magnetic strength for the faulty magnet of 12.2% (2x6.1%) and this accounts well for the 15% reduction of one flux period in each revolution of the stator search coil data. It is therefore assumed that following the pole-tip failure there was stator/rotor contact when the machine was stopped which could have led to thermal soak and local overheating of a rotor magnet. This overheating would have caused local demagnetisation. Without the outlined approach it is likely that this fault condition would have gone unnoticed.
Rotor Pole Piece Magnetic Strength Variation
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CONCLUSIONS Condition monitoring to guarantee reliability and availability of Navy propulsion systems is of the up-most importance. The development of novel electric machines for such purpose has brought about new challenges to the maintenance engineer. Although conventional approaches to monitoring are less applicable to the new motor design outlined, significant advances have been made in the development of a method based on measuring the magnetic flux within the machine. There is great potential in the ability to detect and diagnose incipient faults within both the stationary and rotationary parts of the TFM and also to easily distinguish between local and distributed fault conditions. However monitoring in such a way requires a careful and pre-planned strategy on the instrumentation installed, as the magnetic search coils are intrusive transducers. This is particularly true of those coils to be placed in the rotor section of the machine, as they will be embedded between the pole pieces and magnets on the rotor rim. Also, the number of search coils that may be used on the rotor will depend upon limitations set by the slip ring. Nevertheless these requirements present much less of a problem for the TFM as a committed and proactive approach to monitoring is being undertaken during the development phases. Therefore it is anticipated that a final production version of the machine will come fully instrumented and incorporate an integrated condition monitoring system.
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ACKNOWLEDGEMENTS Rolls-Royce pic are thanked for the support and funding of condition monitoring technology development for electric machines in the Maintenance Engineering Research Group, University of Manchester.
REFERENCES [I]
[2]
[3]
[4]
[5]
[6] [7] [8]
[9]
[10] [II]
B S Payne, A Ball, F Gu, W Li, A Head-to-head Assessment of the Relative Fauh Detection and Diagnosis Capabilities of Conventional Vibration and Airborne Acoustic Monitoring, Proceedings of the 13th International Congress on Condition Monitoring and Diagnostic Engineering Management (COMADEM 2000), Texas, USA, pg 233-242, December 2000. B S Payne, B Liang, A Ball, Modem Condition Monitoring Techniques for Electric Machines, Proceedings of the 1^^ International Conference on the Integration of Dynamics, Monitoring and Control (DYMAC '99), Manchester, UK, pg 325-330, September 1999. W T Thomson, On-Line Current Monitoring to Detect Electrical and Mechanical Faults in Three-Phase Induction Motor Drives, lEE Conference: Life Management of Pov^er Plants, pg 66-73, December 1994. G B Kliman, J Stein, Induction Motor Fault Detection via Passive Current Monitoring, Proceeding of International Conference on Electric Machines, pg 13-17, 1990. B Liang, B S Payne, A Ball, Detection and Diagnosis of Fauhs in Induction Motors Using Vibration and Phase Current Analysis, Proceedings of the 1^^ International Conference on the Integration of Dynamics, Monitoring and Control (DYMAC '99), Manchester, UK, pg 337-341, September 1999. A J Mitcham, Transverse Flux Motors for Electric Propulsion of Ships, lEE Colloquium, pg 3/1-3/6, 1997. Collins Concise Dictionary, ISBN: 0 00 470777-x, Third Edition, 1995. S Fruchtenich, E Pittuis, H Seinsch, A Diagnostic System for Three-Phase Asynchronous Machines, Proceedings 4* EMDA Conference, lEE No 310, London, pg 163-171, September 1989. D G Dorrell, W T Thomson, S Roach, Analysis of Airgap Flux, Current and Vibration Signals as a Function of the Combination of Static and Dynamic Airgap Eccentricity in 3-Phase Induction Motors, IEEE Transactions on Industry Applications, Vol 33, No I, pg 24-34, January/February 1997. J Penman, M N Dey, A J Tait and W E Bryan, Condition Monitoring of Electrical Drives, Proceedings lEE, Part B, Vol 113, No 3, pg 142-148, May 1986. W T Thomson, D Rankin, D G Dorrell, On-line Current Monitoring to Diagnose Airgap Eccentricity in Large Three-Phase Induction Motors - Industrial Case Histories Verify the Predictions, IEEE Transactions on Energy Conversion, Vol 14, No 4, pg 1372-1378, December 1999.
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Condition Monitoring and Diagnostic Engineering Management Andrew G. Starr and Raj B.K.N. Rao (Editors) © 2001 Published by Elsevier Science Ltd.
EUROPEAN PROJECTS - PAYBACK TIME I.D.Jennings Monition Ltd (International), Bondhay Complex, Whitwell Worksop, Nottinghamshire S80 3EH UK
ABSTRACT As the world economy becomes truly global and access to labour markets, at prices which are comparable with those of Western Europe over 50 years ago, becomes a reality for more and more manufacturing industries; our labour market is increasingly pressurised. Unable to compete on price this market must respond to the pressures by upskilling the labour force to manufacture in a technically more advanced way and improve output per person via technology. Also available, as a weapon in this labour market war, is the use of skilled labour associated with the production of high tech, innovative systems and products. To provide a potential outlet for the European labour market, based on future innovative systems and products, we need innovative systems and products. Surprisingly enough now is not the first time that this has been realised. In fact the European Commission has been supporting innovative research, to help produce just such systems and products, with substantial amounts of grant funding for many years. Even though a great deal of extremely useful research has been carried out with this assistance and very many projects have reached very successful conclusions it seems Europe is continuing its long held tradition of inventing lots and marketing little. A tradition which is greatly appreciated by our global trading competition in the USA, Japan, China and South East Asia who gratefully market and sell, to great economic benefit, many of the ideas and concepts so well demonstrated by great numbers of our innovative research projects. The need for marketing and successful commercialisation of these enormous European reservoirs of knowledge and ideas is now, more than ever, becoming of absolutely paramount importance to Western Europe and Europe as a whole. The very survival of Europe's economies depends on us being able to capitalise on this high tech resource, which currently lies severely underdeveloped due to poor marketing and commercialisation. Tragically Europe is almost in the "underdeveloped areas" category when it comes to making money from its innovative brilliance.
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We must improve massively and rapidly, if we don't there may no longer be the generated wealth available to ensure we can carry on funding research and development. Currently we lag noticeably behind both the USA and Japan in investment, per head of population, in this area and cannot afford to fall further. There are many varied and complex reasons why European research project results are not commercialised, as successfully as we would all wish, and work within the European PLAN Network project has been discovering many of them. After 3 years of effort, researching over 70 current and completed projects in the field of engineering, the work has highlighted some extremely disturbing characteristics. It has also identified some very positive aspects which, given support of the correct nature, may well have a tremendously dramatic impact on the whole commercialisation field relating to European R & D . This paper will expand on the above and put forward some of the "ideas for improvement" generated by the PLAN project work. BACKGROUND Many research projects in Europe achieve their technical objectives, but the results are not then exploited and transformed into commercial success in the marketplace. This represents a huge waste of potentially excellent innovative developments, as well as the R&D investment of the European Commission. The main reasons (but by no means the only reasons) why results of European R&D are not exploited are: • Lack of commercial knowledge in business planning and marketing within the R&D project consortium. • Lack of a committed "project champion" with the flair, expertise and determination to take the results from the research stage into real commercial products and services. • Lack of technical expertise in assessing the potential for exploitation across diverse markets. • Lack of knowledge of, and access to, investment sources. • Complications in IPR arrangements which act as a disincentive to commercialisation. There is, potentially, a huge benefit to European industry, and to the European Commission, in overcoming these problems and taking more of the high quality R&D results which are already in the public domain and using them to create new products and services. In order to achieve this, a new methodology is required to drive the process based on sound business practices and the potentialfor return on investment. HOW THE PLAN PROJECT WORK WAS CARRIED OUT Almost entirely uniquely the PLAN project drew together some 70 or more European project leaders who were or are working in the area of Plant Life Assessment. As a network group the wealth of experience of these project leaders, some of whom have over 20 years experience of leading or participating in European, National and even Global projects, would be difficult to surpass. All aspects of the engineering world were represented from large industrial end users of technology through to "blue sky" researchers. Mad inventors. Nutty
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Professors, hard nosed SME business leaders, respected expert consultants and generally a cross section of some of "Europes finest", were the backbone of the PLAN project. All carefully honed into a dedicated, hardworking team by the experienced leadership of the Network co-ordinator, Professor Roger Hurst of the European Commissions Joint Research Centre in Petten, Holland. With such a wealth of experience at the PLAN projects fingertips, it seemed a sensible place to begin by questioning the participants of PLAN. A series of questionnaires was produced, each with a specific purpose and these were distributed to all participants. In general over 60% of all project participants responded, and some cases the response rate was over 90%, which to some extent confirmed the interest being shown in the marketing horizontal theme Examples of the purpose of this series of questionnaires were firstly to establish the level of knowledge of research and development project leaders with respect to marketing and commercialisation. Secondly to establish their opinions as to whether the E.C, devoted enough effort to ensure commercialisation and exploitation plans of proposed projects were sound. Thirdly to establish the current level of knowledge of the marketing and commercialisation process for an R & D project result, from the time at which the R & D ended, to the market launch and beyond. Finally to establish opinions as to what the EC currentiy do to support commercialisation and exploitation of research and development results, and what they could do to improve this assistance and hence improve the commercialisation of such project results. These are four examples of the purpose of this series of questionnaires. They by no means cover the entire field, but give a flavour of what the intentions were, and how the work was approached.
RESULTS OF QUESTIONNAIRES The results were, shall we say, more than interesting, and could perhaps be categorised as follows :• • • • •
Expected but of concern Unexpected and positive Alarming Disappointing Unacceptable.
Examples of each are as follows:"Expected but of concern" With reference to the establishment of knowledge of project leaders with respect to marketing and commercialisation, it was expected that this would not be high since the majority of project leaders are research and development orientated people, but what was of concern is that approximately 50% of the respondents admitted that they were not interested in marketing and commercialisation. An example of an "unexpected but positive"response was that in answer to the questions relating to the establishment of opinions as to whether the EC devoted enough effort to ensure commercialisation and exploitation plans of proposed projects was sound. Despite the fairly negative response from the previous questionnaire it was clear that respondents felt
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that not enough emphasis was placed on the commercialisation and exploitation aspects of projects, and certainly this should be greater. It also should be better supported by the EC. An "al2irming" response came with reference to the knowledge of marketing amd commercialisation and the process of furthering research and development results through marketing and conmiercialisation. It was apparent that some project leaders had almost no knowledge of the commercialisation process from the end of an R & D project to the market launch and beyond. An example of a question which helped to confirm this was when asked what the approximate cost of commercialising a research and development project result which had cost approximately 3 million Euro to develop in the science and engineering field. A number of respondents thought the cost of marketing and commercialisation this result would be in the order of 20 thousand Euro, which clearly demonstrates a very great misunderstanding or lack of knowledge of the process and difficulties associated with taking R&D results to market. An example of a "disappointing" result was that when asked what support the EC currently gave to help with marketing and commercialisation of R & D results, very few project leaders knew of any support at all. Most if not all the remainder only were of aware of extremely limited support. This has to lead to one concluding that either support is not there or it is not sufficiently well promoted. Lastly, as an example of a totally "unacceptable" result that less than 5% of the projects who responded to a question relating to a marketing plan or fully detailed and costed commercialisation plan had in fact got such a document. In fact none of the respondents claimed they had a very detailed document covering the exact process of commercialisation of their R&D result to the point of market launch or beyond. OTHER INTERESTING WORKS Since the commercial success stories of EC supported research and development projects have proven to be somewhat elusive, from the PLAN project work, if figures were to be quantified, then certainly less than 1 in 50 appear to reach the market successfully. The figure is nearer 1 in 100 or even greater. As a result of this, it seemed natural to try to draw comparisons between the way in which EC R & D projects are commercialised and how other bodies commercialise their R&D. Two comparisons were sought, one with a multinational company outlined their approach to innovation and research and development commercialisation and the second was that of the Venture Capitalist's approach to funding potential businesses which are reliant on the results of innovation and research and development Comparison with the multi-national company approach The multi national chosen had a strong European presence and its R & D expenditure budget can be considered for comparison purposes similar to that of the European Commission for the EU. Although the budget is smaller, the percentage spent on R & D as a proportion of turnover is approximately 2%, which roughly equates to the EU expenditure on research and development in comparison to total GDP. The main differences were found to be as follows in the two approaches. All R & D project ideas that 'passed' an initial panel vetting (which were approximately 1 in 5) were allocated an expenditure budget. The submitted R & D
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project ideas came from employee suggestions and schemes designed to encourage employee teams to consider improvement of operations, of product, of mechanical functions etc. At this stage only approximately 10% of the estimated R & D budget required to complete the project was allocated. This allocation would see a project through a feasibility and justification stage. After the feasibility and justification stage, demonstratable results has to be put before a panel which includes Sales and Marketing professionals, and background information supplied has to be sufficiently convincing to gain panel approval and thus gain further funding. Projects must also clearly demonstrate, at this stage, how commercial gain will be achieved from the successful R & D project result. Further funding approved at this stage takes the project to a second demonstrable milestone in development, This had to be completed within an agreed time scale and budget for review at the next stage to gain further ftinding. Focus is placed on proving the idea will work at each stage by clear demonstration to an 'independent' multi-disciplined panel at every evaluation stage, of which there are usually 4 or 5 throughout a 2 or 3 year research and development project. At every stage, including the initial proposal, there must be a commercial plan put forward. The funding decision is based on the technical proof of progress and the ever more detailed costings and commercial gain document . If neither are convincing funding ceases until they are improved or the project is terminated. This leads to some projects continuing to either carry on with technical development to fine tune the results to make them more demonstrable or re-write the commercial plan. There is no budget allocated for doing this work. One in eight of the projects approved at the initial feasibility stage are eventually commercially successful, and this the target for the Research and Development Director. Of course the employees themselves who have the ideas do not carry out the work within the research and development project. The project idea is passed to a research and development team who progress the work on behalf of the initiators. After the successful research and development phase is completed, a Business Panel is assembled to assess the commercialisation costings and planning document before the decision to finally go to market is taken. At this stage approximately 1 in 2 will not be taken forward, even though the R & D was successful. The project can fail on commercialisation grounds solely. The second comparison explored was that of the Venture Capitalists and their attitude towards funding highly risky, innovative businesses who rely on research and development, innovation or new methodologies. Funding of some 70 billion Euro per annum throughout Europe, with about 25% of this currently being invested in high tech companies based on innovation, means the Venture Capitalists can also offer a useful comparison. The main differences between the Venture Capitalist's approach and the European Commission's approach, as one would expect, is largely based on the emphasis on financial return. The venture capitalists require a ftill financial return within a period of 7 years approximately. Businesses they fiind are clearly nearer market than the EC funded research. However, they are prepared to invest for zero return for up to a period of 3 years, occasionally this can be more. They are generally only interested in projects and businesses that require in excess of 5 million Euro funding. Scrutiny of the business plans and commercial exploitation is extremely exhaustive. There is less emphasis on the technical, but the technical plan has to be realistic and convincing. The financial plan, however has to be extremely detailed and be able to stand up to exhaustive cross examination by a panel of financiers, bankers and entrepreneurs.
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The Venture Capitalists use a rule of thumb guide to measure their success. Out of every 10 companies they fund they expect 6 will fail, 2 will break even and return their original investment, 1 will return moderately on their investment, say between 50 and 100%, and 1 becomes a very successful business, returning between 700 and 2,000 per cent on their original investment. In fact, it is not unheard of that the investment returns 30 to 40 times its original value. KEY INDICATORS TO IMPROVEMENTS Assessing the different approaches of the EC, a Multi-National and the Venture Capitalists, to investing in innovation, Research and Development, and from examining the opinions expressed in the questionnaires completed by 70, existing or past, European project leaders within the PLAN Network Project, one can draw certain conclusions. Whilst these conclusions may not be definitive, they can certainly be regarded as pointers and guidelines for future reform. When looking for ways to improve the commercialisation of EU and Nationally funded R & D projects, the following suggestions are put forward. Greater emphasis needs to be placed on the quality and thoroughness of pre- project market research to justify the requirements for the R & D work. • • • • • •
•
Estimated commercialisation costings and plans need to be prepared together with the R & D proposal which assume a successful technical outcome of the R & D work Demonstrable results should be reviewed critically and regularly by a team of evaluators which includes business, financial and marketing professionals. Funding should be staged covering each phase of the R & D work and be dependent on the evaluation results at each phase. Business professionals should be involved heavily from the project proposal stage onwards. A detailed business, sales and marketing plan should accompany the project proposal and be revised at each evaluation to include greater and greater detail. Detailed guidance and help documents should be issued by the EC to help set acceptable standards in terms of the production of business and sales and marketing plans suitable to accompany company proposals at the initial stage and at each evaluation stage. Individual partner budgets and roles should be identified at the proposal stage to ensure commercial awareness and focus. These budgets should cover the 'end of "R & D to market launch" and beyond, and it is these figures that should be revised at each stage of progressive funding. Project leaders should clearly demonstrate personal, commercial and business awareness, and should also demonstrate a clear grasp of market related issues.
ACKNOWLEDGEMENTS Professor Roger Hurst, European Commission, Joint Research Centre Petten, Holland Hans Hartmann Pederson, European Commission, Scientific Officer, Brussels PLAN Project Participants - all of whom the author wishes to thank for their co-operation in helping with the research which enabled this paper to be compiled. European Research Director Pepsi Cola Group European Investment Manager, 31, Venture Capital
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Condition Monitoring and Diagnostic Engineering Management Andrew G. Starr and Raj B.K.N. Rao (Editors) © 2001 Elsevier Science Ltd. All rights reserved.
THE USE OF THE FIELDBUS NETWORK FOR MAINTENANCE DATA COMMUNICATION Houssein El-Shtewi, R Pietruszkiewicz, F.Gu, A. Ball Maintenance Engineering Research Group The University of Manchester Manchester, Oxford Road, United Kingdom, M13 9PL Email: [email protected] Phone:+44 (0)161 275 4390 Web: vyww.maintenanceengineering.com
ABSTRACT The Communication Networks are mainly aimed at the use in process control environment, where the response time of the control action is the most critical aspect in process operations. In contrast, communication of maintenance data still has no high priority of transmission on the network. This is due to some technical reasons, two of these are: Firstly, the data is not so time critical, however maintenance data which passes occasionally when events occur, such as alarm signals of short content can be time critical data. Secondly, such networks are incapable of transmitting both types of data on the same network at the same time, due to the large size of maintenance data. This paper describes in detail the specification of the maintenance data communication on new standard Fieldbus network known as WorldFIP (Factory Instrumentation Protocol), which allows users to specify a profile of a process data.
KEYWORDS: WorldFIP Fieldbus network, maintenance data specification and communication, and status data monitoring.
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INTRODUCTION Various industries have realised the importance of emerging network bus technologies. Among these industries is the automation industry, which is concerned with the use of a network bus for data exchange due to the increase in complexity of I/O driven automation systems. These systems have caused an increase in wiring and interconnection, and as a result the additional cost has risen. A fundamental aim of implementing advanced technology in manufacturing industry is to reduce the production and maintenance costs, and to improve the operational efficiencies. One possible way of reaching this aim is argued to be the use of a new technique of digital data communications. This technique is based on a high-speed single two-way digitad automation network. The objective of implementing this technique is to conmiunicate both control data (critical time) and maintenance data (less critical time) over a smgle digital automation network without disturbing the real time world. This kind of communication network is called the WorldFIP Ffieldbus network, which is being used for the first time in process control systems due to it being an open architecture with a Windows NT platform. It provides higher levels of systems reliability at lower hardware and software costs. The high demand for a fieldbus, simply reflects the benefits of using an industrial network. The fullfunction WorldFIPfieldbusis the only one among many otherfieldbuses,can efficiently communicate maintenance data and control data on the same bus and at the same time by applying a single protocol. All otherfieldbusesaccomplish a similar objective, but they need more tiian one protocol to achieve the same task that WoridFIP Fieldbus does. Most of theses Fieldbuses are specified for processing only one kind of data, time-critical or non time-critical. Fieldbuses in general and WoridFIP Fieldbus in particular have a positive impact on field devices as the latter are influenced to be intelligent enough to perform the computation and diagnostic tasks that are being done by central control units such as PLCs. To unprove the robustness of the distributed control system, the use of intelligent sensors and actuators is advisable not only to reduce data and control traffic, but also the diagnostic information for status or preventative maintenance would be available for the device without additional wiring for diagnostics. Maintenance information has to be defined by the user in a convenientfi-ameand format for easy communication over the network. The distributed architecture of the Fieldbus with the presence of intelligent sensors contributes to distributing control and monitoring systems, and moves the centralised control functions to the shop floor. IMPLEMENTATION OF THE WORLDFIP FIELDBUS IN MAINTENANCE DATA COMMUNICATION. Achieving co-operation between the control systems and condition monitoring system using the same sensors and actuators and processing the data in parallel has resulted in opening new possibilities for plant monitoring. WoridFIP has the feature to provide full integration of the process control data with the condition monitoring data using one network (more details are available at COMADEM 2001 conference papers under the title: The Physical Combination of Control and Condition Monitoring. By R.Pietruszkiewicz). The WoridFIP is capable of supporting message handling on the same bus without any disturbance to the traffic of control data, which has the priority of transmission on the network. The purpose of message handling is to enable the operators in a manufacturing industry to monitor the status data (maintenance information) of every device in the process system. The status is sent over the network in the form of messages upon a request from the supervisory system since they do not have timing constraints. Message transmission should follow the principles of data communication over networks. This means for example, data size, type and protocol of the dialogue between low-level nodes and the supervisory system have to be specified by the user for reliable data communication as shown in figure 1. Data can 368
be transmitted as bits, characters, frames and blocks. Characters are typical of the communication method to transmit written words that may represent the general status such as "healthy"/"faulty" of the machinery that is being monitored. Data can also be represented in numerical values if more details are required. Status data of each node is transmitted over the network under one unique variable name specified earlier by the user to distinguish one variable from another. Each variable can have more than one measured parameter with a different identification.
Request llD 0110
•
F I E L D B U S NE T W O R K ^ (WorldFIP)
Variable ID_01
ID 03
fl
10 bo BO 40
Compressor
Hydraulic rig
Fig. 1 WorldFIP Fieldbus Network Architecture
CONCEPTION OF MAINTENANCE DATA Maintenance data can be defined as the machinery condition while it is in operation. The machinery condition status can be extracted from the machine by means of transducers or intelligent sensors. To reduce the maintenance cost and machine down time and to increase the availability of the machines on the production line, new methods have been developed to keep machinery under continuous control. This development is a result of emerging intelligent field devices and high-speed digital communication networks in industrial process control. Of course not every machine in the plant area has to be monitored, only those, which, on breakdown, give rise to high costs in production loss, maintenance cost and/or in safety aspects. Therefore, the information that represents the machinery status can be monitored directly via monitoring stations used for this purpose at the high level components as shown in figure 2. To make condition monitoring and communication systems more cost effective, maintenance data has to share the same communication network system with the control data at the same time of operation. However, control data has the priority of transmission on the network as it is time critical, low volume and cyclic, in contrast to maintenance data, which has the opposite characteristics.
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High Level Components -Bus arbitrator -CM Controller
-Time-critical variables -Background messages (configuration & diagnostics).
Process variables control Commands & Request commands for condition status.
Fig. 2 Data communication between high level components and low level components on the WorldFIP Fieldbus network.
Communication of Maintenance Data The communication of maintenance data over the WorldFIP Fieldbus network is principally based on the availability of the free bandwidth of the bus and on the time available for handling aperiodic data. WorldFIP is a deterministic protocol that guarantees the arrival of periodic data to the consumer destination on time basis and aperiodic data on request basis, however some aperiodic data such as alarms are treated like periodic data due to their low volume and time response criticality. Maintenance data can be communicated in different forms according to its size and criticality. The following are examples of these forms: -Periodical (regular) conveying of measured maintenance data about the present condition of machine. -Warning messages, which are short in contents, but can reflect any abnormal occurrence in machinery condition already, detected byfielddevices. -Big message contents contain information details to permit fault diagnosis. This information is reported by intelligent field devices upon request from high level components such as condition monitoring systems. Such messages are not time critical and can be delivered only if no urgent tasks need to be sent and when there is afreebandwidth in the network. How is Maintenance Data Communicated on the Fieldbus Network? Maintenance data shares the same Fieldbus network with control data. Maintenance data utilises the free bandwidth in the network to be communicated, this is the property of the WorldFIP, which uses only one protocol to communicate both types of data. The architecture of the Fieldbus network as shovm infigure2, illustrates two main components of the network; high level components (supervisory level), which comprises of control and condition monitoring systems and low level components consisting of intelligent field devices (sensors and actuators), which perform requested tasks. WorldFIP uses a bus arbitrator at the high level components to regulate the scanning time, especially for time critical communication. In the case of maintenance 370
data communication, which is the core topic of our paper, the CM Controller at the supervisory station requests a sensor at thefielddevice level to send the present status of the machinery the sensor is fixed into. The sensor can read different parameters such as temperature, pressure and vibration, which are already specified by the user at this device and uniquely identified. The device also has a unique address over the network as explained in the table 1. The sensor responds back upon the requested information indicating status data with a time stamp. This data can be a summary orfiiUdata (detailed report). The data structure of the parameters is a collection of address, data type, presents status and time stamp. If the response from a sensor indicates normal status condition, the controller does not ask for more information. But if the sensor indicates an abnormal status condition, the controller requests the sensor to resend the status again, or send more information or to send a detailed report. This process depends on how faulty the device is, and continues until the problem is solved. A dialogue model between the intelligent sensor and the master controller is expressed infigure3 a, b. The message that contains the maintenance information is sent over the network as a frame (communication frame), and indicates the stations of both destination and source. Maintenance information is divided into small segments (data packages) at the time of transmission between the two destinations. The division is due to the limit of the maximum data size allowed by the message transfer at the WorldFIP Fieldbus Protocol, which are of 256 bytes data block. Segment transmission is subject to thefi-eebandwidth in the network. HOSTSYSmM Cmdition Monitoring (CM)
Data Type and Size
CM
LJ
Request for status
General Status & Detdled Status
Maintenance Infoimation
Interpret data & Decides on further action
EQUIPMENT e.g.
M^sure data (Smtus)
Request for status again _-_^Contpressor ' Request for more details Wait for next action New data
CM
Fig.3 a: Dialogue model for maintenance data communication between sensor and controller over the WorldFIP Fieldbus network
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TABLE 1 ADDRESS DEFINITION OF NODES AND OBJECTS
Node Address ID_(address & index) ID_0110 20 30 40 ID_0210 20 ID_0310 20
ID: Identified address & index for each node on the network. 1^* two digits = node address 2"^ two digits =node parameter index 0110: 01= node number 1 10= 1^^ parameter (e.g. temperature) at node number 1. 20: 2™* parameter (e.g. pressure) at node number 1. 0210: 02=node number 2. 0310: 03= node number 3.
Variable ID 0110
HOST SYSTEM Condition Monitoring (CM)
Request_ID_0110
1(120 30 40
EQUIPMENT e.g. Compressor
Address Index Name Data Type Time 01 10 Temp. IntegerS (..) 01 20 Pressure Integerl6 (.)
Short report:
IDJaddress & index), status (good, bad), time(..)
Detailed report: ID_(add.& index), status (detailed report), time(..) ID_0110 Type-integer8 Status-good, Time(..) Fig.3 b: Dialogue model for maintenance data communication between sensor and controller over the WorldFIP Fieldbus network
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CONCLUSION In this paper, we provided a general description on how to use the WorldFIP Fieldbus network to support maintenance data communication. The major contribution of this paper is the mechanism of maintenance data communication between the high level components of the network and smart devices at low level. It is very clear to see from this paper that WorldFIP not only has a clear potential in the condition monitoring of industrial applications, but also provides an integration concept of information sharing between tilie control, maintenance and process management system. Therefore, it changed the architecture concept of the ordinary conmiunication systems, and resulted in maintenance data communication, which gained from the new development of applying single Fieldbus protocol and distributed control functions. REFERENCES [1] Besston, J. (1999). WorldFIP in the real time world. In Tech Magazine, issue 3 September 1999. [2] D. D. Kandlur, K.G. Shin, and D. Ferrari. (1994). "Realtime communication in multi-hop networks," IEEE Trans. On Parallel and Distributed Systems 5:10,1044-1056. [3] Eisenbarth, W. (1998). Fieldbus use in industry. Integrated Automated Systems. PEP Modular Computers PEP-8637,1-4. [4] Jacob, P., Ball, A. and Ingram, S, " Fieldbus: The Basis for an Open Architecture Condition Monitoring Revolution", MAINTENANCE, Vol. 11, No.5,1996. [5] Kreidl, J. (1998). Fieldbuses: Emerging Solutions for Factory Automation. Inova Computers GmbH (As seen in RTC Magazine, July 1998), 1-3. [6] r/zewerworA:wr/zeco«rro//er.The industrial Ethernet book, Vol.3, 2000 http://ethemet.industrial-networking.com/articles/ietterweb.asp
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Condition Monitoring and Diagnostic Engineering Management Andrew G. Starr and Raj B.K.N. Rao (Editors) © 2001 Elsevier Science Ltd. All rights reserved.
A DISTRIBUTED DATA PROCESSING SYSTEM FOR PROCESS AND CONDITION MONITORING A.D. Jennings, V.R. Kennedy, P.W. Prickett, J.R. Turner, R.I. Grosvenor School of Engineering, Cardiff University, Newport Road, Cardiff, CF24 3TE, Wales
ABSTRACT This paper describes a distributed data processing system that is used to yield important process and machine condition information. It is designed to make use of idle workstations to run processor intensive jobs, which communicate with a relational database to analyse data recorded by a number of Petri net based data acquisition systems. The distributed data processing system comprises task manager and task processor software modules. The task manager runs on the server and periodically searches each database used by the data acquisition systems for new data and inserts a job in a table, if a number of conditions are satisfied. Each task processor functions as a screen saver, which when activated by the operating system removes the first job from the table and executes the job. A number of data processing methods have been and are continuing to be developed to normalise, pre-process and classify the data before it is presented on dynamic web pages. Additional data processing methods will be developed to analyse data recorded from other types of data acquisition systems and ultimately to automatically send an email or text message when a significant process event occurs. The system therefore can be used to process the acquired data and present it to users as quickly as possible, while making optimal use of existing resources. KEYWORDS Distributed data processing, Petri net based data acquisition. Relational database, Process monitoring, Condition monitoring. INTRODUCTION The distributed processing system described in this paper is part of the research being performed in the hitelligent Process Monitoring and Management Centre in Cardiff University. This Centre is funded by a grant from the European Regional Development Fund with the requirement to help small to medium sized enterprises (SME) in South Wales to optimise their manufacturing processes. Together with helping the collaborating SME, the Centre is also being developed as a
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demonstration facility of an intelligent process monitoring and management system, that uses internet based commimication protocols. Data acquisition systems can very quickly acquire significant amoimts of data. If the data is just stored in files with no mechanisms to manage their size, data overload can quickly occur. Furthermore, data is soon regarded as being obsolete, because corroborative information such as written records and peoples' memories of changes to the process are often lost and forgotten. To prevent data overload and the data becoming obsolete, the data must be analysed, classified and discarded if it is normal. Operators, managers, etc must also be informed when the process is abnormal, so that timely and appropriate corrective action can be taken. Research being undertaken in the IPMM Centre is addressing the problems of acquiring, analysing and displaying the data by using internet based communication protocols. These are used to send the data logged by Petri net based modelling and data acquisition systems, and other specially built data acquisition systems to a database server. A web server that conmiunicates with the database server when building dynamic web pages is also used to send the data to a web browser. SYSTEM ARCHITECTURE Data analysis can be performed on a centralised computer or distributed to a number of computers. Centralised machines, such as mainframe computers are expensive to buy and require staff with specialist knowledge to manage them. Nowadays a lower cost solution is a network of workstations that are linked to a server. In such a configuration a centralised or distributed data processing system could be established, but in the former case a larger server would be required. It must also be borne in mind that workstations are not being used continuously; people leave their desks to attend meetings, for refreshments, etc. Therefore, the micro-processor in a workstation is available for data processing. This is the approach taken by the University of Wisconsin-Madison, with their Condor high throughput computing system (1988). It was originally developed for a Unix operating system, but it is currently being redeveloped for the Microsoft Windows NT operating system. A job is submitted to the master system together with the input data files and the name of the output files. Then when a workstation becomes available (ie no keyboard or mouse input for a period of time), the job is started on the workstation. When using Condor it was found that a job only had very limited access to resources outside of the directory in which the job is executed. In this research access is required to Microsoft SQL server, a relational database that stores the data acquired by a number of data acquisition systems. Although a connection to the ODBC driver could be established, data could not be read from the database. It is the intention of the Condor development team to allow a job access to resources on other computers, but when this development will be complete is unknown. It was therefore decided that a system should to be developed that would directly use the relational database. The system would require a server module or task manager to schedule a job when certain preconditions were met. Also a client program or task processor that would execute a job when the workstation was not being used was also required. The system would also need to eliminate the risk of data contamination, while maintaining data integrity and security.
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DATABASES The database comprises a master database and a process database for each process being monitored. In the master database there are three tables, which are used to list the name of each process database, to relate each data processing method (MethodID) with a path and filename, and to list the tasks waiting to be processed. The first two tables are static and are only changed when a database needs to be added to the distributed processing system or when a new data processing method is created. The tasks table is dynamic, because jobs are regularly being inserted and deleted by the server and client modules. The jobs are in fact sorted according to their priority and the date and time when they were submitted, therefore high priority jobs go to the top of the list. A process database that contains the four tables used by each Petri net based data acquisition system was previously described by Jennings et al (2000). It also contains numerous tables used to store configuration information and the results from each data processing method. In addition, there are three tables that are used to define, control and record the status of each job, as shown in figure 1. i^^^^^m
jobiD
llll
IMJMMJI [MethodID j | | | | | | | | j Input
^P|H| wKti
P^^^^ { | M ^ m l |ConPiguration
9Kt ,HIH
inHlHi Prerequisites UpdatePeriod
.flH H | IMlHi
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SHlDateTime •jBlstatus •HSHH^HMiii^ HHI^^^^SI
jiHIIH HHM
JobID
H^Hj DatBliiio Machine Message
inHH^HH^BfaHHHHH^H
i^^^H^^I
IHHIH
i^^B^HHHH^^HBB^^H
B^^W Figure 1 - Relationship between the Jobs, Status and History tables The Jobs table is static and is used to define the data processing method to be used, the input table(s), output table and the configuration table if one is required. It is also defines how often a job is to be run, the job's priority and if there are any prerequisites. These are used when a job requires the results from another data processing method, before it can be run. Both the status and history tables are dynamic and they are used to control and monitor each data processing method respectively. The status field in the status table is used to show if the job has been inserted in the tasks table, if it is running, if it has been run or has failed. While the history table is used to record on which workstation the job started andfinished,together with any error messages. TASK MANAGER The task manager runs on the server and an outline of its fimction is shown in figure 2. It periodically reads the name of each process databasefi-omthe Process table, which is in the master database. Then for the named process database, it searches the jobs and status tables for all jobs that can be run. It checks that the date and time when the job was last run plus the update period is greater than the current time. It also checks the date and time of any prerequisites that may be set. 377
If the job can be run, then the task manager reads the path and filename of the data processing method from the methods table, which is in the master database. Finally it inserts a string containing the path and filename for the method, the process database and JobID into the tasks table, together with the priority, date and time so that the job is automatically sorted. Process Table
Process DB Jobs Table Job Info 1 \
Task Manager
1 i
Hethods Table
Status, ""^ . DateTine Status Table Job to be run
i Taslcs Table
Figure 2 - Task manager functional diagram TASK PROCESSOR The task processor is designed to work as a screen saver and an outline of its function is shown in figure 3. When it is laxmched by the operating system, it covers whatever in visible on the screen and moves some text around the screen. It then connects to the SQL server, via the ODBC driver and reads the first job in the tasks table. To prevent a task processor running the same job on another workstation at the same time, it is deleted from the tasks table. However, this can fail if another task processor has just deleted the job, so the return code is checked and another job is selected if necessary. Once a job has been selected, the task processor launches the data processing method and supplies two command line augments (process database and JobDD). The data processing method then uses these to connect to the database, find the information required to configure itself and to continue processing data from whence it was last run. It also checks the information and if the method is being run for thefirsttime, it creates and populates the output and configuration tables as required. Then using an SQL statement, data is read from the input table, processed and inserted in the output table. Finally when the data processing method has run for the require time or number of cycles it terminates and the task processor can start another job. Since the task processor is a screen saver, it is terminated by an inputfromthe mouse or keyboard. But this could occur when a data processing method is being run. Clearly the resources being used by the data processing method need to be released as quickly as possible, otherwise the user may complain that the workstation is running too slow. This can be achieved by killing the data
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processing method, moving it to another workstation or by designing the method so that it only runs for a short time. The former case, has the disadvantage that markers to show what data has or has not been processed may not be set. To implement the second case would significantly complicate the task processor, task manager and database structures, because all the internal variables used by the data processing method would need to stored and reloaded. However, the operating system helps by adjusting the allocating resources to the different programs that are running. In which case, it is possible for a user to tolerate a slight drop in performance until the data processing method terminates. In fact testing of the system has shown that the data processing methods run for 7 to 20 seconds and this time can be shortened by changing the maximum cycle count in the data processing method.
Tasks Table
,^ Job to be run
1
Jobs Table Job
Input Table Input Data
Info
\ Data Processing \ \ Status,
Hethod
/
^
^^^^^^ DateTime
Config Data N^
2
/ Configuration Table
Status Table
Workstation, „,. „ DateTixne, Message
History Table
^ ^ ^ ,v ^ Output Data \
Output Table
Figure 3 - Functional diagram of the task processor and a data processing method DATA PROCESSING METHODS At the time of writing, four data processing methods have been implemented. The first of these can be used to count the number of tokens fired by each transition in a Petri net model of a process, per unit of time. Therefore, the number of parts loaded into the process, number of rejects and the number of good parts from the process can be counted every 15 minute, for example. Another method can be used to summarise the route taken by a token through a Petri net. This is useful for following a part and for monitoring the operational sequences of a process. A third method can be used to calculate the values for statistical quality control (SQC) charts. This can be used to monitor the cycle time of the process as well as the health of individual actions in a process. The fourth method can be used to calculate the overall equipment effectiveness (GEE) of the process. This is the product of the production rate, the machine availability and product quality; all of which need to be maximised to achieve a high GEE.
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The library of data processing methods is being added too, with the development of other methods. This includes a method for classifying the values on an SQC chart as normal, trending, etc. It is also the intention to build a method for automatically sending a message, when a significant process event occurs. The message could be an email, a text message to a mobile phone or a handheld computer. SYSTEM PERFORMANCE To measure the performance of the distributed processing system, the Windows NT task manager was used to view a trend of the CPU and memory usage. The effect on the server (Pentium 2, 500MHz, 128MB memory) of the task manager is to increase CPU usage from 3 to 5.8% (average over width of NT task manager window) and memory usage by 26.7%. While the effect on a workstation (Pentium 2, 450MHz, 64MB memory) of the task processor is to increase CPU usage from 2 to 39.8% and memory usage by 36.8%. An increasing number of workstations also marginally mcreases the load on the server's CPU, by 6%, 6.3% and 9%, respectively. But it is the data processing methods when they are run that significantly increases the load on the server's CPU. hi fact, increasing the number of workstations increases the server's CPU usage to 48.6%, 68.2% and 80.2%, respectively. This is due to the SQL server having to run a query on the databases before the results are sent to a workstation. Since the server also builds the dynamic web pages when requested, the performance of the web server was also tested by using a workstation to loading a web page. It was found that even when the server's CPU was highly loaded there was no reduction in the time for the web page to be built and displayed. CONCLUSIONS The distributed processing system described in this paper is part of an intelligent process monitoring and management system, as shown in figure 4. It comprises Petri net based and other specially build data acquisition systems that use a PC and now a data acquisition system that uses a PIC micro-controller. Also it allows dynamic web pages to be created of images of the data, for operators, maintenance and managers. In addition, it will allow messages to be sent to mobile phones, pagers and hand held computers.
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Mobile Communication Devices
1 SQL & Web Server
Petri net based data acquisition system
PIC microcontroller data acquisition system
-*
^
w\
-^ Distributed data processing
Specially build data acquisition system
Figure 4 - Three tier architecture of the intelligent process monitoring and management system
ACKNOWLEDGEMENTS The authors acknowledge the support of the European Union via the ERDF grant to establish the Intelligent Process Monitoring and Management Centre.
REFERENCES Condor high throughput computing (1988). University of Wisconsin-Madison. Available from : http://www.cs.wisc.edu/condor/ [Accessed June 2001]. Jennings AD, Nowatschek D, Prickett PW, Kennedy VR, Turner JR, Grosvenor RI, (2000). Petri net based process monitoring. In proceedings of: COMADEM 2000, 13'^ International congress on Condition Monitoring and Diagnostic Engineering Management, Houston, USA; MFPT Society, 643-650.
BIBLIOGRAPHY Turner JR, Jennings AD, Prickett PW and Grosvenor RI; The design and implementation of a data acquisition and control system using fieldbus technologies; to be published in proceeding of COMADEM 2001. Frankowiak MR, Grosvenor RI, Prickett PW, Jennings AD, Turner JR; Design of a PIC based data acquisition system for process and condition monitoring; to be published in proceeding of COMADEM 200 J.
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Condition Monitoring and Diagnostic Engineering Management Andrew G. Starr and Raj B.K.N. Rao (Editors) © 2001 Elsevier Science Ltd. Allrightsreserved.
THE PHYSICAL COMBINATION OF CONTROL AND CONDITION MONITORING R.Pietruszkiewicz, H.El-Shtewi, F.Gu, A.D.Bali Maintenance Engineering Research Group The University of Manchester Manchester, Oxford Road, United Kingdom, Ml3 9PL Email: [email protected] Phone: +44 (0)161 275 4390 Web: www.maintenanceengineering.com
ABSTRACT Digital fieldbus systems are now the industry standard for automated factory-wide plant control and process data communication. For historical reasons, however, the control and condition monitoring systems have always been kept separate. With the structured communications protocols offered by many fieldbuses, this separation is no longer necessary and indeed is not desirable. The fieldbus concept is now the norm for factory-wide automation systems. Such systems are employed extensively in industry for the real time control of plant, yet the hierarchical communications protocols at the heart of many of these systems permit them to be used for much more than this. In principle, it is quite possible that all control, monitoring and information functions be performed using a single network. The advantages of such integration are many fold, from hardware economics through reliability of operation to the exploitation of control information for maintenance purposes. By design, fieldbus control systems have surplus bandwidth to allow for unforeseen control circumstances. But within routine operation, this spare communications capability can be exploited very effectively for the communication of condition monitoring and maintenance information, and with a guarantee that real time control will not be disrupted. Manchester University has chosen the Europe-leading WorldFIP fieldbus protocol to form the basis of the demonstrator system that it is creating. The ambitious system, believed to be the first in the world, incorporates six complex pieces of industrial equipment and handles all their supervisory, local control, condition monitoring, and maintenance strategy needs.
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INTRODUCTION Condition Monitoring systems aim to increase reliability of the plant due to influencing operation planning depending on the condition of the plant. But even the most advanced Condition Monitoring system is worthless if information about the plant condition does not directly influence the handling of the devices. This direct feedback from the Condition Monitoring system is a key factor for plant improvement. There is a big industrial expectation for a system that combines Control and Condition Monitoring systems making more use of accessible data. The advantages of such integration are many fold, from hardware economics through reliability of operation to the exploitation of control information for maintenance purposes. Looking from a "real business" point of view not many plants can afford to install expensive systems and employing experts to explore, understand and apply the information. How interesting would a frilly automatic diagnostic system be that could work without additional operation personnel? Once installed, an * expert system' would monitor the condition of the plant continuously, looking for trends and preparing strategies for the control of the plant. This represents an investment that would increase safety and reliability with high financial benefits without stretching the budget for maintenance. Manchester University began developing a concept for a Fieldbus System for Machine Condition Monitoring and Control. The aim of the project is to create a system to integrate factory information (Control and Condition) applying the best possible strategy to maintain the plant. THE PHYSICS OF CONTROL AND CONDITION MONITORING On analysing Control and Condition Monitoring data, we will see some significant differences between these two sources of data. The clearest difference is in the size and the time reliability. Comparing size, where condition data requests more space than control data, and the time reliability, where control data is based on stablefrequencyand time reliability (opposite to condition data), we will see that transmission medium must allow for parallel transmission for control and maintenance data. TABLE 1 DATA DIFFERENCES
Feature Size Format Time Reliability Frequency Complexity
Control Data Bytes Variables Time Critical msec Simple Structure
Maintenance Data Kbytes / Mbytes Variables and Samples Non Time Critical On request Complicated Structure
These differences between Control data and Condition data and problems withfindingsingle transmission medium for both sources of data mean that traditional operating methods have formerly separated these two sources of data. The individual systems have been orientated in one area whilst ignoring the other available information. Data used to be collected using separate equipment, stored in separate data bases by separate departments. Control and maintenance did not have common standards of communication. All these factors do not have much influence between the systems, making co-operation between the systems more complicated and less efficient.
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BENEFITS FROM COMBINING CONTROL AND CM DATA What are the benefits of data integration and systems co-operation? A system would benefit on two levels. Co-operation between these two systems offers much more than with them working separately. The low level benefits of a single communication system are: • Hardware reduction (single system equipment, single installation costs): The same sensors can be used for two purposes, namely control and condition. The actuator can perform standard control functions and requested condition tests. • Systems compatibility: New opportunities for operation methods. A control system with easy access to condition data will work with smart condition based systems,, (an easy way to enable advanced operating methods like "condition based control" and "control based condition monitoring"). • No data redundancy and no data transformation: Systems will have direct access to data. Systems operating together using the same data, hence increasing the value of the information. Data can be consumed by any station (sharing the knowledge about devices in the system). The high level benefits of a single communication system are: • Implementation of such a system would provide an advanced way to control and maintain plants, highly increasing the • safety, • reliability, • interoperability, • prediction, • productivity. • Condition systems having a control plan can look ahead for condition diagnostics, which resuh in better control and maintenance processes for the whole plant's structure. • A fully automatic system that won't need a crew to collect and analyse the data, can work and process information 24 hours a day, 7 days a week. Companies that compete with others cannot afford unexpected breakdowns. INTEGRATION METHODS The following problems must be solved in order to provide a solution for our system integration: 1. Control data cannot be disturbed by condition data. 2. Transmission medium has to supply a single communication standard for both systems. 3. Control and maintenance programs have access to the same data. 4. Ensure easy exchange between the systems. 5. The flow of data must be reduced. The use of the Fieldbus network as an integration medium for control and condition solves most of the problems. A fieldbus is an accepted set of communications rules (or protocols), which enables the construction of distributed control and monitoring systems. It is the basis for an advanced networking system used to interconnect field devices (sensors, actuators, and transducers) to higher level factory equipment (PLCs for control or microcomputers for monitoring purposes). Unlike personal computer networks (e.g. ethemet) fieldbuses are
385
optimised for the exchange of relatively short data messages containing control or status information. They can guarantee delivery of messages on a periodic basis or within a limited time. Fieldbuses transmit digital signals along a twisted pair cable, and are replacing the traditional 4-20mA analogue network system. One advantage is that digital signals are more robust in the presence of noise. In addition, cabling costs are substantially reduced by using fieldbus technology because all devices are linked up on the same cable so that wasteful point-to-point communication links are no longer required. Of greater significance, however, is that the Fieldbus permits novel interaction between devices. It enables two-way communication with information no longer being addressed to specific areas of the network but being available to all nodes. This allows full advantage to be taken of existing information, empowering intelligent field devices and leading towards distributed control. FIELDBUS NEEDS Of fundamental importance to the functioning of an integrated network system is the fact that real time control must not be disrupted by the operations of the condition monitoring or management information services. This functionality can be guaranteed by the appropriate use of the established fieldbus message hierarchy. Furthermore, this protocol readily permits the communication of non-time-critical condition monitoring and management information data in the portion of the bandwidth which may be unused by control data at any instance in time. To function effectively, the data exchange required for the condition monitoring function must be minimised, and for this reason it is necessary to employ a distributed processing approach, with intelligent condition monitoring sensors, which function on request from a central condition monitoring controller. Mostfieldbusnetworks currently on the market were designed for only one specific area of activity. WorldFIP belongs to a different group offieldbusnetworks that contain very flexible profiles of data communication. Special WorldFIP features allow for integration. The unique about this network protocol is that WorldFIP allows the construction of communication using time critical variables for Control Data and non-time critical messages, with a greater capacity, for maintenance data. The non-time critical messages have the ability to fit into any space offered by the system depending on the actual condition. That means if there is a situation that requires more control data, the control data will have priority and the maintenance data will be restricted to the available space in the data flow of the network. Messages can be divided or pending but they will not disturb the control data. INTELLIGENT SENSORS Capacities of the Fieldbus are always restricted. To avoid dependency of the system on the bandwidth offered by the network, data traffic should be reduced. This reduction can be achieved by the implementation of lower level equipment in decision-making roles. The intelligent sensors are specially equipped to detect faults and co-operate with the Control and Condition Monitoring. The capabilities of this layer include: • self-calibration of the sensor, • the ability to analyse and make decisions, • downloading and changing software, • communication with the control and condition systems. 386
• •
performing requested tests sampling and sending data to the condition maintenance system.
Stabilisation from faults in the sensor is also required. A simple fault in one of the signals or disconnection from the network should not affect the whole system. Prediction of the sensor behaviour will also be included. The intelligent sensors would be responsible in the first line for local condition monitoring. They will only send data that has failed the standard tests, so healthy data does not need to be sent. With this strategy unnecessary traffic would be reduced to a minimum leaving space for urgent actions and for increasing numbers of devices communicating on a single segment of the network. This is a very economical solution as the use of the extremely fast network is not required. The proposed operating strategy for the Manchester University demonstrator system is described in detail in El-Shtewi (2001).
CREATION OF THE DEMONSTRATOR To give a general solution the system includes various devices representing multiple equipment used in industry. These standard types of devices were considered for testing different types of intelligent sensors and maintenance methods: • Electric Motor (3-phase, 3 kW induction machine) • Diesel Engine (Ford 4-cylinder, 4-stroke, 2.5 litre) • Hydraulic System (Electro-hydraulic position system) • Compressor (Broom Wade reciprocating compressor)
0lfSfI Eii|^»«
\m^k System |
Compressor
f «»4HHrf • MiJWttflrAfttf
Figure 1: Demonstrator
387
|
I Kiectrk Motor
CURRENT DEMONSTRATOR CAPABILITY At this stage of the work, the demonstrator monitors the control parameters and condition of the Electric Motor and Diesel Engine. The remote control/condition station displays information from the local "intelligent system" and the local control device. The station displays only the most important controlled parameters which are sent to and received from the device. The general status of the device (motor condition gauge) is also displayed. This can be seen in Figure 2.
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Figure 2: Remote control/condition station display Any change in condition will be visible on the remote station communication via the Fieldbus network. The station has the ability to relay the current status on to the Internet (or WAN). Locally the "Intelligent sensor" performs the condition monitoring using these different methods: • Instantaneous angular speed measurement for the Electric Motor. • Vibration monitoring for the Diesel Engine. The local station is equipped with condition monitoring diagnostic programs and has access to all parameters (Figure 3). This station is also filtering the information and only necessary data will be transferred via fieldbus to the remote station. If requested, samples of the raw data can be sent to the remote station.
388
Figure 3: Electric Motor "Intelligent sensor" Local Station Display
CONCLUSIONS AND FUTURE WORK The current prototype of the demonstrator will be used for testing the communication standards. A strategy of communication has been developed but it is based on theory, which needs to be proven in practise. This testing stage is designed to yield system improvements and for designing data exchange formats. Development of the system is based on a prototyping method, each part of the programming begins with a basic requirement specification for the developed prototype, which is followed by testing and concludes with a fUU requirement specification. This method has been recognised as a very successful method for developing novel systems. Future work involves, the creation of intelligent sensors for other devices, and the implementation of a condition monitoring station with high level condition monitoring programs and data base storage.
REFERENCES Pietruszkiewicz R (2001) The Communication of Control and Maintenance Data over a Single Digital Automation Network, using Intelligent Sensors and Distributed Processing MARCON 2001 Conference, University of Tennessee USA El-Shtewi H (2001) The use of the Fieldbus Network for maintenance data Communication, Proc. 14th International Congress on Condition Monitoring and Diagnostic Engineering Management (COMADEM 2001), University of Manchester UK Johnson, R., "Looking over the bus systems". Control Engineering 2(13), Dec 1995, pp.5664.
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Condition Monitoring and Diagnostic Engineering Management Andrew G. Starr and Raj B.K.N. Rao (Editors) © 2001 Elsevier Science Ltd. All rights reserved.
THE DESIGN AND IMPLEMENTATION OF A DATA ACQUISITION AND CONTROL SYSTEM USING FIELDBUS TECHNOLOGIES J R Turner, A D Jennings, P W Prickett and R I Grosvenor Cardiff University, School of Engineering, Newport Road, Cardiff, CF24 3TE, Wales.
ABSTRACT A data acquisition and control system designed for an experimental process control rig is described. The system incorporated a Fieldbus distributed input/output system for data acquisition and control from a PC bus master. In the first part of the paper, the physical specification and configuration of the Fieldbus system is described, together with the selection of protocols used for data transmission. Li the second part of the paper, the design considerations of an experimental electronic circuit devised for the data acquisition system is described. The circuit provided an interface between the sensors and actuators of the rig, and the Fieldbus input/output system. The circuit was designed to make most efficient use of the channels available on the Fieldbus system, and also to provide a uniform signal format across all channels. Furthermore, some protective features were incorporated into the circuit, and these are also described here. The third part of the paper outlines a strategy for fault detection using the system described previously. In the final part of the paper, the data acquisition and control software constructed and written for the control of the Fieldbus system by the PC master is described. The software allows the data collected to be stored in a relational database system for further processing, and also provides the user with a real time image of plant operation. The paper concludes by considering the use of such a system for process monitoring tasks, and considers the operation of the system in the context of other work in progress at the Intelligent Process Monitoring and Management (IPMM) Centre at Cardiff.
KEYWORDS Fieldbus, Process Monitoring, Condition Monitoring.
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INTRODUCTION The development of the IntelHgent Process Monitoring and Management (IPMM) Centre within the Cardiff School of Engineering is continuing with funding from the European Regional Development Fund (ERDF). The aim of the centre is to improve the competitive position of collaborating Small to Medium Sized Enterprises (SMEs) within South Wales, by the introduction and use of innovative technologies for remote monitoring of industrial equipment. As part of the research work carried out at the centre, a Fieldbus-based system was built to allow monitoring and control of an experimental process control rig, used for teaching and other research activities. The rig is designed to simulate an industrial process, which involves the conditioning of a process fluid through temperature control, mixing, and delivery of a fluid batch at the required specification. A simplified diagram, figure 1, shows the main features of the rig.
S 3
^ I § fit
Cooler Process tank
6
Stirrer
o
Heater
t3 >
O
Solenoid drain valve
»
Hand-operated drain valve
Sump tank
"•~T"'" Pump
Sump temperature
Figure 1: The main features of the processrigused as a basis for the Fieldbus system During normal operation, water stored in the sump tank is pumped through the piped circuit into the process tank for conditioning by an electrical water heater in the process tank, and then released back into the sump tank when the demanded conditions are reached. Solenoid valves are available for diverting the water through the cooler if required, and also to release the water from the process tank when appropriate. A number of hand operated valves are also available, which can be used to introduce a number of faults into the system for diagnosis (as described later). The rig also incorporates a number of digital numerical displays to allow an operator to read the temperatures andflowratesas measured by the transducers used. Additional displays show the power input to the electrical water heater in the process tank, the state of the on/off solenoid valves and the cooler. An indicator also shows when the process tank is full to capacity. In addition, a stirrer in the
392
process tank allows the water to be mixed during heating, and a potentiometer allows the operator to manually control the power output of the water heater. The Fieldbus system chosen for this application was a Profibus system, manufactured by Siemens. The system makes use of a bus master, which in this case, is represented by a Siemens PC card of type CP5412 A2. The master controls all the low-level data transfer occuring on the bus. A bus slave is then used to connect to the rig actuators and transducers onto the bus, which in this case, is a Siemens ET200M interface. With these components in place, a Profibus system is capable of high data transfer rates of upto 12Mbaud (mega-bits per second). The Profibus system operates a number of different protocols on the bus, and allows the applications developer to choose one depending on the application involved. The protocol used for this application was Profibus-DP, a cyclic protocol which is commonly used for time-critical data transfers, (Jacob, Ingram & Ball 1996). The Fieldbus system was designed to read all the inputs from the transducers on the rig, and also control all the actuators. Two actuators (the pump and the water heater) required additional control strategies for efficient operation (described later in this paper). Since the Fieldbus system has modules available for analogue and digital input and output, it was convenient to classify the actuators and sensors used on the rig in this way. Tables 1-3 list the sensors and actuators on the rig, and the classifications used.
EXPERIMENTAL INTERFACE CIRCUIT An electrical circuit was designed and built to allow the signals from the sensors positioned on the process rig to be efficiently transferred to the Profibus interface and thence to the controlling PC. In order to retain signal integrity, the electrical circuit was designed to transform all the signals from the rig to a 4-20 mA signal for direct connection to the Profibus interface. Since each type of sensor used on the rig has individual signal characteristics, different methods were used to achieve the signal transformation required. These are also described in tables 1-3. A decision was made to keep all the analogue outputsfromthe Profibus interface uniform as a voltage level in the range 0-10 V. Most of the actuators requiring an analogue voltage input required little or no signal conditioning, however in order to control the output of the pump more effectively, a different approach was required. An electrical circuit to transform the analogue voltage output from the Profibus interface into a pulse-width-modulated (PWM) output was designed, and incorporated into the interface circuit boards. This allowed the pump to be powered from a fixed 15 V supply. In order to operate the solenoid-operated valves on the rig, the cooler and the stirrer, the circuit incorporated a number of electro-mechanical relays, which are controlled by the digital outputs of the Profibus interface. In addition, the electrical heater used in the process tank is powered by a 240 V ac supply, and controlled by a solid-state relay. This relay was in turn controlled by a digital output from the Profibus interface, connected in series with the output from a float switch. This switch is used to indicate when the process tank isfrill.This is provided as a safety feature, to prevent the heater being operated when insufficient water is available in the process tank. Additional circuit boards were designed to provide the connections between the digital outputs from the Profibus interface, the appropriate relay mechanisms and the actuators on the rig. In order to control the power output of the heater, procedures were developed in the controlling software to implement a PWM strategy for switching the solid-state relay. Varying the on/off ratio in intervals of 5% provided effective heater control.
393
A number of features were incorporated into the circuit to afford some protection both to the Profibus interface and also the rig hardware. It was found that under certain conditions, the current transmitters used to transform the signal from the temperature sensors could produce a current output capable of overloading the Profibus interface. To reduce this risk, the circuit included a number of zener diodes positioned on the output of the current transmitters. In order to protect other sensitive parts of the circuit, op-amps have been used as buffers.
DETECTION OF SYSTEM FAULTS The system, comprising of the rig, interface circuit, Profibus interface and PC is shown in diagrammatic form in figure 2. ET200M Profibus interface PROFIBUS CONNECTION
PC equipped with CP5412cardasbus master
Sensors and actuators on rig
Figure 2: The system configuration The system as described above was used to develop fault diagnosis strategies using the sensors and actuators on the rig to simulate an industrial process under test. Faults occurring on the rig can generally be classified into 'hard' faults (often rapid failure of one or more system components) and *soft' faults (gradual degradation of one specific component). Experience has shown that many faults can be detected by monitoring the output of the system controller (in this case, represented by a software-controlled PC) and comparing to the output under 'normal' operation. This method of fault detection was tested with this system, by opening a hand-valve to simulate a leakage in the pipe network. In this case, if the system should provide a constant flowrate of water into the process tank for example, the controller output would be measurably higher when a leak has occurred, and would therefore be an indicator to the occurrence of a leak on the rig. Additional information would be required to pinpoint the position of the leak.
SOFTWARE Software for the collection, display, storage and manipulation of data has been written for the system, making use of the driver fimctions available for the Profibus system. The software was written using the National Instruments Lab Windows development environment, which also offers tools for the design and operation of a Windows user interface. Additional add-on toolboxes provide the system developer with the ability to connect and send data to remote databases, also making use of the ODBC
394
drivers available with the Windows 9x/NT operating systems. The application makes use of the multitasking capabilities of a 32-bit operating system, and uses a separate thread for the collection of data and control of the heater from the main loop, which itself handles all the data manipulation tasks and user interface updates. This system permits the collection of data from the Profibus system to occur at a constant rate, and is unaffected by other tasks being carried out by the main processor. The software was developed with a user interface to mimic the operation of the rig. Animated controls show the operation of the cooler and stirrer. The controls are also capable of indicating when a component operates out of its normal range. A screen showing normal operation of the rig is shown in figure 3.
Figure 3: Software screen showing normal operation Li the context of other work in progress at the IPMM Centre, this system operates as a source of process data. This can be treated as real-time monitoring data, or stored for historical analysis, such as trend or statistical process monitoring. The overall aim of the centre is to provide timely, appropriate information to relevant company personnel regarding the status of the process under observation. To this end, a PC server was built, to handle data from the process, convert this data into usefiil information, and provide personalised web-based display to the end users. In order to reduce the significant workload imposed on the server due to the data processing requirements, a distributed data processing system was devised. This operates by breaking down the data processing tasks into small, manageable operations and distributing these to PCs linked to the server via a network. The client software for this has been written in the form of a screen-saver, and thus makes use of PC processor time when the PC is not in use by the user. This system is illustrated schematically in figure 4.
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Personalised web based information display, for - Manager - Operator - Maintenance engineers
INFORMATION OUTPUT
DATABASE, DISTRIBUTED PROCESSING AND WEB SERVER - Microsoft SQL Server - Web server software - Custom Distributed Processing software
DATA INPUT
Rig data acquisition system
Figure 4: The information system in use at the Intelligent Process Monitoring and Management Centre To provide the information required by the end users, methods are required for the processing of the data. Work has been carried out using trend analysis and statistical process monitoring in order to detect the 'soft' faults occurring on the rig. Other methods are under development, which will provide a more 'intelligent' analysis of the data. The data acquisition system can itself carry out basic range checking operations prior to sending the data to the server, and could detect the 'hard' faults which result in a failure of one or more components. In order to reduce the volume of stored data, it is possible to use the range checking operations and only store that data which represents abnormal operation. This will also help to reduce the workload imposed on the server. Further work in this area is being planned. Table 1: Analogue input (in terms of the ET200 Profibus interface) Sensor Flowmeter
Sensor output signal Pulsed 5Vfrequencyoutput
Flow temperature
PTIOO resistance output
Manual analogue input Process tank level
Potentiometer resistance output 0-5 V
Process tank temperature
PTIOO resistance output
Sump tank temperature
PTIOO resistance output
396
Signal transformation ADVFC32 (Analog Devices) frequency to voltage converter XTRlOl (Burr Brown) voltage to current converter XTR103 (Burr Brown) current transmitter XTR103 (Burr Brown) current transmitter XTRlOl (Burr Brown) voltage to current transmitter XTR103 (Burr Brown) current transmitter XTR103 (Burr Brown) current transmitter
Table 2: Analogue output (in terms of the ET200 Profibus interface) Analogue output device Flowrate panel meter Flow temperature panel meter Heater power input panel meter Process tank temperature panel meter Sump temperature panel meter Pump
Required voltage range 0-5 V
Profibus signal transformation Resistor network
0-5 V
Resistor network
0-5 V
Resistor network
0-5 V
Resistor network
0-5 V
Resistor network
0-10 V
Analogue signal to Electronic PWM control using control an op-amp circuit
Control strategy
PWM
Table 3: Digital output (in terms of the ET200 Profibus interface) Digital actuator
Actuator input
Cooler indicator
Digital on (5V)
Drain valve indicator
Digital on (5V)
Diverter valve indicator
Digital on (5V)
Tank full indicator
Digital on (5V)
Cooler
High power digital on
Diverter valve
High power digital on
Drain valve
High power digital on
Stirrer
High power digital on
Heater
Mains power
Signal transformation 1 Direct connection to Profibus interface 1 Direct connection to Profibus interface 1 Direct connection to Profibus interface 1 Direct connection to Profibus interface 1 Mechanical relay controlled 1 Mechanical relay controlled 1 Mechanical relay controlled 1 Mechanical relay controlled 1 Solid-state relay controlled
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Signal control strategy Digital control from Profibus interface As above
As above
As above As above As above As above As above Software PWM controlled via Profibus interface
ACKNOWLEDGEMENTS The authors acknowledge the support of the European Regional Development Fund in the establishment of the Intelligent Process Monitoring and Management Centre at Cardiff University.
REFERENCES Jacob P, higram S and Ball A (1996) Fieldbus: The Basis for an Open Architecture Condition Monitoring Revolution, Maintenance, Vol 11, No 5, pp3-9.
BIBLIOGRAPHY Jennings AD, Kennedy VR, Prickett PW, Turner JR, and Grosvenor RI; A Distributed Data Processing System For Process And Condition Monitoring; to be published in proceedings of COMADEM 2001 Frankowiak MR, Grosvenor RI, Prickett PW, Jennings AD, Turner JR; Design of a PIC based data acquisition system for process and condition monitoring; to be published in proceeding of COMADEM 2001.
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Condition Monitoring and Diagnostic Engineering Management Andrew G. Starr and Raj B.K.N. Rao (Editors) © 2001 Elsevier Science Ltd. All rights reserved.
A NON-LINEAR TECHNIQUE FOR DIAGNOSING SPUR GEAR TOOTH FATIGUE CRACKS: VOLTERRA KERNEL APPROACH. F. A. Andrade, 1.1. Esat Department of Mechanical Engineering, Brunei University, Uxbridge, UB8 3PH. UK.
ABSTRACT This paper introduces the usage of a non-linear signal processing technique, namely the Volterra Series, to the field of vibration based condition monitoring. Furthermore, it includes a critical analysis of the performance of this technique on the early fatigue crack identification problem. For this, a spurgear system was used and vibration signatures were collected using both an accelerometer and a stresswave sensor. Finally, the experimental data analysed includes signatures from one gear in good condition and three faulty gears, each with a fatigue crack of different advancement.
KEYWORDS Vibration condition monitoring, non-linear methods, gear maintenance, maintenance, crack identification, digital signal processing.
INTRODUCTION Although it is possible to represent a non-linear system by dividing it into smaller linear, or quasilinear, sections which are then analysed with the already well established theory for linear systems; it has been recognised that non-linear processes can only be analysed as a whole by non-linear methods. Hence the need for the development and testing of non-linear condition monitoring techniques is real. There are a number of non-linear system identification techniques (Billings, 1980) which can be extended to vibration condition monitoring applications. Some examples are: • Artificial neural networks, which has been widely used as a pattern recognition tool to identify and differentiate vibration signatures from rotating devices with different and or multiple faults. This method has been very effective as a post-processing tool for vibration signatures, however it has not been useful for the analysis of the raw vibration signature; • Functional series methods (including Wiener and Volterra series), which allows the user to select the non-linearity order to be included in the system model, and;
399
•
Heuristic methods such as GMDH (Group Method Data Handling) and parameter estimation methods.
Some of these techniques have been suggested and tried in other areas of research, such as communication systems analysis and biological systems modelling. However, from these techniques only artificial neural networks have been widely applied to industrial condition monitoring applications, as a post-processing tool. From this application it was observed that the neural network is performance is very dependent on the pre-processing technique used to extract the relevant information from the time domain time series. The work presented here introduces and performs a critical analysis of one specific non-linear technique, namely: the Volterra Series. It must be emphasised that although this technique has been successfully applied to other fields, it has not yet been used within vibration-based condition monitoring applications. Furthermore, here it is shown that this technique is very effective in the task of identifying fatigue cracks in spur gears. Finally, the thrust behind this work lies in the fact that Volterra series has already been used with some success in the identification of non-linear biological signals. Also, as is shown here, this technique can be used directly on the raw vibration data (and not as a post-processing technique).
VOLTERRA SERIES: CURRENT APPLICATIONS This section reviews some of the current applications of Volterra series. These are grouped into two main classes: non-linear system modelling, and non-linear system analysis and identification (pattern recognition). Previous applications suggest (Marmarelis, 1978) that the main advantages behind the Volterra approach lies in its ability to model mathematical relations between input and output of a given system. Also, the modelling process does not require any previous knowledge about the system structure being studied. Furthermore, by analysing the calculated Volterra kernels it is possible to information related to the underlying non-linear system under study. This information is of utmost importance and enables the condition monitoring engineer to identify faults in machinery. Non-linear system modelling and identification The identification and analysis of non-linear systems plays a vital role in control theory. Most systems encountered in practice are non-linear. By restricting the operating range these systems can be represented by a linear model. Unfortunately, a fact still remains; non-linear systems can only be adequately represented by non-linear models. From the above premise it is essential to analyse non-linear systems by means of non-linear signal processing techniques. Under this scenario, Koh & Powers (1985) showed how Volterra filters (nonlinear filters with the Volterra series structure) can model and predict the dynamic behaviour of moored vessels due to irregular sea waves. His results show the utility of Volterra filters in studying the non-linear drift oscillations of moored vessels when subjected to random sea waves. Koh's work clearly show that the second order Volterra filter is very effective in modelling the lowfrequency drift oscillation of the barge, and also for predicting the barge response. The latter result is of extreme importance in the control and stabilisation of these systems.
400
Non-linear system analysiSy and pattern detection and recognition The Volterra series approach has been extensively used in the analysis of non-linear biological and physiological systems (Korenberg & Hunter, 1990). It has been found that its main advantage lies in its ability to model a system with no previous knowledge of its structure. Also, it is shown here how Volterra kernel analysis can lead to inferences of the model structure. Some work in this area has been started by a number of researchers, however the Volterra kernel approach has not yet been applied to vibration condition monitoring. One very strong contribution, suggesting the use of Volterra kernels as a pattern recognition tool was put forward by Bissessur. In (Bissessur & Naguib, 1995) the problem of detecting buried pipes is presented. Initially, an artificial neural network is used to discriminate between ground surface and actual pipe reflection from the return of a radar signal. Later the structure of the trained neural network (i.e. a network that correctly maps the site being surveyed) is compared to that of a Volterra series. It is shown that both the neural network and the Volterra series present the same structure. This suggests that these two systems have strong similarities and can share the same transfer function. Bissessur showed that, by obtaining a mathematical formulation of the weights learnt by the artificial neural network and its nodal functions, it is possible to extract a set of Volterra kernels, formulating the Volterra series representation of a system, in this particular case, the pipe detection problem. These results are supported by those given in (Govind & Ramamoorthy, 1990), which discusses the similarities and differences between neural networks and Volterra series.
VOLTERRA SERIES: THEORETICAL BACKGROUND AND EXAMPLE. In order to describe the theoretical background of the Volterra Kernel approach, this section contains a simple numerical example where the Volterra series is used to model a non-linear time-series. The Volterra series emerged from studies of non-linear functional of the form y(t)=F[x(t');t'<=t], and Volterra introduced the representation: 00
00 00
y{t) = J/z, (r, )x{t - r, )^r, + J ^h^ (r,, r^ )x(t - r, )x{t - z, )dT^ dr, -00
.
-00-00
«
(1)
+ . . . + J... -00
\h„{r^,T^,...,T„)x{t~T,)x(t-T^)...x{t~r„)dT^dT,...dT„ -00
for n= 1,2,3,... where: x(t) 2^dy(t) are, respectively, the system's input and output at a given time /, and hn(Tl, T2,..-, Tfq) is the n^*^ order Volterra kernel. The first application of the above functional to the study of non-linear systems was performed by Wiener, in characterising a system as a mapping between its input and output spaces. Wiener showed that another way of expressing the above equation is shown below: y{t) = H\x{t)\ + H,[x(t)] +... + HMO]
(2)
where: oO
oo
H„[x{t)]= l-jh„ih,:;rM'-h)-^(t-Tjdr,...dT„
401
(3)
In this representation the Hn is called the n^*^ order Volterra operator. Also, since most mechanical systems to be analysed are causal systems, it is common to replaced the limits of integration shown above by 0 and oo. The solution of the identification problems based on the Volterra series requires the calculation of the Volterra kernel (Billings, 1980). These can be calculated using different algorithms. In this study the Lee-Schetzen (Cross-correlation technique) algorithm is used. This method is extensively explained in (MarmareUs, 1878; Lee & Schetzen. 1965, Schetzen. 1965), and only a brief explanation will be included here. The cross-correlation technique was proposed in 1965 by Lee & Schetzen, (1965), and relies on the assumption that the n^^ Volterra kernel is directly related to the n^*^ order impulse response of the system. Also Schetzen observed that the kernel h^(Ti) is the system unit response, that h2(Ti, ~ x^ is the two dimensional impulse response of a second order system and that this same principle could then be extended to higher order kernels. In developing this technique Lee and Schetzen showed that the set of kernels /?, could be evaluated by using cross-correlation techniques. Furthermore, they also showed that for a system .S", with a whitegaussian noise input x(t) and a response y(t)^ the kernels can then be calculated according to the schematic diagram overleaf. This approach to kernel estimation was selected due to its simplicity and also because it has several advantages over the Wiener approach. Firstly it estimates the kernels directly, giving an insight on the structure of the system under study. Secondly, this method is simpler as it does not involve Laguerre and Hermite transformations required by the Wiener approach; and finally, this method requires fewer computations than the wiener approach, reducing the computational expense of this technique. Other methods for kernel calculation exist, and those proposed by Korenberg & Hunter (1996) must be mentioned. Although for higher orders it gives the Wiener kernels (Bissessur & Naguib, 1995), instead of the Volterra kernels. Still, it is believed that this technique can be as effective as the crosscorrelation approach, however further studies must be performed to confirm this fact. Finally, more recently Korenberg introduced yet another kernel estimation method: The Fast Orthogonal algorithm (Korenberg, 1988). This could also be an alternative method to the crosscorrelation technique used in this research. Meaning of the Volterra kernels: theoretical example From the theoretical description of the cross-correlation algorithm in Figure 2, it should be obvious that the zeroth order kernel is only an indication of the DC level (i.e. average) of the signal being analysed. Now the meaning of the first and higher order kernels is not so obvious to visualise. In fact these kernels indicate the pattern in which the past values of the stimulus affect the present value of the system response. ''The nth order Volterra kernel is the pattern of interaction among n pieces of the stimulus past with regard to the effect that this interaction has upon the system response " (Lee & Schetzen, 1965). Hence, it can be understood that the first order kernels indicate how a stimulus at a given time in the past affect the present value. Figure 3 shows the first order kernel superimposed on the input signal.
402
y(t)
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A A A A A A
Figure 1: System to be modelled system response
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IAAA/AAAA/,
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^ ^ 2"" order kernel
estimated non-linear response component
0
10
20
30
40
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Figure 2: Volterra kernel estimation process First order kernel analysis
_
h i (left axis)
response (right axis)
-0.06-
Figure 3: Analysis of first order kernel As it can be seen the first order kernel also has a cyclic (under-damped) behaviour with the same frequency of the system response. Also note that, from the analysis of the first order kernel it is obvious that the sample which has the greatest effect on the current sample lies at a lag of 240. While
403
the samples at lag=120, have a strong "negative" effect. These lags were expected as the cycHc nonlinearity of the signal under analysis has a period of 240 samples. A similar approach can be used to interpret the second order kernel. However this is usually more complex, as it assumes that the present system response is dependant on two consecutive inputs happening at respective lags. That means that a positive 2"^ order kernel at position T\, TI indicates that, the present input tends to be positive if a stimulus applied at r\ samples in the past is followed by a stimulus applied at x^ samples in the past. The 2" order kernel will always be symmetrical about T\ andD z^. Hence for practical purposes only half of the kernel matrix is plotted. Finally the process of system identification using the Volterra kernel approach lies on the analysis of the shape of the first and second order kernels. This can usually be very complex. Other parameters can also be used to allow for direct comparison between reponses from different systems. This may include, analysis of first order kernel frequency (for under-damped cyclic systems), and relative strength of linear and non-linear components in a given signal. Note that this relative strength, "% yi\ "% yn' is calculated from the rms value of the estimated linear and non-linear signal components respectively. This is mathematically defined as:
(4)
and where: yi and yn are defined in figure 2.
EXPERIMENTAL SETUP The data here processed was collected at the Condition Monitoring Laboratory, Hertfordshire University. A gear train with all the components of real gear systems was used (Engin, 1998). A schematic of this apparatus is shown in Figure 4. 1. driving gear 2. driven gear 3. transducers 4. proximity sensor
Figure 4: Experimental set-up
In this set-up, fatigue cracks were introduced on the driven spur gear. The geometry and dimensions of these cracks is described in table 1, and table 2 shows the gear (and tooth) numerical description. TABLE 1 SIMULATED CRACKS DIMENSIONS
Cut Geometry Depth (mm) Width (mm) Thickness (mm) Angle C)
Gear Condition F2 1.6 16 0.35
Fl 0.8 8 0.35
404
F3 2.4 25 0.35
TABLE 2 CHARACTERISTICS OF TEST GEARS.
Parameter Type Number of teeth Module Face width [mm] Pressure angle [ ° ] Helix angle [ ^ ] Pitch diameter [mm] Material (mild steel)
Driving gear MA25-20S 20 2.5 25 20° 0° 50 EN8
Driven gear MA25-32S 32 2.5 25 20° 0° 80 EN8
The gear under observation was set at a constant rotational speed of 5Hz and a constant load of 20Nm, applied by a pneumatic brake on the driven gear. The vibration signatures were recorded both by a stress-wave sensor and a magnet-mounted accelerometer mounted on the bearing housing adjacent to the faulty gear. A sample rate of 5.12KHz was used, and signatures with 2048 (two gear revolutions) samples were collected. For the Volterra model the first order memory was set to 1024 samples, as this is equivalent to one full revolution of the gear under observation. The second order memory was set to 100 samples. In all four "gear conditions" were experimented, these were: 1 good gear, and 3 faulty gears (Fl, F2 and F3). For each of these conditions 48 gear revolutions were recorded. From these signatures a time domain average was calculated. It must be noted that this time domain average was performed with the aid of a reference signal, which marked the start of each gear revolution. A proximity sensor adjacent to the driven gear key produced the reference signal. F'inally, the time averaging procedure aimed to maximise the signal-to-noise ratio of the signatures.
DISCUSSION: VOLTERRA KERNEL ANALYSIS The vibration signatures for all four gears (1 good/reference, and 3 faulty gear condition) were analysed and a discussion of the results obtained is included in this section. As expected, for reciprocating systems, the first order kernels are dominated by the cyclic nature of the vibration signatures and very little can be observed from them. In fact all gear conditions lead to the same basic traits on the first order kernel: that is, a kernel presenting a cyclic behaviour with frequency of 480Hz. Spectral analysis of the vibration signatures also indicated this to be the dominant component on the vibration signature (both acoustic emission and accelerometer data). This is illustrated below. Figure 5 shows a sample plot of the vibration signature spectrum together with a sample plot of the first order Volterra kernel for the same gear condition (F3). This figure also illustrates the effect of the time averaging procedure. The plots on the left refer to the raw vibration signatures, while the plots on the right refer to the time averaged vibration signatures. As it can be seen the overall noise level for the time averaged vibration signature is much smaller. This is also reflected on the plots for the first order Volterra kernels.
405
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ftfiU^ 80
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80
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Figure 5: First order Volterra kernels. Now, figure 6 shows the second order Volterra kernels for all the gear conditions. Again the plots on the left refer to the raw vibration signatures, while the plots on the right refer to the time averaged vibration signatures. As it can be seen the observation of the second order kernels for the time averaged signatures lead to the correct identification of the faulty vibration signatures. The kernels for the faulty gear do not show such a strong influence from inputs with respective lag of 10 samples. It must be noted that the results for the raw time data do not lead to fauh identification, as these plots are heavily influenced by the noise present in the collected vibration signatures. These results are confirmed by the analysis of the stresswave sensor signature. It is known that stresswave signature are less prone to noise contamination than acceleration signatures, and both in the raw and time averaged stresswave signatures the fault manifests itself by reducing the effect of inputs with respective lags of 10 samples. Note that these results were obtained using two full gear revolutions (2048 samples) as the system response, and although the Volterra model has a second order memory of 100 samples, only the first 50 coefficients are displayed as these are sufficient to indicate the presence of the fault. From these plots some important observations can be made. It is clearly seen that as the fatigue crack develops the strong influence of periodical pulses (period of 10 samples) and respective lags of 10 samples (i.e. |ri-r2|=10) diminishes. This is also observed for pulses with very short respective lags (i.e. adjacent to the matrix diagonal). This behaviour is not seen on the plots for the raw acceleration data. In fact on the raw data plots it is impossible to visualise any specific trends from a pure visual inspection of the kernel maps. This is attributed to the interference caused by the inherent noise present in the raw signal. This hypothesis is testified by the results obtained from the analysis of both the raw and the time averaged stresswave sensor data, which generates signatures with higher signal-to-noise ratio (Rao, 1996). Figure 7, shows that yhe SWS data produces very similar plots to the time-averaged acceleration data. These results show the importance of the time domain averaging procedure for the effective use of the Volterra kernel approach to vibration condition monitoring, when the signatures present low signal-to-noise ratio.
406
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408
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CONCLUSIONS In this work it has been shown that Voherra kernel analysis can be used for the identification of fatigue cracks on rotating spur gears. This technique has effectively identified the presence of the crack on two different sources of data: vibration signatures (after time-averaging procedure) and stress wave signatures. It must also be observed that the performance of this technique is heavily dependent on the noise level on the collected data. In fact, this method failed to produce meaningful results for the raw vibration data, which presented the highest noise level, out of all the data sets analysed. Below is a summary of the advantages and disadvantages of this method: Advantages • Volterra kernels are able to model non-linear signals, hence allowing the analysis of truly nonlinear systems. • It does not require that previous knowledge of the signal properties be known for the selection of the input parameters. This is a major advantage of this technique as most methods used in vibration condition monitoring require previous knowledge about the signal so that appropriate parameters (window function in TF methods, and the mother wavelet in time-scale decomposition) can be selected before the signal analysis. Disadvantages • This technique is extremely computationally expensive. Depending on the signal length and memory of the Volterra model, the processing can take several minutes. Hence, making it impossible to apply this technique to online real-time condition monitoring. This problem will eventually be overcame as microprocessors become more and more powerful. • This technique is not suitable for long cyclic signals with low signal-to-noise ratio. So, for most real applications of these techniques in conjunction with vibration signatures, a time-averaging procedure is essential to reduce noise influence on time series. • For under-damped signals very long memories are needed to accurately model the system. Finally, this work has shown that Volterra series, which has already been successfully applied to control applications, can also be applied to condition monitoring applications. However the latter application requires great care as the intrinsic noise present in vibration signatures can mask the effect of faults in its early stages.
ACKNOWLEDGEMENTS This work has been partially funded by the Mechanical Engineering Department at Brunei University and the Brazilian Federal Government Scholarship Agency - CAPES. REFERENCES Billings, S. (1980). Identification of non-linear systems - a survey. Proc. lEE pt. D, \.121, pp.272285. Bissessur, Y. & Naguib, R. (1995). Buried plant detection: A Volterra series modelling approach using artificial neural networks. Neural Networks, v. 9(6), pp. 1045-1060. Engin, S. (1998). Condition monitoring of rotating machinery using wavelets as a pre-processor to artificial neural networks. PhD Thesis, Mech. Eng. Dept., University of Hertfordshire. 409
Govind, G. & Ramamoorthy, P. A. (1990). Multi-layered neural networks and Volterra Series: the missing link. Proc. of the 90th IEEE Int. Conf. on Systems Engineering, pp. 633-636. Koh, T. & Powers, E. J. (1985). Second-order Volterra filtering and its application to non-linear systems identification. IEEE Trans, on Acoustics, Speech and Signal Processing, v. 33(6), pp. 1445. Korenberg, M. J. & Hunter, I. (1996). The identification of non-linear biological systems: Volterra kernel approaches. Annals of Biomedical Eng.,\, 24, pp. 250-268. Korenberg, M. J. & Hunter, I. (1990). The identification of non-linear biological systems: Wiener kernel approaches. Annals of Biomedical Eng.,\, 18, pp. 629-654. Korenberg, M. (1988). Identifying non-linear difference equation and functional expansion representations: the fast orthogonal algorithm. Annals of Biomedical Eng., v. 16, pp. 123-142. Lee, Y. & Schetzen, M. (1965). Measurement of the wiener kernels of a non-linear system by crosscorrelation. M. J. ofControl,\. 2, pp.237-524. Marmarelis, P. Z.; MarmareHs, V. Z. (1978). Analysis of physiological systems. The white-noise approach. Plenum Press, New York. ISBN 0-306-31066-X. Rao, B. K. (1996). Handbook of condition monitoring. Elsevier Advanced technology. ISBN 1 85617 234 1. Schetzen, M. (1965). Measurement of the Kernels of a Non-linear System of Finite order. Int. J. Control, V. 1, pp. 251-263. Volterra, V. Theory of functionals and of integral and of integral and integro-differential equations. London: Backie & Sons Ltd. Wiener, N. Response of a non-linear device to noise. Report V-165 Radiation Laboratory, MIT, Cambridge. US Dept. of commerce publication PB-58087.
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Condition Monitoring and Diagnostic Engineering Management Andrew G. Starr and Raj B.K.N. Rao (Editors) © 2001 Published by Elsevier Science Ltd.
DETECTION OF GEAR FAILURES USING WAVELET TRANSFORM AND IMPROVING ITS CAPABILITY BY PRINCIPAL COMPONENT ANALYSIS Nairn Baydar& Andrew Ball Maintenance Engineering Research Group, Manchester School of Engineering, University of Manchester, Manchester, M13 9PL UK, Phone:+44 (0)161 275 4347
ABSTRACT It has already been proven that a wavelet transform can be applied in a diverse area of science, such as acoustics, sound processing, image processing, seismology, biosignal analysis, study of fractals, fluid mechanics, machinery diagnostics, etc. This paper presents an application of wavelet transform in gearbox diagnostics. The paper begins with a fundamental description of the wavelet and then examines the capability of the wavelet to detect fault conditions in the gearboxes. Then Principal Components obtained from a vibration signal are used as a pre-processor to enhance the detection capability of the wavelet transforms.
KEYWORDS Fauh detection, Gear Failures, Principal Component Analysis, Wavelet Transform INTRODUCTION The conventional techniques for fault detection, such as spectrum, cepstrum, time domain averaging and demodulation analysis are based on the assumption of stationarity of the vibration signals. They are well established and have proved to be effective in machinery diagnostics (Stewart, Shives, Randall & McFadden). However, in many cases, these methods are not sufficient to reliably detect different types of faults in early stage and give detail information about their conditions. During the 1990's some time-frequency methods have gained considerable recognition in the field of condition monitoring. Many time-frequency methods exist, some are reviewed in following works (Flandarin, Priestly & Cohen). The early applications of the methods to gear faults in particular began with Forrester's work (1989). He applied the Wigner-Ville distribution (WVD) to averaged gear vibration signals and showed that different faults such as a tooth crack and pitting can be detected in the WVD plot. Later, McFadden and Wang applied the normal WVD and a weighted version of the WVD to gear failure to improve the detection capability of the method (1990). Staszewski and Tomlinson applied the Wavelet Transformation (WT) and Wigner-Ville distribution to a spur gear to detect a broken tooth. They also used statistical and neural networks for the classification of fault conditions (1994). More recent work conducted by Yesilyurt and Ball applied wavelet analysis and 411
Instantaneous Power Spectrum (IPS) to gear faults and identified incipient faults in the gears (1997). A more general study was carried out by Paya, Badi and Esat on gears and bearing faults and they used the wavelet as a processor to classify the types of fauh (1997). Most of the research work based on wavelet transform conducted when the fault conditions were severe in the gearboxes. In machinery diagnostic, it is crucial to detect the fault conditions in the earliest stage possible. This paper firstly presents an investigation carried out concerning a small progressing local fault in gearboxes by using wavelet transform. Then, principal components obtained from the vibration signal were used in the wavelet transform to improve the detection capability of the wavelet. THE WAVELET TRANSFORM (WT) Wavelets occur in sets or family of functions and each is defined by dilation, which controls the scaling parameter, and translation, which controls the position of the kernel in time. In Continuous Wavelet Transform (CWT), time / and time-scale parameters vary continuously. The CWT of a signal x(t) is defined as : CWT,(a,b) = W^{a,b) = - ^ ]x(t) ^ * ( — ) dt
(1)
Where a(a>0) is dilation factor, b is the translation factor and \\f{t) is the mother wavelet or analysing wavelet. The WT is expanded by a family of kernel functions, which are translated and dilated from the mother wavelet and expressed as:
^„,(0 = - ^ K — )
(2)
where \|/ab is the daughter wavelet and i//{
) represents mother wavelet. An arbitrary time signal x(t) a
at /e[0, T] can be decomposed into summation of wavelets at a finite number of scales as : x(t) = M^,+f^Y^w^,^^W(2't-kT)
where
w, = jx(t) dt
(3)
(4)
w^,^^ = jjc(0 W{2't-kT)
0
dt
(5)
0
Expression in Equation-4 and 5 defining WQ and wj^-k are called the wavelet transform of the signal x(t). In these equations the parameter k determines the position of the wavelet in time and 7, which is called the scale, determines how many wavelets are needed to cover the mother wavelets. TEST RIG AND FAULT SIMULATION The fault was simulated by removal of a percentage of the tooth face width on the pinion gear. Gradual fault advancement was studied by removing face width in 10% increments. Testing ranged from normal condition to 50 % removal of the single tooth. The torque on the output shaft was 260Nm, and other specifications of the gearbox are given in Table-1. The vibration signal generated by the gearbox was picked up by an accelerometer. A reference signal obtained from an optical pick-up was then used to synchronise time domain averaging of the vibration data. The signals were sampled at 6.4 kHz.
412
Table-1
Number of teeth Speed of shafts Meshing frequency Contact ratio Overlap ratio
First Stage 34/70 24.3 rev/sec.(input) 827.73 Hz. 1.359 2.89
Second Stage
29/52 6.5 rev/sec. (output) 342.73 Hz. 1.479 1.478
Figure. 1 Test gearbox IMPLEMENTATION OF WAVELET TRANSFORM Many different Morlet wavelet abrupt changes carried out in McDonnel):
possible families of wavelet are available in wavelet applications. In the present study, was chosen as an analysing wavelet since it is well adopted to the problem of locating in the signal (Rioul, Veterli & Morlet). The fast calculation of the wavelet transform is frequency domain. The frequency domain expression of the CWT is (Bentley &
CWT^(a,b) = V^ J x ( / ) H\af) e^^'^^'^df
(6)
Where xff) and Hff) are the continuous-time Fourier transforms of x(t) and h(t), respectively. The calculation is based upon octave band analysis in which each octave is equally subdivided into voices. The calculation of the transform is achieved by implementing discrete version of the Equation.6. The wavelet transform is complex-valued and is presented as a contour plot of its magnitude (modulus) and phase. Now the ability of the CWT will be examined by using the magnitude and phase information of the CWT for gearbox diagnostics. Figure.2 displays the phase and magnitude map of the CWT for various conditions. It demonstrates the ability of the WT to detect and localise the significant change in the vibration signal. Both the phase and amplitude map of the WT reveals the growing local fault in the gearbox. In the phase plot of the WT for healthy condition, the fundamental and second toothmeshing frequency are visible, though the energy of the vibration signal mainly concentrated around the fundamental meshing frequency. Although some change occur in the phase plot of 10 and 20 % fault conditions, most of the signal energy is still concentrated over the fundamental meshing frequency. Therefore, the fault conditions are not clearly identifiable at this stage. From 30 % fault conditions onwards, most of the signal energy appears to be (500 Hz) outside the fundamental meshing frequency and this indicates the presence of a fault in the gearbox. Similarly, the amplitude plot of the WT illustrates that when there is no fault (or a very small fault) the energy of the signal concentrates over the fundamental meshing frequency. With the growing fault the energy of the signal shifts significantly outside the meshing frequency region. The amplitude map seems to be easier to interpret though both, the phase and amplitude of the WT reveals the fault conditions. USING PRINCIPAL COMPONENTS AS A PREPROCESSOR IN WAVELET TRANSFORM A pre-processing of the signal based on principal components analysis (PCA) is introduced before the wavelet transform. The PCA is a statistical technique that transforms correlated original variables into a new set of uncorrected variables using the covariance matrix (or correlation matrix). If observation
413
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(b) Figure.2- The wavelet transform of the vibration signal a) phase b) amplitude
vector X=x/, X2, xj,.. .x„ describes the variables, then the PC A decomposes the observation vector, X as (Martin & Morris): m
x=TP'=t,p:
+t,p; +
+t„pj=Y.t,p^
(7)
(=1
Where pi is an eigenvector of the covariance matrix of X. P is defined as the principal component loading matrix and T is defined to be the matrix of principal components scores. The loading provides information as to which variables contribute the most to individual principal components (pc's), i.e they are the coefficients in the pc's model; w^hilst information on the clustering of the samples and identification of transitions betv^een different operating conditions is obtained from the score. The conventional way of obtaining the PCA involves the formation of the correlation matrix of the variables and then the calculation of the eigenvalues and eigenvectors of this matrix (Jackson). Formation of the correlation matrix and calculation of eigenvalues and eigenvectors computationally takes a longer time. In this work, the Singular Value Decomposition (SVD) technique is used to implement the PCA. In SVD, a data matrix Z is decomposed into products by following equation: X = UXP''
(8)
Where 6^ are eigenvectors, A, eigenvalues and /*^ loading matrix. The main virtue of the SVD is that all three matrices are obtained in one operation without having to obtain a covariance matrix as described above. Implementation of the PCA by the Singular Value Decomposition is coded and executed in MATLAB. The actual vibration signal from the gearbox has 125 rotations and the averaged vibration signal covers a varying number of rotations. For instance, if the averaged vibration signal contains ni rotafions, the signal is split into m individual rotation to form the data matrix, which is:
X =
(9)
Where n represents the data length of each rotation and m represents the number of rotation. Future measurements are tested against the established PCA model by calculating Square Prediction Error (SPE). The SPE is evaluated by taking the square difference between the observed values and predicted values from the normal condition data or the PCA model, which is : SPE = f^{x„-x„Y
(11)
Where Xij and x,. are measured and predicted values respectively. Significant fluctuation in the SPE indicates fault condition in the gearbox (Baydar). In the following analysis, the SPE is used in the wavelet transform to enhance the detection capability of the wavelet. When no fault exists most of the energy of the signal (the SPE) is concentrated in the low frequency region (below 500Hz) as shown in Figure.3. Small fault conditions such as 10 and 20 % of tooth removal cause some changes in both phase and amplitude wavelet plots, though the fault conditions are not clearly identifiable. However, from 30 % onwards fault conditions are more easily idenfifiable in 415
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(b) Figure.3 Wavelet transform with SPE a) phase b) ampUtude
416
"
both phase and amplitude plots when compared to Figure.2. In both phase and amplitude plots of Figure.3, most of the energy of the signal is sharply focused around 150° degrees with the growing fauk conditions. It is also evident that the energy concentration of the signal appears almost across the whole frequency region, which indicates the fault location.
CONCLUSION The result of the present work showed that the wavelet transform on its own is a very useful tool in gear diagnostics and can indicate progressing fault conditions in gearboxes. Both the phase and magnitude information of the wavelet can be used to reveal early fauU symptoms in gearboxes. It was also shown that the pre-processing of the vibration signal by a statistical technique could improve both the phase and amplitude plots of the wavelet. Hence this process enhances the detection capability of the wavelet to make a more reliable decision ACKNOWLEDGEMENTS This work has been supported by Engineering and Physical Sciences Research Council (EPSRC), U.K. REFERENCES Baydar, N. (2000) - The Vibro-Acoustic Monitoring of Gearboxes - EngD Thesis, Manchester School of Engineering, Mechanical Engineering Dept. - The University of Manchester Bentley, P.M & McDonnel, J.T.E (1994) - Wavelet Transforms: An Introduction - Electronic and Communication Engineering Journal - August Cohen, L. (1989) - Time-frequency distributions ~ A Review, Proceeding of the IEEE, Vol,77, No.7, July Flandrin, P. (1989) - Wavelets. Time -Frequency methods and Phase Space , Berlin, Springer-Verlag Forrester, B. D. (1989)- Use of the Wigner-Ville distribution in helicopter transmission fault detection - 1989 Proceeding of the Australian Symposium on Signal Processing and Applications - ASS PA-89, Adelaide,Australia, 17-19 April Forrester, B.D.(1989) - Analysis of gear vibration in the time-frequency domain - 1989 Proceeding of the 44th Meeting of the Mechanical Failures Prevention Group of the Vibration Institute, Virginia Beach, Virginia, 3-5 April Jackson, J. E. (1991) - A user's Guide to Principal Components - ^4 Wiley-Interscience Publication, USA Kronland-Martinet, R & Morlet, J (1987)- Analysis of Sound through Wavelet Transforms InternationalJournal of Pattern Recognition and Artificial Intelligence, 1(2) McFadden, P.D. & Smith, J.D.(1985) - A signal processing technique for detecting local defects in a gear from the signal averaging of the vibration - Proc. Instn Mech Engrs Vol 199 No.c4, IMechE McFadden, P.D.(1986) - Detecting fatigue crack in gears by amplitude and phase demodulation of the meshing vibration. Transaction of the ASME, Journal of Vibration, Acoustic, Stress and Reliability in Design 108, pi 65-170 McFadden, P.D & Wang, W.J. (1990) - Time-frequency domain analysis of vibration signals for machinery diagnostics(l)- Introduction to the Wigner-Ville distribution, Department of Engineering Science, University of Oxford, Report No. OUELI859/90 McFadden, P.D. & Wang, W.J. (1991) - Time-frequency domain analysis of vibration signals for machinery diagnostics(2)- The weighted Wigner-Ville distribution, Department of Engineering Science, University of Oxford, Report No. OUELI 891/90 Martin, E.B, Morris, A.J. & Zhang, J. (1996) - Process Performance Monitoring Using Multivariate Statistical Process Control - lEE Process Control Theory Appl. Vol. 143, No. 2, March 417
Rioul, O & Flandrin, P (1992) - Time-Scale Energy Distribution: A General Class Extending Wavelet Transforms. IEEE Transaction on Signal Processing Vol.40, No. 7, July Rioul, O. & Veterli, M (1991) - Wavelet and Signal Processing - IEEE SP Magazine, October Paya, B. A., Esat, LI & Badi, M.N.M (1997)- Artificial neural network based fault diagnostics of Rotating Machinery using wavelet transforms as a preprocessor - Mechanical System and Signal Processing\l{5)J5\-765 Priestly, M.B (1989) -Non-linear and Non-stationary Time-series Analysis, London, Academic Press Randall, R.B. (1980)- Bruel & Kjaer- Application of cepstrum analysis to gearbox diagnosis - ImechE Randall, R.B (1982) - A new method of modelling gear faults - Journal of Mechanical Design, Vol. 104/259, April Shives, T.R & Mertaugh, L.J (1986) - Detection, Diagnosis and Prognosis of Rotating Machinery Proceeding of the 41^^ Meeting of the Mechanical Failures Prevention Group, Naval Air Test Centre, Patuxent River, Maryland, 28-30 October Staszewski, W.J & Tomlinson, G.R (1994)- The Application of Time-Variant Analysis to Gearbox Fault Detection - PhD Thesis, - Manchester School of Engineering, Mechanical Engineering Dept. The University of Manchester Stewart, R.M. (1977) - Some useful data analysis techniques for gearbox diagnostics - Institute of Sound and Vibration Research, Southampton University, 19-22 Sept. Yesilyurt, I. (1997) - Gearbox fault detection and severity assessment using vibration analysis - PhD Thesis, 1997, Manchester School of Engineering, Mechanical Engineering Dept. - The University of Manchester
418
Condition Monitoring and Diagnostic Engineering Management Andrew G. Starr and Raj B.K.N. Rao (Editors) © 2001 Published by Elsevier Science Ltd.
DYNAMIC ANALYSIS METHOD OF FAULT GEAR EQUIPMENT Peng CHEN^ Fang F E N G \ Toshio TOYOTA^ 'Kyushu Institute of Technology, Kawazu 680-4, lizuka, Fukuoka, Japan ^ Japan Condition Diagnosis Technology Laboratory Incorporated, Takasu 3-11-1, Wakamatu-ku, Kitakyushu, Japan
ABSTRACT In this study we develop diagnosis theory for gear equipment by clarifying the change of dynamic characteristics between normal and abnormal states, and using theories of signal processing, fuzzy and neural network etc. In the this paper, we point out the problems of the traditional diagnosis method using only the dynamic model of normal state, and propose the dynamic model for vibration analysis of fault gears. The dynamic characteristics on fault gears are clarified with solving the non-linear vibration equations by the locus analysis of meshing contact point. We have obtained the vibration waveform of Eccentricity state, wear state and spot flaw state by computer simulations using the method proposed in this paper.
KEYWORDS Gear equipment. Dynamic characteristics, Fauh diagnosis. Vibration analysis
INTRODUCTION Gear equipment is one of most important part of machinery, and its failure may cause great loss of production. Vibration analysis is usually used to the diagnosis of gear equipment in plant, but there are many problems in the process of the gear diagnosis, Randall R. B. (1975), Michael J. Drosjack (1975). (1) Because the vibration signal measured for diagnosis is always contaminated by noise, the symptom of fault is not outstanding and the fault is difficuh to be detected in the early stage. (2) Distinguishing fault types is depended on statistical or experiential way, because the features of the vibration signal measured in each fault state are not made clear theoretically yet. Up to now, there are many papers about gear dynamics, but almost all the methods of dynamic analysis are based on the dynamic model of normal state, or on an approximate analysis method, Mark W. D. 419
(1977), R. Kasuba (1982), T. Nakata and M. Utagawa (1956). In this study, we develop the diagnosis theory for gear equipment by clarifying the change of dynamic characteristics between normal and abnormal states, and using theories of signal processing, fuzzy and neural network etc. In the this paper, we point out the problems of the traditional diagnosis method using only the dynamic model of normal state, and clarify the dynamic characteristics on fauh gears by the locus analysis of meshing contact point. Eccentricity state, wear state and spot flaw state often occur in practical plant. In order to clarify the dynamic characteristics of these three fault states for diagnosing, we have obtained the vibration waveform by computer simulations with the method proposed in this paper.
DYNAMIC MODEL FOR FAULT DIAGNOSIS OF GEAR EQUIPMENT Dynamic model of normal state Formula (1) shows the dynamic model in normal state of a gears. (1) Mx + D{t)x + {K, (0 + K, (t))x = W,+W^ Here, x is the relative displacement in the meshing point. W^ and Wj are the static loads exerting on the gear teeth. M is the equivalence mass on the exerting line of meshing force.
Root
'S2\
circle
Base
circle
0,
Fig.2 : Meshing force of abnormal gear
Fig. 1 : Meshing force of normal gear
The consisting conditions of the formula (1) are that the exerting directions of static loads (PF, .W^) are along the tangent line of base circle of the gears, and the meshing contact point of the gears as shown in Fig. 1. However, when the gear is in fauh state, because the consisting conditions may not be satisfied as shown in Fig. 2, the formula (1) is not suitable to be used to the dynamic analysis in abnormal state of gears. Dynamic model of abnormal state Formula (2) and (3) show the dynamic model of a abnormal gears.
JA=T,,-M,,
(2) JA (3) Here. r^i and T^j are the driving and driven torques. M-^, and Mjj are driving and driven moments exerting on the tooth surfaces. Let j : 1 and 7 = 2 express driving and driven gears respectively, we have
420
^jj = Kn^n^ COSYj, + F,,rr^., cos/jo (5) Here, rr^, and rr^2 ^^^ distances from the meshing points to the gear centres, y ^^ and /^^ ^^^ the exerting angles of the meshing forces. The meshing forces ^, are (6) (V) / — 1/ 2/ (8) ^;y =('-rj,cosr„W, Here, ;' expresses the number of meshing point, x, and x^, express the relative and absolute displacements in the meshing points. Substituting (9) ''s,j(t) = rry cosy,, and formulae (4) - (9) to formulae (2) and (3), we have J A = »','•„, (0 + fVi'-.n (') - K, (OCr,,, W
- '•«2, (0^2 )'•„, ( 0 - ^2(0(^,12 (0^,
- '•^22 (0^2 ^gn (0 - D(t)ir^,, (0^, - '-,21 m J,e,
= -W,r^3,(0-fF,r^,j(0
)'•,.IW - D{t){r^,2 m
+ K,(t){r^,,{t)e, -r^„{1)6,)r^,,{t)
+ K,(0(^„2(0^,
+ D(0(r,,, (0^, -^,2, (0^2)r,i^ it) + D{t)(r^,, {1)9, -r^„, {t)e, )r^,, (/)
(10)
- r,n m_ )r^„3 (0 -'•,,22(0^2)'-..22(0 (H)
Here, D(t) is damping coefficient calculated by D{t) = 2^^M{K,{t) + K,{t))
(12)
^ is damping rate. In normal state, '•si(0 = '-«2(0 = '-„
(13)
The dynamic load can be calculated by ' (14) nt) = F,,+F,, F.,=J^,{t){r^^Xt)0,~r,,Xt)0,) (15) Formulae (10) and (11) are non-linear vibration equations. To obtain the solutions of the equations, the acting directions of the meshing forces and spring coefficients are necessary in each moment. These parameters can be obtained if the meshing contact points are made clear in each moment.
CALCULATING METHOD OF MESHING CONTACT POINT The relative co-ordinates system set on the driving gear ;;,0,x, and the driven gear j^2^^2^2' ^^^ absolute co-ordinate system y^OjX^diXQ, shown in Fig.3*. The co-ordinate values of the two gear surfaces measured on the j^,0,x, and 3^3^2^2 beforehand are •^1,1
y^ _ 0
•^2,2
4,2 =
(16)
>^2,2
0
The co-ordinate values on >'o^2^o ^ ^ ^QA
A),\ -
cos 6^5
yo,i = sin ^5
0
0
- sin ^5 cos ^5
0
0
0 (17)
0 •4,1 + A 1 0
* Fig. 3 appears at the end of the paper
421
•^0,2
A),2 ~ yoj _0
cos ^6 = sin ^6
0
- sin 0(^ 0 cos ^6
0
0
1
(18)
Here, A is the displace O^Oj between rotating axle centres. If the co-ordinate values (x, ,,;^,,) on the driving gear (normal gear) and rotating angle 0^ are given, the meshing point (Xj^, yia) on driven gear (abnormal gear), which contacts with the point (x,,, j^,,), can be calculated by ^0,1 =^0,2' yo,i =yo,2 X,, cos ^5 - y,, sin ^5 = X22 cos ^^ - y2 2 sin ^^
(^^^ (^^)
;c,, sin^5 + >^,, cos^j +A = x, 2 sin^^ - >'2 2 cos^^
/2 j \
The unknown parameters in the above equations are X2 2, J^2.2 ^^^ ^6 • ^ ^ can substitute i9(, to the equations sequentially, and search the point (^32, 3^2,2) ^^m formulae (17) and (18) that satisfy formulae (20) and (21). Then the co-ordinate value (Xcn^oj) ^^ meshing contact point can be obtained.
RESULTS OF SIMULATION AND EXPERIMENT In order to verify the efficiency of the method proposed in this paper, we used the rotating machine shown in Fig. 4 to measure the vibration signals of the gear equipment in normal state, eccentricity state, spot flaw state and wear state. The specification of the gear for the experiment is shown in Table 1. Table 1 accelerometer
Specification of gears for simulation and test Module
2
Width of the tooth
20(mm)
Pressure angle (a)
20°
Number of teeth (normal)
55
Number of teeth (wear)
75
1 Backlash of the normal
the gear for the testgear box
k-load
belt
0.5(mm)
gear Load torque
motor
1.5(N.m)
Fig.4 Rotating machine for tests Normal state Fig. 5 (a) and (b) show the waveforms obtained by simulation and experiment. It can be observed that the results of simulation and experiment are approximately alike in positions of the peak and the form. Eccentricity state Fig. 6 shows the eccentric gear. Fig. 7 shows the waveforms obtained by simulation and experiment. In the same way as the normal state, the results of simulation and experiment are approximately alike in positions of the peak and the form. 422
Wear state In this paper, we show two types of wear state as shown in Fig. (8) and (9). The driving gear is in normal state, and the driven gear is in wear state. Fig. 8 shows the worn gear surface that is approximately expressed by a plane. Fig. 9 shows another type of worn gear surface that is decided by the slip ratio between two teeth during meshing. Fig. 10 shows the waveforms of worn gear obtained by simulation and experiment with the rotating speed lOOrpm and 200 rpm. In the same way as the normal state and eccentricity state, the results of simulation and experiment are approximately alike in positions of the peak and the form.
0.02
TuTie(sec) (a) Tu-ne wave of normal state by simalation (lOOrpm)
<
-0.51 Time(sec) (b) Filtering time vsave of normal state by test (lOOrpm)
(band-pass filter lkHz-7kHz)
base circle center '
Fig.5 : Vibration wave form of normal state
Fig.6 : Eccentricity state
iiillili. 1,111.
1.2 Time (sec) (a) The vibration signal of siniulation(50rpm)
2.4
1.2 Time I.sec.I (a) The filtered -"ribration sigiial of t.est(50ri3m)
•PITB 'WW H i i1 0.3 Time (sec) (b) The vibration signal of sim'ulation(200rpm)
0.6
0.06 Time (sec) (c) The ^dbration signal of simulation(1000rpm)
0.06 Time (sec I (c) The filtered vibration signal of test(1000fpm;i
Fig.7 Vibration waveform of eccentricity state Spot flaw state Fig. 11 shows the gear with spot flaw. In the cases of simulation and experiment, the spot flaw is set on the driven gear. 423
Fig. 12 shows the waveform of spot flaw gear obtained by simulation and experiment with the rotating speed SOrpm, lOOrpm and 200 rpm. The impulse peaks in the waveforms caused by the spot flaw can be observed in this figure.
Befbrewear
I,/'(maximum slip in the tip)
ratio
After wear
'Ltimajdnmanslip in tide tip)
fxttio
Fig.8 : Wear state (1)
( C )
Fig.9 : Wear state (2) [xlO^]i
0.02
Time(sec)
Time(sec)
(a) Time wave of wear state by sirrulation (lOOrpm)
(b) Time vvave of \\<xir state by simulation (200rpm)
Time(sec) (d) Time wave of wear state by lest (2(X)rpm)
Time(sec) (c) Time wave of wear s t ^ e by test (lOOrpm)
Fig. 10 : Vibration waveform of wear state (1) 424
Fig. 11 : Spot flaw state [xlO^] 5
Tiine(secj ( ^ Tittle wave of spot flaw state(50fpm)
Time(sec) ( ^ Time wave of spot flaw staite(50rpm)
Tim e( sec) ( b) Time wa^i-e of spot flaw state(lOOrpm)
Titt\e(sec) (b) Time wavie of spotflaiv stataflOOfpm)
4 S 2 «S 0
f > 0 MiWIWIIIWilipiiW^^^^^^^^ J
Time(sec) ( c ) Time wave of spot flaw state(200rpm)
2.6
U(i|>»li
ft
0)
0.65
-4
Tiine(sec) (c) Time wave of spot flaw state(200i'p«ri)
0.65
Fig. 12 : Vibration waveform of spot flaw state
CONCLUSIONS In this paper, we have pointed out the problems of the traditional diagnosis method using only the dynamic model of normal state, and proposed the dynamic model for vibration analysis of fault gears. The dynamic characteristics on fault gears are clarified with solving the non-linear vibration equations by the locus analysis of meshing contact point. We have obtained the vibration waveform of Eccentricity state, wear state and spot flaw state by computer simulations using the method proposed in 425
this paper. The efficiency of the method proposed in this paper has been verified by comparing simulation resuhs with experiment results. REFERENCES Michael J. Drosjack (1975). Investigation of Gear Dynamics Signal Analysis, Ohio State University Research Foundation Mark W. D. (1977). Analysis of the vibratory excitation of gear system, Basic Theory, JASA 63:10, 1428-1443 Randall R. B. (1975). Gearbox fauh diagnosis using cepstrum analysis, Proc. 4th World Congress on Theory of Machines and Mechanisms Vol.1, 169-174 R. Kasuba (1982). A Method for Static and Dynamic Analysis of Standard and Modified Spur Gears, NASA Conference Publication 2210, AVRADCOM Technical Report 82-C-16, 403-419 T. Nakata and M. Utagawa (1956). The Dynamic Load on Gear Caused by The Varying Elasticity of The Mating Teeth, Proc. 6^^ Japan National Congress for Applied Mechanics, 493
Fig.3 Calculation method of meshing point
426
Condition Monitoring and Diagnostic Engineering Management Andrew G. Starr and Raj B.K.N. Rao (Editors) © 2001 Published by Elsevier Science Ltd.
DIAGNOSIS METHOD OF GEAR DRIVE IN ECCENTRICITY, WEAR AND SPOT FLAW STATES Peng CHEN^ Fang FENG^ Toshio TOYOTA^ ^Kyushu Institute of Technology, Kawazu680-4, lizuka, Fukuoka, Japan 2 Japan Condition Diagnosis Technology Laboratory Incorporated, Takasu 3-11-1, Wakamatu-ku, Kitakyushu, Japan
ABSTRACT After investigating the feature of normal state and fault states by simulation and experiments in last report, the diagnosis method is discussed in this paper For diagnosing eccentricity and spot flaw state, the spectra of enveloped waveform are used to extract the feature frequency components in the spectra. The diagnosis algorithms for diagnosing the two types of fault states are shown in this paper. We have investigated the waveform features of two types of worn gear and found the feature of spectra measured in the wear state. When a gear is worn, its spectrum power in low frequency area becoiines larger than that in normal state. Three symptom parameters in frequency domain are defined to distinguish wear state. We have verified the efficiency of these diagnosis methods by simulations and experiments.
KEYWORDS Gear equipment. Dynamic characteristics, Fault diagnosis, Vibration analysis
INTRODUCTION This paper proposes diagnosing method of gear equipment on basis of the results of dynamic analysis and simulation on fault states of gear equipment, which have reported in our last paper Namely, to diagnose the eccentricity state and the spot flaw state, we present the method based on the spectra of enveloped vibration waveform. To diagnose the wear state, we define symptom parameters in frequency domain, and show the more excellent symptom parameters for diagnosing the wear state.
427
DIAGNOSIS METHOD FOR ECCENTRICITY STATE AND SPOT FLAW STATE Feature of eccentricity state Up to now, it is thought that the vibration signal measured in eccentricity state is a modulated waveform with equivalent amplitude of a sine wave at a circumvolution. However, it is observed by the simulations and experiments that the shape of the vibration signal of the eccentricity state varies with the rotating speed, and is not always the modulated waveform with equivalent ampHtude of a sine wave at a circumvolution. Fig. 1 shows the enveloped waveforms of vibration signal in eccentricity state. It can be observed that the results of simulation and experiment are approximately alike in positions of the peak and the feature frequency. In the case of high rotating speed (>200rpm), the position of the feature peak in the spectra is at the rotating frequency. But in the case of low rotating speed (<200rpm), the position of the feature peak in the spectra is at the several fold rotating frequency due to the above reason. [xlO'j 50 4.5
u 3.3Hz
^AX^^AA-M-A.—^
Frequency
^ i- f
3.3Hz
50Hz '
(a) Spectrum of envelope wave (50rpm)
Frequency
50Hz
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Figure 1: Spectra of enveloped waveform of eccentricity state 428
Feature of spot flaw state The feature of the vibration signal measured in spot flaw state is that an impulse peak appears in the time signals at one circumvolution. In order to diagnose spot flaw state, we calculated the enveloped waveforms as shown in Fig. 2. The feature frequency is at the rotating frequency in all cases of rotating speeds.
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50Hz Frequency (c) Spectrum of envelope wave (200rpm)
Figure 2: Spectra of enveloped waveform of spot flaw state Diagnosis method for eccentricity state and spot flaw state In the case of diagnosing eccentricity state and spot flaw state using spectrum of enveloped waveform, it is necessary to check peaks at the rotating frequency and its harmonic frequency like Fig. 2. Fig.3 shows the flawchart of the diagnosis. If there are peaks at the rotating frequency and its harmonic frequency, there is occuring possibihty of eccentricity state or sopt flaw state. The ccentricity state and the sopt flaw state can be distinguished by the feature of time signal or by the symptom parametr called "Kurtosis factor" , Michael J. Drosjack (1975).
429
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Ni3t eccentricity state and not spot flaw statel
There is a possibility of eccentricity state or spot flaw state Figure 3: Flowchart for diagnosing eccentricity and spot flaw DIAGNOSIS METHOD FOR WEAR STATE Fig.4 Shows time signal and spectra measured in normal states and wear states. It can be observed that the vibration power in the low frequency area of the wear state is larger than that of the normal state. The same phenomenon can also be observed by simulation waveform. In order to reflect the spectnmi feature of wear state, we defined following symptom parameters in frequency domain* l) Power ratio between low and high frequency^ yv/8
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Fig.4 Vibration waveform and spectra of wear state and normal state
Fig. 5 (i) and (iO shows the values of these symptom parameters in normal state and wear state, which are calculated by the simulation spectra and experiment spectra respectively. It can be observed by these graphs that the values of the power ratio between low and high frequency (/?,) are larger in wear state than in normal state, and the values of mean frequency ( pj) and steady factor of waveform (P3) are smaller in wear state than in normal state. The simulation results are very agreeable with the experiment results. 431
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0.43 0.13
(c) Steady figictor of waveform
(c) Steacfy factor of waveftmn
( • ) Calculated by simulation spectra
ncnral \^ear
( • ) Calculated by experiment spectra
Fig.5 Values of symptom parameters Therefore, these symptom parameters shown in equations (1-3) can be used to diagnose the wear state of gear equipment.
CONCLUSIONS On basis of the results of dynamic analysis and simulation on fault states of gear equipment, which have reported in our last paper, this paper proposed diagnosing method of gear equipment. The main results are; (1) We indicated the misconception on eccentricity state by dynamic simulations, and showed the diagnosis method of eccentricity state and spot flaw state using the spectrum of enveloped waveform of vibration signal. (2) Two types of wear state are investigated, and three symptom parameters in frequency domain are defined to diagnose wear state. (3) These results is verified by experiment. We shall continually study on the dynamic characteristics of other fault types of gear equipment, such as misahgnment state, scoring state etc., diagnosis method of multi-shaft gear equipment, and other types of gears, such as bevel gear, worm gear etc. REFERENCES Michael J. Drosjack (1975). Investigation of Gear Dynamics Signal Analysis, Ohio State University Research Foundation.
432
Condition Monitoring and Diagnostic Engineering Management Andrew G. Starr and Raj B.K.N. Rao (Editors) Published by Elsevier Science Ltd.
GEAR DAMAGE DETECTION USING OIL DEBRIS ANALYSIS Paula J. Dempsey National Aeronautics and Space Administration Glenn Research Center Cleveland, Ohio 44135
ABSTRACT The purpose of this paper was to verify, when using an oil debris sensor, that accumulated mass predicts gear pitting damage and to identify a method to set threshold limits for damaged gears. Oil debris data was collected from 8 experiments with no damage and 8 with pitting damage in the NASA Glenn Spur Gear Fatigue Rig. Oil debris feature analysis was performed on this data. Video images of damage progression were also collected from 6 of the experiments with pitting damage. During each test, data from an oil debris sensor was monitored and recorded for the occurrence of pitting damage. The data measured from the oil debris sensor during experiments with damage and with no damage was used to identify membership functions to build a simple fuzzy logic model. Using fuzzy logic techniques and the oil debris data, threshold limits were defined that discriminate between stages of pitting wear. Results indicate accumulated mass combined with fuzzy logic analysis techniques is a good predictor of pitting damage on spur gears. KEYWORDS Gears, Transmissions, Oil debris sensor. Damage detection. Health monitoring. Pitting fatigue INTRODUCTION One of NASA's current goals, the National Aviation Safety Goal, is to reduce the aircraft accident rate by a factor of 5 within 10 years, and by a factor of 10 within 25. One of the leading factors in fatal aircraft accidents is loss of control in flight, which can occur due to flying in severe weather conditions, pilot error, and vehicle/system failure. Focusing on helicopters system failures, an investigation in 1989 found that 32 percent of helicopter accidents due to fatigue failures were caused by damaged engine and transmission components (Astridge (1989)). In more recent statistics, of the world total of 192 turbine helicopter accidents in 1999, 28 were directly due to mechanical failures with the most common in the drive train of the gearboxes (Learmont (2000)). A study published in July 1998, in support of the National Aviation Safety Goal, recommended areas most likely to reduce rotorcraft fatalities in the next ten years. The study of 1168 fatal and nonfatal accidents, that occurred from 1990 to 1996, found that after human factors related causes of accidents, the next most frequent cause of accidents were due to various system and structural failures (Aviation Safety and Security Program, the Helicopter Accident Analysis Team (1998)). Loss of power in-flight caused 26 percent of this type of accident and loss of control in-flight caused 18 percent of this type of accident. The technology area recommended by this study for helicopter accident reduction was helicopter Health and Usage Monitoring Systems (HUMS) capable of predicting imminent equipment failure for on-condition maintenance and more advanced systems capable of warning pilots of impending equipment failures. 433
Helicopter transmission diagnostics are an important part of a helicopter health monitoring system because helicopters depend on the power train for propulsion, lift, and flight maneuvering. In order to predict transmission failures, the diagnostic tools used in the HUMS must provide real-time performance monitoring of aircraft operating parameters and must demonstrate a high level of reliability to minimize false alarms. Various diagnostic tools exist for diagnosing damage in helicopter transmissions, the most common being vibration. Using vibration data collected from gearbox accelerometers, algorithms are developed to detect when gear damage has occurred (Stewart (1977)); Zakrajsek, Townsend, and Decker (1993)). Oil debris is also used to identify abnormal wear related conditions of transmissions. Oil debris monitoring for gearboxes consists mainly of off-line oil analysis, or plug type chip detectors. And, although not commonly used for gear damage detection, many engines have on-line oil debris sensors for detecting the failure of rolling element bearings. These on-line, inductance type, sensors count the number of particles, their approximate size, then calculate an accumulated mass (Hunt (1993)). The goal of future HUMS is to increase reliability and decrease false alarms. HUMS are not yet capable of real-time, on-line, health monitoring. Current data collected by HUMS is processed after the flight and is plagued with high false alarm rates and undetected faults. The current fault detection rate of commercially available HUMS through vibration analysis is 60 percent. False warning rates average 1 per hundred flight hours (Stewart (1997)). This is due to a variety of reasons. Vibration based systems require extensive interpretation by trained diagnosticians. Operational effects, can adversely impact the performance of vibration diagnostic parameters and result in false alarms (Dempsey and Zakrajsek (2001)); Campbell, Byington, and Lebold (2000)). Oil debris sensors also require expert analysis of data. False alarms of oil debris technologies are often caused by non-failure debris. This debris can bridge the gap of plug type chip detectors. Inductance type oil debris sensors cannot differentiate between fault and no-fault sourced data (Howard and Reintjes (1999)). Several companies manufacturer on-line inductance type oil debris sensors that measure debris size and count particles (Hunt (1993)). New oil debris sensors are also being developed that measure debris shape in addition to debris size in which the shape is used to classify the failure mechanism (Howard, et al (1998)). The oil debris sensor used in this analysis was selected for several reasons. The first three reasons were sensor capabilities, availability and researcher experience with this sensor. Results from preliminary research indicate the debris mass measured by the oil debris sensor showed a significant increase when pitting damage began to occur (Dempsey (2000)). This sensor has also been used in aerospace applications for detecting bearing failures in aerospace turbine engines. From the manufacturers experience with rolling element bearing failures, an equation was developed to set warning and alarm threshold limits for damaged bearings based on accumulated mass. Regarding its use in helicopter transmissions, a modified version of this sensor has been developed and installed in an engine nose gearbox and is currently being evaluated for an operational AH-64 (Howe and Muir (1998)). Due to limited access to oil debris data collected by this type of sensor from gear failures, no such equation is available that defines oil debris threshold limits for damaged gears. The objective of the work reported herein is to first identify the best feature for detecting gear pitting damage from a commercially available on-line oil debris sensor. Then, once the feature is defined, identify a method to set threshold limits for different levels of damage to gears. The oil debris data analysis will be performed on gear damage data collected from an oil debris monitor in the NASA Glenn Spur Gear Fatigue Rig. TEST PROCEDURE Experimental data was recorded from tests performed in the Spur Gear Fatigue Test Rig at NASA Glenn Research Center (Scibbe, Townsend, and Aron (1984)). This rig is capable of loading gears, then running gears until pitting failure is detected. A sketch of the test rig is shown in Figure 1. Torque is applied by a hydraulic loading mechanism that twists 1 slave gear relative to its shaft. The power required to drive the system is only enough to overcome friction losses in the system (Lynwander
434
Figure 1.—Spur gear fatigue test rig.
Figure 2.—Spur gear fatigue rig gearbox.
(1983)). The test gears are standard spur gears having 28 teeth, 8.89 cm pitch diameter, and 0.635 cm face width. The test gears are run offset to provide a narrow effective face width to maximize gear contact stress while maintaining an acceptable bending stress. Offset testing also allows four tests on one pair of gears. Two filters are located downstream of the oil debris monitor to capture the debris after it is measured by the sensor. Fatigue tests were run in a manner that allows damage to be correlated to the oil debris sensor data. For these tests, run speed was 10 000 rpm and applied torque was 72 and 96 Nm. Prior to collecting test data, the gears were run-in for 1 hr at a torque of 14 Nm. The data measured during this run-in was stored, then the oil debris sensor was reset to zero at the start of the loaded test. Test gears were inspected periodically for damage either manually or using a micro camera connected to a VCR and monitor. The video inspection did not require gearbox cover removal. When damage was found, the damage was documented and correlated to the test data based on a reading number. Reading number is equivalent to minutes and can also be interpreted as mesh cycles equal to reading number times 10^. In order to document tooth damage, reference marks were made on the driver and driven gears during installation to identify tooth 1. The mating teeth numbers on the driver and driven gears were then numbered from this reference. Figure 2 identifies the driver and driven gear with the gearbox cover removed.
435
Data was collected once per minute from oil debris, speed and pressure sensors installed on the test rig using the program ALBERT, Ames-Lewis Basic Experimentation in Real Time, co-developed by NASA Glenn and NASA Ames. Oil debris data was collected using a commercially available oil debris sensor that measures the change in a magnetic field caused by passage of a metal particle where the amplitude of the sensor output signal is proportional to the particle mass. The sensor measures the number of particles, their approximate size (125 to 1000 |im) and calculates an accumulated mass (Howe and Muir (1998)). Shaft speed was measured by an optical sensor once per each shaft revolution. Load pressure was measured using a capacitance pressure transducer. The principal focus of this research is on pitting damage in spur gears. Pitting is a fatigue failure caused by exceeding the surface fatigue limit of the gear material. Pitting occurs when small pieces of material break off from the gear surface, producing pits on the contacting surfaces (Townsend (1991)). Gears are run until pitting occurs on several teeth. Pitting was detected by visual observation through periodic inspections on 2 of the experiments with damage. Pitting was detected by a video inspection system on 6 of the experiments with pitting damage. Two levels of pitting were monitored, initial and destructive pitting. Initial pitting is defined as pits less than 0.0397 cm diameter and cover less than 25 percent of tooth contact area. Destructive pitting is more severe and defined as pits greater than 0.0397 cm diameter and cover greater than 25 percent of tooth contact area. If not detected in time, destructive pitting can lead to a catastrophic transmission failure if the gear teeth crack. DISCUSSION OF RESULTS The analysis discussed in this section is based on oil debris data collected during 16 experiments, 8 of which pitting damage occurred. The oil debris sensor records counts of particles in bins set at particle size ranges measured in microns. The particle size ranges and average particle size are shown in Table 1. The average particle size for each bin is used to calculate the cumulative mass of debris for the experiment. The shape of the average particle is assumed to be a sphere with a density of 7922 kg/m\
Bin 1 2 3 4 5 6 7 8
TABLE 1 Oil debris particle size ranges Bin range. Average Bin Bin range, 125-175 175-225 225-275 275-325 325-375 375-425 425^75 475-525
150 200 250 300 350 400 450 500
1
9 10 11 12 13 14 15 16
525-575 575-625 625-675 675-725 725-775 775-825 825-900 900-1016
Average 550 600 650 700 750 800 862.5 958
Experiments 1 to 6 were performed with the video inspection system installed on the rig. Table 2 lists the reading numbers when inspection was performed and the measured oil debris mass at this reading. The highlighted cells for each experiment identify the reading number and the mass measured when destructive pitting was first observed on one or more teeth. As can be seen from this table, the amount of mass varied significantly for each experiment. A representative sample of the images obtained fi-om the video damage progression system is shown in Figure 3. The damage progression of tooth 6 on the driver and driven gear for experiment 1 for selected readings is shown in this figure. The damage is only shown on less than half of the tooth because the test gears are run offset to provide a narrow effective face width to maximize gear contact stress. Experiments 7 and 8 were performed with visual inspection. Table 3 lists the reading numbers when inspection was performed and the measured oil debris mass at this reading. Only initial pitting occurred during experiment 7. During experiment 8, initial pitting was observed at reading 5181 and destructive pitting at reading 5314.
436
TABLE 2 Experiments with video inspection Experiment Experiment Experiment Experiment Experiment Experiment 5 1 6 3 2 4 Rdg# Mass, Rdg# Mass, Rdg# Mass, Rdg# Mass, Rdg# Mass, Rdg# Mass, mg mg mg mg mg mg 60 0 60 58 62 1.003 1573 64 0 0 3.285 0 120 2810 3.192 1.418 n^i ^ ^ 3 4 2669 ...,^^nM 150 2.233 1405 4.214 1581 2885 5.113 2296 16.267 2857 11.889 378 7.413 6.396 8.297 2566 10622 12.533 2444 26.268 3029 14.148 518 9.462 ^•^i^i >^.l(Ml 2957 8.704 9328 11.692 W^s-^ i^mmL ^^^'mm5 14430 22.468 2366 13.977 . .tm^^M^ 12368 22.851 14512 24.586 3671 17.361 14688 28.451 4655 23.12 4863 26.227 14846 30.686 15136 36.108 *Note: Highlighted cells identify reading and mass when destructive pitting was first observed.
h:tm
Driven gear
Figure 3.—Damage progression of driver/driven tooth 6 for experiment 1. TABLE 3 Experiments with visual inspection Experiment Experiment Pitting Damage 8 7 Rdg# Mass, Rdg# Mass, mg mg 5181 6.012 13716 3.381 Initial 5314 19.101 Destructive TABLE4 Oi 1 debris mass at completion of experiments with no damage Experiment Mass, 1 Experiment Rdg# Rdg# mg 13 25259 9 29866 2.359 10 20452 5.453 14 5322 11 204 0.418 15 21016 21446 12 15654 2.276 1 16
Mass, mg 3.159 0 0.125 0.163
No gear damage occurred during experiments 9 to 16. Oil debris mass measured at test completion is listed in Table 4. At the completion of experiment 10, 5.453 mg of debris was measured, yet no damage occurred. This is more then the debris measured during experiment 7 (3.381 mg) when initial pitting was observed. This and observations made from the data collected during experiments when damage occurred made it obvious that simple linear correlations could not be used to obtain the features for damage levels from the oil debris data.
437
Prior to discussing methods for feature extraction, it may be beneficial for the reader to get a feel for the amount of debris measured by the oil debris sensor and the amount of damage to one tooth. Applying the definition of destructive pitting, 25 percent of tooth surface contact area for one tooth for these experiments is approximately 0.04322 cml A 0.0397 cm diameter pit, assumed spherical in size is equivalent to 0.26 mg oil debris mass. This mass is calculated based on the density used by the sensor software to calculate mass. If 0.0397 cm diameter pits densely covered 25 percent of the surface area of 1 tooth, it would be equivalent to ~9 mg. Unfortunately, damage distribution is not always densely distributed on 25 percent of a single tooth, but is distributed across many making accurate measures of material removed per tooth extremely difficult. Several predictive analysis techniques were reviewed to obtain the best feature to predict damage levels from the oil debris sensor. One technique for detecting wear conditions in gear systems is by applying statistical distribution methods to particles collected from lubrication systems (Roylance (1989)). In this reference, mean particle size, variance, kurtosis, and skewness distribution characteristics were calculated from oil debris data collected off-line. The wear activity was determined by the calculated size distribution characteristics. In order to apply this data to on-line oil debris data, calculations were made for each reading number for each bin (Table 1) using the average particle size and the number of particles for each of the sixteen bins. Mean particle size, relative kurtosis, and relative skewness were calculated for each reading for 6 of the experiments with pitting damage. It was not possible, however, to extract a consistent feature that increased in value from the data for all experiments. This may be due to the random nonlinear distribution of the damage progression across all 56 teeth. For this reason a more intelligent feature extraction system was analyzed and will be discussed in the following paragraphs. When defining an intelligent feature extraction system, the gear states one plans to predict must be defined. Due to the overlap of the accumulated mass features, 3 primary states of the gears were identified: O.K (no gear damage); Inspect (initial pitting); Damage (destructive pitting). The data from Table 2 was plotted in Figure 4. Each plot is labeled with experiment numbers 1 to 6. The triangles on each plot identify the inspection reading number. The triangles circled indicate the reading number when destructive pitting was first observed. The background color indicates the O.K., inspect and damage states. The overlap between the states is also identified with a different background color. The changes in state for each color were defined based on data shown in Tables 2 to 4. The minimum and maximum debris measured during experiments 1 to 6 when destructive pitting was first observed was used to define the upper limit of the inspect scale and the lower limit of the damage scale. The maximum amount of debris measured when no damage occurred (experiment 10) was above the minimum amount of debris measured when initial pitting occurred (experiment 7). This was used as the lower limit of the inspect state. The next largest mass measured when no damage occurred (experiment 13) was used as the upper limit of the O.K. scale.
2000
4000
6000 8000 10000 12 000 14 000 16000 Reading numbo*
Figure 4.—Oil debris mass at different damage levels .
438
Fuzzy logic was used to extract an intelligent feature from the accumulated mass measured by the oil debris sensor. Fuzzy logic was chosen based on the results of several studies to compare the capability of production rules, fuzzy logic and neural nets. One study found fuzzy logic the most robust when monitoring transitional failure data on a gearbox (Hall, Garga, and Stover (1999)). Another study comparing automated reasoning techniques for condition-based maintenance found fuzzy logic more flexible than standard logic by making allowances for unanticipated behavior (McGonigal (1997)). Fuzzy logic applies fuzzy set theory to data, where fuzzy set theory is a theory of classes with unsharp boundaries and the data belongs in a set based on its degree of membership (Zadeh (1992)). The degree of membership can be any value between 0 and 1. Defining the fuzzy logic model requires inputs (damage detection features), outputs (state of gear), and rules. Inputs are the levels of damage, and outputs are the states of the gears. Membership values were based on the accumulated mass and the amount of damage observed during inspection. Membership values are defined for the 3 levels of damage: damage low, damage medium, and damage high. Using the Mean of the Maximum (MOM) fuzzy logic defuzzification method, the oil debris mass measured during the 6 experiments with pitting damage was input into a simple fuzzy logic model created using commercially available software (Fuzzy Logic Toolbox (1998)). The output of this model is shown on Figure 5. Threshold limits for the accumulated mass are identified for future tests in the Spur Gear Fatigue Test Rig. Results indicate accumulated mass is a good predictor of pitting damage on spur gears and fuzzy logic is a good technique for setting threshold limits that discriminates between states of pitting wear.
15 20 25 OH debris mass* mg Figure 5.—Output of fuzzy logic model.
CONCLUSIONS The purpose of this research was to first verify, when using an inductance type, on-line, oil debris sensor, that accumulated mass predicts gear pitting damage. Then, using accumulated mass as the damage feature, identify a method to set threshold limits for damaged gears that discriminates between different levels of pitting damage. In this process, the membership functions for each feature state were defined based on level of damage. From this data, and a simple fuzzy logic model, accumulated mass measured by an oil debris sensor combined with fuzzy logic analysis techniques can be used to predict transmission health. Applying fuzzy logic incorporates decision making into the diagnostic process that improves fault detection and decreases false alarms This approach has several benefits over using the accumulated mass and an arbitrary threshold limit for determining if damage has occurred. One is that it eliminates the need for an expert diagnostician to analyze and interpret the data, since the output would be one of 3 states, O.K., Inspect, and Shutdown. Since benign debris may be introduced into the system, due to periodic inspections, setting the lower limit to above this debris level will minimize false alarms. In addition to this, a more advanced system can be designed with logic built-in to minimize these operational effects. Future tests are planned to 439
collect data from gears with initial pitting to better define the inspect region of the model and the severity of gear damage. Tests are planned for gears of different sizes to determine if a relationship can be developed between damage levels and tooth surface contact area, to minimize the need for extensive tests to develop the membership functions for the threshold levels. REFERENCES Astridge, D.G.: Helicopter Transmissions-Design for Safety and Reliability. Inst. Mech. Eng. Proc, Pt. G-J Aerosp. Eng. Vol. 203, No 02, pp. 123-138, 1989. Aviation Safety and Security Program, the Helicopter Accident Analysis Team: Final Report of the Helicopter Accident Analysis Team, July 1998. Campbell, R.L., Byington, C.S., and Lebold, M.S.: Generation of HUMS Diagnostic Estimates Using Transitional Data, Proceedings of the 13* International Congress on Condition Monitoring and Diagnostic Engineering Management, Houston, Texas, December 2000. Dempsey, P.J.: A Comparison of Vibration and Oil Debris Gear Damage Detection Methods Applied to Pitting Damage. NASA TM-210371, December 2000. Dempsey, P.J. and Zakrajsek, J.J.: Minimizing Load Effects on NA4 Gear Vibration Diagnostic Parameter. NASA TM-210671, February 2001. Fuzzy Logic Toolbox for use with MATLAB®, January 1998. Hall, D.L, Garga, A.K. and Stover, J.: Machinery Fault Classification: The Case For Hybrid Fuzzy Logic Approach. Proceedings of the 53'** Meeting of the Society for Machinery Failure Prevention Technology, April 19-22, 1999. Howard, P.L., Roylance, B., Reintjes, J., and Schultz, A.: New dimensions in Oil Debris Analysis-the Automated, Real Time, On Line Analysis of Debris Particle Shape. Naval Research Lab, January 1998. Howard, P.L., and Reintjes, J.: A Straw Man for the Integration of Vibration and Oil Debris Technologies. Presented at the Workshop on Helicopter Health and Usage Monitoring Systems, Melbourne, Australia, February 1999. Editor: Graham F. Forsyth. DSTO-GD-0197. Published by DSTO (Defense Science and Technology Organization) Australia February 1999. Howe, B. and Muir, D.: In-Line Oil Debris Monitor (ODM) For Helicopter Gearbox Condition Assessment, January 1, 1998. Hunt, T.M.: "Handbook of Wear Debris Analysis and Particle Detection in Fluids," Elsevier Science Publishers Ltd., London, 1993. Learmont, D., "Rotary Woes," Flight International, No. 4725 Vol. 157, 18-24 April 2000. Lynwander, P.: Gear Drive Systems Design and Application. New York: Marcel Dekker, Inc., 1983. McGonigal, D.L.: A Comparison of Automated Reasoning Techniques for Condition Based Maintenance. Pennsylvania State University Master of Science in Electrical Engineering Thesis. August 1997. Roylance, B.J.: Monitoring Gear Wear Using Debris Analysis-Prospects for Establishing a Prognostic Method. Proceedings of the 5'** International Congress on Tribology, Vol. 4. June 15, 1989. Scibbe, H.W., Townsend, D.P., and Aron, P.R.: Effect of Lubricant Extreme Pressure Additives on Surface Fatigue Life of AISI9310 Spur Gears. NASA TP-2408, December 1984. Stewart, R.M.: Some Useful Data Analysis Techniques for Gearbox Diagnostics. Report MHM/R/10/77, Machine Health Monitoring Group, Institute of Sound and Vibration Research, University of Southampton, July 1977. Stewart, R.M.: Advanced HUM and Vehicle Management Systems Implemented through and IMA Architecture. Proceedings from the 53'** American Helicopter Society Forum, 1997. Townsend, D.P.: Dudlev's Gear Handbook. 2"^ Edition. New York: McGraw Hill, 1991. Zadeh, Lofti, Fuzzy Logic: Advanced Concepts and Structures, New Jersey: IEEE, 1992. Zakrajsek, J.J., Townsend, D.P, and Decker, H.J.: An analysis of Gear Fault Detection Methods as Applied to Pitting Fatigue Failure Data. NASA TM~105950, April 1993.
440
Condition Monitoring and Diagnostic Engineering Management Andrew G. Starr and Raj B.K.N. Rao (Editors) © 2001 Elsevier Science Ltd. Allrightsreserved.
THE GENERALIZED VIBRATION SPECTRA (GVS) FOR GEARING CONDITION MONITORING M. Kljajin University ofOsijek, Mechanical Engineering Faculty in Slavonski Brod, Trg Ivane Brlic-Mazuranic 18, HR-35000 Slavonski Brod, Croatia
ABSTRACT The efficiency of machine condition monitoring is determined by the sensitivity of the methods used for the analysis of signal to the appearance of the defects that decrease the residual service life of the machine and its units. The traditional way to ensure the reliability of the monitoring is based on the increase of the number of different types of measurements and methods of signal analysis. But still another solution of this problem is available. It is based on the development of the basic method of the analysis of the vibration signal that is adapted to the problem of monitoring condition of the machine. This method is extremely sensitive to the appearance of nearly all types of defects in the machine. Below such a method of the gearing vibration analysis based on the measurement of the generalized spectrum is justified. KEYWORDS Generalized Vibration Spectra, Condition Monitoring, Gearing INTRODUCTION In this paper the term gearing is applied to one stage of reduction gearing consisting of two meshing gears (Figure 1). The main focus is on defects that have to be detected in a timely fashion. These include mesh defects that change the condition of contact between teeth in the mesh zone, and individual tooth defects, particularly wear, cracks and spalling (pitting). When defects are present that change the condition of the contact between teeth in mesh, vibration increases at the gear mesh frequency and its multiples. Defects include, displacement of one gear against another, misalignment of shafts and bad lubrication. This increase is also a traditional diagnostic symptom of gear defects. If a specific tooth is worn, cracked or a part of a tooth is missing, then once during each revolution of the faulted gear a shock will occur between the gears. Vibration of the gear supporting structure will increase at multiples of the rotational frequency of the faulted gear. This increase is a traditional diagnostic symptom of the faults identified above.
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Figure 1: Examples of gearings For monitoring condition of gearing that have a stable rotation frequency, it is usual to use the method of spectral analysis of the vibration signal. This method has a number of advantages. First is the ability to separate the vibration components of sources that are from different units of the machine. The most efficient method for this is narrow band spectrum analysis with the frequency resolution of about one tenth of the rotation frequency of the unit under control. In this situation, the variations of the rotation frequency should not exceed 0,1 - 0,5 % of its mean value. It is possible to ensure such a stable rotation frequency, especially on transport, only during the process of vibration measurements (100200 seconds), but from one measurement to another the rotation frequency usually has variations in a wider range. So, the problem is that the choice of the method of the signal analysis should ensure the ability to compare the results of periodic diagnostic measurements and preserve all the important diagnostic information that is present in the vibration signal.
DIAGNOSTIC INFORMATION Many years of investigations of the influence of the incipient defects of the gearing (the defects on the initial stages of their development) on the oscillating forces and the resultant excited vibrations show that, in the most cases, the main forces and the power of the vibration components excited by these forces does not change significantly due to the appearance of the incipient defects. At the same time, small defects have a notable influence on a number of other characteristics of the oscillating forces and vibration of the machine units. Here are the main types of such influence in the descending order of the value of diagnostic information, Alexandrova et al. (1986): • The amplitude modulation of the oscillating forces and corresponding vibration components of the gearing. • The appearance of pulsating moments in the rotating units and thus a frequency modulation of the vibration components. • Changes in the form of the oscillating forces and appearance or increase of new harmonic components in the vibration excited by these forces. • The increase of the oscillating forces and the corresponding components in the vibration spectrum and appearance of new components.
DEFECT DETECTION It is possible to detect the defects of gearing using monitoring only in cases where the natural random fluctuations of the periodically measured vibration parameters are less than the regular changes caused by the defect appearance. The basic method of signal analysis must assure the maximum reliability of the defects detection on the background of the measured vibration parameters fluctuations that can be caused by: • The change in the rotation frequency of the machine between the periodic measurements. • The change in the mode of operation of the machine including changes in load, temperature, etc. • The appearance of construction and technological deviations during manufacturing, assembling or
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repairing of some units of the machine, • Different measurement conditions including the position of measurement point, method of transducer mounting, calibration of the instruments, etc. • The influence of extraneous signals including vibration of other machines, electric fields, etc. Thus, the method most sensitive to the appearance of incipient defects in the gearing is the narrow band spectrum analysis of the vibration signal. Using this method, it is possible to detect the main types of modulation, the change of the shape of oscillation forces and the appearance of new components in the vibration spectrum. But, at the same time, the fluctuations of some parameters of the narrow band vibration spectra due to above mentioned reasons can be so strong that they make the defect detection very hard. Besides narrow band vibration spectra, constant percentage bandwidth spectra are used for monitoring condition of gearing. One example is third octave spectra. The fluctuation of the parameters in such spectra, especially if the variations of the rotation speed do not exceed 10 per cent, is much lower than in the narrow band spectra. But at the same time, it is impossible to detect some defect symptoms by the analysis of such spectra, for example, amplitude or frequency modulation of a certain vibration component. So, it becomes evident that here occurs a problem of an optimal method of spectral analysis of the vibration signal for monitoring condition of the gearing. The desired spectrum should be as informative as a narrow band spectrum and, at the same time, the fluctuations of the diagnostic parameters when the rotation frequency or load or a method of the transducer mounting changes should be minimal.
METHODS The main signal analysis method for gearing condition monitoring is still narrow band spectrum analysis of vibration and noise signals. Among the new technical solutions are automatic spectra processing with the extraction of harmonic components and detection of their amplitudes and frequencies as well as their possible origin. Such automation allows much more reliable trending of the signal component development and predicts their changes, especially in cases of fluctuations of the rotation speed from measurement to measurement. Another important point is separation of condition monitoring of a machine as a whole versus its particular units. In the first case, the measurement points are distant from the primary vibrating units and close to the less noisy ones and condition monitoring is done mainly by the analysis of low and medium frequency vibration. In the other case, the measurement points are chosen directly on the monitored unit case and condition monitoring is combined with diagnostics. Here, most attention is paid to the high frequency vibration. New methods of comparative vibration signal parameters analysis are developed for the final test control after machine assembly or repair, i.e. for condition monitoring by a set. The best way to form and adapt a vibration state standard for new generation condition monitoring and diagnostics systems is to do it jointly with condition diagnostics when only the machines with no severe defects detected by diagnostic routines are used to form vibration state standards. Condition monitoring by a set has one more peculiarity. It is possible use of external vibration exciters or shakers, especially in cases when the unit under control does not generate vibration while the test measurements are being performed. Principally, a new technical solution in the new generation condition monitoring and diagnostics systems is the monitoring of machine or machine unit technical condition. To do so using each of vibration measurement, the automatic diagnostic system makes a condition diagnosis with identification of all possible defects, even in their incipient stage. Next each defect is monitored during its develop-
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ment. This approach allows the prediction of defect development and the accurate prediction of residual service life for the machine or its unit. The condition diagnostics methods used in the first generation condition diagnostics systems were very restricted in their abilities. So, the software programs were developed as expert systems that helped the operator to manage the results of measurements made with the purpose of optimisation of the further accumulation of data and the choice of the most probable diagnosis from many suggested by the expert system. The final results, as usual, were uncertain and the level of operator qualification, the abilities of measurement instrumentation, and the amount of work necessary to obtain additional information defined the diagnosis quality. The further development of condition diagnostic systems was happening in two ways. These differed in their options for the customer to correct and enter new diagnostic symptoms according to his experience and the characteristics of the machines under diagnostics. The existing first generation systems are completely open ones, and the customer can enter any parameters and symptoms he would like to use in diagnostics to detect and identify defects. This type of systems has its own advantages and disadvantages. Its main advantage is the ability to be adapted to both diagnostic objects and measurement instrumentation available for the customer, A highly qualified expert can only do the adaptation so that contradictory rules or very high weight coefficients for the rules that give rather high errors are not entered. The volume of such work is very high and practical use of this system for condition diagnostics may only begin after several months of system adaptation. The previous statement reveals the main disadvantages of open systems. Besides the huge amount of work required for the system adaptation, such a system requires a lot of work to make additional diagnostic measurements not used in condition monitoring, and then entering them into the main expert database. Such measurements are typically needed to increase the diagnostics reliability. The second approach to the condition monitoring and diagnostics systems development is the closed type of diagnostic structure. In this case, all diagnostic rules are chosen and tested by the developers of the system and the system itself is optimised for the selected types of measurements. These systems are oriented to the condition diagnostics of standard machines or their units, e.g. bearings, gears, impellers, etc. Operating such a system does not require any user training in vibration analysis or condition diagnostics as all the measurements, data transfer and condition diagnostics are automated. The fact that these systems are specialised for certain diagnostic measurements results in minimum prices and maximum possible productivity and efficiency of the system. The weak point of this approach is that, in those rare cases when the system needs adaptation for very specific machines or their units, the customers must ask the system developers to develop or supply non-standard diagnostic modules. This situation was observed in cases of machines where it is impossible to attach vibration transducers on the diagnostic unit housing. In this case, special diagnostic modules are developed. These modules usually use the same measurements to make condition diagnostics of severad machine units installed in one housing. As an example, we can mention a task that we have done for the development of modules for rolling element bearings condition diagnostics in the aircraft jet engine. The points needed to make measurements on the bearing housing were not available and the solution was found in the simultaneous condition diagnostics of the bearings and working wheels of turbines and compressors. The development of condition diagnostic modules for standard machine units is a rather difficult problem. Despite the use of known diagnostic methods, it takes several years to develop a reliable automatic module that usually becomes the proprietary knowledge of the developers. As a result, the diag-
nostics efficiency and reliability greatly depend on the choice of the company that developed the condition diagnostic modules and not the company that produced measurement instrumentation and diagnostic system. For example: • The condition diagnostics of bearings is done by the analysis of low frequency fluctuations of friction forces and the power of the high frequency vibration excited by them. To do so, the spectrum of high frequency vibration power oscillations is measured, i.e. spectrum of high frequency vibration envelope. • Condition diagnosis of geared, chain, worm, and other types of mechanical transmissions is done using the analysis of shock loads occurring in the gear interaction, which are transferred to the bearings of the transmission. The shock loads in the transmission can be both positive loads that increase the load on the bearings and negative loads that decrease the bearing load. The changes in loads are also detected by the analysis of vibration envelope spectra measured on the bearings housing. • Condition diagnostics of working wheels rotating in the gas or fluid flow is done by the appearance of an increased turbulence "cloud" in the flow which can either rotate together with the defective blade or appear periodically in the defect zone on the stationary inner surface of the machine body. The properties of this turbulence can be analysed by the envelope spectra of the high frequency noise of the flow or vibration of the machine (pipeline) body excited by the flow. • Defects of electric machines (electromagnetic system of the machine) are found from the appearance of pulsating torque in the machine. This torque may pulsate at different frequencies and may result in changes of machine vibration patterns at a number of machine points and directions. For the identification of these torque symptoms, we use auto spectra of the machine body vibration. Diagnostic modules for other standard rotating machines* units are developed on basis of more complicated physical models that include simultaneous influence of several physical processes. The more detailed information about the physical basis of the rotating machine vibration diagnostics was published in a number of papers, for example Banister (1985), Barkov et al. (1994, 1997), Kljajin (1999a, 1999b) etc.
GENERALIZED VIBRATION SPECTRUM Many years experience in gearing condition monitoring using vibration shows that the Generalized Vibration Spectrum (GVS) gives the best result in the monitoring condition of the gearing. This kind of spectrum is formed from the narrow band spectrum and has a number of properties typical to third octave spectra. Every component of the generalized spectrum is formed from the narrow band components that belong to one-third-octave band with a generalized geometric mean frequency. The amplitudes of the narrow band spectrum components within this band are multiplied, but not added (their energies are not summed) as is done when the third octave spectra are formed from the narrow band spectra. The resulting amplitude of the generalized spectrum component A^ by Barkov et al. (1994) is equal to:
j=i
Where are Afi ~ the amplitude of the j component of the narrow band spectrum in the frequency band corresponding to the generalized spectrum component. A^j - the number of the narrow band spectrum components in the certain band corresponding to the
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generalized spectrum component. The amplitude A^ of the generalized spectrum is uniformly sensitive to the changes or appearance of both the weak or strong vibration components within a certain frequency band. Thus A^ is sensitive to the appearance of weak side bands that occur in the vibration spectrum when the strong harmonic component (carrier) is modulated by some signal and to any other components in the vibration spectrum. When this algorithm (Eqn. 1) was optimised, some weighting coefficients were implemented on the amplitudes of the strong harmonic components within the band corresponding to the generalized spectrum component. Taking into account that the amplitudes of the vibration are usually measured in dB, the amplitude of the generalized spectrum component corresponding to the mean geometric frequency f, can be expressed by Egn. 2:
(2)
He, ^' Where are L^ - the level of the j component of the narrow band spectrum, Cj = 1 +
the weight coefficient for strong harmonic components,
Cj = 1 - for random components.
AXVJ 500
1000 Frec^uency f. Hz
Figure 4: An example of the generalized spectra application in the monitoring condition of a gearbox a narrow band spectrum of a gearbox with no defect. After the weight coefficients were implemented on the strong harmonic components, a very important problem appeared- how to distinguish the harmonic components in the initial narrow band vibration spectrum. An empirical rule can be used for this purpose. A spectrum component can be assumed to be harmonic if it exceeds the random components by 10 dB. An example of a generalized spectrum together with the initial narrow band spectrum is presented on Figure 4 and Figure 5. When defects occurred in one of the gears in a gearbox, the teeth harmonics /^ and 2/^ were modulated by the rotation frequency of this gear, /^. A number of side bands can be found in the narrow band spectrum
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(Figure 5). In the generalized vibration spectrum we see the increase of levels of the corresponding components with /^ and 2/^ (Figure 6).
1000 Freq^uency f, Hz
500
Figure 5: A narrow band spectrum of the same gearbox with a worn gear. CO 120
-^100
I eo
2fz
I n nnin
1
40
Figure 6: The generalized spectrum of the gearbox. Greyed bars represent the increase of the generalized spectra components corresponding to the development of the wear of a gear.
COMMENTS As the generalized vibration spectrum contains the main diagnostic information, it is possible to use it not only for detection of changes in the vibration state of the machine, but also to identify the reasons for such changes - to make the diagnostics of the gearing. The only weak point of the generalized and narrow band spectra analysis is that these methods are not sensitive to the amplitude modulation of the random vibration components. This problem can be overcome by the analysis of both the generalized vibration spectrum and the narrow band spectrum of the envelope of random vibration components measured in the reference points of the machine. The generalized vibration spectra could be used in the stationary and portable diagnostic systems. There can be some peculiarities concerning the choice of the frequency band used for the vibration measurements. So, for the monitoring condition of the machine as a whole, it is recommended to measure vibration in the special reference points of the machine up to 20-40 harmonics of the rotation frequency. For the monitoring condition and diagnostics of separate units of the machine, it is recommended to use generalized spectra measured up to 20-40 harmonic of the rotation frequency and
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even higher frequencies that are determined by the construction peculiarities of the unit. For example, up to 2-3 harmonic of the teeth frequencies in the gearboxes, blade passage frequencies in the turbines and pumps, slot pass frequencies in electric machines etc. The measurements should be made in the control measurement points on the cases of the machines.
CONCLUSION The application of the generalized vibration spectra for the monitoring condition of the gearing can solve the following problems: • Decrease the amount of work on the analysis of the measured vibration and simplify the problem of its automatization with no loss of diagnostic information. • Minimize the number of measurement points on the machine. • Enlarge the list of defect types that can be detected and identify the types of defects. The use of generalized spectra analysis in the stationary and portable monitoring condition and diagnostic complexes has enabled the cost of the systems to be significantly reduced and the efficiency of the defects detection and identification to be significantly increased.
REFERENCES Alexandrova, A.; Barkov, A. V.; Barkova, N. A.; Schafransky, V. (1986). Vibration and Vibrodiagnostics of Electrical Equipment on Ships, Sudostroenie, Leningrad. Barkov, A.V.; Barkova, N.A.; Rogov, S. (1994). Generalized Spectra - A New Concept For Improved Condition Monitoring, Proceedings of the 18th Annual Meeting of the Vibration Institute, Hershey, PA. CoUacott, R.A. (1977). Mechanical Fault Diagnosis, Chapman and Hall, London. Banister, T.H. (1985). A Review of Rolling Element Bearing Monitoring Techniques, IMechE seminar Condition Monitoring of Machinery and Plant, London (6th June 1985). Barkov, A.V. and Barkova, N.A. (1994). Automatic Diagnostics of Rolling Element Bearings Using Enveloping Methods, Proceedings 18th Annual Meeting, The Vibration Institute, June 21-23,1994. Barkov, A.V,; Barkova, N.A.; Azovtsev, A.Yu. (1997). Condition Monitoring and Diagnostics of Rotating Machines Using Vibration, VAST Inc., St. Petersburg. Kljajin, M. (1999a). Envelope Spectrum Methods in Diagnostics of Gearing, Tenth World Congress on the Theory of Machine and Mechanisms (IFToMM), Vol. 6, pp. 2355-2360, Oulu, Finland, June 20-24, 1999. Kljajin, M. (1999b). Monitoring Bearings to Prevent Unplanned Breakdowns in Industry, Case Histories on Integrity and Failures in Industry, (V. Bicego et al. (Ed.)), EMAS Ltd., United Kingdom, page 525. Mitchell, J.S. (1993). Introduction to Machinery Analysis and Monitoring, PennWell Publishing, Tulsa, 1993, page 230.
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Condition Monitoring and Diagnostic Engineering Management Andrew G. Starr and Raj B.K.N. Rao (Editors) © 2001 Elsevier Science Ltd. Allrightsreserved.
USE OF GENETIC ALGORITHM AND ARTIFICIAL NEURAL NETWORK FOR GEAR CONDITION DIAGNOSTICS B. Samanta, K. R. Al-Balushi and S. A. Al-Araimi Department of Mechanical and Industrial Engineering, College of Engineering Sultan Qaboos University, PO Box 33, PC 123, Muscat, Sultanate of Oman.
ABSTRACT A procedure is presented for gear condition diagnostics using genetic algorithm (GA) and artificial neural network (ANN). The time domain vibration signals of a rotating machine with normal and defective gears are processed for feature extraction. The extracted features from both original and preprocessed signals are used in an ANN based diagnostic approach. The output layer consists of two binary nodes indicating the status of the machine - normal or defective gears. The selection of input features and the number of nodes in the hidden layer are optimized using a GA based approach in combination with ANN. For each trial, the ANN is trained using back-propagation algorithm with a subset of the experimental data for known machine conditions. The ANN is tested using the remaining set of data. The procedure is illustrated using the experimental vibration data of a gearbox. The roles of different vibration signals, obtained under both normal and light loads and at low and high sampling rates, are investigated. The results show the effectiveness of the proposed approach in diagnosis of the machine condition.
KEYWORDS Condition monitoring, feature selection, genetic algorithm, gear faults, neural network, rotating machines, signal processing.
INTRODUCTION The use of vibration signals is quite common in the field of condition monitoring of rotating machinery (McCormick and Nandi, 1997a, b; Paya et al., 1997; Shiroshi et al. 1997; Dellomo, 1999; Nandi, 2000; Jack and Nandi, 2000). By comparing the vibration signals of a machine running in normal and faulty conditions, detection of faults like mass unbalance, rotor rub, shaft misalignment, gear failures and bearing defects is possible. These signals can also be used to detect the incipient failures of the machine components, through the on-line monitoring system, reducing the possibility of catastrophic damage and the down time. Although often the visual inspection of the frequency domain features of the measured
449
vibration signals is adequate to identify the faults, there is a need for a reliable, fast and automated procedure of diagnostics. Artificial neural networks (ANNs) have potential applications in automated detection and diagnosis of machine conditions (McCormick and Nandi, 1997a, b; Dellomo, 1999; Paya et al., 1997). However, there is a need to make the operation faster and accurate using the minimum number of features which primarily characterise the system condition v^th an optimised structure of ANN (Jack and Nandi, 2000; Nandi, 2000). Jack and Nandi (2000) presented a GA based procedure for feature selection in condition monitoring of roller bearings. In the present work, the procedure is extended to the diagnosis of gear condition. The features extracted from the time domain vibration signals of the rotating machine are used as inputs to the ANNs. The selection of input features and the nimiber of nodes in the hidden layer are optimized using a GA based approach in combination with ANN. These features, namely, mean, root mean square (rms), variance, skewness, kurtosis and normalised higher order (upto ninth) central moments are used to distinguish between normal and defective gears. Moments of order higher than nine are not considered in the present work to keep the input vector within a reasonable size without sacrificing the accuracy of diagnosis. The roles of different vibration signals, obtained under both normal and light loads and at low and high sampling rates, are investigated. The resuhs show the effectiveness of the extracted features from the acquired and preprocessed signals in diagnosis of the machine condition. The procedure is illustrated using the vibration data of an experimental setup with normal and defective gears (Ligteringen et al., 1997; Ypma et al., 1999). VIBRATION DATA Ypma et al. (1999) presented the measurements from seven accelerometers on a pump driven by an electrical motor through a two-stage gear reduction unit. The first two accelerometers (1,2) were radially mounted near the driving shaft, with an angle of 90° between them, the third accelerometer was used to measure the axial vibration near the driving shaft. The remaining four accelerometers (4-7) were radially mounted on the machine casing, on both sides of the second gear pair. Separate measurements were obtained for two identical machines, one with pitting in both gear pairs and the other with no faults. The sensors were connected to channels (1-7) of a data acquisition system. Four sets of measurements with two levels of load (maximum and minimum) and at two sampling rates (3.2 kSa/s and 12.8kSa/s) were obtained. The signals were processed through low-passfilterswith cut-off frequencies of 1 kHz and 5 kHz respectively for sampling rates of 3.2 kSa/s and 12.8 kSa/s. The number of samples collected for each channel was 16384. In the present work, these time domain data were preprocessed to extract the features for using as inputs to ANN. FEATURE EXTRACTION Signal Statistical Characteristics One set of experimental data each with normal and defective gears was presented by Ypma et al. (1999). For each set, 7 vibration signals consisting of 16384 samples (yO were obtained using accelerometers to monitor the machine condition. In the present work, these samples were divided into 16 bins of 1024 (n) samples each. Each of these bins was further processed to extract the following features (1-9): mean (\i) root mean square (rms), variance {c\ skewness (normalised 3'"'* central moment, 73), kurtosis (normalised 4^ central moment, 74), normalisedfifthto ninth central moments (75- yg) as follows:
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(7
where E represents the expected value of the function. Time Derivative and Integral of Signals The high and low frequency content of the raw signals can be obtained from the corresponding time derivatives and the integrals. In this work, the first time derivative (dy) and the integral (iy) have been defined, using sampling time as a factor, as follows: dy(k) = y(k)-y{k-l)
(2)
iy(k) = y{k) + iy(k-\)
(3)
These derivative and the integral of each signal were processed to extract additional set of 18 features (1027). High- and Low- pass Filtering The raw signals were also processed through low- and high-passfilterswith a cut-off frequency of 640 Hz for the sampling rate of 3.200 kHz. The cut-off frequency for the high sampling rate (12.8 kHz) signals was selected as 1.28 kHz. Thesefilteredsignals were processed to obtain a set of 18 features (28-45). Normalisation Each of the features was normalised by dividing each row by its absolute maximum value for better speed and success of the network training. The total set of normaUsed features consists of 45 x 112 array where each row represents a feature and the columns represent the total number of bins (16) times the total number of signals (7). The procedure of feature extraction was repeated for two load conditions (minimum and maximum), two sampling rates (high and low) and two gear conditions (normal and defective) giving rise to a total set of features of size 4 5 x 1 1 2 x 2 x 2 x 2 . ARTIFICIAL NEURAL NETWORK The feed forward neural network, used in this work, consists of input layer, hidden layer and output layer. The input layer has nodes representing the normalised features extracted from the measured vibration signals. There are various methods, both heuristic and systematic, to select the neural network structure and activation functions (Haykin, 1999). The number of input nodes was variedfi*om1 to 45 and that of the output nodes was 2. The target values of two output nodes can have only binary levels representing 'normal' (N) and 'failed' (F) gears. The inputs were normalised in the range of 0.0 and 1.0. In the ANN, the activation functions of sigmoid were used in the hidden layers and in the output layer respectively. The ANN was created, trained and implemented using Matlab neural network toolbox with backpropagation (BPN) and the training algorithm of Levenberg-Marquardt (Mathworks, 1995). The ANN was trained
451
iteratively to minimize the performance function of mean square error (MSE) between the network outputs and the corresponding target values. At each iteration, the gradient of the performance function (MSE) was used to adjust the network weights and biases. In this work, a mean square error of 10'^, a minimum gradient of 10'^^ and maximum iteration number (epoch) of 500 were used. The training process would stop if any of these conditions were met. The initial weights and biases of the network were generated automatically by the program. GENETIC ALGORITHMS Genetic algorithms (GAs) have been considered with increasing interest in a wide variety of applications (Goldberg, 1989; Michalewicz, 1994). These algorithms are used to search the solution space through simulated evolution of 'survival of the fittest'. These are used to solve linear and nonlinear problems by exploring all regions of state space and exploiting potential areas through mutation, crossover and selection operations applied to individuals in the population (Michalewicz, 1994). The use of genetic algorithm needs consideration of six basic issues: chromosome (genome) representation, selection function, genetic operators like mutation and crossover for reproduction function, creation of initial population, termination criteria, and the evaluation function. Genome Representation In the present work, GA is used to select the most suitable features and the optimal number of neurons in the hidden layer of ANN for diagnosis of the gear condition. The genome (X) contains the row numbers of the selected features from the total set and the number of hidden neurons. For a training run needing N different inputs to be selected from a set of Q possible mputs, the genome string would consist of N+1 real numbers. The first N numbers (xj, i = 1, N) in the genome are constrained to be in the range 1 < Xi < Q whereas the last number XN+I has to be within the range Smin ^ XN+I ^ Smax- The parameters Smin and Smax represent respectively the lower and the upper bounds on the number of neurons in the hidden layer of the ANN. X = {XiX2 .. XNXN+I}^
(4)
Different mutation, crossover and selection routines have been proposed for optimisation (Goldberg, 1989). In the present work, a GA based optimisation routine (Houk et al. 1995) was used. In the GA, a population size often individuals was used starting with randomly generated genomes. This size of population was chosen to ensure relatively high interchange among different genomes within the population and to reduce the likelihood of convergence within the population. Selection Function In a GA, the selection of individuals to produce successive generations plays a vital role. A probabilistic selection is used based on the individual's fitness such that the better individuals have higher chances of being selected. In this work, normalised geometric ranking method was used. In this method, the probability (Pi) for each individual being selected is given as:
\-(\-qy where q represents the probability of selecting the best individual, r is the rank of the individual (with 1 being the best), and P denotes the population size.
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Genetic Operators Genetic operators are the basic search mechanisms of the GA for creating new solutions based on the existing population. The operators are of two basic types: mutation and crossover. Mutation alters one individual to produce a single new solution whereas crossover produces two new individuals (offsprings) from two existing individuals (parents). Let X and Y denote two individuals (parents) from the population and the X'and Y'denote the new individuals (offsprings). Mutation In this work, non-uniform-mutation function (Houk, et. al, 1995) was used. It randomly selects one variable, j , and sets it equal to a non-uniform random number as follows: x\=[x,+{h,-x,)f{G)
if
x,-{x,+a,)f{G) jc,
r,<0.5
if r, >0.5
(6)
otherwise
where ri and rz denote uniformly distributed random number between (0,1); G is the current generation number; Gmax denotes the maximum number of generations; b is a shape function used in the function f(G), ai and bj represent the lower and upper bound for each variable i /(G) = ( r , ( l - - ^ ) ) '
(7)
max
Crossover In this work, heuristic crossover was used. This operator_produces a linear extrapolation of two individuals using the fitness information. A new individual,_ X', is created as per Eqn. 8 with r beinga random number following uniform distribution U(0,1) and X' is better than Y' in terms of fitness. If X' is infeasible (given as r|=0 in Eqn. 10) then a new random number r is generated and a new solution is created using Eqn. 8. F = X + r(X~?)
(8)
r=Y
(9)
;7 = {l ifx]>a„x]
(10)
0
otherwise
Initialization, Termination and Evaluation Functions To start the solution process, the GA has to be provided with an initial population. The most commonly used method is the random generation of initial solutions for the population. The solution process continues from one generation to another selecting and reproducing parents until a termination criterion is satisfied. The most commonly used terminating criterion is the maximum number of generations.
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The creation of an evaluation function to rank the performance of a particular genome is very important for the success of the training process. The GA will rate its own performance around that of the evaluation (fitness) function. The fitness function used in the present work returns the number of correct classification of the test data. The better classification results give rise to higher fitness index.
SIMULATION RESULTS The dataset (45 x 112 x 2 x 2 x 2) consisting of forty-five (45) normalised features for each of the seven signals (7) split in form of 16 bins of 1024 samples each, two load conditions and two sampling rate with two gear conditions were divided into two subsets. The first ten bins of each signal was used for training the ANN giving a training set of 45 x 70 x 2 x 2 x 2 and the rest (45 x 42 x 2 x 2 x 2) was used for validation. The target value of the first output node of the ANN was set 1 and 0 for normal and failed gears respectively and the values were interchanged (0 and 1) for the second output node. Results are presented to see the effects of sensor location, signal processing, load and sampling rates for diagnosis of machine condition using ANN without and v^dth GA. For each case of ANN without GA, number of neurons in the hidden layer was kept at 24 whereas for ANN with GA this was selected between 10 and 30 with only three input features. The training success for each case was 100 per cent. Effect of sensor location Table 1 shows the classification results for each of the sensor locations without and with GA. In cases of straight ANN, first nine input features of the signals and 24 neurons in the hidden layer were used, whereas these values were selected from the corresponding ranges by GA in the second option. The test success was unsatisfactory in case of straight ANN whereas it was 100 per cent for all signals except signal 6 even with only 3 features. TABLE 1: EFFECT OF SENSOR LOCATION
Dataset
signal 1 signal 2 signal 3 signal 4 signal 5 signal 6 signal 7
Straight ANNf No of Test Input features ; hidden success Neurons (%) 24 1-9 83.33 24 100 1-9 1-9 24 83.33 1-9 24 41.67 1-9 24 58.33 24 1-9 58.33 1-9 75.00 24
Input features 5, 29,42 4, 8, 36 32, 34, 39 21,28,39 8,13,39 24, 34,42 5, 39,40
(GA with ANN No of hidden Neurons 18 10 14 25 18 18 10
Test success (%) 100 100 100 100 100 91.67 100
Effect of signal processing Table 2 shows the effects of signal processing on the classification results for the first four signals (1-4) without and with GA. In straight ANN, all the features from the signals without and with signal processing were used whereas in GA only three from each of these ranges were selected. The features from only signals (first row) gave lowest success rate. Here again, the test success was 100 per cent for GA with ANN.
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Effect of load and signal type Table 3 shows the effects of load and signal type (sampling rate) on the classification results without and with GA. In case of straight ANN, all 45 features for first four signals (1-4) with 24 neurons in the hidden layer were used whereas in GA, these values were selected. For low sampling rates, the straight ANN gave 100 per cent but with a much higher training time (321 sec) compared to that with GA (3.24 sec). The times are given for a PIII processor with 533 MHz and 64 MB RAM. The results of straight ANN deteriorated with higher sampling rate. However, with GA the test classification success was 100 per cent for all the cases of load and sampling rate. TABLE 2: EFFECT OF SIGNAL PRE-PROCESSING
Dataset
Straight ANN Input features
GA with ANN
3,4,5
No of hidden Neurons 16
Test success (%) 97.92
Test success (%) 89.58
Input features
signals 1-4
1-9
No of hidden Neurons 24
derivative/ integral high-/low-
10-27
24
95.83
15,17,24
13
100
28-45
24
91.67
30, 33, 39
14
100
pass filtering TABLE 3 .•EFFECT OF LOAD AND SIGNAL TYPE
Load
Dataset Sampling rate
Max Min Max Min
Low Low High High
Input features 1-45 1-45 1-45 1-45
Straight ANN Test No of hidden success Neurons (%) 24 100 24 100 24 79.16 24 93.75
GA with ANN Test No of success hidden Neurons (%) 100 18 3, 14, 32 100 25 14, 27, 41 100 10 10,21,30 100 16 14,31,40 Input features
CONCLUSIONS A procedure is presented for diagnosis of gear condition through genetic algorithm and artificial neural network using the features of time domain vibration signals. The selection of input features and the number of nodes in the hidden layer of the ANN have been optimized using a GA based approach in combination with ANN. The roles of different vibration signals, obtained under both normal and light loads and at low and high sampling rates, have been investigated. The use of GA with only three features gave 100 per cent classification for most of the test cases even with different load conditions and sampling rates. The training time is substantially less than that with straight ANN. The results show the potential application of GA for offline feature selection and the potential use of optimised features and ANN structure for real-time implementation towards development of an automated machine condition monitoring and diagnostic system.
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ACKNOWLEDGEMENTS The financial support to carry out this research through the SQU internal grant IG/ENG/MIED/01/01 is gratefully acknowledged. The dataset was acquired in the Delft "Machine diagnostics by neural network" project with help from TechnoFysica B.V., The Netherlands and can be downloaded freely at the following web-address: http://www.ph.tn.tudelft.nl/~ypma/mechanical.html. The authors thank Mr. Alexander Ypma of Delft Technical University for making the dataset available and providing usefiil clarifications. REFERENCES Al-Balushi, K. and Samanta, B. (2000). Gear fault diagnostics using wavelets and artificial neural network Proceedings ofCOMADEM2000, Houston, Texas, USA, 1001-1010, Dellomo, M. R. (1999). Helicopter gearbox fault detection: a neural network based approach. Transactions of the ASME, Journal of Vibration and Acoustics, 121:3, 265-272. Goldberg, G. E. (1989). Genetic algorithms in search, optimization and machine learning, Addison Wesley, New York. Haykin, S. (1999). Neural Networks: A Comprehensive Foundation. 2"^ Edition, Prentice Hall, New Jersy, USA. Houk, C.R., Joines, J. and Kay. M. (1995). A genetic algorithm for function optimization: a matlab implementation. North Carolina State University, Report no: NCSU_IE_TR_95_09. Jack, L. B. and Nandi, A. K. (2000). Genetic algorithms for feature extraction in machine condition monitoring with vibration signals. lEE Proc.-Vis. Image Signal Process. 147:3,205-212. Ligteringen, R. Duin, R. P. W., Frietman, E. E. E. and Ypma, A. (1997). Machine diagnostics by neural networks, experimental setup. Asci97Setup/paper_970120_2, Hejein (the Netherland), June 2-4. McCormick, A. C. and Nandi, A. K. (1997a). Classification of the rotating machine condition using artificial neural networks. Proc. IMechE, Part C Journal of Mechanical Engineering Science, 211:C6, 439-450. McCormick, A. C. and Nandi, A. K. (1997b). Real-time classification of the rotating shaft loading conditions using artificial neural networks. IEEE Transactions on Neural Networks, 8:3, 748-756. Michalewicz, Z. (1994). Genetic algorithms + Data Structures- Evolution Programs, AI series, SpringerVerlag, New York. Nandi, A. K. (2000). Advanced digital vibration signal processing for condition monitoring. Proceedings ofCOMADEM2000, Houston, Texas, USA, 129-143. Paya, B. A., Esat, I. L. and Badi, M. N. M., 1997. Artificial neural network based fault diagnostics of rotating machinery using wavelet transforms as a preprocessor. Mechanical Systems and Signal Processing, 11:5, 751-765. Shiroishi, J., Li, Y., Liang, S., Kurfess, T. and Danyluk, S. (1997). Bearing condition diagonstics via vibration and acoustics emission measurements. Mechanical Systems and Signal Processing, 11:3, 693705. Tang, K. S. Man, K. F. Kwong, S. and HE, Q. (1996). Genetic algorithms and their applications. IEEE Signal Processing Magazine 13:6,22-37. Wang W. (2000). An investigation into the effects of load on several gear fault diagnostic techniques. International Journal ofCOMADEM3i3, 29-36. Ypma, A., Ligteringen, R. Duin, R. P. W., Frietman, E. E. E. (1999). Pump vibration datasets, Pattern recognition group, Delft University of Technology.
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Condition Monitoring and Diagnostic Engineering Management Andrew G. Starr and Raj B.K.N. Rao (Editors) © 2001 Elsevier Science Ltd. All rights reserved.
FAULT DETECTION ON GEARBOXES OPERATING UNDER FLUCTUATING LOAD CONDITIONS CJ Stander^ PS Heyns^ W Schoombie^ 'Dynamic Systems Group, Department of Mechanical and Aeronautical Engineering University of Pretoria, Pretoria, South Africa ^ Department of Electrical, Electronic and Computer Engineering University of Pretoria, Pretoria, South Africa
ABSTRACT Gearboxes often operate under fluctuating load conditions during service. Conventional vibration monitoring techniques are based on the assumption that changes in the measured structural response are caused by the deterioration in the condition of the gearbox. However, this assumption is invalid under fluctuating load conditions. Experimental data were obtained from a test rig, which is able to apply fluctuating loads of up to 3 Hz on the system. Sinusoidal, step and chirp load fluctuations were considered. Conventional vibration monitoring techniques were applied to the test data in order to indicate the influence of the fluctuating load conditions and the severity of the problem. A vibration waveform normalisation approach is presented, which enables the use of the root mean square and variance statistical parameters to track the trend in the degradation of a gear under fluctuating load conditions. KEY WORDS Gearbox, On-line monitoring, Fluctuating load, Modulation, Vibration monitoring, Normalisation.
INTRODUCTION Monitoring the condition of large gearboxes in the mineral mining industry has attracted greater interest in recent years, owing to the need to decrease the down time on production machinery and to reduce the extent of the secondary damage caused by failures. Effective condition-monitoring systems and strategies would be useful for scheduling the optimal intervals for maintenance, thus minimising unnecessary down time of production equipment. However, when increasing the time intervals between maintenance and inspection, the monitoring strategies and systems need to be reliable so as to ensure that gear deterioration is detected before failure occurs. The cutting-head gearbox of a continuous miner and the drag gearboxes of a dragline are typical examples of large, expensive gearboxes that directly influence production in the mining operation. Smith (1999) states that gearboxes should not be monitored under conditions where the angular acceleration multiplied by the effective moment of inertia exceeds the steady load torque.
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This is why measurements that are taken under free rotational conditions will not give reliable results for analysis. In certain applications, gearboxes should be monitored in the working environment with fluctuating load conditions. Stander and Heyns (2000) present an overview of the techniques available for monitoring the condition of gears. However, most of the vibration monitoring techniques available are based on the assumption of constant load conditions. Wang and Wong (2000) developed a linear prediction method, which is based on the assumption that the vibration caused by a sound pair of gears can be modelled as a stationary autoregressive process. As the stationary autoregressive process is unable to represent the non-stationary transients associated with the localised gear teeth faults, the prediction error increases when localised defects occur. The authors state that the approach is independent of operational conditions, but the exact influence of the load fluctuation on the measured signal is not documented. Baydar and Ball (2000) employed the Instantaneous Power Spectrum (IPS) to detect local tooth faults on a gear under different nominal load conditions. No information could be found about the influence of fluctuating load conditions on the measured vibration signal of a gearbox. Consequently a gearbox test rig was built so that the influence of fluctuating load conditions could be investigated.
EXPERIMENTAL SET-UP The experimental set-up consisted of a single-stage gearbox, driven by a 5 hp Dodge Silicon Controlled Rectifier (SCR) motor. A 5.5 kVA Mecc alte spa three-phase alternator was used to apply the load. The test rig is shown in Figure 1. The gears were manufactured in accordance with DIN3961, Quality 3. Both gears in the pair had 69 teeth. Severe resonance of the gearbox casing will distort the amplitude and phase of the vibration source signal caused by the fluctuating stiffness of the gear mesh. Therefore the gearbox casing was machined from a steel billet to minimise the probability of resonant frequencies within the experimental frequency bandwidth. This precaution was taken to ensure a reliable and representative gear mesh signal. The Alternating Current (AC) of the alternator was rectified and dissipated over a large resistive load, which was kept constant during the tests. The Direct Current (DC) fields of the alternator were powered by an external DC supply in order to control the load that was applied to the gears. A single-phase voltage feedback from the alternator was used in conjunction with Proportional Integral (PI) compensation to regulate the torque applied by the alternator. A command signal input was incorporated into the controller so as to facilitate load control with a signal generator. Load fluctuation rates of up to 3 Hz could be obtained. Tyre couplings between the electrical machines and the gearbox were used in order to reduce the backlash in the system to the backlash of the gears. Acceleration measurements were taken in the vertical direction with a 500 mV/g PCB Integrated Circuit Piezoelectric (ICP) industrial accelerometer and a Digital Signal Processor (DSP), Siglab model 20-42.
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Vibration measurements were taken under four different load conditions and three different levels of damage severity in order to design and evaluate a signal-processing procedure that would be more sensitive to changes in the condition of the gears than to the fluctuations in the load conditions. Tests were conducted under constant load conditions in order to obtain a reference from which to evaluate the signal-processing procedure. The sinusoidal load condition was chosen to evaluate a slowly changing load condition, in contrast to the square load condition that creates a rapid change in load. The chirp load condition refers to a sinusoidal load condition where the frequency increases as time progresses. The chirp load condition therefore represents a wider frequency band of applied load. The specifications on the loading conditions appear in Table I. The initial vibration measurements were taken without any induced damage. Then face wear was induced on one of the gear teeth by artificially removing material from the gear face. In addition, a crack was induced on a tooth 180 degrees away from the tooth with the induced face wear. The damage details are presented in Table 2. Figure 2 displays the 2 mm crack induced on the gear tooth. TABLE 1 LOAD CASE SPECIFICATIONS
1 Load description Constant 1 Sine 1 Square Chirpy
Frequency OHz 0.5 Hz 0.5 Hz 0 . 1 - 2 Hz
Minimum load 14,4 Nm 6.6 Nm 6.8 Nm 10.3 Nm
Maximum load j 14.4 Nm 18.6 Nm 20.1 Nm 17.3 Nm
Figure 1: Experimental set-up TABLE 2 INDUCED DAMAGE SPECIFICATIONS
Material removed from face Crack length
Level 1 gear damage 0.15 mm Nominal 1 mm 459
Level 2 gear damage | 0.3 mm Nominal 1 2 mm 1
Figure 2: Induced crack of 2 mm TABLE 2 INDUCED DAMAGE SPECIFICATIONS
1 Material removed from face Crack length
Level 1 gear damage 0.15 mm Nominal 1 mm
Level 2 gear damage 0.3 mm Nominal
2 mm
1
LOAD MODULATION OF THE MEASURED ACCELERATION WAVEFORM The load variation manifests as a low-frequency amplitude modulation on the Measured Acceleration Signal (MAS). The effect is displayed in Figure 3. The frequency of the Load Modulation (LM) is lower than the frequencies at which local tooth defects will modulate the MAS. The assumption is valid since the torque of most electrical machines decreases as the load frequency increases. The concept is illustrated in Figure 4, showing the frequency of the load being increased from 0.1 Hz to 2 Hz over a period of 5 s. The load modulation clearly indicates the decrease in amplitude as the load frequency increases. Typically the load of an electrical machine will not fluctuate faster than approximately 3 Hz. LOAD DEMODULATION NORMALISATION Statistical moment analysis is probably the oldest analytical technique used in vibration monitoring. Andrade (1999) indicates that the Root Mean Square (RMS) and variance parameters of the MAS on the gear case will sufficiently indicate the degradation of gears. The approach is not computationally intensive and is simple to monitor since it is easy to see the trend in the variation in the parameter values. The properties of the statistical parameters make them extremely attractive for the continuous online monitoring of gearboxes. However, the parameters are strongly influenced by fluctuating load conditions. The aim of the Load Demodulation Normalisation (LDN) signal manipulation is to reduce the variation of the statistical parameters due to fluctuating load conditions. A flow diagram of the signal processing technique is presented in Figure 5.
460
" -w'v A A A [\ A A
11l\ / \ / 5 0.8
^^ 1/
V
1/
\j
\]
y
10
y
\j
\!
15
10
15
Time [s]
Figure 3: Sinusoidal load variation effect on the measured vibration signal
^ 0 .
/ I . vr. , /I' iipV-v/
/Vv-v-^^-'
\\J'-'\fAl
Time [s]
Figure 4: Chirp load excitation from 0.1 Hz to 2 Hz The MAS is de-trended to remove the low frequency drift in the signal. The analytical signal of the MAS has to be calculated so that the amplitude modulation of the MAS can be obtained. An analytical signal is a complex time signal and the imaginary part is the Hilbert transform of the real part. The signal is expressed in Equation (2). a(t) = H{a(t)}
(1)
a(t) = a(t) + ja(t)
(2)
With: a(t) : Hilbert transform of the real signal V
a(t) : Analytical signal a(t) : Real signal 461
The Hilbert transform can be obtained by multiplying the positive frequency components of a signal's Fourier transform by -j (phase shift of minus 90 degrees) and the negative frequency components by +j (phase shift of plus 90 degrees). It is similar to frequency domain integration except that scaling w^ith the frequency is not performed in the Hilbert transform. The magnitude or absolute value of the analytical signal represents the amplitude modulation of the signal. Randall (1987) documents the amplitude modulation retrieval procedure.
Measured Acceleration Signal
De-trend
I
Hilbert Transform
Absolute Value
Low Pass Filter
Multiply by LDNIF
I Normalise the MAS by Division
RMS
Variance
Figure 5: Load Demodulation Normalisation flow diagram
The amplitude modulation signal is low pass filtered to obtain the modulation caused by the fluctuating load. Forward and reversed filtering are used in order to minimise phase distortion. Once the Load Modulation (LM) signal has been obtained, it is multiplied by a Load Demodulation Normalisation Intensity Factor (LDNIF). The LDNIF alters the intensity of the normalisation procedure on the MAS. The condition of the gearbox is indicated by calculating the RMS and variance of the Load Normalised Acceleration (LNA). The LDNIF is obtained by means of an iteration procedure in which the LDN procedure is applied to two signals measured under different load conditions with the same amount of damage. The LDNIF is incremented until the RMS values of the two LNA signals are equal.
462
LOAD DEMODULATION NORMALISATION RESULTS The LDN procedure was applied to the data measured under the four different load and damage severity conditions. The RMS and variance values for the different conditions with and without LDN are shown in Tables 3 to 6. TABLE 3 RMS VALUES OF THE NORMALISED ACCELERATION SIGNALS
ChirpH 1 Damage Constant Sinusoidal Square No 0.4001 g 0.4005 g 0.4015 g 0.4003 g 1 Level 1 0.4024 g 0.4006 g 0.4032 g 0.4008 g Level 2 0.4279 g 0.4240 g 0.4166 g 0.4188 g TABLE 4 RMS VALUES OF THE MEASURED ACCELERATION SIGNALS
Chirp 1 Damage Constant Sinusoidal Square No 0.4005 g 0.3842 g 0.3446 g 0.3922 g Level 1 0.3761 g 0.3496 g 0.3298 g 0.3579 g 1 Level 2 0.3124 gj 0.3191 g 0 . 3 0 8 ^ 0.3284 g A load sensitivity analysis was conducted by calculating the percentage deviation of the RMS and variance values for the various tests from the measurement taken under constant load without gear damage. The data are given in Tables 7 to 10. TABLE 5 VARIANCE VALUES OF THE NORMALISED ACCELERATION SIGNALS
Damage No 1 Level 1 1 Level 2
Chirp Constant Sinusoidal Square 0.1600 g 0.1603 g 0.1611 g 0.1602 g 0.1619g 0.1605 g 0.1625 g 0.1606 g 0.1831 g 0.1797 g 0.1735 g 0.1754 g TABLE 6
VARIANCE VALUES OF THE MEASURED ACCELERATION SIGNALS
Chirp 1 Damage Constant Sinusoidal Square 0.1604 g 0.1476 g 0.1187g 0.1538 g No Level 1 0.1414 g 0.1222 g 0.1087 g 0.1280 g [Level 2 0.0976 g 0.1018 g 0.0952 g 0.1078 U TABLE 7 LOAD SENSITIVITY ANALYSIS OF THE NORMALISED RMS
Damage 1 Level 1 Level 2
Chirp Sinusoidal Square 0.1249% 0.7748 % 0.1749% 5.9735 % 4.1239% 4.6738%
463
TABLE 8 LOAD SENSITIVITY ANALYSIS OF THE MEASURED RMS
Damage Sinusoidal Chirp 1 Square 1 Level 1 -12.7091 % -17.6529% -10.6367% 1 Level 2 -20.3246 % -22.9463 % -18.0025%! TABLE 9 LOAD SENSITIVITY ANALYSIS OF THE NORMALISED VARIANCE
Damage Level 1 1 Level 2
Sinusoidal Square 0.3125% 1.5625% 12.3125% 8.4375 %
Chirp 0.375% 9.625%
TABLE 10 LOAD SENSITIVITY ANALYSIS OF THE MEASURED VARIANCE
Damage Sinusoidal Squ Chirp Level 1 -23.8155% -32.2319% -20.1995% Level 2 -36.5337 % -40.6484 % -32.7930 % Tables 7 and 9 indicate that the RMS and variance values of the LNA are at least five times more sensitive to changes in the level of gear damage than to changes in the load conditions. Tables 8 and 10 indicate that the variation in the percentage deviation of the RMS and Variance values for the different load and damage severity measurements are of comparable magnitude. Therefore it is virtually impossible to monitor the condition of gears under fluctuating load conditions without LDN. CONCLUSION A procedure for processing load demodulation normalisation signals was developed to monitor the condition of gears operating under fluctuating load conditions. The procedure was tested on experimental data measured during sinusoidal, step and chirp load fluctuations for different levels of damage severity. The signal-processing procedure proved to be at least five times more sensitive to variations in the condition of gears than to variations in the load. REFERENCES Andrade F.A.R. (1999), New technique for vibration condition monitoring Volterra kernel and Kolmogorov-Smirnov, PhD thesis. Department of Mechanical Engineering, Brunei University. Baydar N. and Ball A. (2000). Detection of gear deterioration under varying load conditions by using the instantaneous power spectrum. Mechanical Systems and Signal Processing, 14:6, 907921. Randall R.B. (1987), Frequency analysis, Bruel & Kjaer. Stander C.J. and Heyns P.S. (2000). Fault detection on gearboxes operating at varying speed and load. 13^ International Congress on Condition Monitoring and Diagnostic Engineering Management. Houston Texas, 3-8 December 2000, 1011-1020. Smith J.D. (1999), Gear noise and vibration. Marcel Dekker Inc. New York. Chapter 11, 143-151. Wang W. and Wong A.K. (2000). Linear prediction and gear fault diagnosis. 13^'' International Congress on Condition Monitoring and Diagnostic Engineering Management. Houston Texas, 3-8 December 2000, 797-807. 464
Condition Monitoring and Diagnostic Engineering Management Andrew G. Starr and Raj B.K.N. Rao (Editors) © 2001 Published by Elsevier Science Ltd.
DETECTION AND LOCATION OF TOOTH DEFECT IN A TWOSTAGE HELICAL GEARBOX USING THE SMOOTHED INSTANTANEOUS POWER SPECTRUM Isa Yesilyurt ^ and A. D. Ball ^ ^ Afyon Kocatepe Universitesi, Usak Muhendislik Fakultesi, 64100 Usak, Turkey ^ Manchester School of Engineering, University of Manchester, Manchester Ml3 9PL, England
ABSTRACT Combined time-frequency and time-scale methods are effective tools and widely used to describe machine condition. This paper introduces the use of Smoothed Instantaneous Power Spectrum (SIPS) distribution in the detection of a local tooth defect in gears. The SIPS distribution is derived from the Instantaneous Power Spectrum (IPS) distribution defined in the frequency domain and provides a considerable reduction in the ringing effect of the IPS transform. The proposed method was later applied to real vibration data obtained from a two-stage helical gearbox and the results of which show a localised gear tooth defect can be clearly detected.
KEYWORDS Gearbox Fauh Detection, Time-Frequency Analysis, Instantaneous Power Spectrum Distribution, Smoothed Instantaneous Power Spectrum Distribution.
1. INTRODUCTION Maintenance of machines and equipment is of considerable economic importance to industry. Continuity of production, preservation of invested capital, and economic operation can, in the long term, only be assured by an efficient and continuous maintenance process, to predict damage and permit the scheduling of repairs. The condition of machines can be monitored via simple techniques including: aural, and visual inspection, performance monitoring, thermal monitoring, wear debris analysis, and vibration monitoring. Unlike the other monitoring techniques, vibration monitoring is a well-suited technique and widely used to detect, locate, and distinguish failures in rotating machinery. When the problem involves both the detection and location of local faults, time-frequency methods (representing the energy content of a signal as a fimction of both time and frequency) can be used effectively.
465
The Wigner-Ville distribution is one of the most popular time-frequency methods and its windowed version, which is often called the Pseudo Wigner-Ville Distribution (PWVD), has been used successftiUy in gearbox fault detection by McFadden [1] and Staszewski [2]. The main difficulty with the PWVD is that, when applied to a multi-component signal, it produces oscillating interference terms which lie between the main signal components. These terms clutter the time-frequency plane, and complicate the interpretation of the analysis. Many attempts have been made to minimise the adverse effects of the interference terms, and one such study was performed by Choi and Williams [3], resulting in a time-frequency distribution which suppresses the amplitude of the interference terms, but at the expense of loss of detail [4]. An alternative time-frequency method is the Instantaneous Power Spectrum (IPS), the basic concept of which was first introduced by Page [5]; this was later developed by Levin [6], and was finally modified by Hippenstiel and Olivera [7]. With the IPS, the interference coincides with the main signal components, which prevents over-complication of the time-frequency plane, and which also causes reinforcement of the pertinent signal features. This reinforcing action is actually accompanied by an amplitude oscillation of the main signal components (a phenomenon often referred to as "ringing") but this rarely hinders interpretation, and the IPS is useful for the detection of weak signals in the presence of noise. This paper introduces the use of Smoothed Instantaneous Power Spectrum (SIPS) distribution in the detection of a local tooth defect in helical gears. The paper commences with a brief theoretical background to the Instantaneous Power Spectrum distribution (IPS) and then its smoothed version is introduced. The SIPS distribution is derived from the IPS distribution defined in the frequency domain and has significant advantage over the Instantaneous Power Spectrum distribution. It provides a considerable reduction in the ringing effect of the IPS transform. Moreover, a simulated gear test signal is used to show capabilities and advantage of the proposed method over the IPS transform. The paper concludes with the application of the IPS and SIPS methods to real vibration data monitored from a two-stage helical gearbox and the results show that a tooth defect can be clearly detected.
2. THEORETICAL BASIS OF THE IPS The IPS is a member of the Cohen class of bilinear (or quadratic) time-frequency distributions (TFD's). The general form of this class can be expressed as follows: C,(r,a).<|,(4,T)x[^^ + ^ y l^ti-^jrfMtft^
(1)
where x(|i) and x* (|i) denote the time signal and its complex conjugate. The ftmction (|)(^,T) is called the kernel function and its selection dictates the specific TFD. For example, selection of the kernel (|)(^,T) = cos(7r/r) results in Lewin's IPS [6], which can be written: 00
IPS^tf) = i ^x{ty{t
- r)+x{t)x{t
+ r)y^'^'dT
(2)
-co
where x{t) denotes the time signal and * represents complex conjugation. The IPS transform of an analogue time signal results in the instantaneous power spectrum of the signal at a particular instance of time. When time localisation of events is required from the IPS transform, the signal needs to be divided-up into short sections (or weighted) by a real-valued symmetric time window (i.e. w(r) = w*[t), and w(o) = l). If y{t) denotes the weighted (or sectioned) time signal by an appropriate window function, the resuhing IPS transform becomes [7]: 00
IPS,it.f) = ^ j^y{t)y'it-r) + y'{t)y{l + T)y''''dT
466
(3)
In practice, real signals are processed in discrete form, and they are time limited such that x{t) = 0 when t>ta. If x(n) denotes a discrete signal (« = 1,2,3,...) with a sample interval At, and the characteristic window w has length M = 2L-\ where w(k) = 0 when |^| > X, then the discrete time version of the IPS becomes: = Y.[^{n)x{n - k)^x*{n)x{n + ^))]w(0H^>-^^^'"^^
IPs(n,m—] V
^
J
(4)
k=-L+\
where n is the time index, /: is a discrete time variable, and m is the frequency index (m =1,2,3,..,,M). It can be seen that the periodicity of the IPS is 2;r, which is the same as that of the Fourier transform. This means that the IPS can be applied directly to either the real valued signal sampled at the Nyquist rate or to an analytical signal.
3. BASIS OF THE SMOOTHED IPS The IPS transform of the windowed time signal y{t) for a given time t can be interpreted as the Fourier transform of the sum of two symmetrically placed correlation sequence estimates. By using the convolution theorem, the IPS transform of the windowed time signal y{t) given in eqn. (3) can also be expressed in the frequency domain as follows: IPS^{t,f)=^[Y{f)®r{f-^)+Y'{f)0Y{f
+ ^)]
(5)
where Y{f) denotes the Fourier transform of the windowed time signal y{t) and <^ and 0 represent the frequency shift and convolution operator respectively. If a rectangular frequency window H{^) is introduced into eqn. (5), the smoothed version of the IPS transform is obtained:
SIPS^f)
= ^ lH{^)Y{t,f)r{t,f-^)d^+
lH{^)r{t,f)Y{t,f+^)d^
(6)
It can be understood from eqn. (6) that the size of the introduced frequency window //(^) has an important effect on the resulting distribution and leads to two special cases: • If H{(^) = S{<^) is selected, the resulting distribution becomes a spectrogram: SIPS^{t,f) where STFT^{t,f)
= S{t,f) = Y{t,f)r{t,f)
= \STFT^{t,ff
(7)
denotes the Short Time Fourier Transform of the windowed time signal y{t).
• If H{<^) = 1 for all (J, then the distribution becomes IPS distribution: SIPS^{t,f)^IPS^{t,f) (8) To enable computer implementation of the STFTy{t,f), eqn. (6) needs to be expressed in its discrete form. If Y{n, m) denotes the FFT of the windowed time signal with a frequency interval A/", and the introduced frequency window / / has a length 2P +1, then the discrete form of equation (6) becomes as follows: 1 (9) SIPS(n,m) = Y, H{i)Y{n, m)Y^ {n, m - 0 + ^ ^ ( 0 ^ * («' '^)^(«' rn + i)\ j=-p
i=-p
J
where n is the time index {n = 1,2,3,...), m is the frequency index (m =1,2,3,...,A/), and / is a discrete frequency variable. It is known that a spectrogram is a linear transform and hence does not produce any interference in the time-frequency plane when a multi-component signal is analysed. In contrast, the IPS transform, due to its bilinear nature, produces interference when it is applied to a multi-component signal. But, here 467
the interference occurs where the main signal components are located, which results in clearer signal representation. In addition, the pertinent signal components are actually strengthened by the interference at the expense of their fluctuation and this makes detection of weak signal components most likely possible. When the size of the introduced frequency window //(^) is selected between these two special cases, the resulting time-frequency distribution is going to be between the spectrogram and the Instantaneous Power Spectrum distributions. As a result, the resulting distribution will combine the good properties of both the spectrogram and the IPS transforms. 4. NUMERICAL VALIDATION AND PERFORMANCE ASSESSMENT In an attempt to identify the general properties and feature extraction capabilities of the IPS, Spectrogram, and SIPS transforms, a simulated gear vibration signal for a two-stage gearbox shown in Figure. 5 was used. The input shaft of the gearbox was considered to be rotating at 1460rev/min and gears at the first stage were regard as meshing with a ratio of 34:70 giving a fundamental toothmeshing frequency of 827.3Hz. At the second stage, gears were assumed to be meshing with a ratio of 29:52 which produces another fundamental toothmeshingfrequencyat 342.75Hz. The simulated vibration signal generated by each gear stage was comprised of three sinusoidal waveforms, representing the first, second, and third toothmeshing harmonics, and having amplitudes of 1.0, 0.4, and 0.2 units respectively. A local tooth defect was represented by a 5-sample impulse, carried by the first harmonic, of amplitude equal to 80% of that haimonic, and located in time with the 150° pinion rotation position. Finally, the noise generated in the gear system [8] was represented by a zero-mean random component with a signal to noise ratio of 8dB. The generated signal was assumed to be sampled at 6.4kHz giving 263 samples per pinion rotation. During the time-frequency analysis, a Harming window of length 96 was used to weight the signal in the time domain and the centre locations of the windows were chosen with a separation of 5 samples. For the SIPS analysis, a rectangularfrequencywindow of length 11 was used. The magnitude of the time-frequency distributions were represented in the form of 50-line contour plots. Figure 1 shows the time andfrequencydomain representations of the simulated gear vibration signal. In the time domain representation, a time record for one rotation (which had a time span of 263 samples) was considered. The test signal in the frequency domain representation, however, was generated over three pinion rotations to achieve a better frequency resolution. It can be seen that time andfrequencydomain representations individually carmot clearly distinguish all the signal properties. The IPS transform of the test signal is shown in Figure 2. It can be seen that most of the signal energy is mainly concentrated around the toothmeshing harmonics and some of it is distributed over the timefrequency plane which represents a random component of the test signal. The ringing effect of the IPS transform causes rapid fluctuation of these principal components, which results in their 'blobbed' appearance. The presence of the impulse is clearly reflected by the linking of energy content around the 150° position which is not clearly recognised in time domain representation. Figures 3 and 4 show spectrogram representation and the SIPS transform of the test signal respectively. Although the spectrogram exhibits principle signal components, it does not truly reflect the impulse located around 150° position and noise content. It is for this reason that spectrogram representation is not considered in the experimental data analysis. The SIPS transform also exhibits the principle signal components. For the selectedfrequencylength of 11, it provided a significant amount of reduction in the fluctuation of the principle components. In contrast to IPS transform, the SIPS transform is insensitive to the noise content which results in a clearer impulse representation around
468
the 150° pinion rotation position. It can be concluded that the SIPS transform can be used effectively to detect impulsive signal components within the vibration.
3000
^2' 3 '
•rt.
Siftii
0.9
°nmw^f^
l-4-^^•-'^^
100 200 Gear Position (Degrees)
1.5 •o
0.7
W2000
0.6
300
0.6 0.4 0.3
1
0.2
<E0.5 0
A.lJL4lLJisi 500
0.1
^....
1000 1500 2000 Frequency (Hz)
2500
3000
Figure 1: Time and frequency domain representations of the test signal.
200 300 100 Gear Position (Degrees)
Figure 2: The IPS transform of the test signal.
0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 100 200 300 Gear Position (Degrees)
200 300 100 Gear Position (Degrees)
Figure 3: Spectrogram representation of the test signal.
Figure 4: The SIPS transform of the test signal.
6. EXPERIMENTAL RESULTS A two stage industrial gearbox shown in Figure 5 was used for the tests. All the gears were helical gears and induction case hardened. The input shaft of the gearbox was driven by a built in three-phase electric motor with a speed rating of 1460rev/min and the drive pinion at the first stage had 34 teeth meshing with a 70-tooth wheel. The pinion gear at the second stage (driven directly by the 70-tooth gear wheel) had 29 teeth meshing with another 52-tooth wheel. A local tooth defect was introduced to one of the pinion gear teeth at the first stage and its severity was varied by incrementally reducing the tooth face width. The maximum specified gear load on the output shaft of the gearbox was about 260Nm and all tests were carried out under this torque.
469
Introduced Tooth Defect
Figure 5: Faulty test pinion and sectioned view of the gearbox. The vibration signal generated by the gearbox was picked up by an accelerometer and a reference signal obtained from an optical pick-up was then used to synchronise time domain averaging of the vibration data. The accelerometer and reference signals were sampled at 6.4kHz and the raw vibration data was continuously collected over 123 faulty pinion rotations. This was then split into 41 threerotation blocks, and these were averaged together. During the analysis, the averaged vibration signals were weighted by a Hanning window of length 96. The centre locations of the windows were chosen with a separation of 5 samples which gave an overlap between consecutive windows of 91 samples. In addition, a rectangular frequency window of length 11 was used in the SIPS analysis. To enable both the IPS and SIPS transforms to exhibit the resulting time-frequency distribution for one exact rotation of the faulty pinion gear, 359 samples were extracted from the central portion of the averaged signals. The results were presented in the form of 50-Iine contour plots. Figure 6 shows the IPS transform of the healthy gear vibration signal. It can be seen that signal energy is mainly concentrated around 827Hz (which is the fundamental toothmeshing frequency of the vibration generated by the first stage gears) and l654Hz which is the second harmonic component of that frindamental. In addition, some of the signal energy is concentrated around 342Hz and integer multiples of this frequency which denote signal energy generated by the second stage gears. It is clearly seen that the ringing effect of the IPS transform causes rapid fluctuation of these principal frequency components, which results in their 'blobbed' appearance on the contour plots. Furthermore, the IPS transform also exhibits strong impulsive responses and some noise. These features most likely stem from the surface quality of the relatively new meshing gears (before the data for the healthy gear pair was collected, the gears had been run for a total of approximately 2 hours at 75% of their specified frill load). Figure 7 illustrates the SIPS transform of the healthy gear vibration signal. Like the IPS transform, the SIPS transform also exhibits the signal's energy content, but with a lesser degree of sensitivity to the noise content which enables a more clear signal representation. For the selected frequency window size, the effect of smoothing is clearly seen on the signal energy around the toothmeshing frequencies. Figures 8 and 9 show the IPS and SIPS transforms of the gearbox vibration signal with 30% face width removal. It can be seen that both transforms clearly exhibit the presence of a local tooth defect. A most noticeable indication of a faulty condition is the presence of strong energy activity around the 150° pinion rotation position which spreads out in the frequency direction. The effect of smoothing on the SIPS map is notable on the signal energy concentrated around the toothmeshing harmonics which significantly reduces the degree of energy fluctuation. The IPS and SIPS transforms of the gearbox vibration signal with 60%o face width removal are illustrated in Figure 9 and Figure 10 respectively. Here, the increase in fault severity causes a further reduction in mesh stiffness when the defected tooth is in mesh and results in significantly more premature contact of subsequent meshing teeth. Consequently, the resulting fault symptoms are more severe and more localised around the 150° pinion rotation position.
470
2500
2500,-
100 200 300 Gear Position (Degrees)
100 200 300 Gear Position (Degrees)
Figure 6: The IPS transform of the healthy gearbox vibration.
Figure 7: The SIPS transform of the healthy gearbox vibration.
2500
2500 r
100 200 300 Gear Position (Degrees)
100 200 300 Gear Position (Degrees)
Figure 8: The IPS transform of the healthy gearbox with 30% face width removal. 2500
Figure 9: The SIPS transform of the healthy gearbox with 30% face width removal. 2500 r
100 200 300 Gear Position (Degrees)
100 200 300 Gear Position (Degrees)
Figure 10: The IPS transform of the healthy gearbox with 60% face width removal.
471
Figure 11: The IPS transform of the healthy gearbox with 60% face width removal.
7. SUMMARY AND CONCLUSIONS In this study, the use of the Smoothed Instantaneous Power Spectrum has been introduced in the detection and location of a local tooth defect in helical gears. Analysis of simulated gear vibration shows that both the IPS and SIPS transforms are capable of representing most of the signal properties. Due to its bilinear nature, the IPS transform exhibits high frequency amplitude oscillation of the main signal components. In contrast, the SIPS transform provides a significant amount of reduction in the ringing effect of the IPS transform. In addition, the SIPS transform is insensitive to noise content and this provides a clearer time-frequency map and, consequently, a clearer impulse representation. Real vibration data obtained from a double stage gearbox for healthy and faulty conditions were analysed by the IPS and SIPS methods. Fauh detection and location by the IPS and SIPS methods were based upon visual observation of the differing time-frequency patterns of the distributions. When the fault was introduced, clear evidence of the localised tooth defect was found with 30% tooth face width removal. The fault symptoms manifested themselves as energy 'links' between the meshing harmonics across the frequency range when the defected tooth was in mesh. Increasing the severity of the fault to 60% face width removal caused correspondingly stronger fault symptoms around the 150® pinion rotation position where the defected tooth was in mesh.
8. REFERENCES [1]
[2] [3] [4]
[5] [6] [7] [8]
P. D. McFadden and W. J. Wang (1992). Analysis of Gear Vibration Signatures by the Weighted Wigner-Ville Distribution. Proceedings of the Institution of Mechanical Engineers €432/134, 387-393. W. J. Staszewski (1994). The Application of Time-Variant Analysis to Gearbox Fauh Detection, Ph.D. Thesis, University of Manchester. H. Choi and W. Williams (1989). Improved Time-Frequency Representation of Multicomponent Signals using Exponential Kernel. IEEE Trans, on ASSP 37/6, 862-871. I. Yesilyurt, P. J. Jacob, and A. D. Ball (1996). Fault Detection in Helical Gears using Pseudo Wigner-Ville, Instantaneous Power Spectrum, and Choi-Williams Distributions, I: Performance Comparison of Time-Frequency Distributions. Proceedings of 9* International Conference on Condition Monitoring and Diagnostic Engineering Management, Sheffield, 477-486. C. H. Page (1952). Instantaneous Power Spectra. Journal of Applied Physics 23/1,103-106. M. J. Levin (1964). Instantaneous Spectra and Ambiguity Functions. IEEE Trans, on Information Theory IT-10, 95-97. R. D. Hippenstiel and P. M. De Olivera (1990), Time-Varying Spectral Estimation using the Instantaneous Power Spectrum (IPS). IEEE Trans, on ASSP 38/10,1752-1759. T. Masuda, T. Abe, and K. Hattori (1986). Prediction Method of Gear Noise Considering the Influence of the Tooth Flank Finishing Method. ASME Journal of Vibration, Acoustics, Stress, and Reliability in Design 108, 95-100.
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Condition Monitoring and Diagnostic Engineering Management Andrew G. Starr and Raj B.K.N. Rao (Editors) © 2001 Elsevier Science Ltd. Allrightsreserved.
SECURING THE SUCCESSFUL ADOPTION OF A GLOBAL INFORMATION DELIVERY SYSTEM D. Perry and A. G. Starr Manchester School of Engineering, The University of Manchester, Manchester, Ml3 9PL, UK
ABSTRACT This paper is based on a segment of research by the author in the area of global asset management. It concentrates on an information system based on e-mail and Internet technologies that provides part of a global distributed solution for the manufacturing business sector. The requirement for successful adoption of the information delivery system forms the topic of the paper. For proactive decisions to be made, accurate, directed and punctual business performance information is vital within organisations. Maintenance derived information is typically isolated and unused outside maintenance departments. However, available technology today, allows for the storage, comparison and analysis of vast quantities of raw data. The potential possibility of unlocking this valuable wealth of information provides firms with huge opportunities. Further, to obtain the maximum benefit, delivered information has to be tailored to specific individual needs. Key Performance Indices provide the type and direct relevance of information individuals require. In order to ensure maximum organisational confidence in the system, it has to provide secure and effective means of transferring internal and external information at regular intervals. Consequently, the paper discusses how the necessary trust can be built up in the system to provide a successful adoption of the technology. Therefore, the paper shows a methodology that can aid participation for the introduction and business integration of the information delivery system. KEYWORDS Asset Management, Information, Technology, Adoption, Key Performance Indices.
473
INTRODUCTION This paper focuses on the requirements for a business information delivery system. It is primarily concerned with supplying information that can provide an organisation with the ability to care for its physical assets more efficiently. The system in question has been designed to fulfil the requirement of an innovative Global Maintenance Support System based upon distributed Internet technologies. The system has been developed to satisfy customers of a consultancy software firm as well as the current and potential future needs of the consultancy itself The diagram shown in Figure 1 shows the three generic levels of the system. The diagram shows the interconnections between system requirements, data and information that make up the generic connections in the system. The arrows represent interconnections between distinct entities. Req uirements Customer Requirements
r
yr
Consultancy Requirements
w
Data
^f
Store &/or Distributed Links kL
^
^ ^
^f Customer Data
ii
. .J
1
^y
Info rmati on
^
r
Customer & Consultancy Reports
Individual Delivered Information
1
i i i i 1 i i i
Figure 1: Diagram to show Generic Interconnections of System At the top of the diagram are the maintenance related information requirements of the customer and the consultancy. Maintenance related data and other related business data is the central aspect of the diagram. This comprises the existing computer systems of the customer and from a centrally collated store run from the consultancy. Due to current technology and research, the size, type, location and storage, access and capability of data is not an objective of the paper, although, it is viewed by the author as important factor later in the design of the system when choosing software and hardware requirements. The delivery of information is shown at the bottom of the
474
systems diagram feeding back into requirements and providing the desired outputs. The author sees these three areas as the basic building blocks or segments for production of the system. However, it is not sufficient to justify the creation of the information delivery system on the basis of a generic model. Four fundamental questions need to be answered prior to system build in order to achieve the required objective of a successful adoption. The questions are: 1. 2. 3. 4.
Who the system is for? What is the system doing with the business data? When should the information be provided? How should the implementation be achieved?
Each of these questions has been discussed to form individual sections of this paper. WHO THE SYSTEM IS FOR? The information delivery system has to send maintenance derived data, combined with other selected data to firm personnel as useful information. Therefore, providing a powerful source of knowledge for individuals. It is important not to send the information to only the trained maintenance practitioners. Predominately, the individuals will not be from a maintenance background. Current Computerised Maintenance Management Systems have been designed for use by trained maintenance staff and therefore cannot provide the requirements for the rest of the business. Enterprise resource planning systems and other business systems do try to provide a broader framework. However, in the author's opinion they still lack the detailed data and manipulation facilities that a maintenance practitioner requires. The author believes that maintenance data is required for providing information throughout organisations. This enables improved efficiency in actual business practice, improved decision making and overall asset contribution to profitability. This means that maintenance effects businesses throughout structured departments and throughout hierarchies. The implications of predicting a potential plant failure or plant shut down using Condition-based Maintenance has ramifications to different segments of the business. To illustrate this point the author gives the following example. Information ^' ^' "
Stores Raw Material Delivery
•
;
\ Service \ Plant A \ Delivery \ Sales \ Critical \ Sub\ New \ Stajf j Failure 1 contracted 1 Contracts 1 / Predicted 1 Logistics 1
/
t
1
Information Figure 2: Diagram to Illustrate Examples of the Dimensions of Maintenance Information to Stated Segments of Business
475
The diagram in Figure 2 demonstrates that maintenance information is required in areas of the business other than production. Examples of the type of information for each segment of the business are stated below. Stores Any information regarding disrupted production can provide the business with the opportunity to delay deliveries. Reducing associated raw material and inventory costs. Plant A Maintenance predicts the plant failure from Condition-based Maintenance system and advises production of the plant availability. Delivery The business uses a sub-contracted delivery service. Advance warning of disrupted throughput of production provides the opportunity to re-arrange deliveries and reduce payment for none required services. Sales The prediction of critical failure of the plant provides the sales team to not take on new contracts that potentially could not be supplied. Reducing the possibility of disgruntled customers that tarnish the image of the business. Service The service staff is the provider of product support at the customer. Disruption in supply from Plant A can be minimised to the affected customer if they are aware early enough of impending product delay. Following this discussion regarding the requirements for maintenance related business information, the next section of this paper describes the type of information that needs to be delivered. WHAT IS THE SYSTEM DOING WITH THE BUSINESS DATA? The information that is provided by the system has to be tailored to the individual company requirements. The chosen type of information are Key Performance Indices. These provide wide-ranging performance indicators for the business. It is therefore important that maintenance information be integrated into the relevant business measures. This can occur manually in a paper-based system, through integration of a Computerised Maintenance Management System or Enterprise Resource Planning System. To achieve the seamless integration an analytic application provides the tool to extract the relevant data. This solution provides access to the data in a user-specific
476
construction manner. Without the need for high cost, long implementation and business wide disruption that has led to the decline of Enterprise Resource Planning [Beyond ERP (2000)]. Performance indicators should be designed to enable an overall perspective of the business. Having both an integrated and independent relationship with business goals, strategies and objectives as stated by Wireman (1998). This means that Key Performance Indices can include information from a corporate and financial outlook as well as the more manufacturing perspective of efficiency and effectiveness. To show the types of information provided by Key Performance Indices the following equations are examples for each business area of Stores (Eqn. 1), Plant (Eqn. 2), Delivery (Eqn. 3), Sales (Eqn. 4) and Service (Eqn. 5). The equations are purely examples to illustrate the variety of information. The number of chosen indices for each stated area of the business depends entirely on the business requirements of the firm. Total Cost of Maintenance Spares in Stores Total Cost of Whole Business Inventory in Stores Availability
x
Performance Efficiency
x
'^
Quality Rate '^
Number of Late Deliveries Total Number of Deliveries
(3)
Total Achieved Sales Revenue Total Potential Sales Revenue
(4)
Total Emergencv (call out) Staff Hours Total Staff Hours
(^)
Maintenance can have an impact on all the equations that are stated. Therefore, maintenance effects the performance of the stated business segments. The severity varies due to the uniqueness of the business. Eqn. 2 can be referred to as Overall Equipment Effectiveness. It involves specifically measuring the performance of equipment. Currently, this is one of the few examples of a performance measure that crosses operational boundaries. This is in relation to production, quality and maintenance. It should be stressed that the indices need to be measurable in the business. This means that, time as a factor should be considered with each example. The nature of this time function is discussed in the following section. WHEN SHOULD THE INFORMATION BE PROVIDED? The technological ability to provide performance information to a business in real time is readily available. However, there is a cost benefit analysis that needs to be
477
clearly understood for the desirability of such an application. Particularly in relation to providing and collecting expensive data into the system. The information provided by the system is only as good as the data that is placed in it. For example, stocktaking can be both a laborious and costly exercise, although, accurate data may be required. The key performance indices themselves are another factor against providing a real time system. It may not be practical to compile the data on demand resulting in the performance indices not reflecting a true picture of the business. An example of this is trying to collate data that has not been placed into the company system, even though it is common business knowledge that it has taken place. This can undermine the future value of the system to users. There is also the factor of information overload on users. If the information is provided too frequently the novelty and impact on the importance can be forgotten. The other extreme is having the information delivered irregularly and infrequendy which then provides a detrimental affect on the successful adoption. Information supplied by indices needs to reflect internal and external factors that may not be measured in the very short term (i.e. hourly or daily). Examples, such as buyer behaviour pattern or staffing fluctuations caused by holiday periods. However, it is important to bear in mind that information provided by the system should be comparable with historical data. Therefore, if providing a real time solution is not practical on the type of delivered information, the periodic delivering of the information is the alternative. The decision regarding the regular time of delivery depends on the needs of the individual firm. Examples from both internal and external factors that influence this decision are: > > > >
the current situation regarding their internal data systems their information system strategy the business sector of the firm management and individual requirements.
HOW SHOULD THE IMPLEMENTATION BE ACHIEVED? This section focuses on the implementation of the technology into the businesses from the perspective of the psychological affects on the users and the company. This section has been included because from the author's personal experience, failure on the introduction of new technology is not caused by problems with the technology directly. It is caused by a lack of acceptance within the firm. Business support for the system is tackled by the information delivery system being introduced, combined and supported by a consultancy training and business development product. This initially provides the top management support that is a fundamental factor to successful deployment. Further, it allows for the data and the key performance indices to be supplied to the system in a structured, documented and controlled form.
478
To gain trust of the users the information supplied to the individuals has to conform to four factors: > > > > >
the appropriate form of information delivered at a consistent and regular periodic time to the right person at the correct place in a secure and reliable form
The presentation of information shown to users has to be understandable to the majority of employees irrespective of their knowledge and place in the company hierarchy. Therefore, simple graphical charts and pictures will be presented. The recipient of the information receives a personalised delivery. This provides users with a personal attachment and apathy. Also, the nature of the information delivery system (i.e. to provide regular directed and structured information) provides a tool to aid interim and immediate reinforcement of company goals. Makin et al., (1996) suggest that interim reinforcers help maintain long term goals while inmiediate reinforcers can be stronger in their influence on individuals than delayed reinforces. An example of this is a firm trying to increase its production output by 20%. The achievement of the project may be forecasted to take the business 12 months. The performance indices shown in Eqn. 2 can be used to provide regular updates and help manage and control of equipment. Providing the information to enable immediate and interim reinforcement. CONCLUSIONS The purpose of the information delivery system is to take isolated maintenance derived data from the maintenance department, combine it with various sources of business data and then provide and translate it into useful information to all employees regardless to whether they have any existing maintenance knowledge. Unlocking this potential source of information using the adoption process that has been stated enables proactive maintenance decisions to be made through a business. The Key Performance Indices provide the type and direct relevance of information individuals and therefore the delivery system require. This tailored information allows individuals to make empowered decisions because instead of being told what they have to do, they are provided with the information to make their own informed choice. The Key Performance Indices also provide the requirement for the exact sources of defined data. This data has to be controlled and monitored to prevent poor or incorrect data being placed into the system. Conununication within the enterprise is another beneficiary of the use of the system. The information provided can break through functional and departmental boundaries. Allowing a new understanding of maintenance and wider management issues to be received by all employees.
479
The integration of computer systems on a business is highly expensive. Using the techniques described the minimum disruption is caused providing no negative reaction to the introduction of the technology prior to the system starting operation. The final conclusion is that a high level of trust and confidence has to be attained for successful adoption. This can be enhanced by features supplied within the system itself and by the support offered from top management. The uptake can be measured by reviewing overtime of whether or not individuals actually read the information. This performance information can provide the means to improve and adapt the system to the changing requirements of individuals.
REFERENCES Beyond ERP, Manufacturing Engineer, 79:5, October 2000, 210-213. Kelly A. (1997). Maintenance Strategy - Business-centred Maintenance, ButterworthHeinemann, Oxford, UK. Makin P., Cooper C. and Cox C. (1996). Organizations and the Psychological Contract, The British Psychological Society, Leicester, UK. Wireman T. (1998). Developing Performance Indicators for Managing Maintenance, Industrial Press, Inc., New York,
ACKNOWLEDGEMENTS The author would like to thank the support and advice of Wolfson Maintenance in the production of this paper. Specifically Dr I. Kennedy, Dr A. J. Doyle and Dr M. Niblett for their input and insight into some of the ideas behind this paper.
480
Condition Monitoring and Diagnostic Engineering Management Andrew G. Starr and Raj B.K.N. Rao (Editors) © 2001 Elsevier Science Ltd. All rights reserved.
DESIGN OF A PIC BASED DATA ACQUISITION SYSTEM FOR PROCESS AND CONDITION MONITORING M.R. Frankowiak, R.I. Grosvenor, P.W. Prickett, A.D. Jennings, J. R. Turner Cardiff University, School of Engineering, Newport Road, Cardiff, CF24-3TE, Wales
ABSTRACT This paper describes the design of a data acquisition system, based upon a Microchip PIC microcontroller, that is being developed for condition and process monitoring. It presents the considerations made in the development of hardware and software to allow the deployment of a monitoring system based upon the Petri-net concept. The methods by which the data collected by the system can reach a centralised database are described. In doing so consideration is given to the available connectivity options for the micro-controller based monitoring system used. The benefits and limitations of such a micro-controller based system are discussed using some initial results obtained by the experiment. KEYWORDS Monitoring, Micro-controller, Petri-net, CAN bus, Internet. INTRODUCTION This paper describes the use of a micro-controller to perform monitoring tasks based upon the concept of Petri-nets. This work is part of the research currently being conducted by the Intelligent Process Management and Monitoring (IPMM) Centre of Cardiff University. The aim of the IPMM Centre is to promote the development of process monitoring technology. Initial consideration of the deployment of a micro-controller based monitoring system provided sufficient success to support this development (Christopoulos 2000). It is based upon the use of Petri-nets for monitoring purposes, which is an established part of the IPMM research activities (Prickett et al. 2000; Jennings et al. 2000). New and powerfiil devices have been released by the micro-controller industries. These are being used, in work such as that described here, to support applications that previously demanded a much greater level of resources. In the case of process monitoring the cost of these existing systems often prevented their widespread adoption.
481
The PIC (J^ Microchip Tech. Inc.) family of micro-controllers is part of this new generation. A considerable number of applications based upon them have been developed, taking advantage of their reduced size and power consumption, as well as affordable prices. This paper outlines the development of monitoring hardware based upon the PIC family's provision of digital and analogue inputs, as well as communication facilities; PIC micro-controllers are provided with a subset of devices that enable their integration using a Control Area Network (CAN). CANs are widely used by industry and are one of the most accepted Fieldbus standards (Scott and Buchanan 2000; Rufmo et al. 1998). The work also considers the management of the data generated by the monitoring system. This is normally uploaded to a remote database through the Internet. Internet connectivity has to be considered (Prickett et al 2000; Kennedy et al. 2000). It represents the natural way to get access to data collected by distributed monitoring systems. It also promotes the use of powerful computational structures, which can be far awayfromthe monitored process, but can be used to handle the data in an appropriate manner. The Internet also offers the facility to make use of an existing and well developed structure to access the process information from anywhere. The amount of program and application memory required to support these frmctions and the ability of PIC based systems to meet these needs is considered below. A PC based Petri-net monitoring tool previously developed within the IPMM Centre enables the modelling of a process as a sequence of events (Prickett et al. 2000; Jermings et al. 2000). The same technique was considered to be a good basis from which to develop a monitoring system based on a micro-controller. A Petri-net model represents a logical and clear way to describe a process. It allows hardware and software independence, once the process modelling the Petri-net can be easily integrated into the existing hardware and software. If the process changes the model can be edited, but the monitoring system does not change. Limitations associated with the number of input lines for monitoring purposes, as well as the size of process to be monitored are considered in detail below. HARDWARE CONSIDERATIONS The PIC family of micro-controllers offers a range of devices to suit a number of applications. This project is currently using the PIC18C452 device. The most attractive features of it, from the point of view of this application, are the digital I/O ports, the analogue inputs, the programmable timers and serial communication ports. These features are software configurable and their number and availability may vary according the application. Equally important are the size of the available program and application memory, last one designated as file registers. Full details are available at Microchip homepage (2001). The concept of Petri-nets for monitoring utilises signals driven by the process to control the firing sequence of the model's net transitions. By using multiplexing techniques, a total of 24 digital inputs are allowed through one of the 8 bit ports of the PIC chip. Four analogue and two pulse inputs were also planned to enable some other more complex monitoring fiinctions. Currently a Universal Synchronous Asynchronous Receiver Transmitter (USART), which allows a wide range of configurable baud rates, is connected to a standard RS-232C communication port on a PC-based computer. Monitoring data collected by the system is displayed on the PC and can be transferred to a centralised database server. As a next step, a CAN bus will be used to enable distributed system integration, as well as to establish a centralised node which will be responsible for data transfer to an existing database server cormected to the Internet. Figure 1 shows a block diagram of the hardware structure. Despite the fact that this specific chip requires a clock up to 40 MHz, the system was designed to operate based on a 20 MHz clock. This lower frequency was chosen to be within the limits imposed by 482
the CAN controller (MCP2510), avoiding more then one oscillator on the circuit. One of the embedded internally clocked timers was configured to generate a time base that updates a real time feature. Interrupts are generated at periodic intervals to read and store the digital input levels, without requiring special software polling to perform it. All peripherals were configured to request service through interrupt lines. Software construction becomes easier, once specific blocks are linked to the respective peripheral requesting actions.
RS-232C Interface
I
M
8 X digital inputs
I
M
8 X digital inputs
jj
8 X digital inputs
P I C
CAN Node
4 X analogue inputs
^
2 X pulses inputs
Figure 1: Basic hardware structure, with a proposed CAN node
SOFTWARE CONSIDERATIONS The PIC18C452 micro-controller uses 16 bit wide instructions, offering a large range of instructions and consequently making programming easier. A number of instructions for data manipulation are also available, which is ideal for applications like the one under consideration. The basic structure of the software developed is shown in Figure 2. The initialisation block sets up the micro-controller operating mode and selects and configures the peripheral devices used by the application. The main body of the application software runs continuously, checking the existence of a new event that may require the system intervention. To make fiill use of the available resources the application uses interrupt services for the peripheral devices, such as timers and communication interfaces. These devices request the specific and appropriate intervention only when necessary, avoiding any unnecessary processing. Table 1 presents the description of the tasks performed by each of the main components shown in Figure 2. Initialisation Block
: : : :
Monitoring Net Checking
Timer Interrupt
Command Analysis
Receive Data Interrupt
Message Construction
Transmit Data Interrupt
1 1 : ;
: Main Body
Interrupt Services
Figure 2: PIC based monitoring software structure 483
TABLE 1 APPLICATION SOFTWARE COMPONENTS DESCRIPTION Description
Software Component Net checking
The transitions are checked to detect whether or not the input states matches with a firing condition. When selected, a message to notify the event is requested. Affected places are updated.
Command analysis
A set of commands is provided to perform some basic tasks. It includes set date to set up the calendar date, set time to initialise the real time clock and the reset command, which forces the system to return to the start point of the Petri-net.
Message construction
When a transition is enabled, a message indicating that a token has been fired is written and loaded in a buffer, accessed by the data transmit interrupt service. This message contains the transition number and date and time associated with the event.
Timer interrupt
Updates the system real timer and calendar. The state of the sensors monitored is also updated by the interrupt.
Receive data interrupt
Stores received data in a system buffer.
Transmit data interrupt
I Updates the system real timer and calendar. The state of the sensors monitored is also updated by the interrupt.
PIC BASED MONITORING A Petri-net monitoring system based on an embedded micro-controller like the PIC18C452 can represent a cheap and easy to install solution. It is important that the monitoring hardware and software implementation should not be dependent upon the process if we are to avoid the necessity of providing a new hardware/software set-up for each different process. To prevent this from happening, the Petrinet describing the particular process is placed into a separate data table. The use of data structures with the PIC18XXX family is facilitated by the existence of a set of instructions to retrieve data from a table. Thus a new Petri-net can be created to monitor any new or changed process, input into the data table and integrated into the PIC based monitoring system. At the moment this data table has to be compiled together with the application software. However the development of a PIC micro-controller, based on flash memory technology will result in a more flexible, user-friendly alternative system. The Petri-net monitoring tool developed and deployed within the IPMM is based upon an adaptation of the original theory (Peterson 1981). This allows enabled transitions to be associated with process events using signals provided by existing sensors (Pricket et al. 2000; Jennings et al. 2000). Figure 3 represents a generic example of this kind of situation. The sensors have their state regularly polled to detect a firing condition. All input conditions must match to enable the transition to be fired. Monitoring is thus based upon the firing of transitions. The model produced to enable a Petri-net to be designed for use with a system using a PIC microcontroller has a significant difference. It considers transitions as static and places as dynamic entities and thus monitoring is based upon the movement of tokens through places. This consideration means
484
that places can to be separated from the structure that defines the model and is controlled by the transitions.
®
Input Place - with a token External Sensor
Transition
O
Output Place
Figure 3: Adapted Petri-net transition for monitoring purposes Figure 4 shows a block diagram representing the considered data structures. The transition data structure basically points to a sensor mask (i.e. identifies the source of input signals), and to input and output places for each transition. Places are thus seen as individual entities that can have the number of tokens they contain incremented or decreased, depending upon their condition regarding the fired transition, i.e. inputs from transitions into places increase the number of tokens present in any place, whilst outputs from places to transitions decrease the number. Transition Identification Sensors Mask Input Place Pointer
End of Input Places Output Place Pointer Token Identification End of Output Places
Number of Tokens
(a)
(b)
Figure 4: Transition (a) and place (b) data structures An important observation is that transitions are considered static because they do not change during the monitoring process. Even though transition structures can vary with the different numbers of places and inputs from one application to another. Places within the Petri-net will however exhibit changes to the number of tokens they contain, which will vary with time and with the particular route taken by the tokens through the Petri-net. 485
When a transition is enabled and a token is fired, the associated data string is sent to a PC computer, where a dedicated application, connected to a remote server, records the event in a database. Figure 5 shows the PC application window, associated with this task. Commands to set the date and time, and restart the Petri-net running on the PIC are available on the application window. Functions to make the database selection are also available.
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[ BESET )
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Figure 5: Transition firing PC application window In the database the transition number, date and time of the event are recorded in separate columns, as shown in Figure 6. Data analysis is possible through the use of traditional tools.
TIME liiLmtMl 15:27:15 TD01 TD03 7039 TD10 TD15 TD01 T004 T009 TD10 •rai5
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15:27:41 15:28:11 15:29:17 15:30:42 15:30:42 15:32:25 15:33:05 15:33:28 15:34:18
DATE 21/D4/2001 21/04/2001 21/04/2001 21/04/2001 21/04/2001 21/04/2001 21/04/2001 21/04/2001 21/04/2001 21A34/2001
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r• '
d
Figure 6: Database table The experiment that originated the data shown in Figure 6 was based on a scale module of an ASEA hydro forming press, which is controlled by a Siemens S5-95U PLC. A Petri-net with 14 places and 15 transitions, monitoring 7 external digital signals, was used to describe the sequence of events present on the process. The number of places and transitions represents 5% of the total capacity of the proposed system. Despite the simplicity of the process that was monitored, these numbers show the capabilities of this kind of monitoring solution. The use of more than one monitoring module on the same process or machine is possible and is under development at this moment. Figure 7 shows the equivalent Petrinet designed usmg a PC computer based application developed by the IPMM Centre (Jennings et al. 2000). Since the data is available in a database server connected to the Internet, application based on the e-monitoring (Kennedy et al. 2000) concepts can extract the information necessary for management purposes.
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Figure 7: PC based Petri-net monitoring tool The project is being further developed to utilise the newly developed PICs to produce monitoring devices that can access the database without the intervention of the PC computer playing a role as a gateway. It demands that the monitoring system based on the PIC micro-controller must be able to get access to the Internet. Several cases have been reported showing the use of PIC micro-controllers with Internet applications (Microchip web site 2001). Nevertheless it is still a challenge to provide Internet access protocols and monitoring tasks running on the same system. The different nature of the applications and the amount of resources demanded, specially file registers, suggests the most promising way forward is to deploy separate systems, one dedicated to the monitoring task and the other for Internet access. CAN is seen as offering potentially the best choice to inter-connect these communication and monitoring systems. In this way it may also be possible to link multiple systems across more complicated processes. This technology is supported by the PIC family of microcontrollers and the standard and the support necessary for its use are already available. This network tends to be relatively small, restricted to the process and with small size of data strings to be transmitted. All these fit quite well with CAN.
FINAL CONSIDERATIONS Petri-nets for monitoring purposes, based on micro-controller, are a useful tool. The challenge now is to develop this technique and build a monitoring system that allows the deployment of distributed monitoring along the process or at different parts of a machine. This system must then collect the necessary data and provide tools allowing data analysis and communication. The benefits of monitoring techniques are already well known. The use of the new developments, like those based on PIC family of micro-controllers, can bring some good options for a wider employment of monitoring, merging the benefits of distributed applications with those of reasonable cost. A considerable amount of research is being conducted to enable embedded systems to get connectivity facilities, especially those related with the Internet. It denotes that very compact systems will have an 487
important place in the development of a wide range of applications. Nevertheless, research has to be made to allow all the different and necessary parts involved to be joined together, ensuring a reliable structure that can be expanded and configured to suit a range of applications. ACKNOWLEDGEMENTS The authors acknowledge the support of the European Union via the ERDF grant to establish the Intelligent Process Monitoring and Management Centre. M. R. Frankowiak is a PhD student supported by CAPES, a Brazilian Federal Agency for Post-graduation Education. REFERENCES Christopoulos A. (2000). PIC Microcontroller Application. Project Report. Cardiff School of Engineering. Jennings A.D., Nowatschek D., Prickett P.W., Kennedy V.R., Turner J.R. and Grosvenor R.I. (2000). Petri net Based Process Monitoring. In Proceedings ofComadem 2000. Houston, USA: MFPT Society. 643-650. Kennedy V.R., Jennings A.D., Grosvenor R.I., Turner J.R. and Prickett P.W. (2000). Process Monitoring Using Web Pages (e-Monitoring). In Proceedings ofComadem 2000. Housto, USA: MFPT Society. 877-883. Microchip Technology Inc (2001). Microchip Technology products. Microchip Website. Arizona, USA. Availablefrom:http://www.microchip.com/14010/helper.htm. [Accessed 02 April 2001]. Peterson J.L. (1981). Petri Net Theory and the Modelling of Systems, Englewood Cliff, Prentice-Hall. Prickett P., Grosvenor R., Jennings A., Kennedy V., Nowatschek D. and Turner J. (2000). Developing an Internet Based Intelligent Process Monitoring and Management Centre. 3'^ International Conference on Quality Reliability and Maintenance. Oxford: IMechE ISBN 1860582567, 79-82. Rufino J., Verissimo P. and Arroz G. (1998). Embedded Platforms for Distributed Real-Time Computing: Challenges and Results. In Proceedings of the Second IEEE International Symposium on Object-Oriented Real-Time Distributed Computing. Saint Malo, France: IEEE Comp. Soc, 147-152. Scott A.V. and Buchanan W.J. (2000). Truly Distributed Control Systems using Fieldbus Technology. In Proceedings off^ IEEE International Conference and Workshop on the Engineering of Computer Based Systems. Edinburgh, Scotland: IEEE Computer Society. 165-173. BIBLIOGRAPHY Turner JR, Jennings AD, Prickett PW and Grosvenor RJ; The design and implementation of a data acquisition and control system using fieldbus technologies; to be published in proceeding of COMADEM2001. Jennings AD, Kennedy VR, Prickett PW, Turner JR, and Grosvenor RI; A Distributed Data Processing System For Process And Condition Monitoring, to be published in proceedings ofCOMADEM200I.
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Condition Monitoring and Diagnostic Engineering Management Andrew G. Starr and Raj B.K.N. Rao (Editors) © 2001 Elsevier Science Ltd. All rights reserved.
APPLICATIONS OF DIAGNOSING OF NAVAL GAS TURBINES Adam CHARCHALIS Mechanic-Electric Faculty, The Naval Academy Gdynia, Poland,
ABSTRACT A high combat readiness is the fundamental feature of contemporary vessels from cutters to aircraft carriers. It results from introducing the new fighting means and from the application of small-overall dimensions, small mass and high power gas turbine engines for a vessel propulsion system. The gas turbine engines are used on various types of vessels from cutters to aircraft carries. They represent a part of the homogeneous propulsion as well as combined propulsion. Nowadays they are applied for propelling high speed vessels in all significant world fleets. The application of turbine engines as the main propulsion engines of a vessel impels, according to requirements, operation procedures. It alters considerably the usage of gas turbine engines in the navy from the ones in aviation. The relatively low load is characteristic to the operation of the marine engine. The time of operation on average and maximum load constitutes only up to several per cent of the whole time of its operational usage. That is the reason why the efficiency of operating marine engines is considerably lower than the planned (designed) efficiency. The paper presents general possibilities of identification of the operating states of naval gas turbines by the Base Diagnostic System application. The system configuration, testing means and methods are demonstrated within the paper. The system was implemented into the Polish Navy Vessels with turbine engines. Measuring positions, the data base constantly being updated and diagnostic software allow carrying out the operation of turbine engines according to their actual conditions.
KEYWORDS Naval gas turbine, technical diagnostics, diagnostic system, testing means and methods, diagnostic software.
BASE DIAGNOSTIC SYSTEM OF NAVAL GAS TURBINE ENGINES The usage of naval gas turbines requires a professional technical supervision. Such a requirement caimot be fulfilled by crews of small vessels which are mainly provided in the Polish Navy, Therefore, it was decided to support the crews of such vessels by the „Base Diagnostic System of Naval Gas
489
Turbine Engines" (BDS) [3,5]. The system is introduced for periodical inspections of engine condition and particularly in case of: • annual maintenance, • necessity of the prolongation of mean time between major repairs, • identification of an abnormal running found during routine maintenance. The Base Diagnostic System consists of a serious of diagnostic positions and provides the possibility of complex examination of engine conditions by EDP (Electronic Data Processing) application. The BDS (consequently) is capable of working out the prognosis for the engine future operation. An operating decision is worked out on the basis of appropriately prepared measurements of the engine parameters. They are subsequently converted into diagnostic parameters according to the elaborated flow diagrams for computer programs. It was established during the BDS creation that diagnostic information would be gathered: • systematically - from the operating documentation of the vessel (an engine log-book), • periodically - by an automatic measuring-registering device, • periodically - by examination of special parameters describing the conditions of an engine e.g. measurement of vibrations and impurity of oil, endoscopic examination, registration of start-up and lay-off processes and so on, • periodically - on the basis of interviews with experts and expert's opinion. The BDS enables accomplishing the following tasks: a) detection of the engine conditions that may lead to defects and even to the break-down, b) making a diagnosis of the ship's supervising-measuring system, c) carrying out a current evaluation of the engine characteristics - fouling intensity of engine passages and, if it is necessary - restoring to the engine original state by washing the passages, d) keeping data base of each engine and vessel and making a prognosis of changes of the engine operation condition. From the technical point of view the BDS is equipped with a special supervising-measuring device capable of carrying out numerous (foregoing) tasks on the grounds of measured values of various parameters of the engine. The system configuration is based on operating experiences from aviation but the computer measuring system, diagnostic software, research methods and data base are original. In order to work out all tasks securing the proper gas turbine operation the BDS is equipped with following special apparatus: 0 for measurements of vibration parameters and their analysis, 0 position for oil examinations on metal particle contents and other impurities, 0 computer measuring system of start-up and lay-off parameters, 0 computer measuring system of operational parameters, 0 programmable analyser of high-changeable signals, 0 endoscopes, 0 automatic test equipment for safety devices and supervising-measuring apparatus of the engines, 0 computer data base. The methods of gathering of diagnostic parameters for the condition evaluation of different engine's sub-assemblies are presented in Figure 1.
490
GAS PASSAGES TESTING OF SAFETY DEVICES AND SUPERVISING-MEASURING APPARATUS
ENDOSCOPIC EXAMINATIONS
AUTOMATIC CONTROl ' SYSTEM OF THE ENGINE
'-^''.c: - 4^F!55S^.^ MEASUREMENT OF THERMODYNAMIC PARAMETERS OF THE GAS COMBUSTOR WITH ELEMENTS OF THE ENGINE AND POWER PLANT FUEL SYSTEM
ACCElfiRATION ©ICELIRAHON CONTROL SHAFTING AUGMENT
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^T KINEMATIC SYSTEM WITH ELEMENTS OF THE ENGINE AND POWER PLANT OIL SYSTEM
T
MEASUREMENT OF TEMPERATURE INEQUALITY OF THE EXHAUST GAS
/cS'^Kf;^;i^,twl
MEASUREMENT OF THERMODYNAMIC p i PARAMETERS OF THE GAS
fAAAA EXHAUST GAS ANALYSIS MEASUREMENT OF PHYSICAL AND CHEMICAL LUBE OIL PARAMETERS , MEASUREMENT OF FUEL CONSUMPTION VIBRATION MEASUREMENT AND THEIR SPECTRAL-CORRELATION ANALYSIS VIBRATION MEASUREMENT OF THE FUEL SYSTEM ELEMENTS
..^^^^
^.^^iiw^^jr
ENDOSCOPIC EXAMINATION
Fig. 1. Methods and means of gathering diagnostic parameters.
COMPUTER MEASURING SYSTEM FOR EVALUATION OF PROPULSION CHARACTERISTICS AND OPERATING CONDITIONS OF A COMBINED PROPULSION SYSTEM The measuring system gathers diagnostic information about the elements of a propulsion system and allows an evaluation of propulsion characteristics in different floatation conditions. The system has been designed with perennial experiences of the naval gas turbines diagnosing [7]. The system is capable of simultaneous measurement of operating parameters (about 160 parameters) of four turbine engines operating within COGAG propulsion system and ship motion parameters. In comparison with the measuring set of the engine start-up it is extended with measurements of a ship velocity, fuel consumption, mass flow rate, exhaust analysis and thermal field. Figure 2 shows a diagram of the gathering of diagnostic parameters for marine power plants with gas turbines.
491
The magnitudes measured during experimental sea trials are the base for calculations of those diagnostic parameters which are able to estimate the condition of the engine passages and make it possible to elaborate an adequate operating decision concerning maintenance activities, connected with the restoration of the propulsion characteristics [1,9]. An example of the exhaust temperature distribution in gas passages behind the gas generator for different engine loads is presented in Figure 3. These distribution diagrams are used to make diagnosis of the engine fuel feed system.
EXHAUST
Fig. 2. Diagram of measurements of naval gas turbine.
-0.8N -1.0N -1.2N ~ 605 oC 695 oC 710 oC 0.4 N 0.6 N 555 oC 580 oC
Fig. 3. Exhaust temperature distribution in the gas generator outlet. The measurements enabled the calculation of changes of those parameters which describe the energy states along the passages. These changes were then used to estimate the quantitative and qualitative influence of engine condition on the compression and expansion processes. The examination of the conditions of engine passages showed that in order to make a diagnosis it is necessary to consider changes in slip (considered as a difference between HP and LP shaft speed) between measured and design values: 6(AnL.p) [3]. Since, at a given setting, the fuel control system ensures that nHp remains constant at its design value, this evaluates as: ^^U'i
where: Art/j, = n,P
{measured)
'lA\clesisn)
0 and 1 are the analysed states of the engine
492
COMPUTER DATA BASE The computer data base, built on the grounds of IBM-PC computer, consists of: • files, containing memorised information which is systematically and periodically led into the data base, • programmes which are able to memorise the information and transform it into parameters diagrams and decision tables. By this means the diagnosis of the actual operating state of the engines is made easier. The data base consists of five fundamental programmes for data leading from: • measurements with the computer measurement system - RAMB, • the ship's operating documentation - BDOST-U, • oil examinations - BAZ-DOL, • the vibration measuring system - VIBRATION, • thermoscopes - TEMPERATURE. All of these programmes allow the preliminary analysis of data, an elaboration of results tables and diagrams. DIAG-C programme provides the shattering of measuring data files, related to load: continuous running and start-up. BAZ-DM programme provides connection between data taken from measurements with the computer measuring system and the results led from the operating documentation. A comparison of various diagnostic parameters is carried out after that. ROZRUCH programme conducts the parameters computation of start-up and lay-off parameters in options as follow: „cold" and „hot" start-up and lay-off The programme evaluates a state trend for the engine subassemblies. The transformed information from the programmes: „ROZRUCH", BAZ-DM", „BAZDOL", „DRGANIA" and „TEMPERATURE" is transferred to EXPERTISE" programme which provides comparison to the model data and by means of that - takes an operating decision. The block diagram of the data base organisation is presented in Figure 4.
REFERENCES 1. Charchalis, A., Korczewski, Z., Diagnostics of gas turbine passages on the grounds of measured thermogasodynamical parameters, Modelling, Measurement and Control 1994, C, Vol. 40, Nr 2, France (in English). 2. Propulsion systems of fast vessels with turbine engines, Marine Technology Transactions Polish Academy of Sciences Gdansk vol. 4, 1993, pp.38-48 3. Charchalis, A., Diagnostic system of naval gas turbines, Przeglqd Mechaniczny, 1993, Nr 1, pp. 4-5, Poland, (in Polish). 4. Experimental diagnostics of naval gas turbines, ISROMAC, Honolulu 1998, s. 5. Charchalis, A., Measuring systems applied in marine gas turbine diagnostics, Internal publication of the Polish Naval Academy, 1993, Nr 903, pp. 75-84, (in Polish). 6. Charchalis, A., Mironiuk, W., Szubert J., On the basis of vibration measurements. Internal publication of the Polish Naval Academy, 1993, Nr 903, pp. 109-122, (in Polish). 7. Charchalis, A., Computer measuring-diagnosing system of naval gas turbines, WOS£ publication, 1993, Nr 1 /U, pp. 101 -106, (in PoUsh).
493
VIBRATION MEASUREMENT SYSTEM
OIL EXAMINATION ^^l^,*,?r^- r
BAZ-DOL
)
IMPURITY PARAMETERS
TEMPERATURE DIAGRAMS
MODEL DECISION TABLES
f VIBRATION 1
VIBRATION PARAMETERS AND ANALYSIS
{TEMPERATURE)
THERMOSCOPE READ-OUT
^DECISIONN^
Fig. 4. Block diagram of the data base organisation.
Charchalis, A., Korczewski, Z., Dyson, P., Evaluation of operating conditions of the passages of naval gas turbines by gas path analysis. The Institute of Marine Engineers London, England, 1996, (in English). Charchalis, A., Grz^dziela, A., Diagnosing the ship shafting alignment by means of vibration measurement IMAM2000 - Naples
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Condition Monitoring and Diagnostic Engineering Management Andrew G. Starr and Raj B.K.N. Rao (Editors) © 2001 Elsevier Science Ltd. Allrightsreserved.
DIAGNOSING OF NAVAL GAS TURBINE ROTORS WITH THE USE OF VIBROACOUSTIC PARAMETERS Adam Charchalis, Prof, and Andrzej Grz^dziela, Ph. D. Polish Naval Academy, Gdynia Institute of Construction and Propulsion of Vessels
ABSTRACT In the paper results are presented of vibroacoustic research on balance control of gas turbine rotors and assessment of their permissible operation times. In the paper results of vibroacoustic research on balance control of gas turbine rotors and assessment of their permissible operation times are presented. The work is a part of implementation of an the integrated diagnostic control system of gas turbines installed on Polish Navy ships fitted with COGAG propulsion systems.
KEYWORDS diagnostic, vibration, gas turbine, rotors, balancing
INTRODUCTION Exploitation of naval ship propulsion systems is a complex task due to specific features of marine environment as well as demand of maintaining high level of serviceability and reliability of the ships [1]. From the side of operators, doubts are often expressed concerning maintenance times or making decision on further exploitation of the object. The vibroacoustic monitoring systems of ship propulsion systems were first time applied in the middle of 1980s [5,7,8]. Application of periodical diagnostic procedures or on-line monitoring systems makes it possible to operate ship propulsion systems in accordance with their current technical state. In the case of ship gas turbines the hourly period of scheduled maintenance or repair surveys is presently the criteria for maintenance time determination. Though such exploitation strategy makes early scheduling of maintenance operations and their logistic assurance possible, but it simultaneously contributes to increase of costs because of its replacement system of elements (technically often still serviceable ones) as well as it makes impossible to early detect primary symptoms of failures occurring before the end of maintenance time [2,3,4]. Application of vibroacoustic techniques to the gas turbine propulsion systems, as an element of multisymptom diagnostics, is justified not only regarding the same turbines but also for investigations of
495
mutual geometrical position of elements of torque transmission system, as well as of control of electric energy generating sets.
OBJECT OF INVESTIGATIONS The 1241 RE missile corvettes, among other Polish Navy ships, are also subject to a permanent basic diagnostic system. They are fitted with COGAG gas turbine propulsion systems. Their configuration scheme is presented in Fig. 1.
Fig. 1. A scheme of the propulsion system of 1241 RE missile corvette 123456789-
Starboard service turbine unit ( TZSM PB) of abt. 3000 kW output Starboard service reduction gear ( PM PB) Starboard peak-power reduction gear (PMS PB) Starboard peak-power turbine unit (TZSS PB) of abt. 9000 kW output Port side peak-power turbine unit (TZSS LB) of abt. 9000 kW output Port side peak-power reduction gear ( PMS LB) Port side service reduction gear ( PM LB) Intermediate shaft Port side service turbine unit ( TZSM LB ) of abt. 3000 kW output
To obtain reliable data on diagnostic parameters, investigations of the gas turbines installed in the presented propulsion system were carried out by means of the multi-symptom diagnostic model whose one of the main features is recording and analysing vibroacoustic signals. The investigations were aimed at determination of permissible in-service unbalance and appropriate assemblage of turbine rotors on the basis of selected vibroacoustic parameters, and - finally - determination of their permissible operation time resources. The investigations were based on the following assumption: If technical state degradation of gas turbine rotor sets is a function of their operation time ( at a load spectrum assumed constant) then it is possible to select from the recorded vibration signal spectrum such parameters whose changes can be unambiguously assigned to the operation lime. If critical values of the vibroacoustic parameters are known then it is possible to estimate a permissible operation time period on the basis of changes with time of the investigated parameters. During realisation of the investigations in question the use was made of producer's guidelines for permissible values of vibration parameters, as a initial comparative and verifying element. Because DR 76, DM 76 496
and DR 77 turbines are similar to each other an uniform measurement method was applied to all the considered engines (at observing individual values of symptoms).
REALISATION OF THE INVESTIGATIONS For realisation of the investigations the measurement instruments: GC-89 analyser and FFT-2148 analyser, of Bruel & Kjaer, were used making it possible to collect and process measured data. Measuring transducers (accelerometers) were fixed to steel cantilevers located on the flange of the lowpressure (LP) compressor only. It was decided to carry out the investigations with the use of the transducer fixed to the LP compressor flange for lack of transducers and equipment suitable for measuring signals at the temperature as high as 200°-^ 300° C occurring on the HP compressor flange. The fixing cantilevers characterised of a vibration resonance frequency value different enough from harmonic frequencies due to rotation speed of the turbine rotors. The measurements were taken perpendicularly to the rotation axis of the rotors. Such choice was made on the basis of theoretical consideration of excitations due to unbalanced shaft rotation, and results of preliminary investigations ofthe object [6]. As signals usable for the „defect-symptom" relation the following magnitudes were selected by the turbines' producer: Ysnc - 1^^ harmonic value of vibration velocity amplitude connected with the LP compressor , - Yswc - the same but connected with HP compressor - Yrms - root-mean-square value of vibration velocity amplitude within the range of 35 Hz ^ 400 Hz. The changes ofthe vibroacoustic symptoms were analysed in function of service time within the ranges: for DR 76 and DM 76 engines : from 0 to 2000 hours for DR 77 engines : from 0 to 1000 hours. The choice was justified by the time-between-repair values scheduled by the turbines' procedure. For purpose of these investigations a simplification was made consisted in assuming values of the afterrepair turbine vibroacoustic symptoms as those ofthe new turbine. To make such assumption was necessary due to rather low number of the investigated objects (only eight turbines of each type). The following limit values of rms vibration velocity amplitude were specified by the turbines' producer: for DR 76 and DM 76 engines : permissible value of Yrms equal to 24 [mm/s] , - permissible value of harmonics Y equal to 17 [mm/s] for DR 77 engines : - permissible value of Yrms equal to 30 [mm/s], - permissible value of harmonics Y equal to 20 [mm/s] For further diagnostic inference the criterial 1^^ harmonic values of HP compressor was rejected for the reason of an important influence of damping decrement on recorded values of Yswc signals. Determination of the maintenance time on the basis of a Yswc signal value is possible only indirectly by analysing Yrms and Ysnc signals.
497
Results shown in Fig. during ship operation features, but also of expected maintenance turbines' procedure.
3 and 4 of the investigations of changes of the considered values of symptoms indicate that the maintenance time is a function of not only turbine design a selected exploitation policy. At the considered service load spectrum the time for both types of turbines was two times longer than that specified by the
As it was necessary to adjust operation procedures to warranty terms it was decided to establish twoway control of cleanness of the gas flow part of the turbines: 1^^ - by means of the endoscopic method and 2" - assessment of changes of the vibroacoustic parameters. The control is carried out at least two times a year for all gas turbine engines in service. Its scope also contains recording the values of the operational parameters whose changes could be an initial symptom of failures of the coupled devices as well as elements of the fuel supply system. All information is recorded and stored in the database of the system in operation. Results of the maintenance time assessment on the basis of Ysnc parameters for DR 77, DR 76 and DM 76 engines are presented in Fig. 2 and 3. Engine DR 77 N=1,2
signal Ysnc
E E
o oo oo oo o o o o oj i on o oO ' o « -o o T r r - -o o o co oo (o oD ao o) Co oN jo oi no oc oo oT -o o cocDcnr operation time
Fig. 2. Maintenance time assessed by means of Ysnc parameter Engines DR 76 & DM 76 N=1,0 signal Ysnc
operation time
Fig. 3. Maintenance time assessed by means of Ysnc parameter
498
In order to obtain uniform diagnostic procedures regarding unbalance assessment of the turbine rotors the dimensionless parameters characterising that states were applied. On the basis of theoretical considerations as well as results of other diagnostic investigations carried out for some years the following parameters were selected as those most sensitive: 51 - ratio of the mean vibration velocity amplitude of a given rotor ( 1^^ harmonic) and the velocity component relevant to 2"^^ harmonic excitation frequency of the rotor in question 52 - ratio of the mean vibration velocity amplitude of a given rotor ( \^^ harmonic) and the velocity component relevant to 3*^^ harmonic excitation frequency of the rotor in question. From an analysis of the results the following minimum values of SI and S2 parameters were determined: - for DR 76 and DM 76 engines : SlSNC = minl.5 S2SNC = min 2.5 SlSWC-minl.5 S2SWC = min 2.5 - for DR 77 engine : SlSNC = minl.5 S2SNC = minl.8 SlSWC = minl.7 SlSWC-min2.9 where : SNC stands for LP compressor, SWC - for HP compressor. By analysing the kinematics system, the front internal bearing of the HP compressor rotor was selected as the most dynamically and thermally loaded one. By means of harmonic analysis of the vibration excitations connected with the bearing's work regarding the internal shaft unbalance it was possible to determine permissible values of the velocity amplitude VR of the vibrations characteristic for frequency difference of the rotor velocities of HP and LP compressors. They are as follows: r^ harm VR = 8 mm/s 2"''harmVR=1.6mm/s The presented method was verified by investigating also other parameters characterising technical state of the engine in function of operation time, such as skid, endoscopic control, starting parameters, lubricating oil contamination etc. Moreover, the permissible diagnostic parameter values specified by the producer were taken as those verifying the assumed vibration symptoms. The accelerometers were fixed in the same way as that assumed in the turbine producer's model of vibration energy propagation. Changes of values of SI and S2 parameters are presented in Fig. 4 and 5. COMMENTS TO RESULTS OF THE INVESTIGATIONS Two-way realisation of the investigations made reliable verification of the investigation results possible. The following detail conclusions were drawn for further diagnostic inference: • For DR 76 engines: Ysnc vibroacoustic parameters are diagnostically susceptible at the engine load N = 1.0, and/or DR 77 engines : Yrms and Ysnc parameters at the engine load N = 1.2. • Changes of 1 ^^ harmonic values connected with HP compressor rotors (Yswc) and LP ones (Ysnc) at the work of DR 76 and DR 77 engines at BJ load are hardly noticeable in function of operation time therefore their operational susceptibility is too low.
499
Changes of values SI i S2 (SNC) DR 77 engines in functon of operation time
CM
6
C CO
. 4
•\
\~+~
I
E3
I
I
-S1 I
I
•S2
I 1 *0 operation time
Fig. 4. Changes of values of SI and S2 parameters in function of operation time for DR 77 engine
!
Changes of values S1 i S2 (SNC) DR 76engines in functon of operation time 98-
•
i
;: 6 ^ 5.
S2
I' S 3
• i"
^;
S 1
5 2 1n0
200 400 600 800 1000 1200 1400 1600 1800 2000 operation time
Fig. 5. Changes of values of SI and S2 parameters in function of operation time for DR 76 engine
•
Changes of Yrms parameter with operation time are not unambiguous hence it is of a low diagnostic merit. On this basis „symptom value - operation time" relationships were determined, and the time to next maintenance finally assessed. The engine load was assumed the criterion for estimation of the compulsory maintenance time for DR 76 and DM 76 engines, evolving from exceedance of permissible symptom values during normal operation of the engine. For the calculation of Y(t) values the factor k = 1.1 ( covering 10% measurement error) and the user confidence factor m = 1.05 was applied as follows : Y(t) = k • m • Yr(t) where : Yr(t) -vibration parameter function of operation time 500
For DR 77 engine Ysnc parameter is diagnosticaily susceptible because it leads to a shorter maintenance time at the considered maximum load. By taking into account the between-repair-time period for DR 77 engines amounting to 1000 hours the expected maintenance time of 2600 hours was assessed in accordance with its technical state on the basis of Ysnc parameter values (Fig. 2). DR 76 turbine engines are installed in the considered M - 15 E service propulsion system. The load N = 1.0 of them was assumed the criterion for determining their maintenance time basing on exceedance of permissible values of the considered symptoms at normal engine operation. For estimation of the engine's maintenance time according to its technical state such parameter was selected whose normal service changes determine the maintenance time shorter at a higher load. This was based on two following assumptions: • Forces connected with various unbalance forms, manifested in recorded vibration signal changes, increase along with rotational speed increase ( hence also with engine's load) • Resuhs of the investigations at the load N = 0,6 of DR 76 service engines and N = 0.8 of DR 77 peak-output engines have been rejected as the least credible ones. At these loads the engines operate within resonance speed ranges therefore the results could not be the basis for technical state assessment of the engines. Hence for estimation of their maintenance time, Ysnc parameter was selected and its value of 3150 hours was determined from exceedance of the permissible value (Fig. 3).
FINAL REMARKS AND CONCLUSIONS • • • • • •
Application of the proposed approach makes managing the engine's operation time much more rational, especially at its end. The proposed approach is non-invasive and does not require taking the ships out of service. Realisation of investigations of the kind makes it possible to collect data for a database of the future monitoring system of ships, expected to improve their operational features. Experience gained during the investigations would be utilised for other power plants equipped with gas turbines. The proposed diagnostic method is a coherent element of Basic Diagnostic System used by Polish Navy for many years. The proposed exploitation method leads to important economical profits and especially to reliability improvement, a first-rate problem.
From analysis of the presented results the following detail conclusions dealing with vibroacoustic investigations can be offered: - For further research the following target operation times of rotor systems (at the assumed load spectrum), required for their maintenance should be assumed : - 3150 hours - for DR 76 and DN 76 turbines - 2600 hours - for DR 77 turbines. - Assessment of the engine technical state by means of SI and S2 vibration parameters makes it possible to flexibly utilise engine operation time in the case of not performing repair operations. - Periodical control of Ysnc parameter trend development enables to credibly represent changes of a given parameter in function of operation time. - The Ysnc parameter was selected the same for both considered engines due to its unambiguous dependence on operation time and similar character of its changes. 501
Control of SI and S2 parameters during exploitation makes it possible to assess state of contamination of the gas flow part of the considered turbine engines, and exceedance of its permissible value could be taken as a signal for necessary washing of their compressor units. The proposed maintenance time resources concern only the rotor sets. Assessment (in the proposed time instant) of serviceability of the coupled devices, fuel supply and lubricating systems was not included into the scope of the present investigations.
REFERENCES 1. „Banek T., Batko W. : „Estymacja zaburzeh w systemach monitoruj^cych". Wyd. AGO, Krakow 1997 2. Charchalis A.: „System diagnozowania okr^towych uktadow nap?dowych z turbinowymi silnikami spalinowymi". Problemy eksploatacji, 27, 4'1997 3. Charchalis A.: „Diagnostyka okr^towych turbinowych silnikow spalinowych". Kongres diagnostyki technicznej '96, vol. I, 4. Charchalis A., Mironiuk W., Szubert J.: „Diagnozowanie okretowych turbinowych silnikow spalinowych na podstawie pomiaru drgah". Wyd. AMW, Gdynia, 1993 5. Downham E., Woods R.: „The rationale of monitoring vibration on rotating machinery" ASME Vibration Conference, Paper 71- Vib-96, September 1971 6. Grzadziela A.: „Ocena stanu technicznego ukiadu wirnikowego okretowych turbinowych silnikow spalinowych". XXVII Ogolnopolskie Sympozjum „Diagnostyka maszyn" ? 7. Lyon R., Deyong R.: „Design of a high-level diagnostic system". , vol. 106, January 1984 8. Meyer H.G.: „Reduction of shipboard vibrations". MER , November 1984.
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Condition Monitoring and Diagnostic Engineering Management Andrew G. Starr and Raj B.K.N. Rao (Editors) © 2001 Elsevier Science Ltd. Allrightsreserved.
COMPUTER IMAGE ANALYSIS OF DYNAMIC PROCESSES E. Chrpova, L. Pfevratil and V. Hotaf Technical University of Liberec, 461 17 Liberec, Czech Republic
ABSTRACT Our research work focuses on the development and application of the means for computer image analysis of dynamic processes. The goal is to affect the quality of the products. The work was intended to develop a suitable software toolbox, complete with hardware equipment, measurements of production processes, and analyses of the measurements from a carding process, a weaving process, a paper production process and others. For the monitoring and obtaining parameters of analysis, we use three software tools - fractals, CIE colour space, and statistic measures. There are based on two distinct requirements for the algorithms. Firstly, they must be flexible enough to enable the best parameters for analysis to be selected and compared with other methods. Secondly, they must be fast enough for on-line control. Our investigation confirmed that many production processes could use the same principles of analysis in its processes. Our research shows the possibilities of application of three software tools in various processes. Our activity in this problem was supported by the project EU Inco Copernicus - Noviscam, Erbicl5CT960700. In this paper we attempt to clarify how changes in the production processes cause corresponding changes in the end products. Selected measurements are provided as means of documentation.
KEYWORDS Monitoring, quality control, image processing, signal processing, random processes.
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INTRODUCTION The work was intended to develop monitoring system for random processes based on video image during the production phase. The system consists of hardware equipment, data evaluation implemented in software and determination of acceptable tolerances related to final product quality. The research team has investigated applications of the methods in a paper production process, a carding process and a weaving process. The example of application to the carding process is shown in fig. 1. The captured processes are fast, they have chaotic properties and they are difficult to control with standard tools of process control.
Fibre web of production
Flock-fibres
^^m^^ output product carding machine
thermobonding Figure 1: Monitoring system
MONITORING SYSTEM Monitoring system is created by hardware equipment and by developed software tools.
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Hardware equipment 1. 3'CCD colour video camera JVCKY-FSSBE with variable focal lens Pickup device: 1/3-inch interlines CCDx3 Effective number of pixels: 440 000 pixels Colour separation optical system: F 1,4, RGB 3-colour separation prism S/A ratio: 58 dB Horizontal resolution: 750 TV lines (Y signal) 580 TV lines (RGB signal) Variable focal lens HZ - G6350U. 2. Digital Video Cassette Recorder DSR-20P System: DV Track pitch: 15iim Time mode: Drop frame system This recorder was purchased for simulation of the real production processes. This recorder cooperates with the measuring system. 5. Lightening Lightening reconmiended for digital video COMLITE 55W/12-950, 3 000 lumen with illumination by OSRAM DULUX L 2G11. 4, Accessories 2 beams and 4 lighting stands. One is for the camera and one is for the lightening. 5. Video and graphic system - dps Reality Computer: Intel PIII/450, SDRAM 128MB, ASUS P3B, AGP16 MB, SCSI UW, CD ROM 36speed, HDD SCSI Cheetah 18,2 GB, FDD 3,5", Industrial case 19". Dps Reality card provides the means for capturing images in real time. Dps Reality will work with any application that can read or write to any of the file formats supported by Virtual Tape File System, including SGI, BMP, PIC, TIF, IFF, VPB, RAS and RLA. Dps Realiy is simple interface with a trim table, timeline and preview windows designed with the compositor in mind. The heart of the system is tiie Reality card. Its on-board SCSI controller, designed to control Ultra Wide SCSI drives means that does not rely on the computer's bus to transfer video to disk. The video data is captured and stored in a 32-bit 4:2:2:4 MJPEG or uncompressed YUV format. The Reality card has a single input/output cable, which carries auxiliary, balanced and unbalanced audio, and component, composite and S-Video signals in both inputs and outputs. It provides analogue signal processing and character generator support. The two screens shown on right side of the window are waveform monitors. These waveform monitors have a vertical scale calibrated from -30 to + 130 units. (In NTSC this scale is in IRE units). For both NTSC and PAL a level of 100 corresponds to white. This is the correct level for brightest portions of the video signal. For PAL signals a level of 0 corresponds to black.
Software tools For the monitoring and obtaining parameters of analysis, the team uses three software tools that were developed in house named NOVISCAM technique (NOVIS, Video colorimeter, Statistics), shown on fig. 2. There are two distinct requirements for all used algorithms. Firstly, they must be flexible enough
505
to enable the best parameters for analysis to be selected and compared with other methods. Secondly, they must be fast enough for on-line control. Data can be analysed using various methods. The investigation considered new approach using fractal analysis in comparison with conventional statistical method as shown in fig. 3. The fractal analysis is described elsewhere. This paper uses results obtained on statistical bases (average value - x, standard deviation - s, coefficient of variation - v = s/x).
Statistical analysis
Fractal analysis
Video colorimeter X, s, V, tolerances
Box procedure
Rescaled range
Agregated variance
Figure 2: Analysis of data
MONITORING AND DATA ANALYSIS For the analysis a data record from CCD camera was used. Shots are transmitted to a computer as a data record and analysed. Images in a digital form are represented as matrix with values of pixels, and on the images windows are designated. Average values of pixels in the windows (fig.3) are read from images. The values are saved, and create time series. These time series are analysed by fractal analysis and statistical analysis. Single images with windows ^.^^
^^'^^ ^^^"^ of window 1 with the threshold
! . !,
.lr''M
R/S Dimension
i,h.
fii^^im I 'i^^:^-rJ
image
r ^ set.. , ISO-grey
Figure 3: Monitored image by fractal analysis
506
Box Dimension
Fractal analysis The fractal analysis is based on evaluation of the time series, which were estimated by using "Iso-gray set", Rescaled Range Analysis (R/S) and "Box procedure" (fig.3). For this analysis was used software Matlab and the results are described elsewhere (1,2,3). Analysis by video colorinoieter Video colorimeter expresses the colour of measured flat object on-line (fig.4). This is the way, how to compare one colour to the next with accuracy. This analysis identifies a coloiu* expUcitly. That is, it differentiates a colour firom all others and assigns it a numeric value. This analysis enable to compare data obtainedfi-omthe most commonly used spectrophotometers. The image of an original picture is mostly projected through a set of appropriate optical filters and separated to three components of indirect trichromatic reproduction - red, green and blue (RGB). The optical signal generates electrical charge, large proportionally to the light flow, in 2D matrix of sensors of video camera, every element of CCD field behaving as a micro-photometer. The charge is shifted in rows and sequentially read out and matrixes from RGB representation to YUY-video devices native colour space. The video signal from camera is fed to an analogue-to-digital converter. The digitised representation, also known as Y,Cb,Cr according to ITU R.601, is stored in a frame buffer and analysed by software in real time. It is conveyed to the production process control system via TCP/IP network then. Brightness (Y)
Width of monitoredflat product
Measured data: CIEXYZ X Y Z CIExyY X Y Y CIELah L* a* b* Figure 4: Monitoring and data analysis by video colorimeter
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The averaged values of Y,Cb,Cr components are transformed to CIE 1931 XYZ, trichromatic components x,y,Y components, as well as to CIE 1976 Lab components. The lab colour model has been chosen as an optimum in the given application.
Statistical analysis This analysis is devoted for measurement of Non-uniformity of the flat fabrics. The program processes a video-signal, which was originally received by the dpsReality video-card and passed to the program in a suitable digital form. The processing uses statistical analysis (fig. 5) of the data to determine deviation from a uniform condition in running mode statistically. While the 'ruiming' mode employs real time processing and analyses continuous flow of frames, the static processing is applied to selected single frames and allows more detail analysis. Y,Cr,Cb i i upper X
1
/W\/\AA/\ff
I,^s;s„.„>
lower variation in width Figure 5: Principles of data analysis
*Running* mode In this mode the computer divides every frame into a selected number of vertical stripes (stripes in the direction of material moving under the camera). The mean value of brightness and that of two colour components are then calculated for each stripe. These values are then plotted in graphs so that all three-colour components can be monitored in time. Depending on the application of the system these components represent for example the density of raw material for carding, density of fabric etc. for the whole monitored width. The program also determines the value across the whole width. The values are plotted so that the operator can monitor the changes in signal intensity and colour in time. At the same time the program determines standard deviation, minimum and maximum value and deviation of the frame values from the mean value. The program also checks if the calculated values are within the specified tolerances (one for each frame component). Cases over the tolerance limits are recorded and reported. It is possible that the recorded parameters either gradually drift or suddenly change to different levels. In such cases a new central value of the tolerance field can be selected even during the run of the program using a specific key on the control panel. Static mode In this mode the program analyses individual frames selected by the 'collect frame' command. On this command the last frame is transferred from current memory and statistically processed. The frame is divided into a specified number of vertical and horizontal stripes. For each such generated oblong the program calculates the mean value of colour components, minimum and maximum values and
508
standard deviations across the wholeframe.CUck of a mouse on a specific part of theframeprovides values at this point. A 'survey' window is also a part of the program. This window shows the picture recorded by the video camera in real time and allows of the feed rate of the recorded material. The value of this feed rate is important for determination of the overlap of individualframes.This overlap must be eliminated from processing so that the processed sections form a continuous image corresponding to monitored material and no data are processed twice. TABLE 1 Datafromstatistical analysis in "running" mode Sample of flat products 1 - fibrous web - good quality 2 - fibrous web - worse quality 3 - fibrous web - bad quality 3a - bad quality of fibrous web 4 - paper (bad quality) 5 - paper (good quality) 6 - woven fabric on M 8300 (quaUty) 7 - woven fabric on inspection frame 8-fibrous web from 1 production line 9 - fibrous web from 1 production line
Record on Mean value Maximum 1 Maximum relative video tape of brightness relative increase reduction 7,4 % 4,7 % 28:51 to 123 29:31 6% 6,7 % 29:34 to 127 30:14 25,1 % 32,4 % 30:18 to 132 31:27 8,6 % 11,2% 30:18 to 135 30:35 0,8 % 1,7% 32:55 to 146 33:53 0,5 % 0,7 % 33:56 to 146 34:56 0,8 % 0,8 % 37:17 to 133 38:15 0,8 % 38:18 to 135 1,1 % 39:16 5,6 % 6,1 % 42:24 to 165 43:22 6,2% 7,2 % 143:24 to 167 44:24
Relative standard deviation 2,3 %
Time 1 frame high 1,5 s
2,4 %
1,5 s
8,9
% 1,5 s
3,2%
1,5 s
0,4 %
1,5 s
0,2 %
1,5 s
0,2 %
12 s
0,3 %
0,6 s
2,2 %
0,2 s
2,4%
0,7 s
Notes: real feed rate = height offield/timefor passing the height; brightness values from 15(black) to 255(white). Technical parameters The video-signal contains 25 frames per second. In order to process the signal in real time the time for processing single frame data cannot be longer than 40 ms. This is what is happening in the described case. The speed of recording also limits the feed rate of progress of the recorded material. Specifically the feed rate must be lower than the height of the recorded field multiplied by the recording frequency. Under normal conditions with the height of the recorded field 40 cm the feed rate of the recorded material cannot exceed 10 m/s. This is a very high value which is generally not achieved in production.
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The resolution of the recording is also important and can be a limited factor in some potential applications. What comes to mind is the analysis of the quality of woven fabric, where is a need to detect missing wefts and similar defects. The recorded field of a camera typically contains 720 * 576 pixels. For a required resolution of 2 pixels per mm the camera can record less than 40 cm of material width.
APPLICATIONS This project investigated three applications: the paper production process, carding process and weaving process. Paper production The basic procedure of the paper production process is as follows: the raw materials are slashed of with water to obtain a homogeneous suspension. The suspension is distributed on to a wire screen that is similar to a conveyor belt, and water is filtered off Vacuum pumps drain some of the remaining water. The paper web is then compacted and is pressed between rolls. The last part of the production is drying on hot cylinders. Carding process The carding process produces a fibrous web (flat layer of fibres) from flock of fibres and the web is input material for production of threads which are the basic material of flat fabrics as are for example weaving and knitting fabric. The fibrous web can be also used as basic flat material for non-woven textiles. The uniformity of the flat layer of fibres is main parameter important for textile process. Weaving process The weaving process uses 2 systems of threads (weft and warp). Woven fabrics are made on looms by weaving. The measurement was made on multiphase weaving machine M 8300. The measurements were conducted at an inspection frame and a weaving machine. The image record contains good and poor quality. The image data record is currently being analysed.
DISCUSSION Results from the carding process, the paper production process and weaving process show the possibility of using statistical method, appHed brightness and colour signals from video camera. It seams that the use of statistical methods provides an alternative to fractal analysis. Our investigation opens the way for process control, which will reduce the number of defects and will result in high quality of the product.
ACKNOWLEDGEMENTS The investigation has been carried out in the framework of EU Inco-Copernicus programme in collaboration with the University of Hertfordshire, England, Turboinstitut, Slovenia, the University of Bremen, Germany, BIRAL, England and FILPAP, Czech Republic.
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CONCLUSION 1. The system for monitoring random production processes has been developed. 2. The system is based on video image and suitable signal processing. 3. The system has been applied to paper and textile production processes.
REFERENCES Hotar,V., Chrpova,E-5 Lang,M, Philpott,D. (2000). Application of Fractal Dimension in Carding, Paper and Other production processes. Final Report of Sub Team - CeVis (University of Bremen), Technical University of Liberec. Chrpova,E., Hotaf,V., Lang,M. (2000). Application of "NOVISCAM TECHNIQUE" and Fractal, Dimension in Paper, Glass and Textile Production Processes.lSQW?VD'2000, Bled, Slovenia. ISBN 961-6238-38-8, 86-98. Chrpova,E., Hotaf,V. (2000). Application of Fractal Dimension in Textile Production Processes. The Textile Institute, 80th World Conference, Manchester, England. ISBN 187037245X.
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Condition Monitoring and Diagnostic Engineering Management Andrew G. Starr and Raj B.K.N. Rao (Editors) © 2001 Elsevier Science Ltd. All rights reserved.
INVERSE METHOD OF PROCESSING MOTION BLUR FOR VIBRATION MONITORING OF TURBINE BLADE
Tadao KAWAl', Masami ITO^ Yoko SAWA^ and Yasuhiro TAKANO^ ' Associate Professor, Department of Mechanical Engineering, Nagoya University Furo-cho, Chikusa-ku, Nagoya, 464-8603, Japan ^ Engineer, Production Engineering Laboratory, Matsushita Electric Co. Ltd. ^ Master course student, Department of Mechanical Engineering, Nagoya University ^ Engineer, Toyota Motor Co.
ABSTRACT Among vibrations to occur in a turbine, vibrations of a main shaft or a blade are very dangerous. A lot of systems monitoring vibrations to
occur to a main shaft were developed, and they have been
commercialized. On the other hand, it is very difficult to measure vibrations of a turbine blade during operation because blades are united with a main shaft and turn. Accordingly, the method to estimate vibration of the blade is limited to measure vibration of a blade at rest or to analyze it by the finite element method. If it become possible to measure vibration of a blade in an operation state, a designer can design the more reliable and reasonable turbine and an operator can maintain the turbine system more safely. In this study, we propose the technique to measure vibration of a turbine blade in a turn using an image processing. By using the CCD camera, we capture the end face of a blade in a turn and determine a position of a blade from an image data, and then estimate amplitude of vibration statistically. The motion of the blade at high speed makes a captured image blur and degrades the estimation of the position of the blade. By processing the blurred image as the inverse problem, we can estimate the position of a blade precisely. One piece of image data is provided by a turn of once. Because natural frequencies of a blade are higher than the rotational speed, we are requested to plot the probability density of the position of the blade for estimating amplitude of the vibration. Under a supposition that the blade vibrates sinusoidally, the probability density for the blade vibration can be calculated. By comparing the probability density for sine wave with the processed probability density
513
for the blade, the amplitude of blade vibration can be evaluated. The rotating speed of the blade and the shape of an end face of a blade affect the estimation of the position of the blade. We check these effects and verify the high accuracy of our technique experimentally.
KEYWORDS Turbine blade, Vibration, Motion Blur, Image Processing, Inverse Problem, Statistical Method 1 INTRODUCTION Recently, the techniques to reduce the total cost and to support the machine life become more and more important in the engineering. Because the production skills are well developed and the production costs are reduced extremely, many companies turn their attention to support their productions.
Instead of building a new plant, it is strongly required to let all kinds of system
including turbine systems work for a long term because of the severe economic situation. On the other hand, a use of a long term damages a machine and harms its reliability. This is why the reliable monitoring systems are required to check the conditions of the system and its damage. Many diagnosis systems to check the rotor vibrations of a turbine have been developed ^'^^-^-^^ and, actually, works at a lot of facility. On the other hand, there is not a useful means to measure the vibration of the turbine blade at a real operation state because it rotates around the main shaft of the turbine. On this account, the techniques to predict fatigue life of a joint of a blade are limited to a measurement of a vibration mode of a blade by a standstill state or the FEM analysis.
If the
technique to measure the vibration of the blade directly in a real operation state is developed, the more reliable design of the turbine and its work are guaranteed. Image processing has a number of attractive features; e.g., it is the non-contact measuring method and many size of the object is measurable by changing its lens system. In this study, we capture the end face of a turbine blade using the image measurement device installed in turbine case and estimate amplitude of the vibration of the blade in its operation state. Because the rotational speed of the turbine is extremely high, the captured image of the end of the blade is blurred.
In addition, the round end face of the blade and the non-uniform light intensity
make the estimation of the blade vibration difficult. We overcome problems mentioned above using approach of inverse problems and developed the system for measuring the vibration of turbine blade with high accuracy. 2 ANALYTICAL MODEL In this study, we develop the technique to measure amplitude of the blade vibration by processing the captured image of the end face of the blade using a CCD camera. Under the supposition of the turbine blade to vibrate with the first mode only in rotary direction by analysis, we treat the image data as one dimension about rotational direction.
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1 pixel of object ^ 1 2
i
3
OriginaMmage: x 4
i;niP__l—^w^
2mm
Fig. 1 Measuring System Blurred image: b
Fig. 2 Process of making blur image Figure 1 shows a conception diagram of a measurement system. In the case of capturing an image of the moving object with finite time, the image blurs ^"^^^^l In addition, an image is digitized and quantized into pixels by CCD. In the following analysis, the matrix having the rotational speed of the blade v and the opening period of the camera At expresses the motion blur. At first, imagine that the end face of the blade is located in one flame of an image data. The frame is expressed by the one-dimensional vector of which size is M. Also, the gray level of the background is set to be zero. Equation 1 relates the exact image data x to the blurred image data b. Ax = b
(1)
where A is a M by M matrix, x and b being M vectors. The matrix A is a matrix to blur an original image x (in the following secfion, we named A as the blur matrix). Figure 2 illustrates the process of making a blur matrix. At first, divide the duration of a photographic exposure At into A^ sessions imaginary. One pixel of object image moves v At /A/^ during the session of At IN (see white block in Fig.2). Also during the session, each photoelectric conversion element of a CCD device gets the light energy (see hatched block in Fig.2). After the light energy is accumulated during the whole duration of a photographic exposure of At, each element of a CCD gives output voltage. By applying above idea to the whole object image, the blur matrix is constructed. 3 ANALYSIS METHOD 3.1 Reconstruction of image Because it is very difficult to check the vibration of all blades, we develop the basic method to
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measure anplitude of only one blade as the first step in this study. We capture an image of end face of the blade once at a rotation. If a blade does not vibrate, a position of a blade is always the same. However, a position of a blade changes according to the vibration amplitude of the blade at the moment of capturing image. Usually, the natural frequency of the blade is extremely higher than the rotational speed of the shaft. So, amplitude of blade vibration cannot be determined directly according to position data given once at a rotation. In this study, we gather a lot of position data of blade and process statically to get amplitude of the blade vibration. At first, the method of measuring the precise location of blade by processing the blur image of the end face of blade is explained. Theoretically, a location of the blade is estimated by restoring a blurred image data. Here, the position of the blade was defined as the position of the left end of the blade. x = A-'b
(2)
On the contrary, various noises in a captured image data make the equation 2 ill posed, and give bad reconstructed image data. In this report, we use an inverse matrix depending on the singular value decomposition to regulate the inverse problem as described in section 4.3. 3.2 Estimation of amplitude by statistical technique Generally speaking, first natural vibration is easily induced and gives important effects to the system. In this report, we assume the case that the blade vibrates with its first mode in the rotary direction and develop the method to measure its amplitude. In the assumption of the blade vibrating sinusoidally with an constant amplitude, we can estimate its probability distribution by equation 3^^^ [(x^X'-x'V p{x)^\\ J [O
\x\<X >I \x\>X
(3)
where X is the amplitude of vibration.
•
-3
-2
-1
Mill
0 0 Pe«Won[piMO
1 1
2
3
Fig.3 Histogram of estimated positions
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4
As mentioned above, one position data is given by processing one image data. By repeating this process many times, the histogram of blade position is illustrated. According to the assumption of sinusoidal vibration, we can estimate amplitude of vibration by matching the theoretical distribution function to the experimental histogram in Equation 3. Here, the amplitude is determined at the condition that the correlation between both data has the maximum value. Figure 3 shows the experimentally measured histogram with probability distribution curve determined by the above criteria. 4 THE PROBLEMS IN POSITION ESTIMATION AND ITS IMPROVEMENT 4.1 Background data In Chapter 3, the background data is assumed to be zero. However, in the experiment, a background in the captured image data gives important effects on estimating the location of the blade. These are a degradation of gray level and the border distortion. Lightning at the experiment and capturing an image at high degree of gray level improve the effect of a degradation of gray level. rAw^-Av/v^^'^-'-vV
1T^
0-
—1
VMA AAA/VVWM/
< -11-•« 0- — I
(a)
ft
^^AM/yyi
r"*
r^\ i
VVA-V^ KyVvi.'^/vv'A, NhrM^ .
'<
-1-
0
^\P^\AJ\
1
100
200
300
Position [pixel]
Fig.5 Eflfect of rotational speed error on reconstmction (a) Reconstruction with +3% error in rotational speed (b) Reconstruction with -3% error in rotational speed (a)
^ 3D
to
13) 2D Position[pixel]
29D
^^
3D
H
(b)
Fig.4 Image data and reconstruction data (a)Blade image with background (b)Blurreddataof(a) 0
(c)Blurred data with background
100
200
Position [pixel]
(d)Reconstruction from (c)
Fig.6 Blurred data ant its reconstruction
(e)Blurred data without background
(a) Blurred data without background
(f)Reconstruction from (e)
(b) Reconstmctionfrom(a)
517
300
To estimate the effect of the border distortion, we transform an image Xb with background illustrated by Fig.4(a) to blurred image by Equation 3 as shown by Fig.4(b). Because of the finite length of original image data Xb, Fig.4(b) shows a border distortion induced by the blur matrix described in Chapter 3. As the same manner, because a captured image data bb is constructed with infinite data, the reconstruction of bb by Equation 2 fails as shown by Fig.4(d). To avoid this distortion, we subtract a background data bo from a captured data bb and reconstruct the image by Equation 2 (Fig.4(e)). Here, the original captured data is simply subtracted by the left side value. The improved reconstructed data is shown in Fig.4(f). 4.2 Rotational speed irregularity The instant rotational speed of the blade is obtained as the sum of the steady rotational speed of the shaft and the instant speed by the vibration of itself. A precise rotational speed of the shaft is obtained by a rotary encoder, however, the speed of vibration of the blade could not be estimated before the measurement of an amplitude of the blade vibration. Therefore, a vibration speed may degrade the reconstructed image data. Fig.5(a) and (b) show the effect of the vibration speed on the reconstruction. The vibration speed is changed from -3% to + 3% of the rotational speed of the shaft. This result shows a small effect of the vibration speed of the blade on the reconstruction quality. Therefore, the reconstruction is not so sensitive to a rotational speed irregularity. 4.3 Influence of noise and improvement of precision An inverse problem is easily affected by noise in data and becomes unstable in solving it. In this section, we intend to evaluate the effect of noise on the reconstruction and regulate its unstableness. As explained in Chapter 2, the light from a moving object is accumulated at each photoelectric conversion element of a CCD device during a photographic exposure and makes a captures image blur. In the above accumulation process, lights from various sources and a sensitivity irregularity of each element of a CCD device may be noises. These noises are formulated as w in Equation 4. (A+w)x=b
(4)
where the term w is assumed to be the random noise with a Gaussian distribution. In this report, following two techniques are used in order to reduce influence of noise. 1. A inverse matrix depending on the singular value decomposition is used. 2. A template matching technique is applied to improve accuracy in determining the position of the blade. 4.3.1 Singular value decomposition It is very effective in regulating an inverse matrix to remove small singular values and their vectors from the transform matrix A. In the case of removing many singular values, an inverse matrix
518
becomes stable, but detailed information is lost. In this report, removed singular values are decided depending on the following criteria. Threshold value=MAX(SIZE(A))*NORM(A)*EPS*MAX(SIZE(A)) MAX(SIZE(A)): Maximum size of matrix A NORM(A): Norm of matrix A EPS: Floating decimal point relativity precision A 3.2 Application of template matching Above mentioned methods improve the estimation of the position of the blade, however, its accuracy is not enough (see Fig.6(a)and (b)). To achieve more improvement in accuracy, a template matching technique is applied. A captured image of rotating blade is checked its similarity with a template image of the stopping blade. The template matching is carried out according to the following procedures. (1) A template image is put at left edge position of a frame of image data. (2) Calculate a coefficient of correlation between a template and re-constructed image data. (3) Move a template rightward one pixel and calculate a coefficient of correlation again. (4) Continue above (3) until the right edge of a frame and find the position where a coefficient of correlation has the maximum value. This position is set as the position of the re-constructed blade position. Fig. 7 shows the estimated position of a blade. Under the condition listed in Table 1, we estimate amplitude of the blade vibration and get a value of 28.6 pixels, which is very close to the given value of 30 pixels. 5 CONCLUSIONS In this study, we proposed the technique to estimate the vibration amplitude of the turbine blade by an image processing, and found the following.
Table 1 Simulation conditions Rotational speed of Turbine
1200(rpm)
Radius of Turbine
2500(mm)
Speed of Shutter
O.Ol(ms)
Size of image
300(pixel) 0.028(mm)
Size of pixel Number of Image
500
Thickness of blade
2(mm)
Frequency of Blade
147(Hz)
Amplitude of Blade
30(pixel)
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1
Original image Estimated image
100
150
200
250
300
Position [pixel] Fig.7 Original and estimated blade image
(1) It is not necessary to remove the background image data from the captured image data in order to reduce influence of the border distortion. (2) A precise instant position of the moving blade is estimated by the template matching between the stopping blade image and the re-constructed image. (3) The amplitude of the blade vibration of 28.6 pixels, being very close to the given value of 30 pixels, is statistically calculated. REFERENCES [1] Rao, J. S. and Shingote, G A. ,1994, "On-line Expert System for Rotor Fault Diagnosis", Proc. Turbo Machinery Asia'94, Singapore, p.94. [2] M. Tanaka, M. Sakawa and K. Kato,, 1997, "Time-Frequency Analysis for Mechanical Vibration Data using Wavelet Transform", V^ Int. Conf on Engineering Design and Automation (EDA'97), pp.374-378. [3] T. Toyota, T. Niho, P. Chen, 1999, "Failure Detection and Diagnosis of Rotating Machinery by Orthogonal Expansion of Density Function of Vibration Signal", Proc. EcoDesign'99: 1^* Int. Symposium on Environmentally Conscious Design and Manufacturing, pp.886-891. [4] Gonzalez, R. C. and R. E. Woods, 1992 "Digital Image Processing", Addison-Wesley. [5] J. R. Parker, 1997, "Algorithms for Image Processing and Computer Vision", John Wiley &Sons, Inc. [6] J. S. Bendat, A. G Piersol, 1971, "Random Data: Analysis and Measurement Procedures", John Wiley &Sons, Inc.
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Condition Monitoring and Diagnostic Engineering Management Andrew G. Starr and Raj B.K.N. Rao (Editors) © 2001 Elsevier Science Ltd. All rights reserved.
ARTIFICIAL NEURAL NETWORK PERFORMANCE BASED ON DIFFERENT PRE-PROCESSING TECHNIQUES F. A. Andrade, I. I. Esat Department of Mechanical Engineering, Brunei University, Uxbridge, UBS 3PH. UK.
ABSTRACT Condition monitoring systems aim to detect machinery faults, preferably in their early stages. This provides important information to the maintenance team and reduces the plant operational and maintenance costs. A popular tool for automated condition monitoring systems uses artificial neural networks, whose performance is heavily dependent on the quality of the input training data, which must accurately contain the main features of the signal to be classified. This work compares the usage of two feature extraction techniques (cepstrum and cepstrum reconstruction), as pre-processing tools for automated monitoring systems. Here, a neural network is used to diagnose the condition of a model drive-line, consisting of many rotating parts. Including a pair of spur gears, bearings, and an electric motor. Firstly, the model drive-line was ran in its normal condition, and later it was run with different gear faults (simulated cracks of different sizes) introduced intentionally. The real time domain vibration signatures from the drive-line under different conditions were pre-processed using the different pre-processing techniques. The pre-processed signals are used as input to neural networks that perform fault detection. It is shown here, that the cepstrum reconstruction technique does in fact outperform the basic cepstrum technique as a feature extraction tool for automated monitoring systems. Hence although more computationally expensive, cepstral reconstruction is a better choice of preprocessing technique for automated condition monitoring systems.
KEYWORDS Condition monitoring, Predictive Maintenance, Neural Networks, Gears, Fault diagnosis, Cepstrum, digital signal processing. INTRODUCTION Condition monitoring systems aim to detect machinery faults in their early stages. This provides important information to the maintenance team. Today, most monitoring systems rely heavily on the expert judgement of the maintenance engineer, who is able to identify faults, diagnosing the condition of internal components, which are inaccessible without machine disassembly. The 'birth' of the fault 521
is indicated by changes in the working conditions of the machine. For rotating devices such as bearing and gearboxes vibration signal is very commonly used for fault diagnosis. In the quest for automated condition monitoring systems, not requiring the human/expert judgement from maintenance engineers, artificial neural networks (ANN) became a popular pattern recognition tool. Also, it is known that the performance of ANN is heavily dependent on the quality of the input training data, which must accurately contain the main features of a vibration signature. In this study the feature extraction effectiveness of two signal pre-processing techniques, namely cepstrum and cepstrum reconstruction, is compared. These techniques are used to pre-process vibration signatures, generating smaller input vectors to feed artificial neural networks. Researchers have already shown the feature extraction capability of cepstrum in a wide range of fields (Randall, 1982; Gao, 1996a, 1996b; Braccialli, 1997;), suggesting the use of cepstrum as an efficient pre-processing technique for ANN (Wu, 1997). In this study, it is shown that cepstral reconstruction does, in fact, outperform the basic cepstrum as a pre-processing feature extraction technique to be used in conjunction with ANNs. For this comparison, the problem of identifying fatigue cracks from its early stages is tackled. This is a real problem, which has already been studied by a number of researchers using a wide range of approaches (Staszewski, 1992, 1996). Particular attention must be paid to the work of McFadden (1986), which uses phase modulations of the gear meshing frequency for crack identification; the work of Boulahbal (1997), which uses the wavelet transform and the work of Lin (1997), which uses a nonlinear dynamical systems approach. All these approaches are effective, and have their own advantages, but are also more computationally expensive than cepstral analysis. The comparison between the pre-processing techniques is based on the performance of two similar multi-layer back-propagation ANNs (equal topology and learning parameters). The first network was trained with input vectors from the cepstrum technique, and the second with vectors from the cepstrum reconstruction technique. Finally, the specific problem under investigation is the identification of early tooth fatigue cracks on spur gears. Gears in good condition, and with simulated fatigue cracks (faulty condition) were used to generate the experimental data.
THEORETICAL BACKGROUND This section includes a short description of the theoretical background of the techniques under analysis. Cepstrum and Cepstrum Reconstruction Cepstrum (C) and Cepstrum Reconstruction (CR) aim to express the vibration signatures in terms of its spectral and harmonic contents. A formal definition of these methods is given by (Oppenheim, 1975): c{n) = IDFT{\og\DFT{x{n)}),
and; (1)
x{n) = IDFT{cxp\DFT{cin)}),
(2)
where: t(n) is the real cepstrum of the input signal x(n)\ and x(n) is the cepstral reconstruction (or inverse cepstrum) of c(n). DFT is the discrete Fourier transform and BDFT is the inverse discrete Fourier transform. The output of the cepstrum and CR analysis is a vector in the cepstral domain. This is shown as a plot, similar to a spectral plot, but with quefrency [Hz'^] along the x-axis. Figure 1 show the CR of
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vibration signatures from gears in normal (NO) and faulty (F3) conditions. As it can be seen, both plots are very similar, and present the same basic traits. This is shown by the discernible peaks at x=0. 2 Hz"\ which is the angular frequency of the output shaft. Also, on a smaller scale along the quefrency axis, both plots shows peaks as x=0. 0062 Hz"^ which relates to the gear meshing frequency at 160Hz. Cepstrum Reconstruction - NO
Cepstrum Reconstruction - F3 2 ir
0 -ll|
0.15 0.2 0.25 Quefrency (Hz-1)
0
0.05
0.1
0.15 0.2 0.25 Quefrency (Hz-1)
0.3
0.35
0.4
Figure 1: Cepstrum reconstruction plots Finally it must be noted that simple visual inspection of these plots does not lead to a reliable identification of the gear conditions, as both the plots present the same basic traits. Therefore, ANNs are used as a pattern identification tool to assist in signal classification. Neural Networks Background information on back-propagation ANNs is available in a number of publications (Rumelhart, 1986; Lippmann, 1987), only a brief summary is given here. In short, ANNs can be seen as complex transfer functions which are built by algorithms that aim to mimic the learning behaviour of the human brain. Neural networks consist of many interconnected artificial processing neurons/nodes. These nodes are collected in layers forming the complete network. Each node on the network provides a threshold of a single value by summing up the product of each input value with its respective weight. The node will process this summed input value with a non-linear activation function, giving an output value. The activation function can take many shapes, the most common being the sigmoid function, f(x)=l/[l+Exp(-x)], which output a value between 0 and 1. This output value is either fed to another node on a subsequent level, or it will be the actual output of the network (if no subsequent levels exist). This process is illustrated in Figure 3. where: mathematically, the node output is defined as: Output
Output =
f\Y,^.j^+e,
(3)
Figure 2: Schematic operation of a neuron In this study the ANN are trained with the back-propagation algorithm (Rumelhart, 1986). This is a supervised learning technique, where sets of input vectors are fed along with the desired network output. During training, the actual network output is then compared with the desired output. From the discrepancy between these values a mean square error (MSE) is calculated and back propagated along the network, allowing the learning algorithm to adjust the network weights and bias values. This minimises the MSE between actual and desired output ANN output. This back-propagation process is repeated until an acceptable low value MSE is obtained. Indicating that the network has been successfully trained. At this stage the network is then able to process and classify new data sets (unseen previously). 523
EXPERIMENTAL SETUP/DATA COLLECTION The experiments of the present work were carried out in the Condition Monitoring Centre at the University of Hertfordshire. Figure 4 shows a schematic diagram of this rig, full details can be found in (Engin 1998).
1. brake calliper 2. brake shaft 3. brake disc universal joint
5. 6. 7. 8.
base plate output shaft driven gear spacer block
9. driving gear 10. input shaft 11. Kopp® Variator 12. AC motor
Figure 3: Layout of experimental rig
The gears are driven by a 1.2 kW electric motor^^. The motor speed is controlled by a variable speed drive^^ and was set to 8Hz (giving 5Hz on the output shaft). On the other end, the output shaft is connected to a brake disk^ by a universal joint"^. A load torque of 20 Nm is applied to the gear system by the brake calliper^ This prevents backlash between the driving^ and the driven^ gear.
Gears In this experiment a gear train with a tooth ratio of 1:1. 6 was used. Table 1 shows important gear details. All gears were manufactured to the standards DIN3965 (material specification), DIN3962 (tooth geometry specification). TABLE 1 CHARACTERISTICS OF TEST GEARS.
Parameter Type Number of teeth Module Face width Pressure angle Helix angle Pitch diameter Material (mild steel)
[mm] ["] [°] [mm]
Driving gear MA25-20S 20 2.5 25 20° 0° 50 ENS
Driven gear MA25-32S 32 2.5 25 20° 0° 80 EN8
A numerical analysis of this gear system shows that the gear meshing frequency is 160Hz (for the output shaft rotating at 5 Hz). In all, 5 gear conditions were studied. These are: • normal operating condition (NO); • worn-out gear (WO) showing pitting and scoring; • gear with small, medium and large fatigue crack (Fl, F2 and F3 respectively) A schematic diagram describing the implemented fatigue cracks is included in Figure 4, and a full description of the implemented fatigue cracks is included in Table 2.
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XT \
LFigure 4: Diagram of fatigue cut on spur gears TABLE 2 CUT GEOMETRY AND ILLUSTRATION OF CRACK ANGLE
Gears
depth [mm]
Fl F2 F3
0.8 1.6 2.4
Cut Geometry thiclcness width [mm] [mm] 8 0.3 0.3 16 0.3 25
Angle ["" ] 40 40 40
Experimental Setup and Instrumentation An accelerometer mounted vertically (Badi, 1995) on the output shaft bearing housing, was used to acquire the vibration signatures. The accelerometer is connected to an A/D card on a personal computer via a signal amplifier. A sampling frequency of 5.12 kHz was used to convert the analogue signal from the accelerometer into a digital time series, and the gear system was run with standard automotive lubricant (15W). In all 5 test cases were studied (NO, WO, Fl, F2 and F3). For every test case, 16 sets of real time domain vibration signatures were recorded. Each set contains 2048 points (i. e. two revolutions of the driven gear). Implementation From the recorded vibration signals the cepstrum and cepstrum reconstruction vectors for the signatures of all test sets were calculated. As already shown in Figure 1, pure visual inspections of the time domain, cepstrum or cepstrum reconstruction plots do not lead to an accurate diagnosis of the gears under analysis. From the calculated vectors, the ten most dominant peaks were selected from all the test sets. This served as the input to a back-propagation ANN. It must be noted that the 10 chosen components in fact consist of 10 magnitudes and their 10 corresponding quefrencies. Hence, each input vector to the neural network consists of 20 samples. The dominant features for all the test sets were grouped in two text files. In each file 8 patterns of data represented each gear condition (i. e. each file consisted of 40 patterns of data for the 5 cases). The first file was used to train the back-propagation neural network, and the second file was used to test the trained network. It must be emphasised that the ANN did not see the test patterns during the training stages. The topology of the hidden layer of the ANNs was found heuristically (usual trial and error method). The network topology was set constant for comparing the different pre-processing techniques. The input layer contains 20 nodes, as determined by the input vector, and the output layer contains5 nodes, as determined by the number of gear conditions (i. e. BN, NO, Fl, F2, F3).
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Under this scenario the networks were trained to distinguish between the 5 different gear conditions by giving a non-zero response on a specific node, according to the condition of the gear under analysis (Fl, F2, F3, NO and WO). The topology of the network for this test was 20:8:5. It must be noted that this test poses a demanding task for the networks, as the trends introduced by a small and/or medium fatigue crack (F2) are similar to those introduced by a large fatigue crack (F3). Therefore, in this test the performance of the ANN is directly dependent on the quality of the training data, and hence on the feature extraction capabilities of the selected pre-processing technique.
RESULTS In this section the mean square error (MSE) of the networks during its training stage and the network output on the training and test sets are shown. The MSE charts (figures 5 and 6) show the evolution of the network performance during the training stage. This consists of 10000 iterations of the backpropagation (Rumelhart, 1986) algorithm. The network output charts (figures 7 to 11) show how the ANN classified the different test conditions. On these plots the dashed line shows the correct classification. As a reminder there are 5 test conditions, and for each condition there are 16 test sets (8 used for training and 8 used for testing the trained network). Hence in all there are 40 sets for training the network and 40 to test the network. Figures 5 and 6, below, show the normalised mean square error (MSE) during the training stages of the artificial neural networks. As it can be seen the training convergence/speed for the network using the different pre-processing techniques are very similar, with both networks showing good training convergence at the completion of training (10000 iterations). This similarity is also reflected on the performance of the network on the training data.
i
Figure 5: Cepstrum (C) ANN
Figure 6: Cepstrum Reconstrucion (CR) ANN
Now, figures 7-11 show the performance of the cepstrum and the cepstrum reconstruction ANN. The performance of the network on the training data is very similar, correlating with the charts for the mean square error for the two networks. However it must be noted that the performance of the networks on the test data is quite different. As it can be seen the classification accuracy of the network fed with cepstrum reconstruction input vectors is much higher than for the accuracy of the network fed with the cepstrum vectors.
526
Cepstrum ANN
Cepstrum Recontruction ANN test data
training
test data
training
t o gO.5
^v-v^M.
k-A-_ Fl i F21 F3 JNOJWOl Fl I F2! F31NdWQ pattern sequence
Fl I F2 i F3 iNOiWOi Fl I F2 I F31 NOlwO
40
pattern sequence
40
Figure 7: Output for ANNs, node 1
pattern sequence
pattern sequence
40
40
Figure 8: Output for ANNs, node 2
pattern sequence
40
pattern sequence
Figure 9: Output for ANN, node 3
1 f
iiii'1L
n
o
I 0.6
'
^
L \A.vW ^ y ^ \| ' pattern sequence
40
Figure 10: Output for ANN, node 4
527
40
Cepstrum ANN
Cepstrum Recontruction ANN test data
training
t o
i 0.5
I. Fl I F21 F3 iNOlwoi Fl I F2 | F3 INdwO pattern sequence
40
pattern sequence
40
Figure 11: Output for ANN, node 5 DISCUSSION As it can be seen from figures 5-6 and 7-11, both networks performed very well on the training data,, giving an output of 1 at the appropriate output node assigned for each specific gear condition. However, for the test data the performance was not so impressive. This was expected as the classification task at hand is not so simple. Minor variations of the crack size leads to very little changes in the vibration signature of the gears under analysis. This test was chosen specifically for this reason, so it requires greater efficiency in the feature extraction capability of the pre-processing techniques used in conjunction with the artificial neural network. Finally, on the test sets the performance of networks trained with the cepstrum reconstruction input vectors were much better than the performance of the networks trained with the basic cepstrum input vectors. This can be observed visually on figures 7-11.
CONCLUSIONS The results presented and discussed in this study show that both cepstrum and cepstrum reconstruction were successfully used to pre-process vibration signatures from gears in good and faulty conditions. Also, artificial neural networks were successfully trained to classify these pre-processed signals. The pre-processing by cepstrum and cepstrum reconstruction was performed on blocks of 2 gear revolutions, increasing the quefrency resolution. From the resulting vectors, certain features of the preprocessed signal were selected and used as the input to a back-propagation neural network. The neural network was trained to distinguish in detail the different gear conditions investigated. In all 5 cases were studied, 3 cases for gears with fatigue cracks (of different sizes) one case for a normal operating gear, and one case for a gear presenting signs of wear (pitting and scoring). In this scenario, the neural network trained with the cepstrum reconstruction data showed a higher performance than the network trained with the cepstrum data. In summary, from the investigations carried out the following points can be concluded: • The visual inspection of the cepstrum and cepstrum reconstruction plots alone, requires a high degree of experience for an accurate description of the gear condition. • The selection of the dominant features of the cepstrum and cepstrum reconstruction plots allow for an automated diagnosis of the system. • The combination of cepstrum reconstruction and neural networks lead to a better diagnostic system, than that obtained by combining cepstrum and neural networks. 528
•
Artificial neural networks were effectively used to reliably classify the gear condition, requiring no judgement from human operators.
ACKNOWLEDGEMENTS This research is partially supported by the Brazilian Government (CAPES), and Brunei University. REFERENCES Badi, M. N. M. & Dodd, L. J. (1995). Single and multiple fault detection along a model drive-line. COMADEM 95, Canada. Bogert, B. P. et al, (1963). The quefrency analysis of time series. Proc. Symp. Time Series Analysis, M. Rosenblatt, Ed., New York, John Wiley & Sons. 209-243. Boulahbal, D; Golnaraghi, M. F. & Ismail, F. (1997). Gear crack detection with the wavelet transform. Proceedings ofDETC'97, ASME Design Engineering Technical Conference. Bracciali, A; Cascini, G (1997). Detection of corrugation and wheelflats of railway wheels using energy and cepstrum analysis of rail acceleration. Proc. ImechE J. of Rapid Transit, pt. F. 211(2), 109116. Engin, S. (1998). Condition monitoring of rotating machinery using wavelets as a pre-processor to artificial neural networks. PhD Thesis. University of Hertfordshire. England. Gao, Y. and Randall, R. B. (1996a). Determination of frequency response functions from response measurements: 1. Extraction of poles and zeros from response cepstra". Mechanical Systems and Signal Processing, 10(3), 293-317. Gao, Y. and Randall, R. B. (1996b). Determination of frequency response functions from response measurements: 2. Regeneration of frequency response functions from poles and zeros. Mechanical Systems and Signal Processing, 10(3), 319-340. Lippmann, R. P. (1987). Introduction to computing with neural nets. lEEEASSP Magazine. Lin, D. C ; Golnataghi, M. F. and Ismail, F. (1997). The dimension of the gearbox signal. Journal of Sound and Vibration„20S(4), 664-670. McFadden, P. D. (1986). Detecting fatigue cracks in gears by amplitude and phase modulations of the meshing vibration. Transactions of the American Society of Mechanical Engineers, Journal of Vibration, Stress and reliability in Design 180, 165-170. Oppenheim, A. V. and Shafer, R. W. (1975). Digital signal processing. Prentice-Hall Inc., New Jersey. Paya, B. (1998). Vibration condition monitoring and fault diagnostics of rotating machinery using artificial neural networks. PhD Thesis, Brunei University. England. Randall, R. (1982). Cepstrum analysis and gearbox fault diagnosis. Bruel & KjaerApp. Note, 233-280. Rumelhart, D. E. ; McClelland, J. L. and Williams, R. J. (1986). Learning internal representations by error propagation. Parallel distributed Processing. 7, Cambridge, MA. MIT Press. Staszewski, W. J. and Tomlinson, G R. (1992). Report on the application of signal demodulation procedure to the detection of broken and cracked teeth utilising the Pyestock FZG spur gear test rig. Technical Report, Dynamics and Control Research Group, Department of Engineering, University of Manchester. Staszewski, W. J. (1996). Gearbox Vibration diagnostics - an overview. The 8th Int. Congress on Condition Monitoring and Diagnostic Engineering Management, Sheffield, UK. 16-18 July. Wu, Q. Z. ; Jou, I. C. and Lee, S. Y. (1997). On-line signature verification using LPC cepstrum and neural networks. IEEE Trans, on Systems Man and Cybernetics, ptB, 27(1), 148-153. 529
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Condition Monitoring and Diagnostic Engineering Management Andrew G. Starr and Raj B.K.N. Rao (Editors) © 2001 Elsevier Science Ltd. All rights reserved.
FAULT ACCOMMODATION FOR DIESEL ENGINE SENSOR SYSTEM USING NEURAL NETWORKS A. Badri, E.Berry, F.Gu, and A.D.Bali Maintenance Engineering Research Group The University of Manchester Manchester, Oxford Road, United Kingdom, M13 9PL Email: [email protected] Phone:+44(0)161 275 4308 Web: www.maintenanceengineering.com ABSTRACT Engine control systems include several sensors, which are used to acquire information about different engine parameters. A fault in any sensor may develop into system failure causing the engine to stop. This has led to the need to introduce a fault-tolerant sensor system (FTSS) that can handle faults in sensors. Fault-tolerance can be achieved by hardware redundancy or analytical redundancy. Hardware redundancy is the addition of extra components to replace the faulty component in the event of failure. However, a greater number of sensors will be required which increases costs, reduces reliability, and increases complexity. As most engine parameters are related to each other, information about one parameter can be obtained from others. This characteristic can be used to create analytical redundancy, which can be employed to accommodate sensor failures. Due to their learning and adaptation capabilities, artificial neural networks are very appealing for the purpose of providing fault tolerance capabilities in automotive engines. The paper considers the application of neural networks to accommodate failures in sensors. A faulty sensor is reconstructed based on the data from related sensors. The effectiveness of this method is tested using real data collected from a fully automated diesel engine test rig. Three sensors are considered: Engine speed, Manifold absolute pressure, and Throttle position.
KEYWORDS Fault accommodation. Fault-tolerant, Redundancy, Neural networks. Network training, Data reconstruction
INTRODUCTION There are many application areas of fault-tolerance in computing, and recently the demand for faulttolerance has appeared in other fields such as industry and transportation. Traditional techniques for achieving fault-tolerance are based on redundancy: hardware redundancy (extra components), 531
software redundancy (extra information), and/or time redundancy (extra delay time for signal propagation), Johnson (1989). As the plant, which is to be controlled and monitored becomes more complex, greater numbers of sensors will be required, which makes the use of the hardware redundancy insufficient due to the associated higher costs, reduced reliability, and increased complexity. A cheap way to obtain improved reliability, increased availability, and also achieve different goals such as safety, maintainability, and performance, is to introduce a FTSS which is based on analytical redundancy. This FTS integrates different techniques that can handle on-line (operational) faults in an overall system with the following properties: 1 -Fault detection: by employing intelligent software that observes the operation of components. 2-Fault isolation: to prevent any simple fault from developing into failure at the system level. 3-Fault accommodation: using a re-configuration technique or signal replacement with appropriate estimation. Fault detection and isolation techniques are well known and widely developed, Simani & Fantuzzi (2000), but methods for fault accommodation have just recently started. Estimation of a signal replacing a measurement from a faulty sensor is one method of fault accommodation. This work employs a generalised regression neural network (GRNN) to accommodate sensor faults for diesel engine sensors.
DATA COLLECTION A 2.5 litre Ford turbo charged fully automated diesel engine test rig was used. Three sensors (Engine Speed, Manifold Absolute Pressure and Throttle Position) were selected. The sensors were connected to a PC via a CED 1401 Plus interface. Measurements were carried out over a speed range of 12002400 RPM in steps of 200 RPM with 16 readings for each step. The speed was changed by remotely changing the demand of the throttle actuator. The sensor signals were converted into numerical values using a MATLAB program. The raw data is illustrated in fig 1. Measurements Space
1000 1.9
MAP(Volt)
1.5
1
Throttle position(Volt)
Figure 1: Distribution of measurements
532
NEURAL NETWORK MODEL A generalised regression neural network (GRNN), Demouthe & Beal (1997), Wasserman (1993) was employed to model the relationship between these parameters. The GRNN used had two layers of neurons, with the first layer being radial basis functions and the second layer consisted of linear neurons. The network was trained off-line. The data consisted of 112 samples for each measured parameter. The data was divided into two halves, the first half used to train the network, and the second half used to test the trained network. The network was such that the first layer had 56 radial neurons, and also the second layer had 56 neurons. The spread of radial basis functions in a GRNN is fixed by a spread parameter. This controls the trade-off between over-fitting and under-fitting. The spread parameter that gives good fitting to the training data and acceptable generalising to new data was selected for the Engine Speed sensor (0.0035), Manifold Absolute Pressure sensor (0.035), and Throttle Position sensor (0.002).
SIMULATION RESULTS The network was trained using the first half of the data. The network was then tested using the second half of data. The testing was carried out to predict the capability of the network to reconstruct the lost data as a simulation for complete sensor failure (no output signal at all). Throttle position and manifold pressure data were used as the inputs to reconstruct engine speed data (output). Fig. 2 shows the network training and prediction for engine speed data. Manifold pressure and Throttle Position data reconstruction results are shown in figures 3 and 4 respectively, where engine speed and throttle position data was used for manifold pressure data reconstruction, while manifold pressure data and engine speed data was used for throttle position data reconstruction. (a)Tralning Residuals(solid line:original data;x line:prediction) 3000 M W M K X X
60 30 40 50 Sample number (b)Network Test Errors and prediction for validation data (solid line:original data;x line:reconstructed data) 3000 20
30 Sample number
Fig. 2-Network training and prediction for Engine speed data 533
(a)Training Residuals(solid line:original clata;x line:prediction)
60 30 40 50 Sample number (b)Network Test Errors and prediction for validation data (solid line:original data;x line:reconstructed data) 20
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Fig. 3-Network training and prediction for Manifold pressure data (a)Training Residuals(solid line;original data;x line:prediction) _2.5
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It can be seen that the network produces good prediction results for training and reconstructing the data of missed parameters using data from other related parameters. Figures (5) to (7) present the results of the error analysis for data reconstruction for each sensor. The error in speed data reconstruction, which is the difference between the original data and the reconstructed data, has an average value of 14.6 RPM. This means that we can estimate the engine speed to within 15 RPM, which is acceptable The throttle position sensor, which is a potentiometer, has the lowest error 0.001015 Volt due to it being less sensitive to noise and interference. Network Test Error
20 30 40 Measurement samples
Fig. 5- Error analysis for engine speed data reconstruction X 10
Network Test Error
20 30 40 Measurement samples
Fig. 6- Error analysis for manifold pressure data reconstruction 535
Networtc Test Error
X 10
10
20 30 40 Measurement samples
Fig. 7- Error analysis for throttle position data reconstruction
CONCLUSIONS AND FUTURE WORK Fault accommodation by parameter estimation using neural networks has been demonstrated. Good results have been obtained which give motivation to apply it on-line. Neural networks may also be used for fault detection and isolation. Future work will involve the investigation of these capabilities to achieve a fault tolerant sensor system using only one technique.
REFERENCES B. Johnson. (1989). Design and Analysis of Fault-Tolerant Digital Systems. Addison-Wesley Publishing Company H. Demouthe & M. Beale. (1997). Neural Network Toolbox User's Guide. Mathworks P. D. Wasserman. (1993). Advanced Methods in Neural Computing, Van Nostrand Reinhold S. Simani & C. Fantuzzi. (2000). Fauh Diagnosis in Power Plant using Neural Network. Information Science. 121:2000, \25-\36.
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Condition Monitoring and Diagnostic Engineering Management Andrew G. Starr and Raj B.K.N. Rao (Editors) © 2001 Published by Elsevier Science Ltd.
THE APPLICATION OF NEURAL NETWORKS TO VIBRATIONAL DIAGNOSTICS FOR MULTIPLE FAULT CONDITIONS A J . Hoffinan\ N.T, van der Merwe\ P. S. Heyns^ C. Scheffer^ and C. Stander^ ^School for Electrical and Electronic Engineering, Potchefstroom University for CHE Potchefstroom, 2520, South Africa ^Department of Mechanical Engineering, University of Pretoria Pretoria, 0002, South Africa
ABSTRACT Vibration analysis has long been used for the detection and identification of machine fault conditions. The specific characteristics of the vibration spectrum that are associated with common fault conditions are quite well known, e.g. the BPOR spectral component reflecting bearing defects and the peak at the rotational frequency in the vibration spectrum indicating the degree of imbalance. The typical use of these features would be to determine when a machine should be taken out of operation in the presence of deteriorating fault conditions. Reliable diagnostics of deteriorating conditions may however be more problematic in the presence of simultaneous fault conditions. This paper demonstrates that the presence of a bearing defect makes it impossible to determine the degree of imbalance based on a single vibration feature, e.g. the peak at rotational frequency. In such a case it is necessary to employ diagnostic techniques that are suited to the parallel processing of multiple features. Neural networks are the best known technique to approach such a problem. The paper demonstrates that a neural classifier using the X and Y components of both the peak at rotational frequency and the peak at BPOR frequency as input features, can reUably diagnose the presence of bearing defect and can at the same time indicate the degree of imbalance. Different supervised and unsupervised neural classification techniques are then evaluated for their ability to reliably model the degree of imbalance, while also identifying the presence of defects.
KEYWORDS Vibration analysis, bearing defects, imbalance, deteriorating fault conditions, automated diagnostics, neural networks, Kohonen feature maps, nearest neighbour rule, radial basis function networks.
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1. INTRODUCTION In a system with multiple fault conditions present, such as bearing defects, unbalance and looseness, it is important to distinguish between the fauh conditions. A situation may arise where certain fault conditions are stable while the conventional analytical procedures indicate degradation of the fault. This phenomenon occurs due to the presence of other fault conditions that are deteriorating. The objective of this research was to develop a condition monitoring strategy which can make accurate and reliable assessment of the presence of specific fault conditions, and which can furthermore distinguish between deteriorating and stable faults in a system with multiple fault conditions present. The proposed strategy involves the identification of fault conditions by vibration analysis, incorporating neural networks to either model the status of fault conditions, or to discriminate between different fault conditions. Vibration measurements were taken on a simple test rig that is subjected to increasing imbalance in the presence of a bearing defect. Various investigations into the detection of multiple fault conditions using vibration monitoring has been conducted by other researchers (McCormick & Nandi 1997, Paya 1997). No specific mention could, however, be found of a bearing defect in conjunction with an imbalance condition. The focus of this article is to demonstrate a strategy for the recognition of multiple fault conditions, applied specifically to the above mentioned fault condition. 2. EXPERIMENTAL SETUP Multiple fault conditions were induced on a Vib demo VIB 2.100 Pruflechnik AG test rig. The test rig consisted of three plumber block bearings, which support a shaft. An outer race defect was induced on the centre bearing of the test rig. Residual imbalance was induced on to the shaft of the system, using weigths of 0, 12, 18 and 24 grams respectively, to simulate a deteriorating secondary fault condition. Acceleration measurements were taken on the centre bearing and on the bearing closest to the induced imbalance. Vibration measurements were taken with 100 mV/g ICP accelerometers in both the horizontal and vertical directions during the four tests. A DSP Siglab analyser model 20-42 was used to collect the data. The rotational speed of the system at 1626 rpm was measured with an ONO SOKKIHT-4100 digital tachometer. The frequency band was set between 5 Hz and 10 kHz for the analysis. 3. FEATURE SELECTION AND EXTRACTION The vibration signals form a multivariate feature space. The required number of training samples for a classifier generally increases exponentially as a function of the number of features, assuming uncorrelated data (Hoffinan & Tollig, 1998). Furthermore, the performance of the classifier is closely linked to the quality of the features. The extraction of a compact feature set, which can still capture most of the correlation inherent in the original sample space, is thus very important in a multivariate setting. Suitable feature extraction methods highlight the important discriminating characteristics of the data, while simultaneously ignoring the irrelevant attributes (i.e. noise).
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Figure 1: Test rig The frequency domain provides a useful feature set for machine diagnostics (Rao, 1996). Machinery defects are related to specific frequency domain features (Norton 1989, Rao 1996). It is well suited to the detection of periodical machinery vibrations. Impulsive vibrations, on the other hand, are better analysed in the time domain. Envelope spectra analysis is a technique especially suitable for the early detection of damage to rolling element bearings. The technique essentially consists of a bandpass filter that reduces components unrelated to the bearing. Envelope detection of the signal is then performed by full-wave rectification and low-pass filtering, after which spectrum analysis can be applied (Norton, 1989). It is well known that defects in rotating machinery can be monitored with vibration frequency domain analysis. The frequency spectra of a bearing, with increasing imbalance being imposed by adding weights to the setup, are shown in figure 2. For this paper specific features from frequency domain analysis were extracted to predict multiple faults in the experimental setup. These features can be calculated using common condition monitoring techniques. For frequency domain analysis, the frequency band of 5 Hz to 10 kHz was investigated. Time signals were recorded at a sampling rate of 25.6 kHz. For spectrum calculation, typically 20 processing averages v^th a resolution of 1 - 2 Hz were used. The following features were extracted from frequency domain analysis: • Amplitude of vibration spectrum at Rotational Frequency (RF), in horizontal and vertical directions. • Ball Pass Frequency on the Outer race (BPFO) of the defect bearing, in horizontal and vertical directions. • Higher Frequency Domain components (HFDs) indicative of bearing defect. • Ball Pass Frequency on the Outer race (BPFO) of the defect bearing, obtained from the envelope spectra. The amplitude at RF is commonly used to detect imbalance, while the BPFO component is indicative of a defective bearing. Should both fault mechanisms however be present, no single feature can completely distinguish between the different fault categories. This is illustrated in figure 3.
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Figure 2 : Increasing imbalance on channel 1 (0 ~ 1 kHz) Bemmg d«fed Normal defect x Imbalancr. Og k I2g g I8g b 24g r
Figure 3 : Scatterplot of rotational frequency X and Y components 4. DEVELOPMENT OF CLASSIFIERS Several different approaches for modelling class membership in a data set with ANN are possible. Firstly one can work with afixednumber of known classes (typically identified through an exploratory analysis using SOM techniques). Alternatively one can start with a reduced classification problem, with each class representing a well defined fault condition, and then increase the number of classes as additional data becomes available. Secondly one can either indicate class membership by an associated output value of 1 for the corresponding ANN output (whereas a 0 indicates the opposite), or one can use the outputs of the ANN to indicate, on a continuous scale, the severity of the presence of a condition. Classifiers were trained to distinguish between the following six classes: • Imbalance masses of Og, 12g and 24g (with no bearing defect) - classes 1 to 3. • Imbalance masses of Og, 12g and 24g (with an outer race bearing defect) - classes 4 to 6.
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4.1,
Kohonen self-organising maps
The well known Kohonen self-organising map (SOM) (Kohonen, 1998) was firstly employed. After normalising the features identified in section 3, a feature matrix must be prepared to be used as input to the SOM. The trained SOM, displaying all four features are shown in figure 4. The relationships between the features can be deducted from this figure. It is clear that the two features indicating unbalance is low towards the left hand side of the map, and increase towards the right hand side. The features indicative of bearing defect display a different behaviour. These features divide the map in two distinct regions, lower (high values) and upper (high values). With these relationships in mind, the Best Matching Unit (BMU) labels of training data are plotted onto the map, shown in fig. 5 (left hand side). It can be seen from fig. 5 that eight regions are formed on the map. The lower half of the map are BMUs indicative of a defect bearing. The top half is for the normal bearing. Furthermore, unbalance increase from left to right. Hence, the SOM can distinguish between the normal and defect bearings with certain levels of unbalance present. The right hand figure displays the BMU trajectory for the training data. From this, it can be seen how the BMUs are found as the training of the SOM commence through the data set as arranged in fig. 5. RF vertical (27 1 Hz)
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Class labels and Best Matching Unit trajectory on SOM
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4,2.
Nearest neighbour rule (NNR) classifiers
The one nearest neighbour rule (abbreviated as NNR) has been successfully applied in the past to a variety of real world classification problems. As the NNR effectively stores or memorises the training set, the computational resources required can become a problem with large data sets (e.g. in data mining appHcations). We use a technique which allows continuous class membership values, instead of the binary class labels usually associated with the NNR. While the NNR selects the closest sample, irrespective of the class label, our approach is to select the closest class (or cluster) to the sample. The output value of this nearest neighbour rule with class membership (NNRC) can vary continuously, while the NNR always assigns a discrete class label (based on the distance to the closest neighbour). This enables the NNRC to be appUed in the modelling of a primary fault mechanism (an outer race defect) in the presence of a secondary fault mechanism (an imbalance mass) (Van der Merwe et al, 2001). The advantage of NNRC relative to NNR in classification problems would be that a degree of class membership can be obtained, as is possible for example in FLNN (fiizzy logic neural networks). The advantage of the NNRC over other modelling techniques, such as neural networks (multilayer perceptrons or radial basis fixnctions), is that no lengthy training time is necessary. Figure 2 shows the output of a NNRC classifier trained on observations fi-om all six classes. The BPFO feature indicating a bearing defect condition increases firom front to back, while the RF feature indicating imbalance condition increases form left to right. The data for the NNRC technique has been normalised with respect to length (norm of one).
Figure 6 : Response of NNRC classifier trained on aU 6 classes (training samples shown in black)
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4.3.
Radial basis function (RBF) networks
RBF classifiers normally perform well for classification problems with well-defined clustering in the data set. Such clustering could be achieved for this problem by utilizing the appropriate combination of features. Figure 7 shows the output of a RBF classifier trained on observations from all six classes. The BPFO feature indicating a bearing defect condition increases from fi"ont to back, while the RF feature indicating imbalance condition increases form left to right. The abiUty of this network to generahze was tested by feeding it with unseen Og, 12g, 18g and 24g data, measured on bearings with and without defects. It achieved 100% accuracy on the Og, 12g and 24g data. The 18g data was classified as either 12g or 24g, with the presence of bearing defect always handled correctly. It would hence be successful in classifying unseen inputs as having either very little, moderate or severe imbalance, as well as identify the presence of a defect. A network trained on the data for the normal bearing only, could however not correctly distinguish between the 12g and 24g classes for data collected from a defective bearing. This illustrates the necessity to use as representative a training set as possible in training such a neural classifier.
Outpuls of RBF network for own training data
iefect - BPOR x channel Imbalance - RF x channel
Figure 7 : Outputs of RBF classifier trained on all 6 classes (training samples shown in black)
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5. CONCLUSION Three different neural classification techniques were evaluated for their performance on a condition monitoring problem requiring the identification of multiple fault mechanisms. The SOM proved to be an easy to use tool for initial data processing and for identifying hidden relationships between the features. It also requires very little human intervention. It can be concluded that the SOM can be used for identification of multiple faults if the features are chosen and normalised correctly and if the class labels of the training data are known. It was shown that, once the classification problems is well defined, both NNR and RBF classifiers can accurately discriminate between different combinations of multiple fault conditions, as well as identify the degree of severity of deteriorating fault conditions. It was demonstrated that incomplete training sets will lead to faulty diagnostic decisions. RBF classifiers potentially have an advantage above SOM classifiers in terms of training speed. NNR techniques have the advantage of not requiring any optimization during training, that can reduce the computational requirements. Once trained, RBF classifiers however have a speed advantage over NNR techniques for the evaluation of new samples, which may be a problem for NNR classifiers in the case of large training sets. REFERENCES Hoffinan A. J. ToUig C. J.A. (1998). Neural network recognition of partial discharge signals. South Afiican Power Engineering Conference. Cape Town. Kohonen T. (1998). The self-organizing map. Neurocomputing. 21, 1-6 Paya B.A., Esat I.I. and Badi M.N.M. (1997). Artificial neural network based fault diagnostics of rotating machinery using wavelet transforms as a preprocessor. Mechanical systems and signal processing. 11:5.751 -765. Haykin S. (1994). Neural networks: A comprehensive foundation, Macmillan publishing company. Norton M.P. (1989). Fundamentals of noise and vibration analysis for engineers, Cambridge University Press. Rao B.K.N. (1996). Handbook of condition monitoring, Elsevier Science. Koncar N. (1997). Optimization methodologies for direct inverse neurocontrol. Master's thesis. University of London. Van der Merwe N.T., Hoffinan A.J., Stander C , Scheffer C , and Heyns S.P. (2001). Identifying multiple faults in rotating machinery, South Afiican Power Engineering Conference, Cape Town.
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Condition Monitoring and Diagnostic Engineering Management Andrew G. Starr and Raj B.K.N. Rao (Editors) © 2001 Elsevier Science Ltd. Allrightsreserved.
APPLYING NEURAL NETWORKS TO INTELLIGENT CONDITION MONITORING W Li \ R M Parkin \ J Coy ^ A.D. Ball \ F. Gu ^ ^ Wolfson School of Mechanical and Manufacturing Engineering, Loughborough University, Loughborough, LEI 1 1 AH, UK ^ Technology Centre, Consignia, Swindon, SN3 4RD, UK ^ Manchester School of Engineering, University of Manchester, Manchester, Ml3 9PL, UK
ABSTRACT In this paper we present the application of self-organising map (SOM) networks to the study of machine intelligent condition monitoring. The SOM networks are able to classify machine conditions with little priori knowledge. The condition-indicating information is condensed into the network's synaptic weights which can be used for further analysis. The paper is organised as follows. Firstly basics of SOM networks are discussed with emphasis on input vector distribution approximation. Secondly, the applications of SOM networks are verified with numerical examples. Thirdly, the SOM networks are applied to approximate the distribution of machine generated acoustic signals. Spectral information is extracted from network synaptic weights and used to compare different conditions. The spectral interpretation of the SOM synaptic weights provides an efficient and novel analysis technique. KEYWORDS Self-organising map, condition monitoring.
INTRODUCTION The condition monitoring of diesel engines employs a variety of techniques such as vibration analysis, acoustic analysis, thermography, combustion pressure analysis and instant speed analysis (Priede 1980). The signal processing methods range from time domain statistical parameters to the complicated joint time-frequency analysis (Li 2000). One of the problems encountered with the applications of these individual signal processing methods is that a diesel engine is a far more complicated dynamic system than bearings and gearboxes. Hence it is not always possible to classify the non-linear characteristics of diesel engine conditions with the above
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linearly based signal processing methods. From the viewpoint of engine condition monitoring, it is more desirable to apply the non-linear pattern recognition and classification strategies in order to analyse engine conditions. It has been noted that neural networks are able to approximate almost any non-linear functions (Schalkoff 1997). This provides the fundamental fact that it is possible to model the non-linear dynamics of diesel engines from their outputs. The applications of neural networks to machine condition monitoring and fault diagnosis have been reported recently. Paya et al. (1997) investigated the condition monitoring of rotating machinery using backpropagation (BP) neural networks. The discrete wavelet transform was first applied to pre-process the vibration data before feeding them to the neural network. Both signal and multiple faults were successfully detected and classified. Li et al. (2000) adopted some of the common normalised indices such as peak-peak, mean, Kurtosis, and bearing characteristic frequencies to train a feedforward network in an effort to diagnose motor and rolling bearing faults. The model was verified with both the simulated and real time vibration data. Zhang, et al (1996) applied the SOM to study the bearing related machinery faults in which the normalised and dimensionless vibration data in the range of [0.0, 0.1] were fed to the network for classification. One of the first applications of neural networks in engine fault diagnosis was put forward by Scaife et al (1993). They applied neural networks to identify engine component failures. Thompson et al (2000) set up a neural network based engine model in predicting diesel engine exhaust emissions. The advantage of the neural network model is that the knowledge of engine performance governing equations and the combustion kinetics of emissions formation is not required. However, it can be seen from the above references that mostly vibration and emission related data are used in neural networks for classification. On the other hand, although acoustic signals were found containing very useful information about diesel engine conditions, they have not been investigated by neural network approaches (Li 2000). This paper will investigate the engine generated acoustic signals using the SOM. A novel interpretation method is proposed in analysing network's synaptic weights. SELF-ORGANISING MAPS This section briefly introduces the foundations of self-organising maps (SOMs) to be used in this paper. A self-organising map (SOM), also known as topology-preserving map, is formed by a topological structure of input patterns. In this map the spatial locations of the neurons in the lattice are indicative of intrinsic statistical features contained in the input patterns. Usually, the map is arranged in a one- or two-dimensional plot yet higher dimensions are not commonly used due to representation difficulties. The architectures of the SOM network have two basic types: the Kohonen model and the Willshawvon der Malsburg*s model as shown in Figure 1 (Haykin 1999). It can be seen that both models arrange output neurons in a two-dimensional lattice structure.
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Figure 1: Self-organising map architectures (a) Kohonen model, (b) Willshaw and von der Malsburg model The Kohonen model, proposed by Kohonen (1982), outlines a topological mapping which optimally projects a fixed number of vectors into a higher dimensional input space and is suitable for data compression. The second model, jointly introduced by Willshaw and von der Malsburg (1976), is composed of two separate two-dimensional lattices of neurons connected together. The input neurons are projected onto the output lattice but unlike the Kohonen model, this model has strong biological grounds in that it is trying to explain the mapping relationship between the retina and the visual cortex. The purpose of the self-organising map (SOM) is to project the input patterns onto a one- or twodimensional map so that the output neurons are arranged in a similar manner as that of the input space. The learning algorithm of SOM can be stated as follows: Step I: Initialisation Like any other neural network the synaptic weights connecting the input layer and hidden or output layer are initialised first. One common way of initialising the weights is to assign each weight with a small random value. Step 2: Competition Given an input pattern, the network computes the values according to a chosen discriminant function. Among the output neurons, only one particular neuron with the closest relationship to the input vector is picked up and labelled as the winning neuron. Suppose an input vector picked randomly from an input space is denoted by
0) where m is the dimension of the input space. The synaptic weight vector connecting the J th neuron and the input vector is denoted by
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hence the /th neuron is picked up as the competitive neuron. This winning neuron best matches the input vector in the sense of Euclidean distance. Step 3: Cooperation Once the winning neuron is picked up, the centre of a topological neighbourhood for co-operation is determined. The next step is to select the neurons within the neighbourhood and there are a number of methods for determining the neighbourhood neurons such as rectangular functions, hexagonal functions and Gaussian functions. Step 4: Adaptation The key step in self-organising training is to update the synaptic weights so that the Euclidean distance is minimised as much as possible. The updating strategy is defmed as
w,(/ + i) = w,(0+;7W^,(0k0-w,(/)J
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where Tj{t) is the learning rate at step t, and hj.(t) defines the radius of the topological neighbourhood around the winning neuron i. From the above equation, it can be seen that only the weights of those neurons defined within the topological neighbourhood of the winning neuron / will be updated. The synaptic weights lie outside the neighbourhood will remain unchanged. As the winning neuron / best matches the input vector x in the sense of the selected Euclidean distance metric, the above leaning strategy is able to move the synaptic weight vectors towards the distribution of the input vectors. NUMERICAL EXAMPLES In this section, two simulated examples will be given to demonstrate how the SOM classifies the input vectors, hi both examples, a two-dimensional input vector space is used and a 10 by 10 neuron lattice is adopted. Figure 2 shows the classification results of a two-dimensional randomly distributed input space. Figure 2 (a) plots the original input distributions lie in the rectangular areas of [-0.25, 0.25, -0.25, 0.25] and [0.25, 0.75, 0.25, 0.75] respectively. Clearly they can be classified into two classes by the dashed line. The distribution after 500 epochs is shown in Figure 2(b). It can be seen that most weights are located around the centre of the plane which do not show clear separation. With the increase of iteration number to 1,000 in Figure 2(c), more and more weights are re-organised towards the bottom left and top right comers where the original input distributions are located. When the iteration number is
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further increased to 10,000 epochs, the distribution of network weights in Figure 2(d) looks more like the original input distribution in that more weights fall into the above rectangular areas. (a) 2-D distribution
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Figure 2: 2-dimensional input distribution and synaptic weight distributions Figure 3 demonstrates the approximation of two simulated time domain signals. One is a sinusoidal wave with an oscillation frequency of 10 Hz and the other is a randomly distributed noise with amplitudes between [-0.2 0.2]. (b) 500 epochs
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Figure 3(a) shows the input sinusoidal signal with the added noise. As the input dimension is two so the synaptic weights are plotted separately in the following three figures. It is expected that each weight vector should approximate one of the input vectors. Figure 3(b) show the resuh after 500 epochs. It can be seen that the plots of the two weight vectors give some information about the input signals. For instance, the dotted line signal has amplitudes within [-0.05, 0.05] while the dashed line signal lies between -0.75 and 0.50. When the iteration is increased to 1,000 epochs, the amplitudes of both weight vectors increase to some extent. For example the amplitudes of the sinusoidal signal extend between -0.8 and 0.85 and the amplitudes of noise signal are within [-0.08, 0.08]. With the iteration is further increased to 5,000 epochs, both amplitudes are increased correspondingly. Furthermore, the shape of the dotted weight vector looks more like a sinusoidal signal. This implies that SOM networks are trying to approximate the input signals in terms of amplitudes and shapes. ANALYSIS OF REAL DATA To verify the efficiency of SOM networks, experiments were carried out in a reciprocating laboratory and acoustic signals were collected for analysis. Faults were seeded in engine cylinders by adjusting cylinder valve clearances to different levels. The normal clearances for exhaust and inlet valves were set at 0.38/0.20 mm. The exhaust and inlet valve clearances in cylinders 1 and 4 were increased to 0.70/0.40 mm and 0.45/0.50 mm respectively. Eight segments of acoustic signals under the same load and speed were measured and fed to the SOM network with a two dimensional lattice of 15 rows and 15 columns (225 neurons). The training iteration was set at 5,000 epochs in all cases. As the input vector dimension is 8, it is impossible to represent all the synaptic weights in the conventional two or three-dimensional plots. It is noted, from the above examples, the synaptic weights contain the condensed information about the input vectors. Hence it is proposed to compare the averaged spectral information of the synaptic weights. Figure 4 shows the spectral comparison of synaptic weights under 1000 rpm and 30 Nm load.
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Figure 4: The synaptic weight comparison at 1000 rpm
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The spectral components are calculated by performing the Fourier analysis of network weights and selecting those dominant peaks. It can be seen from the above figure that the normalised amplitudes of the faulty data are higher than that of normal data in most cases. The amplitudes of the first component are related to Fourier transform in which all the data are added up together, hi Figure 4, the spectral amplitudes for fault in cylinder 1 are higher than the other two. This can be explained by the fact that the clearances in cylinder 1 are the largest. Figures 5 and 6 display the same spectral comparison of network weights at 1500 rpm and 2000 rpm. Again the amplitudes of fault #1 are the highest and those of normal amplitudes are the lowest. By comparing these three figures at different speeds, it can be seen that when the speed is higher, the amplitude differences among those higher-order components are bigger. For instance, the amplitudes for the faulty conditions in Figure 6 are much higher than that of the normal ones. On the other hand, they are relatively close to each other when the speed is lower as shown in Figure4.
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The above results demonstrate that, by plotting the spectral information of SOM network's synaptic weights, it is possible to detect engine faults through acoustic signals. CONCLUSIONS This paper investigated the applications of self-organising map (SOM) networks to the condition monitoring and fault diagnosis of diesel engines through acoustic signals. The SOM networks are able to approximate the distribution of input vector space with smaller number of neurons. Unlike supervised networks such as backpropagation, SOM does not need detailed/^norr information about input space. This makes SOM networks more suitable for problems such as diesel engine condition monitoring in which priori information about faults is not always readily available. The numerical examples demonstrated that SOM networks could faithfully classify and approximate the original distributions of input vectors. The results from engine acoustic signals showed that network synaptic weights contain valuable information about machine conditions. This paper proposed a spectral decomposition method to highlight the condition indicating information. The representation of synaptic weights' spectral components was able to distinguish the amplitude differences between engine valve related faults. REFERENCES Haykin, S. (1999). Neural networks: a comprehensive foundation. 2"^ ed. New Jersey: Prentice Hall. Kohonen, T. (1982). Self-organising formation of topologically correct feature maps. Biological Cybernetics 43, 59-69. Li, B., Chow and M. Y., Tipsuwan, Y. (2000). Neural-network based motor rolling bearing fauh diagnosis. IEEE Transactions on Indmtrial Electronics, 47:5,1060-1068. Li, C. J. and Yu, X. (1995). High pressure air compressor valve fault diagnosis using feedforward neural networks. Mechanical Systems and Signal Processing. 9:5, 527-536. Li, W., 2000. A study of diesel engine acoustic characteristics. Thesis (PhD). The University of Manchester, Manchester, UK. Paya, B.A., East, I. I. and Badi, M. N. M. (1997). Artificial neural network based fault diagnostics of rotating machinery using wavelet transform as a preprocessor. Mechanical System and Signal Processing, 11:5,751-765. Priede, T. (1980). hi search of engine noise - an historical review. SAEpaper 800534, 2039-2069. Scaife, M. W., Charlton, S. J. and Mobley, C. (1993). A neural network for fault recognition. SAE paper 930S61. Schalkoff, R. J. (1997). Artificial neural networks. London: McGraw-Hill. Thompson, G. J., Atkinson, C. M., Clark, N. N., Long, T. W., Hanzevack, E. (2000). Neural network modelling of the emissions and performance of a heavy-duty diesel engine. Proceedings of Institution of Mechanical Engineers, 214, Part D, 111-126. Willshaw, D. J., von der Malsburg, C. (1976). How patterned neural connections can be set up by selforganisation. Proceedings of the Royal Society ofLondon, Series B, 194,431-445. Zhang, S., Ganesan, R., Xistris, G. D. (1996). Self-organising neural networks for automated machinery monitoring systems. Mechanical Systems and Signal Processing, 10:5, 517-532.
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Condition Monitoring and Diagnostic Engineering Management Andrew G. Starr and Raj B.K.N. Rao (Editors) © 2001 Elsevier Science Ltd. All rights reserved.
DATA MINING IN A VIBRATION ANALYSIS DOMAIN BY EXTRACTING SYMBOLIC RULES FROM RBF NEURAL NETWORKS Kenneth McGarry and John Maclntyre School of Computing, Engineering and Technology, University of Sunderland, St Peters Campus, St Peters Way, Sunderland, SR6 ODD, UK ken.mcgarryQsunderland.ac.uk
ABSTRACT Neural networks are becoming an increasingly popular technique for modelling data with complex and/or non-linear relationships. Diagnostic systems for condition monitoring applications fall particularly into this category, especially those using spectral vibration data. However, neural networks do have some major disadvantages compared with rule based diagnostic systems. The most important criticism is the lack of any explanation system, which would open up the neural networks internal operation for scrutiny. This paper illustrates how the internal parameters of an RBF network can be converted into symbolic rule format. The rule extraction algorithm is described in detail.
KEYWORDS Neural networks, data mining, rule extraction, knowledge discovery.
INTRODUCTION Neural networks have been apphed to many real-world, large-scale problems of considerable complexity. They are useful for pattern recognition and they are robust classifiers, with the ability to generalise in making decisions about imprecise input data (Bishop95). They offer robust solutions to a variety of classification problems such as speech, character and signal recognition, as well as functional prediction and system modelling where the physical processes are not understood or are highly non-linear. Although neural networks have gained acceptance in many industrial and scientific fields they have not been widely used by practitioners of mission critical applications such as those engaged in aerospace, military and medical systems. This is understandable since neural networks do not lend themselves to the
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normal software engineering development process. Knowledge extraction by forming symbolic rules from the internal parameters of neural networks is now becoming an accepted technique for overcoming some of their limitations. In this paper we describe our method of extracting knowledge from an RBF network which is classed as a local type of neural network. KNOWLEDGE EXTRACTION FROM NEURAL NETWORKS In recent years there has been a great deal of interest in researching techniques for extracting symbolic rules from neural networks. Rule extraction has been carried out upon a variety of neural network types such as multi-layer perceptrons, Kohonen Networks, recurrent networks. The advantages of extracting rules from neural networks can be summarised as follows: •
The knowledge learned by a neural network is generally difficult to understand by humans. The provision of a mechanism that can interpret the network input/output mappings in the form of rules would be very useful.
•
Deficiencies in the original training set may be identified, thus the generalisation of the network may be improved by the Addition/enhancement of new classes. The identification of superfluous network parameters for removal would also enhance network performance.
•
Analysis of previously unknown relationships in the data. This feature has a huge potential for knowledge discovery/data mining and possibilities may exist for scientific induction.
In addition to providing an explanation facility, rule extraction is recognised as a powerful technique for neuro-symbolic integration within hybrid systems (McGarry 99a).
RADIAL BASIS FUNCTION NETWORKS Radial basis function (RBF) neural networks are a model that has functional similarities found in many biological neurons. In biological nervous systems certain cells are responsive to a narrow range of input stimuli. Figure 1 shows a network trained the Xor data set for illustration. This network has two input features, two output classes and four hidden units. The RBF network consists of a feedforward architecture with an input layer, a hidden layer of RBF ''pattern" units and an output layer of linear units. The input layer simply transfers the input vector to the hidden units, which form a localised response to the input pattern. Learning is normally undertaken as a two-stage process.
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bias = -0.5118
widrti = 0.555
Figure 1: RBF network
RBF Training The accuracy levels stated in the tables are the best out of up to 10 test runs. Training of the RBF networks required the setting of three parameters, the global error, the spread or width of the basis function and the maximum number of hidden units. The value assigned to the global error setting can result in fewer hidden units being used than the maximum value. If the error value is not reached, training would terminate when the maximum number of hidden units has been assigned. The training and test data for the construction of the RBF networks were generally split 50/50. Data Sets The data used in this work was created by the VISION project which was funded by the Brite EuRam initiative of the European Union (Project No. BE95-1313) •
Vibration data A. This is a synthetic data set composed of simulated data generated by finite element modelling (FEM). The data was gathered as part of the VISION project in which the University of Sunderland was a key partner. The data is composed of 1028 examples with nine input features and three output classes. The nine input features represent vibration spectra that are indicative of the physical operating condition of a particular type of rotating machine such as a motor or fan. The three output classes are composed of two faults and the normal operating condition.
Vibration data B. This is a more complex data set produced by the VISION project and is a combination of real world data, simulated FEM data and test rig data. This data is a subset of that used to produce the final diagnostic model incorporated into end product. It consists of 1862 examples with 20 input features and eight output classes (McGarry 99b). Table 1 displays a list of FFT parameters with selected faults in the SIMU column.
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FFRPM FF2RPM FF3RPM FF4RPM FF5RPM FFHarmPow FFlO-1000 FF500-1000 FFlOOO-2000 FF312-1200 FF1200-10000 FFRMS FFMaximum FFMaximumFreq
SRms SStanDevi SAverage SMeanDevi SMaximum SMinimum SKurtosis SSkewness SCrestFactor
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Table 1: Statistical Parameter Number Identifier DATA MINING AND KNOWLEDGE DISCOVERY Over the last 10 years data mining has aroused a great deal of interest as a solution to the problems associated with searching for informative patterns in large data bases. Artificial intelligence techniques can provide the means to build automated, adaptive systems. Data mining (DM) is a process concerned with the semi-automatic extraction of interesting patterns from databases. The search for interesting and potentially useful knowledge requires the definition of what makes an interesting pattern? This is the most important question concerning DM as it is all too easy for the DM process to produce large quantities of patterns, which can overwhelm the user. Many patterns will be either: too regular and therefore trivial or non-informative, or too random and therefore meaningless. Over the years much research has been carried out on the "interestingness" problem (Piatetsky 94). The actual data mining process may involve a number of artificial intelligence techniques. Some techniques which have been successfully used in the past are decision trees, inductive logic programming, genetic algorithms, neural network and case based reasoning. Data Mining for Condition Monitoring Applications Over the years vibration consultants have amassed huge databases of spectral information. This information is usually taken during routine analysis of machinery and the diagnosis may be conducted on site but more usually back at the consultant's premises. Hence the requirement for mass storage and a comprehensive database archive system. In addition to the storage of the actual vibration data details such as customer site, type and location of the machine, the measurement locations and types of transducer used may be kept. It is possible to identify several sources of new knowledge that may reside within the accumulated data store: •
Discovering new machine faults. The likelihood of discovering some new configuration of mechanical defect is most unlikely considering the relative maturity of the electromechanical field. However, it is possible for a new product such as a pump or a bearing assembly to suffer from a design error that may exhibit an unusual frequency signature during operation. 556
•
Discovering new features to identify known machine faults. This is potentially the most useful area for data mining and knowledge discovery. In recent years a great deal of research effort has been expended in an attempt to discover those frequency components that lead to an accurate diagnosis of machine condition. It is evident that a great deal of diagnostic knowledge still remains to be discovered.
•
Discovering the cause of spurious diagnosis. The ability to detect and discover the cause of an incorrect diagnosis will greatly improve the reliability of a diagnostic system. Currently this feature is very difficult to implement, as this requires the intervention of a human operator to detect the incorrect diagnosis and some further analysis of the rogue input data. Usually, a precise explanation of why any given input data was incorrectly diagnosed as a fault is impossible.
The data mining technique described in this paper involves the process of training several RBF networks on vibration data and then extracting symbolic rules from these networks as an aid to understanding: i) RBF network internal operation, ii) the relationships of the parameters associated with each fault class.
LREX: LOCAL RULE EXTRACTION ALGORITHM The development of the LREX (Local) algorithm was motivated by the local architecture of RBF networks, which suggested that rules with unique characteristics could be extracted. The basic premise of our rule extraction process works on the assumption that each hidden RBF unit can be uniquely assigned to a specific class. The LREX algorithm is composed of two modules: the mREX module extracts IF..THEN type rules based on the premise that a hidden unit can be uniquely assigned to a specific output class. However, hidden unit sharing occurs within networks trained on non-linear or complex data. This phenomena reduces rule accuracy as several hidden units may be shared amongst several classes. The second module, hREX was developed to identify which hidden units are shared between several classes. The extracted rules are IF..THEN type rules hidden may appear and details are provided elsewhere in the literature (Mcgarry Ola). mREX: Input-to-output mapping The functionality of mREX algorithm is shown in figure 2. The first stage of the mREX algorithm is to use the W2 weight matrix (see figure 1) to identify the class allocation of each hidden unit. The next stage is to calculate the lower and upper bounds of each antecedent by adjusting the centre weights using the Gaussian spread. The lower and upper limits are further adjusted using a statistical measure S gained from the training patterns classified by each hidden unit. S is used empirically to either contract or expand each antecedent's range in relation to the particular characteristics of these training patterns. A single rule from the vibration A domain is presented in figure 3.
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Input: Hidden unit weights fi (centre positions) Gaussian radius spread o Output weights W2 Statistical measure S Training patterns Output: One rule per hidden unit Procedure: Train RBF network on data set Collate training pattern "hits" for each hidden unit For each hidden unit Use W2 correlation to determine class label Use "hits" to determine S Select S format {min, max, std, mean, med) For each fj. X lower = ju- a* S X upper = //+ a* S Build rule by: Antecedent = [Xlower; Xupper] Join antecedents with AND Add class label Write rule to file Figure 2: mREX rule extraction algorithm
Rule 2: IF((RPM1 >= -0.44144 ) AND (RPMl <= 1.2237) AND (RPM2 >= -0.44317) AND (RPM2 <= 1.2219) AND (RPM3 >= 0.15902) AND (RPM3 <= 1.8241) AND (RPM4 >= -0.58981) AND (RPM4 <= 1.0753) AND (RPM5 >= 2.3602) AND (RPM5 <= 4.0253) AND (HarmPow >= -0.58495) AND (HarmPow <= 1.0802) AND (RlO-1000 >= -0.82373) AND (RlO-1000 <= 0.84138) AND (R500-1000 >= -0.39743) AND (R500-1000 <= 1.2677) AND (RMS >= 8.529) AND (RMS <= 10.1941)) THEN..MISALIGNEMENT Figure 3: mREX extracted rule for misalignment (Vibration A data)
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Enhancements to the Extracted Rules A number of features were added as a postprocessing function to the rule extraction algorithm. Although these additions are not directly a part of rule extraction they do increase the understandibility of the extracted rules and therefore add value to the overall process of data mining: removal of superfluous antecedents, removal of redundant rules and the addition of a default rule. A fragment of the rule set for the vibration A domain is presented in figure 4.
Rule 1: IF(R500-1000>=-0.0029052 AND R500-1000 <= 0.2086) THEN..OK Rule 2: IF((R10-1000 >= 0.00019207 ) AND (RlO-1000 <= 0.041637) AND (R500-1000 >= 0.37334) AND (R500-1000 <= 0.87877)) AND THEN..MISALIGNMENT
Figure 4: Extracted rules from the Vibration A domain with superfluous rules removed The use of information theory was made to reduce the number of antecedents within a rule. The information measure or information gain about an input feature represents the importance of that feature to resolve class identity. Features with very low values may be excluded from the extracted rules and will thus simplify the rule and aid comprehensibility. Removal of redundant rules was accomplished by identifying those rules which only classified a small number (1-2) of examples. A small amount of accuracy was lost but reduced the rule set by 25 rules in the case of the vibration A data. The number of redundant rules varied with the size of the original RBF network and reflects the inefficiency of the training algorithm used. The addition of a default rule improved the accuracy of rules from vibration data A. This is always a dangerous strategy as the rules are forced to make a classification decision regarding the input i.e. the class with the largest number of rules had all of its rules removed, the remaining rules would check for the other classes and if these failed then the default class would be selected. The accuracy results of the basic and three rule type variants are presented in table 2. Data set
RBF (%)
Vibration A Vibration B
89 94
Basic rule(%) 73 80
Antepruned(%) 50 45
Rule pruned(%) 65:25 80:6
Default rule(%) 70 84
Table 2: Accuracy comparison between original RBF network, basic rule, pruned antecedent, pruned rule and default rule systems. Analysis of the hREX rules proved to be interesting as the RBF networks have up to 20-40% of their hidden units shared between the various output classes. Overall, pruning rules with "superfluous" antecedents produced a very poor result. The rating assigned to each antecedent (input feature) was calculated on an individual basis and did not include inter-
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variable relationships. However, further inspection of those rules that had antecedents pruned but had good accuracy highlighted some interesting details. It would appear that the usual FFT parameters (i.e. the RPM's) for determining fault type and severity appear not to be required by several rules. These rules seem to have modelled facets of the original data set learned by the RBF network. However, the rules are valid, accurate and account for 15-20% of the total number of extracted rules.
CONCLUSIONS The work described in this paper has tackled the issue of rule extraction from RBF networks. The rules provide the user with lower and upper bounds for each parameter associated with a particular class of fault. Deficiencies in the original RBF were identified by the removal of those rules (i.e. hidden imits) that either do not contribute or contribute little to the overall classification capability. The removal of superfluous antecedents enables a smaller sized rule to be constructed and thus aids comprehensibility. This technique has also identified rules that use parameters not typically associated with fault diagnosis and thus has aided a better understanding of the data set. Future work will examine other methods for antecedent removal that will assess the importance of inter-variable relationships instead of the information measure currently used which examines the importance of each variable individually.
ACKNOWLEDGEMENTS This work was partly funded by the ISEBERG project, ERDF project number 69/11/002. The authors would like to thank Zuhair Bandar and Tony Browne for their useful comments on certain sections of this work
REFERENCES Bishop, C. (1995), Neural Networks for Pattern Recognition, Oxford University Press. McGarry, K. and Maclntyre, J. (1999a), Hybrid diagnostic system based upon simulation and artificial intelligence, in "Proceedings of the International Conference on Condition Monitoring", University of Wales, Swansea, 593-600. McGarry, K., Wermter, S. and Maclntyre, J. (1999b), "Hybrid neural systems: from simple coupling to fully integrated neural networks". Neural Computing Surveys 2:1,62-93. McGarry, K., Wermter, S. and Maclntyre, J. (2001), Knowledge extraction from local function networks, in "Seventeenth International Joint Conference on Artificial Intelligence", Seattle, USA. (Piatetsky-Shapiro and Matheus 1994) Piatetsky-Shapiro, G. and Matheus, C. J. (1994), The interestingness of deviations, "Proceedings of AAAI Workshop on Knowledge Discovery in Databases".
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Condition Monitoring and Diagnostic Engineering Management Andrew G. Starr and Raj B.K.N. Rao (Editors) Crown copyright © 2001. Published by Elsevier Science Ltd. Allrightsreserved.
APPLICATION OF COMPONENTIAL CODING IN FAULT DETECTION AND DLVGNOSIS OF ROTATING PLANT B S Payne', F Gu', C J S Webber^ and A D Ball^ ' Maintenance Engineering, Manchester School of Engineering, University of Manchester, Ml4 9PL, [email protected], www.maintenance.org.uk ^ QinetiQ Ltd, Malvern Technology Park, St Andrews road, Malvern, Worcestershire, WR14 3PS, [email protected], www.QinetiQ.com
ABSTRACT A novel data processing technique called Componential Coding (which is achieved using a self-organising auto-encoder neural network) has recently been developed as a tool for condition monitoring of rotating plant. This paper outlines the background to the technique and describes two alternative architectures for its implementation. The development of approaches in order to implement condition monitoring are then discussed. Finally an example of the capability of Componential Coding is given using real data from an induction motor test facility. Conventional methods of condition monitoring are well established but exhibit some shortcomings [1, 2, 3] related to the requirements for prior knowledge and development of machine-specific tools. Componential Coding however is a generic technique that can be applied with little domain specific knowledge. The approach was originally developed for high dimensional data (typically for image processing) [4]. In order to apply Componential Coding to condition monitoring of rotating machinery the algorithm was developed in two forms: one for single-channel data and one for multi-channel data inputs. This paper considers the training of the network, how it may be used in condition monitoring and also both empirical and automatic optimisation of the network in order to improve the detection and diagnostic capabilities. The results obtained through the application of both real vibration and acoustic data from a conventional induction motor operating under healthy and faulty conditions are then presented and discussed. KEYWORDS Neural network, componential coding, auto-encoder, condition monitoring, induction motor. INTRODUCTION The novel technique outlined in this paper is based on an unsupervised neural network structure and was developed for data processing [4]. The network adapts its data-model to its training data, in such a way that on average it is able to reconstruct each input data pattern
British Crown Copyright 2001/QinetiQ. Published with the permission of the controller HMSO.
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with optimal accuracy. The model's reconstruction accuracy is optimised by gradient descent on the mean squared (vector) difference between input data patterns and their respective model-based reconstructions. When presented with new, anomalous data that have properties statistically different from the data on which it was trained, the network will reconstruct the anomalous features of the new data less than optimally; the discrepancy (between the anomalous input data pattern and the network's training-model based reconstruction of that pattern) can be used to highlight anomalies in the new input data pattern that were not represented in the training data. Componential Coding has the advantage that it makes no assumption about the basis function construction - it does not assume an input data pattern is made up of sine waves (as in a Fourier Transform approach) or wavelets (as in a Wavelet Domain approach); instead, the basis functions, which constitute the adaptive parameters of the data model, are derived (without supervision) by the process of adaptation to the training data. One of the most advantageous features of the network is its translational invariance which means that signatures can be recognised at any instance in time without the requirement for reference marks in the raw data. Additionally the network may be applied directly to the data with no pre-processing requirements and it has also been proven to be robust even in the presence of measurement noise. A special choice of (soft) non-linear neural threshold function gives rise to the emergent property of * sparse' or 'componential' coding [4], which means that the neurons of the network develop adaptively into a set of feature-detectors for the key features of the data; this allows the possibility of direct physical interpretation of the basis functions that result from the adaptive training. The network's classification and data compression capability was originally demonstrated in the processing of images of multi-dimensional data sets [4]. In order to apply the network to condition monitoring of industrial machines, novelty detection is achieved through interpretation of the error between the measured signal reconstruction (using the network model) and a healthy baseline signal. Higher amplitudes of the error therefore indicate a larger change in the system from its baseline. NETWORK ARCHITECTURE ALTERNATIVES Two variants of the auto-encoder have been developed. One is for single channel inputs and named an Individual Channel Architecture Network (ICAN). The other is for multiple channel inputs, named a Joint Channel Architecture Network (JCAN) [5]. ICAN employs multiple, independent networks where each one is trained with a single channel of data. It is intended to be used when measured variables from different transducer channels have very different data structures. An example of a requirement for this is in the monitoring of cylinder pressure and torque from a diesel engine, which are measured using quite different kinds of sensors. This architecture allows the neurons of each channel to be tuned independently for best novelty detection. Unlike the JCAN network, the ICAN network does not allow the possibility of picking up correlations between different sensor channels however. JCAN is a single network that is trained using multiple data channels. This is particularly useful when many different channels measure data from similar sensors (such as the measurement of tri-axial vibration). The output of a particular channel produced by JCAN depends not only on the information of one channel but also on that of all the other channels, allowing data fusion between different sensor channels. The JCAN network is sensitive to correlations between different sensor channels, allowing such correlations to be exploited for
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condition monitoring. This network also reflects the phase information between channels so that this feature may be retained for condition monitoring. In order to implement JCAN, a relationship between channels was developed implicitly through a partial circular correlation operation [5]. The ICAN network is actually a constrained special case of the more general JCAN network, having the special property that each neuron of the network receives only one channel of input. TRAINING OFTHE AUTO-ENCODER The purpose of training the autoencoder is to derive a set of optimal basis fiinctions and other adaptive parameters (the so-called scale parameters) that best reconstruct the input training data (figure 1). The basis function configuration is, however, predescribed by the number of basis functions and the number of elements in each function.
No of Basis Functions, Length of each Basis Function, Threshold and Softness Set at Beginning Bulk of the code is in this block.
Random Basis Functions Created
Input Training Data Patterns (Recommended that 100 revolutions are represented)
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Data Reconstruction
Basis Functions Updated using Gradient Descent Algorithm
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If Error < E, or Number of Iterations = n then training is finished.
Final Basis Functions and Network Configuration
Figure 1 ~ Training of the Auto-Encoder Network
The training of the auto-encoder network involves the following basic steps: 1. The basis functions are initialised with random values and scaled into unit vector-length. 2. For the current configuration of basis functions, a correlation function is computed between each basis function and the current input data pattern and the scale values are calculated. A gradient vector for each basis is calculated using a gradient descent algorithm. The basis functions are updated according to the gradient vectors and then scaled to unit vector-length. Steps 2 and 4 are reiterated until the basis functions are satisfactorily capable of reconstructing the training data patterns or the maximum number of iterations has been reached. CONDITION MONITORING USING THE AUTO-ENCODER Condition monitoring of a piece of rotating plant may be implemented at four different levels using the auto-encoder:
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Level 1: Level 2:
Level 3: Level 4:
Overall abnormality detection can be achieved through the input data pattern reconstruction errors obtained from the model. Abnormality detection of individual sensors can be achieved via the signal reconstruction error of the individual channel or decomposition of the total error to individual channels. Fault discrimination (or severity assessment) can be based on the amplitude of reconstruction errors. Fault diagnosis is possible by characterisation of the basis functions and scale values.
NOVELTY DETECTION AND DIAGNOSIS The fundamental tasks in condition monitoring involve novelty detection and diagnosis. From the network model and training principles, three approaches for the development of models for monitoring are possible. These approaches are based on the input data pattern reconstruction error, scale parameter re-calculation and basis function re-estimation. However this paper will present only the application of the more basic input data pattern reconstruction error and a preliminary interpretation of the basis functions in both the time and frequency domains. Input Data Pattern Reconstruction Error Once network training (with a healthy baseline data set) is complete, the final configuration of trained basis functions is used (unchanged) in the subsequent monitoring phase. Anomalies in new data that were not represented in the training data will show up as large differences between an input data pattern and the network's reconstruction (based on the trained model) of that pattern. This difference (reconstruction error) therefore provides an overall and generically applicable assessment of novelty and thus allows fault detection. It can then be used to achieve a value defined as the Discrimination Index, DI: pj^Eappiied-Etest
Equation 1
^test
Where ^applied IS the crror between the reconstruction of a newly applied input data patterns (from machine monitoring) and itself, and Etest is the error between the reconstruction of the test input data patterns compared with the training data patterns. DI is therefore a normalised measure of difference. OPTIMISATION FOR FAULT DETECTION The auto-encoder performance depends heavily on the network parameter configuration (particularly the number of basis functions and threshold value) and therefore a systematic approach to optimisation was performed. Requirements of optimisation were that the approach used should be generic in order to maintain the network's application-flexibility and also capable of dealing with an objective function (DI) surface with many plateaux and jumps (as network parameters are changed). This led to the development of an automatic optimisation procedure (using an Evolutionary Algorithm) that required little information about the system to be monitored. However it was also recognised that a subjective performance assessment based on gradual and systematic parameter adjustment would be
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useful for understanding the auto-encoder behaviour and for the development of a generic optimisation method. Therefore, in parallel with automatic optimisation, empirical optimisation was also pursued by the manual adjustment of parameters within the search space. Such an approach validated the use of automatic optimisation and also proved its reliability. RESULTS The first application of the network was made to data recorded from a 3kW three-phase induction motor in a test facility (figure 2) that allows the seeding of a variety of faults. Two channels of data were recorded; horizontal surface vibration on the drive end (using an accelerometer) and airborne acoustics (using a microphone placed 200mm horizontally away from the motor casing). Data was collected under healthy operation of the motor and with the seeding of 20V and 40V imbalance faults in one of the three phases of supply. In practice phase imbalance faults may be due to the uneven use of a single phase (eg the single-phase lighting in a large building may be powered unevenly from one phase) or loose electrical connections for example. Control Box and Hall Effect Current Sensors
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Examples of the raw acoustic data and its representation in a time-frequency domain are given in figure 3. The time-frequency representation shows that there about 8 major frequency components (in the range provided) and the fluctuations of the amplitudes along the time axis are relatively small. In line with the theory when 20V phase imbalance is introduced there is an increase in the amplitudes at lOOHz. Figure 4(a) illustrates the network convergence characteristics for the fiised vibration and acoustic data following this optimisation. The convergence is based upon the best fit determined between training data and its reconstruction and the y-axis therefore indicates the residual, as mean square error (MSE) between them. Convergence is gained at around 50 training iterations indicating that the network has been trained sufficiently at this point. Figure 4(b) shows the scale parameters associated with each of the 25 basis fimctions found at the end of the training process. The value of the scale parameter for basis 20 is significantly higher than for the others. This may be due to noise influences, as the actual optimised basis function (figure 5) has more higher frequency content than in the others.
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The optimised basis functions are illustrated in figure 5 in both the time and frequency domains. The basis functions are very similar to the raw data in the time domain and it may be observed those for the vibration data fluctuate more than the basis functions for the acoustic data (as in the raw data itself). There is also dissimilarity between the individual basis functions as confirmed by the Euclidean distance, probability density functions (PDFs) and dot product [6]. This dissimilarity suggests that the basis functions reflect distinct timedomain features.
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The spectra of the basis functions (and also of the original data) are dominated by lower frequency components. This illustrates that the basis functions also record frequency information during training, which provides the network with the ability to find frequency novelty. Comparing the averaged spectra of the basis functions with the original spectra it was found that both are very similar for the acoustic data but there is more dissimilarity for the vibration data (particularly in the low frequency range). This indicates that the vibration signal has a relatively low signal-to-noise ratio (SNR). Table 1 illustrates the detection results by using both the ICAN and JCAN derivatives. The ability to detect the seeded phase imbalance faults is measured by the discrimination index (DI). It should be noted that JCAN produces a single DI value as both the vibration and acoustic data are effectively fused together. On the other hand ICAN produced a DI value for each type of data. The results illustrate that the DI values are non-linear with the introduction and increasing severity of the fault condition, this is to be expected as the network itself is non-linear. Both ICAN and JCAN demonstrate good fault detection although ICAN has proved particularly good at discriminating the more severe condition of 40V imbalance. However the DI provides no means of diagnosing the fault condition.
Table 1 - JCAN and ICAN Detection Results
Fault detection and diagnosis of the most common machine faults are conventionally well understood in the frequency domain. It is via the analysis of frequency features that fault diagnosis is routinely achieved in condition monitoring. As the spectral components of the basis functions correlate with well with the original signal spectrum it is also possible for the auto-encoder to diagnose faults through frequency domain approaches. Since the reconstruction errors represent the novelty of a new measurement, the spectrum of the error signal correspondingly characterises the changes in the frequency structure. Changes in the
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frequency components can therefore be used for the identification of the occurrence of a particular fauh [6]. Figure 6 compares the spectra of the original measured data from the motor and that of the network residuals. The two graphs on the left-hand-side represent the vibration data and the two on the right-hand-side represent the acoustic data. The introduction of phase imbalance leads to an increase in the energy content (amplitude) in the 50-200Hz range of the raw vibration data and a decrease in amplitude in the higher frequency range. In the corresponding residual spectrum this higherfrequencyreduction may also be observed with increasing fault severity. In the measured acoustic signal spectrum, little change in the frequency structure can be seen with the introduction of the fault condition. However distinct and gradual changes are apparent in the corresponding acoustic signal residual. These results therefore illustrate the capability of the auto-encoder network in fault detection, diagnosis and severity assessment and its potential in extracting useful information from the residual [6]. Spectra of Original Signal and Residual Signal for Acoustic Data
Spectra of Original Signal and Residual Signal for Vibration Data ft
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Figure 6 - Diagnostic Potential in using Residual Signals for both Vibration and Acoustic Data (ICAN)
CONCLUSIONS This paper has demonstrated that the self-organising network requires very little knowledge of the system under investigation for the purposes of fault detection. The use of the discrimination index (DI) as a single-valued fault detection indicator provides a straightforward basis on which automated warning levels may easily be implemented. The choice of the network derivative to be used (JCAN or ICAN) should be based upon the data being collected. However for the induction motor condition monitoring data considered, both derivatives were found to be very capable of detecting the phase imbalance condition. This is explained by the fact that although the vibration and acoustic data was collected from different sensor types (therefore suggesting the use of ICAN), both sets provide similar information content (therefore allowing the successful application of JCAN) [7]. Interestingly it was found that for fault diagnosis, airborne acoustic data was more sensitive than motor case vibration. This is an important discovery because of the ease in which acoustic measurement may be implemented (it is a non-contact approach and provides global machine information content) [7]. In general for the motor data it was found that residual spectrum analysis could capture both primary fault features (up to about 100 Hz) and
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secondary fault features (up to about 800 Hz). Additionally it was found that the autoencoder is particularly robust and network modelling is possible even if data is very noisy. FURTHER WORK In order to address the computational overheads experienced during the optimisation phases of the network, future work will aim to identify the bottlenecks and then make speedimproving modifications in these areas. It is also anticipated that improvements may be made to the evolutionary algorithm. Given the highly successful results obtained to-date it is intended that the auto-encoder will now be applied to data from a novel motor being developed by Rolls-Royce. It is expected that this application will fully utilise some of the benefits of the auto-encoder such as translation invariance [4], the ability to deal with multi-channel inputs and the fact that little information is available about the fault characteristics of this new machine. ACKNOWLEDGEMENTS This work was carried out as part of Technology Group 10 of the MoD Corporate Research Programme. Rolls-Royce pic is thanked for its support in application of this novel network architecture. In addition, the contribution made by Hugh Webber at QinetiQ during the course of the work is acknowledged. REFERENCES [1]
[2]
[3]
[4] [5]
[6]
[7]
B Payne, B Liang and A Ball, Modern Condition Monitoring Techniques for Electric Machines, Proceedings of the 1^* International Conference on the Integration of Dynamics, Monitoring and Control (DYMAC '99), Manchester, UK, pg 325-330, September 1999. B Payne, A Ball and F Gu, An Investigation into the Ways and Means of Detecting, Locating and Assessing the Severity of Incipient Turn-to-turn Stator Shorting Faults in 3-phase Induction Motors, Proceedings of the 13*^ International Congress on Condition Monitoring and Diagnostic Engineering Management (COMADEM 2000), Texas, USA, pg 195-202, December 2000. B Payne, A Ball, F Gu and W Li, A Head-to-head Assessment of the Relative Fault Detection and Diagnosis Capabilities of Conventional Vibration and Airborne Acoustic Monitoring, Proceedings of the 13^^ International Congress on Condition Monitoring and Diagnostic Engineering Management (COMADEM 2000), Texas, USA, pg 233-242, December 2000. C J S Webber, Emergent Componential Coding of a Hand-written Image Database by Neural Self-organisation, Network: Neural Systems, Vol 9, pp 433-447, 1998. F Gu, M Desforges, B Payne and A Ball, Application of a Self organising Autoencoder to the Condition Monitoring of a Novel Electric Motor, Final Report, Phase One, Internal Document No: MERG-0699, May 1999. F Gu, B Payne, M Desforges and A Ball, Optimisation of a Self-Organising Autoencoder for the Condition Monitoring of a Novel Electric Motor, Final Report, Phase Two, Internal Document No: MERG-0600, April 2000. B Payne, L Bo, W Li, F Gu and A Ball, The Detection of Faults in Induction Motors Using Higher Order Statistics, Proceedings of the 5th Annual Maintenance and Reliability Conference (MARCON 2001), Gatlinburg, Tennessee, USA, May 2001.
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Condition Monitoring and Diagnostic Engineering Management Andrew G. Starr and Raj B.K.N. Rao (Editors) © 2001 Elsevier Science Ltd. All rights reserved.
BEARING FAULT DETECTION USING ADAPTIVE NEURAL NETWORKS Y SHAO and K. NEZU and T. TOKITO Department of Mechanical System Engineering, Gunma University 1-5-1, Tenjing-cho, Kiryu City, 376-8515, Japan
ABSTRACT An effective method is presented for improving the signal to noise ratio by adaptive neural networks. A comparison of failure detection capabilities of a filter using LMS algorithm and a filter using adaptive neural networks under heavy environment noise is made. Experimental results have shown that using adaptive neural network is an effective means to extract early symptoms of bearing fault under such conditions.
KEYWORDS Bearing Fault, Adaptive Neural Networks
INTRODUCTION Vibration monitoring is recognized as being an effective tool when applying a program for machine condition monitoring. Vibration monitoring is based on the principle that all systems produce vibration. When a machine is operating property, vibrations are small and of constant amplitude; however, when a fault develops and some of the dynamic processes in the machine change, the vibration spectrum also changes. For localized defects in rolling element bearings, each time a rolling element passes over a defect, a vibration impulse is generated. These impulses are related to the size and type of the fault, and all current vibration monitoring techniques are based on the recording and quantification of these vibration impulses. Numerous methods have been developed for detecting localized faults in bearings. For example, statistical methods (I), time andfi-equencyfieldanalysis methods (2), analysis of bearings
571
vibration features (2), ad^tive noise canceling techniques (3X4) and fuzzy and neural networks identification methods (5) However there still does not exist an ideal way to diagnose the early failure of a rolling element bearing subject to heavy noise conditions. Adaptive noise canceling (ANC) is a method for estimating signals corrupted by additive noise (6)(7). For the failure diagnosis of rolling element bearings, Thomas (1982) and Shao and Nezu (1996) described methods for improving the signal-to-noise ratio by the application of adaptive noise canceling (ANC) and asynchronous adaptive noise canceling (AANC) using the least squares (LMS) algorithm, respeaively (3)(4). However, since rolling element bearing noise is impulsive, only the signal-to-noise ratio in limited ranges was improved because the methods all used linear ad^ive filtering algorithms. In machine operating environments, the linear adaptivefilteringusing the standard LMS algorithm will have poor performance characteristics due to high variance gradient estimates resulting fi-om non-Gaussian noise. In this paper, an adaptive network is used to solve this problem, by which the above two types of noise can be better eliminated at the same time. Section 2 formulates the problem of bearing fault signals and noise and briefly describes the algorithm for networic training. A new bearing fault diagnostic procedure using adaptive neural network is developed in section 3, and simulation and experimental results are presented in Section 4. Conclusions are provided in Section 5. MODEL OF ADAPTIVE NEURAL BETWORK FOR BEARING FAULT DIAGNOSTIC Definitions of Signals in Various Layers of Multi-Layer Perception N.
N,
7^
7^
Nu
^
£
Fig. 1 Definitions of Signals in Various Layers of Multi-layer Perception A multi-layer perception, shown in Fig.l, consists of many adaptive linear combiners with a non-linearity at the output. The input-output relationship of such a unit in layer 1 of the network is characterized by the nonlinear difference equation *(/)
xf^'>=^|
(1)
with the output being the ith node in the (/+l)th layer. The parameter A is a bias term, equivalent to a weight with a constant +1 input.
572
The error signal is defined to be the difference between some desired response and the actual output of the network. eXn) = d,-yXn)
i=l, 2, ..., A^w
(2)
where di is the desired response at the hh node of the output layer, yi{n) is the output at the hh node of the output layer, and NM is the number of neurons in the output layer of the neural network, referred to hereafter as the M h layer; n refers to the number of iterations of the algorithm. The sum of error squares produced by the network defines the cost function 1 ^«
1 ^^
(3)
Using an approximation to the gradient descent technique. The weight update equation is ts^v^^(n +1) = w^^(n) + b.w^^(n)
(4)
The weights are changed in proportion to the negative of the gradient. The update term is defined to be
A < W = -//V<4^(«)
(5)
where/I is a learning-rate parameter and V ^ e(n) is the gradient of the cost functionfi(n)with respect to the weight Wi^. The output j^/«j is therefore y,^x'^'^(p(netf'-'') (6) where net'f'-''^net'f':''^jmtf-''
(7)
^^Yu^T'^^^T''^^? p=\ Model For Bearing Noise Canceling Using the Adaptive Neural Network The basic model for noise canceling is shown in Fig. 2. Signal Si is corrupted by a noise No and received at the primary sensor when the bearing is running under abnormal conditions. The sensor output signal is denoted the combined No/Si signal S, S i is the bearing fault symptom signal, and No is machine noise. A reference noise Ni, which is related to noise No in some unknown way but uncorrelated with signal Si, is received at the reference signal. N and S are used as inputs for the adaptive network. The neural networks are adaptively trained to predict the combined No/Si signal Sfi-omnoise signal N. The network error £ will be equal to S, the signal minus the interfering noise signal, where s contains only the Si signal. Thus, the adaptive neural algorithm can learn to cancel noise adaptively. For the initialization of all weight and biases, the parameters of the neural network may be assigned a subset of values drawn at random from the training set. The learning-rate parameter u and the filter window width are important factors that will affect the result of the adaptive neural filtering. The
573
filtering result in the learning-rates and thefilterwidths will be discussed in section 4.
Model of Machine Noise
Adaptive Noise Canceling
Fig. 2 Adaptive Filter in a Machine Noise Canceling
SIMULATION RESULTS The performance of the adaptive neural filter was examined by simulating an adaptive network with a rectangular sliding data window of length N. The network was driven by a corrupted test signal with a constant rectangular amplitude (frequency lOOOHz). To examine the ability of the networic to adapt to iK)ise, the network was presented with Gaussian noise with a zero mean and a standard deviation of 1.8. The output sequence was broken into blocks of 2n samples (n>5), which were then processed using the FFT. Detection was attempted on the results of the FFT calculation. The clearance ratio (CR) and peak ratio (PR) were chosen to evaluate the performance of adaptive neural filter The clearance ratio (CR) and peak ratio (PR) were defined as follows Clearance Ratio CR-^Y
Peak ratio
PR --
I
[yv^ii)
Max{X(k)) Max{Y(k))
(8)
(9)
where, X are the results of the FFT calculation of input data x, Y are the results of the FFT calculation of output data y, Yj is the peak amplitude in the keyfi-equencyof the signal feature. The relationship between learning rates and evaluation factors (CR factor and PR factor) is shown in Fig 3. The FFT calculation had a data length of 4096 points. Figure 3 shows that there is a good stability range of noise canceling for adaptive neural filter when the learning rates are chosen between 0.0003 and 0.07. When the learning rate is 0.002, the relationship between the width of the sliding window and the evaluation factors (CR and PR) is shown in Fig. 4 The CR and PR increased with the increased sliding window widthfi-om64 to 8192 points. The most suitable result was found at point 8192 for CR and PR, but the rangesfi-om1024 to 8192 were also to be able to satisfy thefilteringrequirements.
574
The variation of adaptive neuralfilteringwithfilteringtime is shown in Fig. 5. The input sequence data was divided into 16 blocks, with 512 samples in each block. Figure 5 shows that the adaptive neural filter will be stable after 0.124 second. The difference between error and actual signal performance of filter for neural network is shown in Fig. 6. Within 0.15 second the network error signal forms a very good estimate of rectangular signal.
I 0.4 , 0.25 \£* i 0.2 L
- • - CR: factor I - * - P R : factor
y J
1 -B 0.8 c? 0.6 ig 0.4 (S!
«, 0.3 o
0.8 g
I 0.2
0.6 2
7^ 0.1
Q.
^
^
^
0.4
0.1
64
0.2
0.05
128
256
512 1024 2048 4096 8192
Length of Data 0.004 0.01 Learning rate
0.07 -Clearance Ratio •
- Peak Ratio.
Fig.4 Relationship between Width of Sliding Window and Evaluation Factors
Fig. 3 Relationship between Learning Rate and Evaluation Factors 0.35
f-^t
0.3
t •
/
.o 0.25
f
§ °-2 2 0.15
«
O
0.1 0.05
/
0 ' C
1 — w i c a i a i i o c nauui
0.5
1 Time (Second)
1.5
I
Fig. 5 Variation of Neural Filter with Fihering Time
Fig. 6 Difference between Error and Actual Signal Performance of Filter for Neural network
EXPERIMENTAL RESULTS EV AN ACTUAL BEAREVG RUNNING SYSTEM The test bearing was artificially localized defects induced by an electric-discharge machine. The test has three types of vibration under different loads and speeds. Normal bearing vibrations can be caused by in either identical or diflferent bearing types. One defective bearing was installed. A comparison of estimated bearing signal waveform Fig. 8(b) with the raw bearing Fig. 8(a) shows that these two waveforms are very similar. In Fig. 8(c) and (d), the detective bearing waveform is shown along with its spectrum. In Fig. 8, the performance of neural filter is not very clear thought the impact structure (due to the fault) is easily to recognizable, but Kurtosis value is increased to a value of more than raw bearing signal.
575
The Kurtosis factors of the three failure sizes for the two filtering algorithms are shown in Fig.9. Generally, the Kurtosis is 3 when the bearing is running under normal conditions. Figure 9 shows that the kurtosis was greater than 3 for three sizes of bearing failure, and that the adaptive neural filtering algorithm is more suitable at identifying bearing faults than the standard LMS algorithm's. The improved ratios of Kurtosis values in three failure sizes (large size, medium size, small size) between the adaptive neural algorithm and the standard LMS algorithm are 3.93, 2.61 and 1.13 times.
nM*M)'M»^>>Of
(a)
30 25 » Neural NetwoK • LMS AliorithiTi
(b) •i
:^.
J:
J=
03
J
a*
J=
U
-«:
JL
S 15
JL_
1.0
(c)
^ W f l l V ^ l ^ i ^ Hz Medutn Defect Size
(d)
'pfiMfy^^^
(a),(b) Time Waveform (cXd) Spectral Fig. 7 Waveform and Spectral of Outer Race Failure
Fig. 8 Performance of Filter for Neural Network and LMS Algorithm
CONCLUSIONS Statistical analysis showed that the ANF method was more able to identify a bearing fault than standard LMS algorithm. The values of Kurtosis in the adaptive neural algorithm are 1.13—^3.93 times as high as they were in standard LMS algorithm. The experimental results also show that there is a wide, stable range (0.0003—0.07) of learning rates for the adaptive neural filter. Reference [1] D. Dyer, R. M. Stewart," Detection of Rolling Element Bearing Damage by Statistical Vibration Analysis", Trans, of the ASME Journal of Mechanical Design, Vol. 100,229-235, April, 1978. [2] S. Braun, B. Datner, "Analysis of Roller / Ball Bearing Vibrations", Trans, of ASME Journal of Mechanical Design, Vol. 101, 118-125, Jan. 1979. [3] G. K Chaturvedi, D. W Thomas, "Bearing Fault Detection Using Adaptive Noise Canceling", Trans, of the ASME Journal of Mechanical Design, Vol. 104, 280-289,1982. [4] Yimin Shao and Kikuo Nezu, D^ection of Self-aligning Roller Bearing Fault Using Asynchronous Adaptive Noise Cancelling Technology, JSME International Journal, Vol.42,No.l, March 1999, pp.33-43. [5] C J. Li, S. M. Wu," On-Line Detection of Localized Defects in Bearings by Pattern Recognition Analysis", ASME of Journal of Engineering for Industry, Vol. Ill, 331-336, November, 1989. [6] "Neural Network Toolbox User's Guide", The Math Works, Inc. [7] Simon Haykin, "Adaptive Filter Theory", Prentice-Hall, Inc. A Simon & Schuster Company Upper Saddle River, New Jersey 07458, 1996.
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Condition Monitoring and Diagnostic Engineering Management Andrew G. Starr and Raj B.K.N. Rao (Editors) © 2001 Elsevier Science Ltd. All rights reserved.
ANALYSIS OF NOVELTY DETECTION PROPERTIES OF AUTOASSOCIATORS Sang Ok Song, Dongil Shin, and En Sup Yoon Institute of Chemical Processes, Seoul National University, Seoull51-744, Korea ABSTRACT In this work we review PCA and various non-linear PCA methods from the autoassociator point of view. Autoassociator is used to identify and remove correlations among problem variables and can be used to detect abnormality condition of various processes where an early warning of an abnormal condition is required. Feature extraction methods such as PCA and neural network can be an excellent tool of building autoassociator. Several autoassociators based on statistics and neural network have been reviewed and their autoassociative reconstruction properties and abnormality detection performances have been analyzed for a nonlinear 3-dimensional example. Results show that principal curves & neural network, principal curves & splines, and self-supervised MLP successfully reduces dimensionality and produces a feature space map resembling the actual distribution of the underlying system. Also these methods can be reliable solutions for novelty detection and their characteristics are discussed. KEYWORDS Autoassociator, novelty (abnormality, fault) detection, PCA, nonlinear PCA, principal curves, SOM INTRODUCTION Autoassociator is a model that the output can estimate the correct value of the input. Autoassociator reduces dimensionality and produces a feature space map resembling the actual distribution of the input. Therefore any feature extraction method can be a tool of building autoassociator. The appHcations of the autoassociator include abnormal condition detection, missing sensor replacement, and so on. In this work we analyze the projection properties and abnormality detection performance of autoassociators. One of the characteristics of abnormality detection problem is that only normal patterns are available while abnormal ones are not. The output of autoassociator reproduce the input and the autoassociation error defined as the Euclidean distance between the input and output vector can be used as a criterion for abnormaUty condition. If the error is small, then the input can be considered as normal and if it is large, then the input can be abnormal. In section 2, we give the brief explanation of the basic structure of autoassociator and review various methods for building the autoassociative model. In Section 3, we analyze the feature extraction properties and abnormahty detection performance of autoassociators for a 3-dimensional mathematical example. 577
AUTOASSOCIATORS We consider the basic structure of autoassociator as follows Auto-assockthn error =
I
' ^
•|x-x|^i
ImnctkM
vmrtmbf
trntction
1
'^1*'^
Figure 1. The basic structure of autoassociator The function G and F are the mapping and dems^ping function of autoassociator and Z is the matrix of feature variables extracted from the input variables. This leads to the dimensionality reduction formulation, where the encoding is given by function G performing mapping from the input space to a lower-dimensional feature space, and the decoding is given by the function F mapping from feature space back to the original input space, x is the reconstructed output with the same dimension as the input space. Therefore, autoassociator can be achieved by estimating function G and F. Principal Component Analysis Principal component analysis (PCA) is a favorite statistical tool for data compression and information extraction. PCA finds combinations of variables that describe major trends in a dataset. Mathematically, PCA relies on an eigenvector decomposition of the covariance or correlation matrix of the process variables. However PCA identifies only linear correlations between variables. Principal Curves and Surfaces The notion of principal curves and surfaces (or manifolds) has been introduced in statistics by Hastie and Stuetzle (Hastie and Stuetzle, 1989) in order to approximate a scatterplot of points from an unknown probability distribution. A smooth nonlinear curve called a principal curve is used to approximate the joint behavior of the two or more variables. The principal curve is a nonlinear generalization of the first principal component and the principal manifold is a generalization of the first two principal components. Conceptually the principal curve is a curve that passes through the middle of the data. For a given distribution, a particular point on the curve is determined by the average of all data points that project onto that point. When dealing with finite data sets, we must project onto a neighborhood of the curve. This self-consistency property formally defines the principal curve. E(X\Z
= argmJn||F(z')- ^' )= F ( G ( X ) )
(1)
where E denotes usual expectation. The individual components of (1) can be conveniently interpreted as the encoding and the decoding mappings. Therefore the procedure of obtaining principal curves is given in two steps. The projection step: This step corresponds to encoder mapping step, where the data are projected onto the current estimate of the principal curve. G(x) = argmm||r(z)-X^f
578
(2)
The conditional average step: This step corresponds to decoder mapping step, where locally weighted regression and smoothing scatterplots are used to estimate F(Z)=E(X|Z)
(3)
After iteration, the Euclidean distance between the data set and estimated principal curve is calculated Self-supervised MLP Autoassociator can also be performed using the multiplayer perceptron architecture to implement the mapping functions F and G in a bottleneck. This approach is called self-supervised operation referring to the fact that during training the output samples are identical to the input samples. Selfsupervised MLP are also known as bottleneck MLP, nonhnear PC A networks (Kramer, 1991), or replicator networks (Hecht-Nielsen, 1995). The simplest form of self-supervised MLP has a single hidden layer of A: nonlinear units and m linear input/output units encoding m-dimensional samples {k < m). In order to effectively construct a nonhnear dimensionality reduction, the mapping functions, F and G in figure 1 must both be nonlinear. This suggests that a 3-hidden-layer network should be used.
Figure 2. The architecture of self-supervised MLP The bottleneck (middle) hidden layer in Figure 2 has linear units. If the training is successful, the final network performs dimensionality reduction the original m-dimensional sample space to the kdimensional space of the bottleneck hidden layer. An MLP network shown in Fig 2 may be conceptually appealing for nonlinear dimensionality reduction and autoassociator. Batch Self-organizing Map Self-organizing map (SOM) is closely related to the principal surfaces approach. The fundamental idea of self-organizing feature map was introduced by Marlsburg (1973) and Grossberg (1976) to explain the formation of neural topological maps, which has been successfully appHed to a number of pattern recognition and engineering appHcation. However, the relationship between SOM and other statistical methods was not clear. Later it was noted that Kohonen's method could be viewed as a computational procedure for finding discrete approximation of principal curves (or surfaces) by means of a topological map of units. The batch version (Luttrell, 1990; Kohonen, 1993) of the selforganizing map algorithm is closely related to the principal curves algorithm. The feature (Z) space can be discretized into a finite set of values called the map. Vectors Z in this feature space are only allowed to take values from this set. An important requirement on this set is that distance between members of the set exists. We will denote the finite set of possible values of the feature space as
'^ = {y/,,y/^,-',y/,}
579
(4)
The elements of this set are unique, so they can be uniquely specified either by their index or by their coordinate in the feature space R*. Since the feature space is discretized, the principal curve or manifold F(Z) is defined only for values Z e T . Therefore this function can be represented as a finite set of centers taking values
Cj=¥{^{j%
J = l,...,b
(5)
In this way the units provide a mapping from the discrete feature space 4^ to the continuous space R'". The elements of ^ define the parametrization of the principal curve or manifold. The encoder funcion G is then, G(x)=^[argmm||c,-X|f]
(6)
Discrete representation of the principal curve, along with a kernel regression estimate for conditional expectation (3) results in the batch SOM algorithm. The locations of the units in the feature space are fixed and take values Ze^. The locations of the units in the input space R'" will be updated iteratively. Principal Curves & Neural Networks The principal curve gives a generalization of the first linear principal component, but the algorithm does not produce a nonlinear principal component model in the sense of a principal loading. Rather, for each data point, an associated score and corrected data point are calculated. One approach to using these scores and corrected data points would be to store them in a computer and interpolate among them when a new data point is measured. Such an approach is cumbersome and depending on the size of stored results, it could be expensive in terms of computer time. As alternate approach, Dong and McAvoy (1996) presented an NLPCA method that integrates the principal curve algorithm and neural networks. The nonlinear function F is defined as the nonlinear principal loading function. For an arbitrary nonlinear relationship expressed by F, a neural network can be an available approach to use to model F because of its universal approximation property (Homik et al., 1989). In this approach, two models are needed. The first one maps the m-dimensional data set onto the ^-dimensional nonlinear principal scores. The second one maps the ^-dimensional principal scores onto an m-dimensional corrected data set. The architecture of the neural network for implementing NLPCA is shown in Figure 3. Pflpdpai Ctirv^
IstMLP
2ndMLP
Figure 3. The architecture of the principal curves & neural networks
580
COMPARISON OF RECONSTRUCTION PROPERTIES DETECTION PERFORMANCES OF AUTOASSOCIATORS
AND
ABNORMALITY
We analyze the abnormality detection properties of following methods for simple 3-dimensional mathematical example. All subsequent analysis was performed using the Matlab 5.2. - Method 1 : PCA - Method 2 : Principal curves & neural networks - Method 3 : Principal curves & splines - Method 4 : Self-supervised MLP This example was used to illustrate nonlinear process monitoring by Dong and MacAvoy (1996). A system with three variables and only one factor is considered. 1 = ^ + ^1
(7)
2=t^-3t + e2 3=_r'+3^'+e3
(8) (9)
where e\, ej, e^ are independent noise A^(0,0.01), ^G[1,2]. hi the first 200 samples, data calculated according to these equations, and these data are taken as the normal condition. 100 samples are used as the training data and other 100 samples are used as the normal test data. After the first 200 samples, there are small changes made in X3, and the system becomes Xi=^ + ei
(10)
x^=t^-?>t-\-e^
(11)
X3 =-1.1/'4-3.2/'+^3
(12)
100 samples are calculated for abnormal test data, hi method 2 and 3, discrete principal curves are estimated by the batch version of self-organizing map algorithm. Principal curve estimate is provided by 10 centers for the training data. In method 2 neural networks are used to obtain principal curves from these centers and reconstructed data fi^om principal curve estimates. In method 2 we use cubic spline interpolation to obtain principal curves from these centers. In method 2 each neural network has 5 hidden nodes and in method 4 the autoassociative neural network has a 3:5:1:5:3 architecture. Next figures show reconstructed data of each method for normal training data. Three methods except PCA can explain nonlinear correlation of the training data. However because the selforganizing map uses a discrete feature space, the output values (Z) of the first network and the input values of the second network in method 2 are only allowed to take values in the principal score value set {0, 1,..., 10}. For this reason the value of reconstructed data in method 2 is one of the values of finite centers. Therefore more centers we use in method 2, the smaller the training error is. Figure 6 shows the reconstructed data for the use of 50 centers in method 2. Method 3 and Method 4 have almost same projection properties. In principal curves & splines the training data points are projected to the closest points on the principal curve. In self-supervised MLP reconstructed data points form a curve that passes through the middle of the data as like the principal curve.
581
- Original Function ! • Training Data i O Reconstructed Datai
Figure 4. The projection data point for training data in linear PCA (error=0.8561) - Original Function • Training Data O Reconstructed Data
- Original Function • Training Data O Reconstructed Data
Figure 5. Principal curves & neural networks (10 centers, error=0.2350)
Figure 6. Principal curves & neural networks (50 centers, error=0.0926)
- Original Function • Training Data O Reconstmcted Data
- Original Function • Training Data O Reconstructed Data
Figure 8. Self-supervised MLP (error=0.0727)
Figure 7. Principal curves & splines (error=0.0773)
Next Figures show the SPE charts of four methods for normal (the first 100 samples) and abnormal (the second 100 samples) test data. The dotted line is the threshold that is maximum value of the first 100 samples. The number of values that exceed this threshold for the second 100 SPE can be one of the criterions how well the model detects abnormality.. Figure 9 shows that the SPE of linear PCA is similar for both the normal and abnormal conditions and one of the SPE of abnormal data exceed the threshold. It is not surprising that linear PCA fails to detect this abnormality because the
582
example data has nonlinear correlation and linear PCA cannot capture the nonlinear relationship. For method 2 (principal curves & neural networks) with 10 centers, 40 of the second 100 SPE values exceed the threshold, while for using 50 centers, 47 of the second SPE values exceed the threshold. It can be seenfromthis result that Method 2 with 50 centers can detect the novelty more effectively than 10 centers. Because the value of reconstructed data is almost same as the value of the closest center, if the distances between centers too large to discriminate between noise in the normal data and abnormality, the SPE for this model cannot the criterion of abnormality detection. However if too many centers are used to obtain a good result, you can have an over-parameterized model. There exist some kind of tradeoff problem between the number of center used and the model complexity. For method 3 (principal curves & splines), 53 of the second SPE values exceed the threshold and for method 4 (self-supervised MLP) 51 exceed the threshold. In principal curves & splines algorithm principal curve can be obtained from small number of centers by using cubic spline interpolation. Input data points are projected to the closest points on the principal curve, the SPE of abnormal data become large. Therefore abnormality conditions can be detected well using small centers in comparison with method 2. However principal curves & splines algorithm with many centers produces an over-parameterized model as like principal curves & neural network algorithm. Self-supervised MLP has as similar performance as principal curves & splines. It is not very difficult to train network for this 3-dimensional example. However sometimes the network is not trained well and then the abnormality performance is very poor. It is expected that if the system is more complex, it is more difficult to train MLP network. • - ^ ,........>....,,<................ J CO
V
1
m
\mhi1 i
0
iiibiuy
100
1 •
Figure9. The SPE of linear PCA
Figure 10. Principal curves & neural networks (10 centers)
100
i
2O0
Figure 11. Principal curves & neural networks (50 centers)
200
Figurel3. Self-supervised MLP
Figure 12. Principal curves & splines
583
CONCLUSIONS In this paper various autoassociators based on statistics and neural network have been reviewed and their autoassociative reconstruction properties and abnormahty detection performances have been analyzed for a nonlinear 3-dimensional example. Principal curves & neural network, principal curves & splines and self-supervised MLP can successfully extract nonlinear information of the example data. Discrete principal score values are estimated by the batch SOM algorithm and principal curves & neural network method require many centers to train the mapping and demapping function network well. Principal curves & splines method shows the best abnormality detection performance for this example, but if the size of system is large, this ^proach can be difficult to handle the large amount of data because their values should be stored in a computer. Self-supervised MLP also shows good autoassociator properties, but has difficulties of training MLP networks with several hidden layers, and sometimes shows questionable results. All autoassociators are performed in unsupervised manner and then they have model parameters such as the number of centers and hidden nodes associated with the complexity of the system. Tuning these parameters appropriately is important to obtain good abnormality performance.
ACKNOWLEDGEMENTS We acknowledge the financial aid for this research provided by the Brain Korea 21 Program supported by the Ministry of Education and the National Research Lab Grant of the Ministry of Science & Technology. In addition, we would like to thank Research Institute of Engineering Science and the Automation & Systems Research Institute in Seoul National University.
REFERENCES Bourland, H. and Y. Kamp (1988), Auto-association by multiplayer perceptrons and singular value decomposition, Biol. Cyber. 59, 291-294. Cherkassky, V. and Mulier, F. (1972), Learning from data: concepts, theory, and methods, JOHN WILEY & SONS, INC. Dong, D. and MaAvoy, T. J. (1996), Nonlinear principal component analysis-based on principal curves and neural networks. Com. Chem. Engng. 20:1,65-78. Grossberg, S. (1976), Adaptive pattern classification and universal recoding, I: Parallel development and coding of neural feature detectors, Biol. Cyber. 23, 121-134. Hastie T. and W. Stuetzle (1989), Principal curves, J. Am. Stat. Ass. 84:406, 502-516. Hecht-Nielsen, R. (1995), Replicator neural networks for imiversal optimal source coding, Science 269, 1860-1863. Homik, K., Stinchcombe, M. and Whit, H. (1989), Multilayer feedforward neural networks are universal approximators, Neural Networks 2, 359-366. Kohonen, T. (1993), Things you haven't heard about the self-organizing map, Proc. IEEE Int. Joint Conf On Neural Networks, San Francisco, 1147-1156. Kramer, M.A. (1991), Nonlinear principal component analysis using autoassociative neural networks, AIChE J. 37, 233-243. Luttrell, S. P (1990), Derivation of a class of training algorithms, IEEE Trans. NN 1, 229-232. Marlsburg (1967), Self-organization of orientation sensitive cells in the striate cortex, Kybemetic 14, 85-100. Wise, B. M and Gallagher, N. B. (1996), The process chemometrics approach to process monitoring and fauh detection, J. Proc. Cont. 6:6, 329-348.
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Condition Monitoring and Diagnostic Engineering Management Andrew G. Starr and Raj B.K.N. Rao (Editors) © 2001 Elsevier Science Ltd. All rights reserved.
CONDITION MONITORING OF A HYDRAULIC SYSTEM USING NEURAL NETWORKS AND EXPERT SYSTEMS Markus A. Timusk^
Chris K. Mechefske^
^Department of Mechanical and Materials Engineermg, The University of Western Ontario, London, ON, Canada, N6A5B9 ^Department of Mechanical Engineering, Queen's University, Kingston, ON, Canada, K7L 3N6
ABSTRACT This paper reports on the computational approach of an autonomous condition monitoring and diagnostics system monitoring the operational parameters of a machine with multiple inputs of varied types. The particular application is the hydraulic system of machinery used in the mining of Western Canada's Oil Sands. The system, analyses transducer datafromthe machinery and determines the machine's condition, using various signal processing techniques and a combination of a neural network and an expert system. If at any given time a decision is made by the diagnostics system that a fault is imminent, it will communicate failures and relevant information to maintenance staff via the company local area network. The system makes use of both neural network and expert rule based approaches for fault detection of a system with many different types of inputs to address the practicalities of implementation. These inputs include mechanical vibrations, slowly changing mechanical parameters and operator inputs. The combination of Artificial Neural Networks (ANN) and expert systems attempts to address some of the weaknesses of either approach being used independently. While the ANN is being trained onlme, the expert system will serve as the primary mechanism for failure detection. Gradually, as a database of fault and normal running data is accrued and classified, the role of the ANN will become more prevalent. All of the signal processing, computation and communications are being implemented using commercially available, low cost, PC based data acquisition and monitoring technology. Once the system has been trained unnecessary parameters and corresponding hardware and transducers can be removed. This approach addresses the practicalities of implementation of a monitoring system to a machine where little advanced knowledge exists of the machine's behaviour. The structure of the expert system and the ANN will be somewhat parallel. The reasoning for this is that what one scheme does not catch, the other will. A challenge with this type of parallel system is the avoidance of false alarms. Preliminary resuhs indicate that this is an economical and effective approach, flexible enough to be implemented for various other types of machinery and industries. KEYWORDS Novelty Detection, Hydraulic, Expert System, Neural Network, Autoencoder. 585
BACKGROUND The focus of this project is to design and implement an autonomous condition monitoring system developed to give early warning to maintenance personnel of any imminent failures of a large hydraulic system. The major challenge was to develop a solution through experimental and theoretical approaches while still providing value to the company with a proven system at the end of the project. The machinery being monitored for this application is a hydraulic system powering a large conveyor to move oil sand used in the mining industry. The hydraulic system consists of three nine-cylinder variable displacement piston pumps driven by constant speed AC motors operating at constant synchronous speed. The pumps are configured in parallel and output into a common manifold. The output of the system is a hydraulic motor, which propels the conveyor. The only operator control for the system is the overallflowrate of hydraulic fluid that is regulated by the variable displacement of the pumps. MONITORING AND DATA ACQUISITION SYSTEM Hardware Configuration HYDRAULIC SYSTEM
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E^ZIK" Figure 1: Hardware Configuration The hardware utilized for the data acquisition, analysis and communications for this system is PC based. The main advantages of a PC based system over other system configurations are cost, programming versititly and easy upgradablility. The high speed data acquisition of the vibration signals is being performed by a National Instrumements PCI Bus Data Acquisition Card. The lower speed acquisition of the other machine parameters such as pressures,flowsand speeds are being collected using a National histruments FieldPoint Network Interface. Figure 1 shows the system configuration schematically. Signals and Transducers Signals being monitored in this system fall into two groups, high speed vibration signals and slowly changing parameters. There are nine high speed mechanical vibration signals emanating from the hydraulic
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pumps and 18 relatively low speed DC signals emanatingfromtransducers and the Programmable Logic Controller (PLC). The low speed signals include system parameters such as pressures, flows, speeds, operator input, electrical current draw of motors and others. Vibration signals being monitored are measured using piezoelectric accelerometers mounted in vertical, horizontal and axial directions. Software Two high level programming languages have been employed to develop the software which runs the fault detection and communications protocols for the system. The main program that runs the overall system is being written in National Instruments proprietary language. LabVIEW. LabVIEW will run the user interface, control the data acquisition hardware, the data collection and storage, preliminary signal processing and communications. The software component of the system, which will perform the major signal processing and fauh detection, will be written in MATLAB. The Neural Network Toolbox is being used within MATLAB to create and run the neural network component of the program. After development, training and testing, the MATLAB program will be called as a scriptfromwithin the main LabVIEW program. There will be two possible mechanisms for operators to interface with this system. Actual programming and configuration of the system will be performed on the main PC which runs the application. The other interface for this system will be via an embedded script, which will report warning conditions and impending failures via email. If time allows, a script producing a web page detailing current machine parameters may also be developed. Another possibility for communications is through the PLC to the control interface of the machine operator. All of these communicationsftmctionscan be developed using LabVIEW routines. DIAGNOSTIC ROUTINE In order to meet the requirement of providing a proven and robust diagnostics program that can be put into service right away as well as provide a more advanced (and also experimental) system which would make up for some of the deficiencies of the traditional method, it was decided to combine the use of an expert system with a neural network approach. The logic behind this strategy is that if the expert system fails to detect an imminent failure, the neural network may be able catch it. A chart describing the structure and dataflowof the diagnostic routine is shown in Figure 2. Expert System The maintenance staff at Syncrude possess an enormous wealth of knowledge and expertise on the equipment at the mine. Ideally, an intelligent monitoring system should capitalize on as much of this knowledge as possible. In the case of the hydraulic system for this application this information took several forms. Various measurements of the machinery were used, including a vibration history acquired vsdth a portable data acquisition unit, plant information data logged over a period of time and anecdotal knowledge. In order to capitalize on this resource, it was decided from the outset that it would be beneficial to base the entire diagnostics system on a knowledge based system. The diagnostic conclusions of the Neural Network would Action as simply another input to the expert system. This allowed for the output of the Neural Network to be taken in the context of the expert rule base. Another advantage of incorporating the expert system was the benefit of getting immediateftinctionalitywhile the Neural Network routines are being developed.
587
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Neural Network Diagnostic Routines and Novelty Detection The traditional method of Neural Network based condition monitoring is to train a network to distinguish between and classify positive and negative examples of machine behavior. This approach works well in the lab where one is able to artificially seed a fault into the machine (i.e. weld an imperfection or blip into a gear tooth) and then record this data as the sample of negative examples. However, negative examples are exceedingly hard to come by in real world applications such as this one. In most cases machinery is prohibitively expensive, failures are difficult to describe in absolute terms and rarely occur in the same way twice and it is impractical to artificially induce a failure. A type of monitoring strategy which requires no failure or negative instance data is known as Novelty
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Detection. Novelty detection methods are classified as methods which proceed by examining instances of a concept, trying tofindtheir commonalities and generalizingfromthem [1]. A niimber of techniques have been investigated for novelty detection for use in condition monitoring of machinery. Techniques include statistical methods, Parzan Windows, Nearest Neighbor, Gaussian Mixture Models, Neural Network Based Models; including Autoencoders and Kohonen Self-Organizing Maps. A review of these methods is presented by Taylor and Addison [3]. The novelty detector typically works in two stages. In the first stage, known as the learning stage, it is presented with numerous examples of patterns representing positive instances (data from a normally running machine). After being trained on positive instances the detector is then put into action processing data that it has not seen before. If presented with data that belongs to the class of the positive instances (the training data), the detector regesters a low indication of novelty (novelty score). If the data is significantly different (novel)fromthe class of data it was trained on, it will register a high novelty score. The inevitable dilemma when designing a novelty detection scheme is the establishment of the novelty threshold. By the very nature of this type of application, we have no data which represents a negative instance (or fault condition). A compromise must be made between a threshold that is set low in order to ensure sensitivity for early fault detection and a high threshold which is resilient against false alarms. Another limitation of the novelty detection approach to condition monitoring is that although an abnormal or fault condition may be detected by the system, a classification of the actual fault will not be provided. The Autoencoder: An Approach to Novelty Detection The autoencoder is a Neural Network technique based on the feed forward neural network structure and has been used successfrilly in novelty detection applications [3,4,5]. Typically, the inner layers possess fewer neurons than the input/output layers. This shape tends to compress redundancies while preserving features which characterize the data. An autoencoder is trained by teaching it to be able to reproduce a certain class of signal or pattern (the positive instance). While going through the training routine, the target data set and training data set are one in the same. HopefiiUy, if the positive instance data group possess enough similarities, which are recognized by the autoencoder, it becomes effective at reproducing these signals with a small reproduction error between the input and corresponding output. When analyzing data which belongs to a class other than the training class the reproduction error will be larger. It is this difference in reproduction errors that forms the criteria for classifying novel from normal data. Hidden
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Figure 3: General Structure of an Autoencoder Preprocessing and Feature Extraction for the Autoencoder In order to ensure that the autoencoder was trained to the highest possible standard, several steps were
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performed to preprocess the data before introducing it to the autoencoder. In order to ensure equal influence of individual parameters, the entire feature vector was normalized to lie between zero and one. Elements which did not correlate with the other parameters and outlier data were also discarded. For training, vectors were shuffled into random order. The vibration signals were converted to the frequency domain and windowed at even intervals across thefrequencyspectrum. Constant percentage bandwidths were not deemed necessary since the machine operates at the constant synchronous speed of the electric drive motors. Crest factors were extracted at eachfrequencywindow. This yields a subvector which attempts to characterize each vibration signal. Several configurations of feature vectors are being evaluated through experimentation. The following is an example of a feature vector used: FV=[{ pump 1 crest factors}, { pump 2 crest factors}, ( pump 3 crest factors}, {12 other machine parameters}]
Data Selection For Training After performing a visual and statistical analysis of data accrued from several weeks of consecutive operation, the machine appears to operate in many different modes depending largely on the swash plate angle of the pumps. The swash plate angle regulates theflowrate of the system, which in turn controls the speed of the conveyor. Figure 4 illustrates actual machine data sorted in ascending order of the corresponding swash plate angle. A histogram plot of the occurrences of various swash plate settings recorded over a long period of service revealed which modes of operation rarely occur and need not be modeled (Figure 5). Rather than train a single autoencoder network for all system modes, it was found that better performance was obtained when separate encoders were configured to correspond to individual modes of a particular parameter. The modes which were used for training the autoencoder were those which were revealed to be the most prevalent in the histogram plot. Ultimately, these selected modes of observation also serve to define the rules for the expert system, which govern when and which autoencoder network is looking at the data. A characteristic of the system which could corrupt the training of the novelty detection autoencoder network and create false alarms while the network is in service was the occasional transient operating condition or surge experienced by the drive. This type of transient mode, due tofluctuationsin ore density, does not correlate with or represent the physical deterioration or a mechanical fault of the system. For this reason, transient spikes were removedfromthe training data of the autoencoder. During operation, any event which is not sustained for a period longer than a level specified by the program, will be ignored. Novelty Detector Performance There are a seemingly infinite number of possible configurations and adjustments that can be made to this type of novelty detection scheme in order to realize tfie end performance goal; to achieve the largest difference in reproduction error between the positive and negative data. These parameters include the ratio of nodes between the hidden layer and the input and output layers, the number of hidden layers, the learning rate, amount of training epochs used for training and the type of transfer functions used. A network with fewer nodes in the hidden layer tends to generalize better while more nodes may be necessary for the network to learn complex functions. The number of learning epochs use must be carefully selected so as not to over fit the data.
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Figure 5: Histogram of operating mode distribution
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DISCUSSION Despite the aforementioned limitations of the novelty detection approach to fault detection, it is concluded that when combined with an expert system, this is an effective approach to the outlined and similar problems. It is anticipated that this research will help to further the understanding of this complex mechanical and hydraulic system and provide operating and maintenance personnel with information needed to understand component condition and warning of imminent failure. If proven effective, this will have direct input into the operating and maintenance plans with the goal of eliminating costly breakdown maintenance. It is hoped this research will have a real benefit to enable companies like Syncrude to reduce operating and maintenance costs, and maximize production and overall efficiency. ACKNOWLEDGMENTS The authors would like to thank Syncrude Canada Ltd. and the members if its reliability department for their enthusiastic cooperation and generous technical andfinancialcontributions to this project. REFERENCES 1. Japkowicz, Nathalie (1995). A Novelty Detection Approach to Classification, Proceedings of the Fourteenth International Conference on Artificial Intelligence pp. Montreal Canada. 518-523. 2. Taylor, Odin; Addison Dale. (2000) Novelty Detection Using Neural Network Technology, Proceedings ofCOMADEM2000, 13^ International Congress on Condition Monitoring and Diagnostics Engineering Management. Houston, Texas USA. 3. Addison, J.F.; Wermter, Stefan; Maclntyre, John. (1999). Effectiveness of Feature Extraction in Neural Network Architectures for Novelty Detection. Artificial Neural Networks 7:10, Conference Publication No. 470. 4. Demuth, Howard. (1998), Neural Network Toolbox User's Guide, The Math Works, Inc. 5. Li, James; Yu Xueli, (1995). High Pressure Air compressor Valve Fault Diagnosis Using Feedforward Neural Networks", Mechanical Systems and Signal Processing, Vol9, pp. 527 - 536. 6. Zhang, S.; Ganesan R, (1995). Self-Organizing Neural Networks for Automated Machinery Monitoring Systems, Mechanical Systems and Signal Processing, Academic Press Vol 10, pp. 519 -532.
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Condition Monitoring and Diagnostic Engineering Management Andrew G. Starr and Raj B.K.N. Rao (Editors) © 2001 Elsevier Science Ltd. Allrightsreserved.
MULTI-LAYER NEURAL NETWORKS AND PATTERN RECOGNITION FOR PUMP FAULT DIAGNOSIS L. Wang\ A. D. Hope^ H. Sadek^ ^ School of Engineering Science, University of Southampton, Southampton, SO 17 IJB, UK ^Faculty of Technology, Southampton Institute, Southampton, SO 14 OYN, UK
ABSTRACT Multi-layer Neural Networks have been widely applied to pattern recognition in the last twenty years. A large amount of research has been conducted to investigate good performance networks for practical problems including machinery fault diagnosis. However, due to the complexity of Artificial Neural Networks, there is no easy way to select the best models from amongst the numerous possibilities. Resulting from the fact that networks are initialised randomly, the other problem that multi-layer neural network users often experience is how to achieve network repeatedly. In this paper, a systematic way of finding the best performance networks for prediction of certain pump faults is presented. By comparing a series of network structures, network training times, simulation errors, and correct prediction rates, the performance of the networks were evaluated. The network repeatability was also achieved through Matlab programming. A water pump vibration monitoring system has been set up as a laboratory test-rig and common faults such as pump impeller wearing problems, impeller imbalance problems, and pump bearing faults were simulated on the test-rig. Vibration signals under different pump conditions were sampled and pre-processed before being applied as inputs to multi-layer neural networks. Suitable feature extraction methods for these problems were discussed to establish the patterns for neural network classification.
KEYWORDS Condition monitoring, fault diagnosis, artificial neural networks, pumps, bearings, vibration.
INTRODUCTION Artificial Intelligence is a relatively new field which first emerged in the late 1970s. In the last thirty years, various artificial intelligence technologies have been developed for different applications, such as knowledge-based Expert Systems (ES), Fuzzy Logic (FL), Artificial Neural Networks (ANNs). Compared with conventional computing programs, these technologies are systems with human-like 593
reasoning and decision-making abilities, and are also robust and noise tolerant [Kirkham and Harris (1997)]. Neural networks take a different approach to problem solving compared with conventional computers. Artificial neural networks are composed of a large number of highly interconnected neurones working in parallel. They learn by examples. Artificial neural networks have been widely used for pattern recognition because of their ability to generalise and to respond to unexpected patterns. The key strength of neural networks is their ability to recognise patterns in incomplete or 'noisy' data. [Lippmann (1987)] Neural networks are specified by the network topology, node characteristics, and training or learning rules. These rules specify an initial set of weights and indicate how weights should be adapted during use to improve performance. Both design procedures and training rules were the topic of much research over the last twenty years [Lippmann (1987)]. As a result, Artificial Neural Networks have become popular techniques for data analysis. Artificial neural networks, especially multi-layer networks, as advanced signal processing techniques, are combined with vibration analysis techniques for automatic detection/diagnosis of machinery faults. Typical examples are bearing faults [Liu and Mengel (1992)], rotating shaft faults [Ogunfunmi and Chen (1993)], and pump problems [Ilott and Griffiths (1996)]. However, finding the best performance network for a specific problem can be very difficult. In this paper, a systematic approach for training and finding the best performance networks for three different pump problems were presented, i.e. pump impeller imbalance problems, impeller wearing problems, and bearing defects.
FEATURE EXTRACTION When presenting data to a neural network it is usually beneficial to employ feature extraction techniques, which can effectively reduce the input dimensions of the network. By the inclusion of a feature extraction algorithm, the training time of the network can be greatly reduced and the generalisation capability of the trained network can be improved. A wide variety of signal processing techniques can be used to extract features from machine vibration data. Techniques range from calculating simple single figure measurements, such as crest factor, shock pulse measurements and statistical moments, through to more sophisticated techniques, such as maximum entropy spectral estimation and higher-order statistics analysis [McCormick and Nandi (1997)]. For different applications, different feature extraction methods may be used to achieve a better result. Understanding the problem is essential for selecting the most suitable methods. In this study, vibration signals were collected during tests with simulated faults on three different parts of the pump. Three different feature extraction methods were applied for these problems. Pump impeller imbalance problem The pump impeller imbalance faults were simulated by removing material from one side of the impeller [Wang and Hope (2000)-1]. The level of imbalance was judged by the amount of material being taken off from the impeller. Five levels of impeller imbalance were produced and artificial neural networks were used to classify these pump conditions. As the impeller imbalance problem developed, the vibration levels of the pump at the running speed would increase. The associated harmonics should not be significant unless a misalignment problem was also present. Because the pump ran at 50 Hz the R.M.S. values of the vibration power spectrum in the following three frequency bands were used to represent the impeller imbalance patterns: 40~60Hz, 90-1 lOHz, and 140~160Hz .
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Pump impeller wearing problem Six levels of impeller wear were also simulated by removing the corners of the impeller blades [Wang and Hope (2000)-2]. Artificial neural networks were designed/trained to classify these six wear levels. Features of impeller wear faults were extracted to form the inputs for artificial neural network training and testing. The vibration power spectrum between 0 and 500hz was divided into ten-50hz frequency bands (0-50,50-100,...,450~500hz) and the R.M.S. values of the power spectral density of each band were calculated. From all ten sections a pattern was formed which related to one impeller condition. The results have shown that this is an efficient feature extraction method for this particular problem. Pump bearing defects Bearing defects were created by E.D.M. machining on the bearings (inner race, outer race, ball and cage) [Wang and Hope (2001)]. Six different bearing conditions were simulated. The inputs to the networks for training were obtained by pre-processing the vibration data collected from the housing of the defective bearing on the pump. The data pre-processing included both time-domain R.M.S. calculations and frequency-domain band-frequency R.M.S. calculations. In the frequency domain, the R.M.S. values over the frequency range (0-5 kHz) and another ten frequency bands between 0 to 4kHz were calculated to form the input vectors for the neural networks in addition to the time domain R.M.S. values. The ten frequency bands were 0 - lOOHz, 100- 200Hz, 200- 300Hz, 400- lOOOHz, 1000- 1500Hz, 1500- 2000Hz, 2000- 2500Hz, 2500- 3000Hz, 3000- 3500Hz, 3500- 4000Hz. They were chosen based on features of the signals from the bearing characteristic defect frequencies and also the fact that faulty bearings tend to create high resonance harmonics of the defect frequencies and sidebands around them. Artificial neural networks were then designed and trained to classify these six bearing faults.
NETWORK DESIGN AND NETWORK PERFORMANCE The performance of a multi-layer back-propagation neural network depends on many factors, among which the network structure is an important one. It is observed that different network structures may differ greatly in terms of general performance. There is no existing well-defined criterion to be followed in selecting network structures. In many cases, the network structures finally employed are chosen randomly or based on empirical ways, such as experience, trial and en'or and so on. This may not guarantee that the selected network structure is the best or nearly the best. Since the number of input and output neurones is determined in advance by the input and output dimensions of the problem to hand, the structure of a network to be selected depends on the composition of hidden layers. In theory, no more than two hidden layers are needed for approximation of any function [Lippmann (1987)]. Therefore, one or two hidden layers are enough for most applications. The number of nodes in the hidden layer must be sufficient to achieve good performance networks. However, if too many nodes are used, the network also captures and memorises insignificant patterns or noise in the training samples. As a resuh, its reasoning ability is reduced. For many years, the number of neurones in the hidden layer was randomly varied to find an acceptable network for certain problejns [Steele (1996)]. Besides the two most important issues mentioned above, there are other issues which need to be considered when applying multi-layer neural networks in fault diagnosis, including the training/learning algorithms of each layer, the size of training set, the performance of a neural network, etc.
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Network learning is based on the definition of a suitable error function, which is then minimised with respect to the weights and biases in the network. Back-propagation, has been the most popular, most widely implemented of all neural network training/learning algorithms. It corresponds to a propagation of errors backward through the network. The important contribution of the back-propagation technique is in providing a computationally efficient method for evaluating the error derivative. However, backpropagation learning algorithms are based on a gradient search, which is an algorithm that seeks out a local minimum and thus may not yield the exact mapping [Hush and Home (1993)]. Moreover, the generalisation of neural network depends on the size of the training set as well as the size of the network. The larger the training set size, the better generalisation of the network. Generally speaking, a larger number of data samples will do a better job at representing the problem being dealt with [Hush and Home (1993)]. The performance of the network was normally assessed by the percentage of the test data the network classified correctly. It is usually tested by evaluating the generalisation of the network on new data outside the training set. In this study, the application of multi-layer back-propagation networks in pump fault diagnosis was investigated. For all the three pump faults, different feature extraction methods were applied as described above according to the nature of the problems. Regarding the structure of the multi-layer neural networks, only one hidden layer networks were considered at this stage. The number of neurones in the hidden layer was varied from 1 up to 40. 'Purelin',' logsig \ and 'tansig' are three most popular leaming/transferring algorithms introduced in Matlab [Demuth and Beale (1997)] and were used in this work.
Adjusting Transferring functions:
purelin, tansig, logsig Numbers of hidden layers 0) Numbers of neurones in the hidden layers
(1 to 40)
Best Network
• • a
Simulation/ evaluation
Q Weights and bias • Number of hidden layers • Numbers of neurones in the hidden layers • Transferring functions a Training time
Networks Accuracy rates Training time
Figure 1: The programme for determining the best performance networks
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The best performance networks for a particular problem were achieved by running a Matlab programme according to the transferring function, the numbers of neurones in the hidden layer, the weights and biases, the training time and the accuracy rates. The procedure is shown in figure 1.
RESULTS AND DISCUSSIONS The best performance networks were obtained after a series of systematic trainings for the three pump faults. The results are given in table 1. TABLE 1 Neural network training results for the three different pump problems Impeller imbalance
Impeller wearing
Bearing with defects
Best classification accuracy rate (%)
100
100
96.7
Training time (minutes)
1.73
0.68
22
Number of hidden layer neurones
12
6
10
Hidden layer
tansig
logsig
logsig
Output layer
logsig
logsig
logsig
Transfer function
Classification accuracy rate Successful classification rate of a network is obviously very important for all the fault diagnosis problems. The higher the accuracy rate, the better the network performs. As shown in the above table, 100 per cent accuracy rate is not always achievable. It will depend on the nature of the problem. As the complexity of the problem increased, it will become more difficult for the network to classify the input patterns. For the first pump fault, i.e. the impeller imbalance faults, more than one network design achieved 100% accuracy rate [Wang and Hope (2000)-!]. With the impeller wearing problem, only one network achieved 100% accuracy rate. For the pump bearing faults, due to the size of the defects made on the bearings, which made the classification job much more difficult, no one network design was found to achieve 100% accuracy rate from over 200 network selections. Training time The training time of a network is not as critical as the classification accuracy rate of a neural network. This time will only be required during the network training period. After the network is trained, the simulation and real fault classification periods are relatively short. This means that as long as the best performance network is obtained within a reasonable training time, the actual pattern identification procedure is relatively easy. However, although it is not as critical as the classification accuracy rate, it is obviously important that the training time is kept as short as possible during the application of artificial neural networks for machinery fault diagnosis. From table 1, the training time for the first two pump problems were relatively short while for the third, i.e. the bearing condition classification problem, it was rather long. This was due to the difference in the complexity between the three classification problems. On the other hand, for a certain pattern classification problem, the training time increases remarkably with the number of neurones in the hidden layers. [ Wang and Hope (2001)] 597
Transferring functions From table 1, the transferring functions used in the best performance networks are mainly 'logsig' and 'tansig', especially 'logsig'. 'Purelin' is a linear transfer function and is not good at adapting for nonlinear problems such as machinery fault diagnosis. 'Logsig' and 'tansig' are two 'sigmoid' functions which are commonly used in back-propagation networks, in part because they are differentiable. From the training, it was found that the training period of networks with the 'purelin' learning function were much quicker than networks with the other two functions. But the other two functions result in better networks for the fault classification problems. To reduce the training time, changing the transferring functions from 'logsig' and 'tansig' to 'purelin' can be helpful. To improve the network performance, the 'tansig' or 7og5/g' transferring function could be better choices.
CONCLUSIONS Three different pump faults were simulated on a test-rig at different levels to train the neural networks. The performances of the neural networks were evaluated based on their correct classification of the pump conditions. A systematic way of approaching the best performance multi-layer neural networks for fault diagnosis was proposed by Matlab programming. By applying this programme, the networks were trained and evaluated constantly rather than randomly. Therefore, the best performance networks were achieved with more confidence.
REFERENCES Boek M. J. (1991). Experiments in the Application of Neural Networks to Rotating Machine Fault Diagnosis, IEEE International Joint Conference on Neural Networks, 769-774. Demuth H. and Beale M. (1997). Neural Network Toolbox-For Use with MATLAB, User's Guide, Version 3.0, The Math Works, Inc. Hush D. and Home B. (1993). Progress in Supervised Neural Networks-What's New Since Lippmann, IEEE Signal Processing Magzine Vol.10, 8-39. Ilott P.W. and Griffiths A. J. (1996). Diagnostic Engineering of Pumping Systems Using Artificial Intelligence, Proceedings ofCOMADEM, 451-460. Kirkham C. and Harris T. (1997). Smart Computing Technology Benefits in Condition Monitoring, Condition Monitor 132, 6-8. Lippmann R. P. (1987). An Introduction to Computing with Neural Nets, IEEE ASSP Magzine 4:2, 422. Liu T. I. and Mengel J. M. (1992). Intelligent Monitoring of Ball Bearing Conditions, Mechanical Systems and Signal Processing 6:5, 419-431. McCormick A. C. and Nandi A. K. (1997). Classification of the Rotating Machine Condition Using Artificial Neural Networks, Proceedings of IMECHE 211:C, 439-450. Ogunfunmi T. and Chen Z. (1993). Vibration Signature Analysis Using Boltzmann Neural Networks, Intelligent Engineering Systems Through Artificial Neural Networks Vol.3, 763-768. Steele J. (1996). Detecting Cavitation in Hydraulic Pumps Using Artificial Neural Networks, Intelligent Engineering Systems Through Artificial Neural Networks, 909-914. Wang L. and Hope A. D. (2000)-1. Using Artificial Neural Networks as a Diagnostic Tool for Pump Impeller Imbalance Faults, Proceedings oflCME, Shanghai, China, November, 2000. Wang L. and Hope A. D. (2000)-2. Vibration-based Condition Monitoring for Pumps in Waste Water Industry, INSIGHT, 42:8, 500-503. Wang L. and Hope A. D. (2001)-3. Fauh Diagnosis Using Artificial Neural Networks for Waste Water Pump Bearings, Proceedings of Condition Monitoring 200, Oxford, UK, June, 2001.
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Condition Monitoring and Diagnostic Engineering Management Andrew G. Starr and Raj B.K.N. Rao (Editors) © 2001 Elsevier Science Ltd. All rights reserved.
DEVELOPMENT OF AN AUTOMATED FLUORESCENT DYE PENETRANT INSPECTION SYSTEM T. D. Moore and Dr. A. Starr Department of Maintenance engineering, Manchester School of Engineering, University of Manchester, Oxford Road, Manchester, Ml3 9PL, UK email: [email protected]
ABSTRACT This paper concentrates upon the topic of automated fluorescent dye penetrant inspection (FPI). Particular attention is paid to the inspection stage of the process where the difficulties and problems are discussed. Previous research into automated inspection is reviewed including a section on developed automated inspection systems. The paper summarises the technical and practical progress made towards developing an improved inspection system.
KEYWORDS Dye penetrant, visual defect inspection. Automated inspection, Review of FPI, Image processing, classification, decision system.
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1.
INTRODUCTION
Fluorescent dye Penetrant Inspection (FPI) is one of a multitude (BindT yearbook, 2000) of Non Destructive testing (NDT) methods used within industry. FPI offers a cheap and reliable method for detecting surface breaking defects. The aim of the process is to produce a high contrast fluorescent dye "indication" on a components surface where defects occur. Ideally no other dye indications should be present. In reality other false or non relevant indications occur which complicate the inspection problem (NDT course notes, 1999). A manual visual inspection follows the penetrant processing stage. This inspection is completed under UV-A light to excite the dye which fluoresces a yellow/green colour. This increases the visibility of the dye indications. Two penetrant inspection images are shown in figure 1:
Figure 1: Images of Penetrant processed components. At first glance the problem of automating the inspection can appear trivial. However upon closer inspection the level of complexity and skill involved in the process is realised. The crux of the problem is in deciding which of the fluorescent indications on the components surface are due to defects and which are false or non-relevant indications. The decision process by which a manual inspection is carried out involves component specific knowledge and experience of the method and component. A plan of how the inspection decision is reached is shown in figure 2.
600
INDICATION?
KEY: # -DECISION POINTS.
FALSE
RELEVANT
NON-RELEVANT
ACCEPTABLE?
FAILURE
PASS
Figure 2: The Inspection task decision tree. There are two types of non defect indications "False and Non - Relevant Indications". Table 1 (Liquid penetrant course, 1999) shows the most common causes of both types of non-defect indications. TABLE 1 EXAMPLES OF FALSE AND NON RELEVANT INDICATIONS.
1 1 1 1 1
FALSE INDICATIONS Background fluorescence Penetrant contamination Contact with other components Fingerprints Handling tool marks Lint and dirt Smudges and runs
NON RELEVANT INDICATIONS Fillets Joints (non welded) Press fits (e.g. gears) Splines Key ways Threads Undercuts
1 1 1 1 1 |
The final stage of the problem deals with indications that are considered to be due to defects. Typically these are assessed against a quality acceptance standard using characteristics such as size, location and concentration of defects within a region. Often a magnified white light inspection of the defects producing the indication is performed as a final check.
601
The manual inspection process produces good results however a drive for continual improvement is necessary (Lovejoy, 1996). Many problems with the manual inspection method can be identified; such as the subjective evaluation method used and inspector fatigue. The subjectivity is introduced as the inspection results are based on an individual judgements (Burkel, 1990) which will vary from inspector to inspector. Inspector fatigue can results in the non-detection of defects, leading to unacceptable variation in process performance. Automation has the potential to alleviate many of these problems allowing the decision process to be standardised reducing process variation. Alternatively visualisation aids can ease the inspection task improving capability. Visualisation aids can reduce inspector fatigue and provide hard information that was previously unavailable. Research also suggests that visualisation aids would prove more popular than fullautomation with NDT personnel. Automation can help increase the understanding of the knowledge required to complete an inspection and produce a tangible asset for the company implementing it. A more consistent record of the inspection results can also be created and stored electronically which allows for more complex data analysis. 2.
REVIEW OF AUTOMATED PENETRANT INSPECTION SYSTEMS:
A brief review of three systems have been presented in this report. Following extensive research it appears that these three systems are the only attempts at automated FPL 2,1 The University Of Cincinnati System (Nui et al.,1995): The University of Cincirmati developed a software based system to analyse penetrant images. The software is capable of separating "true" indications from "false". Synthesised dye penetrant images were produced by implanting crack images into images of real in-service aeroengine components(ref). The decision is reached by processing the image with three modules Module 1: Pre-classification - The image is filtered to remove noise and reduce the number of indications to be processed. Some defect indications are also removed at this stage which increases the system error. Module 2: Feature extraction - This module reduces the image data content to a matrix of eight descriptive features; Module 3: Pattern classification - The features extracted are used to classify each indication as a crack or non-crack using four different classification methods: The performance of each classifier was evaluated using two indicators: •
False alarm rate (False positive) - classified as being a crack when it was not.
•
Miss rate (False negative) - classified as being a non crack when it is.
Table 2 shows the results for the average often independent tests:
602
TABLE 2 PERFORMANCE RESULTS FOR FOUR DIFFERENT CLASSIFICATION METHODS.
CLASSIFIER Fisher linear Bayes classifier. KNN ANN 2.2
FALSE NEGATIVES (Percentage) 0.6 1.9 7 12.8
FALSE POSITIVES (Percentage) 4 3.6 2.1 1.5
TOTAL CALL RATE (Percentage) 97.7 97.2 95.4 92.9
General Electric's (GE) Penetrant System (Meyer, 1983), (Anon, 1990):
"Automated Fluorescent Penetrant Inspection Module" (AFPIM) is one of the modules from It was developed between 1978 and 1985 and was used to inspect aerofoil components.(ref)
(IBIS).
The system consists of two main modules: - Laser scanning sensor system. - Decision making software. The first component aquires the data from the scene, which was then used in the second component to reach a decision. The scanning system developed allows for accurate manipulation and positioning of the component which was then scanned by a dark blue HeCd (helium cadmium) laser. The sensor information was sent down a fibre optics cable and passed through a filter to remove the wavelength of light corresponding to the excitation laser. The remaining light was fed to photomultiplier tube detector where its intensity properties could be measured. The decision system used a threshold method to mark areas on the component which may be of interest. The accept/ reject decision was undertaken by a decision tree based upon the flaw type and criticality which were in turn are defined by the maximum flaw diameter, area and aspect ratio of the indication. The most difficult aspect of the AFPIM development involved the setting of the accept/ reject level. This is an important topic for any condition monitoring / quality control application. An accurate classification is required in order to get minimum false positives and negatives. The problem was tackled by performing thousands of test scans, the resuhs of which were compared to data obtained from the expert inspectors. The correlation between the two sets of data led to the development of powerful algorithms that allowed the inspection system to be developed beyond a prototype. The final system could inspect up to 20 blades per hour with a minimum defect detection size of 0.030 inches and a confidence in detecting defects of 90 %.
603
2.3
Tiede's Inspection System (Abend, 1998), (http.V/www.tiede.de):
Tiede have developed the only commercially available automated penetrant inspection system. Configurations can contain up to 8 CCD cameras connected to a standard PC. The heart of the system is the softw^are which controls the penetrant processing parameters and reaches an inspection decision. Pattern recognition methods are employed, along with a masking process which allows only selected regions on a components surface to be inspected. It is suggested that good results can only be achieved if the system is used in conjunction with a "Quality Assurance Package QAP" which monitors and controls all of the parameters that affect the output from the processing stage of the penetrant process.
2,4
Conclusions And Discussion Of The Reviews:
Each of the systems reviewed above have advantages and disadvantages. The current research being completed by this author aims to draw from the best features of each system and combine them to produce a new improved system. The significant technological improvements over the past decade allows for new solutions to the problem to be considered or become practical. The proposed system will draw from the best features of each system as highlighted below: The University Of Cincinnati System used an effective spatial feature extraction method to reduce the data content of a penetrant image, the features were then used to train a decision system. This approach together with the features identified will be incorporated within the new development. General Electric *s (GE) Penetrant Inspection System used real aerofoil components and as such dealt with complex issues of imaging the entire component. The problem was overcome using blade clamping methods. The images were also filtered allowing only the light emitted from the penetrant dye to be captured. Both of these features have been incorporated into the development plan for the new system. A test scan approach as adopted by GE to validate the system is also being considered. Tiede^s Inspection system includes a modem CCD, knowledge and pattern recognition based decision system which is considered to offer many advantages. The masking techniques will also be used in the development of the new system. In addition to the automation of the inspection task the new system is also aiming to increase the understanding of the inspection task and provide the users with useful data and images. The data and images can help explain how and why the inspection decision was reached. This will increase trust in the new technology, an important issue that is often overlooked. If the technology is to become a commercial reality users must have confidence in the systems performance.
604
3.
SYSTEM DEVELOPMENT.
A flowchart for a proposed system is shown in figure 3 (Boyle, Hlavac and Sonka, 1995). punpiwp
The Image analysis system.
FIGURE 3: A flow chart of the proposed system.
The system can be broken down into five main components:
3,1
•
Image acquisition system;
•
Image pre-processing and segmentation system;
•
Feature extraction system;
•
Data pre-processing, analysis and visualisation;
•
The decision system.
The Image Acquisition System (http://catalog, coherentinc. com):
Obtaining good quality images of a penetrant inspection scene is complicated. The perfect image would be that as visible to the human eye, however many factors compound to prevent such images being obtained. The required image must be in focus with indications clearly visible with good definition. A high contrast is required between the indications and the background to ease the image segmentation task. 605
The low light level in the inspection booths create many problems when attempting to obtain a good quality image. A UV filter was used to remove UV light from the images, increasing the indication contrast. However, low light levels resulting from the filtering increase the difficulty in obtaining focused images. The system decided upon includes a high accuracy grey scale CCD with a 640X480 pixel resolution connected to an industrial frame grabber.
3.2
Image Pre-Processing And Segmentation System;
Image filtering
•
Image segmentation
•
Preparation of feature extraction images.
FIGURE 4: The stages of image pre-processing and segmentation The purpose of this stage is to present the image data in a form suitable for feature extraction. This is achieved by separating the penetrant indications from the rest of the image using an intensity thresholding segmentation method based on the average intensity of the edges in the images. The image is then separated to form two new images one containing large or "crack-like" indications and another containing the remaining indications. Each image is then analysed separate creating many separate images containing either a single indication or a region of porosity image. These images are then sent to the feature extraction module.
3J
Feature Extraction System (Image processing toolbox, 1998);
The purpose of this stage is to reduce the data content used to describe the problem this will speed up any training methods used and allow knowledge based approaches to be developed. Features are initially chosen using a common sense approach, selecting features that define important discriminatory characteristics of the indication in question. Currently the computer code to extract twenty eight features has been developed. These can be grouped into three sets: • • •
3,4
Binary shape description features: e.g. The eccentricity of an indication; Grey level derived features: e.g. Standard deviation of the intensity values; Porosity and global features: e.g. the average area of all indications.
Data Pre-Processing, Analysis And Visualisation;
There are three main objectives of this stage: To prepare the data for analysis and ease the decision process To analyse the data to learn more about the problem. To produce a method to visualise the data and results The analysis of the data can help decide which methods should be used when building a decision system, several data analysis, dimensional reduction and classification prototypes have been developed however little information about the problem can be extracted until several system improvements have been made namely:
606
The features extraction methods need to be refined; The images collected need to be improved; More images are required. Once more data that describes the problem accurately has been produced the data can be fed into the developed algorithms and the results can then be analysed. A better understanding of the methods required to solve the problem can then be established.
3.5
The Decision System,
The fmal stage of the prototype system development will involve classification of the indications as defect or non-defect. In the future the number of possible classifications may be extended to provide more useful and detailed information. Currently many different approaches are being considered however it appears likely that a hybrid knowledge and data driven system may be created depending upon the availability of images that describe the full range of possibilities.
4.
FUTURE WORK.
-
Improvements to the image acquisition system leading to the acquisition of numerous images capturing the required information from the inspection scene;
-
Improvement to the feature extraction and image pre-processing system that results in data which better describes the image scene;
-
Build a basic knowledge based decision system to establish a benchmark for performance.
-
Development of a novel non-linear dimensional reduction technique to simplify the presentation and classification of the data;
-
Development of the classification system and user interface.
-
Incorporate knowledge of the problem into the system to ease the classification task and produced data that better describes the problem.
-
Research into the bleed out of indications over time and the incorporate the findings into the image analysis system.
-
Development of a user interface and method to display results.
607
REFERENCES: Abend.K (Feb 1998). SAE Special Publications. Fully Automated Dye-Penetrant Inspection of Automotive Parts, v 1337, p 13-19 Anon. Aerospace Engineering, Aircraft engine inspection,v 10(8), p 39-41. Burkel. H. R (1990). Materials Evaluation. Automated fluorescent penetrant inspection of aircraft engine structures, 48(8),p 978-981. Boyle. R, Hlavac,.V and Sonka. M (1995). Image processing, analysis and machine vision. Chapman and Hall. Image Processing Toolbox User's Guide (1998), (COPYRIGHT 1993 - 1998) The Math Works, Inc. All Rights Reserved. 24 Prime Park Way, Natick, MA 01760-1500. Liquid Penetrant Inspection Training course level 3 training notes (1999). The south West School of NDT, Cardiff Lovejoy. D (1996). Insight: Non-Destructive Testing and Condition Monitoring. The past, Present and future of penetrant testing, 38(7), p 509-511. Meyer. D. L (1983). Helicopter society. The integrated Blade inspection system (IBIS), p206-212. Niu A et al (1995). The International Society for Optical Engineering. An automated inspection system for detecting metal surface cracks from fluorescent penetrant images, 2423, 278-291. Non-destructive testing level 1 and 2 course notes.(1999) Rolls Royce training centre Mickleover, Derby. The British institute of NDT (BiNDT) yearbook (2000). Published by BiNDT.
608
Condition Monitoring and Diagnostic Engineering Management Andrew G. Starr and Raj B.K.N. Rao (Editors) © 2001 Elsevier Science Ltd. All rights reserved.
NON-DESTRUCTIVE FAULT INDUCTION IN AN ELECTRO-HYDRAULIC SERVO SYSTEM Zhanqun Shi''^, Deying Cui', Zhigang Wang', Jizhong Wang' & Hong Yue' 'The School of Mechanical Engineering, Hebei Unviersity of Technology, Tianjin, 300130, China ^Maintenance Engineering of Manchester, [g Research Group, The University I Manchester, M13 9PL, UK
ABSTRACT This paper aims to solve the problem of high research expense on the fault implementation of electrohydraulic servo (EHS) systems. Due to high integration and high sensitivity of the system, any failure induced in it may cause the whole system crashed. This makes the fault implementation high risk. In order to make the research on fault diagnosis easier, a non-destructive approach is developed in this paper. On a test rig, several kinds of faults are implemented by adding additional circuits in the system. The first one is implementation of circuit failures in the torque motor coils of the EHS valve. It is carried out by means of a switch circuit. The second kind of fault is leakage of the actuator, which is carried out with a parallel loop to the main supply loop of the actuator. All these faults or failures are repeatable without any destroy in the system itself. The test results have been given at the end of this paper. The results show that this is an economical way and provides researchers in this field a convenient approach in fault analysis of EHS systems.
KEYWORDS Fauh induction, fault simulation, electrohydraulic servo system, non-destructive test, actuator, servo valve
1 INTRODUCTION As more and more electro-hydraulic servo systems are used in various control tasks, their reliabilities and condition monitoring become more and more important. Some researchers have concentrated on the fault study in the electro-hydraulic servo systems. Bull, DR (1996) has developed failure modes and effects analysis method. J Watton (1995,1998) gives some neural network applications in diagnosis of fluid power systems. Shi, Z (1999) introduces a wavelet approach in fault diagnosis of an EHS system.
609
However, the applications on condition monitoring of EHS systems are far less than that on other mechanical systems. The major obstacle in this area is that the implementation of fault in monitored system is difficult or sometimes impossible. When a researcher wants to check the results of his analysis, he has to make some faults in the system or wait for the occurrences of the same faults. Unfortunately, the waiting time may quite long, and the occurrences may be different. In the other hand, any fault seeded into the system will make it lose its precise and performance permanently, sometimes may damage the system. Unlike the fauh diagnosis in other mechanical units such as bearings and gearboxes, no faults in this test rig can be repeated. If one has to destroy a very expensive unit once a time, his research cost would be very expensive. There are two possible ways to solve this problem. One is digital fault simulation (DFS). The other is to simulate faults with non-destructive approach on the test rig. Even in DFS approach, a rig test is still needed to confirm the results. One has to implement faults on test rigs. This makes it essential to develop a non-destructive test approach. This paper makes an effort in this direction. Faults can be seeded by adding some additional components and by means of the normal functions of these components. The principle is discussed with an example to simulate faults in the actuator and the servo valve of an EHS system. From the additional connected hydraulic circuits and electrical circuits, faults such as leakage in the actuator and shortcut as well as breakdown in the servo valve are simulated. The most important is that all the faults in this way can be repeated arbitrarily.
2
NON-DESTRUCTIVE FAULT INDUCTION IN EHS VALE
2.1 Common faults in electro-hydraulic servo valve The operating principle of an EHS valve can be found in literatures such as Watton J (1989) and Lu W (1996). An EHS vale is the most important unit in an EHS system. It is very sensitive to oil contamination, pressure stability, environment and other factors. All these may cause the EHS vale in following faults. (1) The spool can not move, this in turn lead to not move in actuator; (2) Coil failures, such as circuit shortcut, break down and loose connection; (3) Null offset is larger than expected; (4) Output flow rate is less than expected; (5) The performance of the valve is decreased. 2.2 Fault simulation to the electro-hydraulic servo valve It is very difficuh to seed all faults mentioned above in an electro-hydraulic servo valve. In this paper, only one type of fault is simulated. It is oriented the coil of the EHS vale. The fauhs such as coil shortcircuit, break down and loose connection can be carried out by the simulating circuit, which is connected outside the valve. Fig. 1 shows the principle of the torque motor v^th an amplifier. Equation (I), (2) give the currents in coil I and coil 2 when there is an input signal u^ .
do
^tt + ^„", = h (Z, + K, -f r^,) + i,Z, ^K-^
610
(1)
^bh~f^uU, =i2{Zf,+R,2+r.) Where /, ij
currents in coil 1 and coil 2
7?^,
resistant in coil 1 and coil 2
R^2
r^, and r^, inner resistant of amplifier to the two coil Z^ resistant of comijion line
+ i,Z,-N
(2)
dt
K^ u^
gain of the amplifier signal voltage input into the amplifier.
Uf^^
voltage that produces balance current on coils
O.
magnetic-current
Kl K2 Magnet
^^ Magnet
Coil and flux
K3
S
vy
H ^
K4 W5
S
Fig 1 Principle of the torque motor
Figure 2 Circuit to simulate coil faults
TABLE 1 THE FAULTS INDUCED INTO COILS Switches
Kl
K2
K3
K4
K5
^,
R2
Faulty free
+
-
-
-
-
-
-
Breakdown
-
-
-
-
-
-
'
Coil 1 SC
+
+
-
-
-
-
-
Coil 2 SC
+
-
+
-
-
-
-
Both coil SC
+
+
+
-
-
-
-
-
-I-
-
+
-
+
Coil 1 partly SC
+
-
-
+
Coil 2 partly SC
+
-
-
-
('+'
Switch on;
'-' Switch off;
611
SC Short circuit)
In healthy condition, R^^ = R^2 ^ ^ ^pi = ^pi • ^^ ^^^l^s occur in coils, there may be R^^ ^ R^j o^ ^pi ^ ^p2' For example, if the right coil is shortcut, r^, =0, if it breaks down, ^p, = °o. The same situation will be in the left coil. The faults in coils can be simulated outside the coils. Fig. 2 shows the simulation circuit. By turning the switches Kl, K2, K3, K4 and K5 on and off, the simulation can be carried out. Table 1 shows the situation of the switches and the faults simulated. Where i?i and R^ represent the changeable R^^ R^2 • The loose of connection can be simulated by means of any switch. 3 NON-DESTRUCTIVE FAULT INDUCTION IN ACTUATOR 3.1 The common faults in actuators The most common fauh in an actuator is leakage. As shown in Fig. 3, there are two kinds of leakage. The one is an internal leakage, which means a mini-flowfromthe high-pressure chamber to the low-pressure chamber. The other is an outside leakage, which means the flowfromthe inside of the actuator to the outside. The reason of an internal leakage is enlarged clearance between the piston and the bore. It may also because the failure of seals. The reason of an outside leakage is mainly because the failure of seals. 3.2 Faults induction in actuator As shown in Fig. 4, a solenoid directional valve and an orifice valve are connected in parallel to the actuator. There is no leakage at all in the actuator. The fluid flows AQ from p^ to P2 can be used to simulate the leakage in the actuator. The orifice valve is used to regulate the flow rate, and theflowmeter to measure the simulated leakage flow. With this parallel circuit, the leakage influence on the velocity of the actuator can be studied wititiout any damage to the system. The conditions are as follows. When the solenoid directional control valve is in neutral position, the situation of the actuator is in leakage free. If it is in the left position, a fluid can flow from p^ to pj, which takes the same frmction as the internal leakage fault.
:J^
Flowmeter
e-Ag Internal leakage
Outside leakage Pi
Figure 3 Leak in actuator
Solenoid directional control valve Pi
Kffi
~ Flow control valve
IL
Fig. 4 A simulation of actuator fault
612
Provided that the supply flow rate is g , the velocity without leakage is by equation (3):
Vo=f
(3)
A In leakage case, the leakage flow rate Qi in Fig. 3 is described in equation 4. Qi=K,(p^-P2)
(4)
Equation (5) gives the velocity in internal leakage situation. - = Vo-Av
(5)
In the parallel result in Fig. 4, however, the flow rate of the orifice valve is: AQ = R(p,~p,y
(6)
A proper designed orifice valve can give ^ = 1, and the resistance coefficient R can be tunable. By adjusting R, the leak flow rate in equation (4) can be carried out in equation 6, ie: AQ = Qi By changing the flow control valve, R, different amounts of leakage can be simulated, and their influences on the speed can be studied. At the right position of the direction control valve, the simulated results give the induction of the outside leakage faults. Again, the leakage can be controlled by means of the flow control valve.
\r \L
0
10
20
30
40
50(b) 60
70
80
90
1 30
0
10
20
30
40
50(c) 60
70
80
90
1 X)
10
20
30
40
50 60 input (sec)
70
80
90
100
y
0
Figure 5 Test results in simulated coil faults 4 IMPLEMENTATION AND TEST RESULTS The non-destructive fault simulation approach is implemented in an EHS position control system. An electrical circuit is developed to simulate the coil failures, and a hydraulic circuit is buiU to simulate leakage in the actuator. The results are satisfied to fauh analysis in the EHS system. As an example,
613
Figure 5 shows the test results in coils breakdown and coil 1 short circuit. Where figure 5(a) shows the healthy condition, 5 (b) shows the break down case and 5(c) the short circuit case.
i
y ^ '
•
1
1 1/
1
1 nl 0
J
10
20
30
40 50 60 No. of samples
70
80
90
100
Figure 6 Leakage fault in actuator Figure 6 shows the testing results in leakage of the actuator. Comparing to the healthy condition, one can find that when an actuator leaks its response will be slower than of normal condition. This result again proves that the non-destructive approach works very well.
5
SUMMARY
The fault study in electro-hydraulic servo system is not easy due to its high expense. This paper proposed a novel approach to study faults in the system by means of additional connected hydraulic or electrical circuits. Two methods are detailed to simulate the leakage and the stick-slip faults in the actuator and electrical circuit faults in the torque motor of the servo valve. These methods allow the key but expensive components to be used repeatedly. In addition, combined with numerical fault simulation, these methods will achieve an effective way for fault study. REFERENCES [1] D. R. Bull, et al (1996). A computational tool for failure modes and effects analysis of the hydraulic systems, FPST, Vol.3,113-119. [2] J. Watton, et al (1995). An on-line approach to fauh diagnosis of fluid power cylinder drive systems, Proc. Instn Mech Engrs, Vol. 208, Part I, 249-262. [3] J Watton (1989). Fluid power system—modeling, simulation, analog and microcomputer control, Prentice Hall [4] Lu Wanglong (1996). Practical methods to reasoning and elimination of the failures in hydraulic machinery, Hunan science and technology press, China. [5] T. T. Le, J. Watton and D T Phan (1998). An artificial neural network based approach to fault diagnosis and classification of fluid power systems, Proc. Instn Mech Engrs, Vol. 211, Part I, 307-317 [6] Zhanqun Shi, Andrew Ball and Fengshou Gu (1999). Fauh diagnosis of hydraulic control system based on wavelet and NN, Proc. ofDYMAC'99, Manchester, UK, 517-520.
614
Condition Monitoring and Diagnostic Engineering Management Andrew G. Starr and Raj B.K.N. Rao (Editors) © 2001 Elsevier Science Ltd. All rights reserved.
IDENTIFICATION OF CONTINUOUS INDUSTRIAL PROCESSES USING SUBSPACE SYSTEM IDENTIFICATION METHODS R. J. Treasure & J.E. Cooper Manchester School of Engineering, The University of Manchester Manchester M13 9PL
ABSTRACT In this paper we use a subspace algorithm to calculate a state space model of a simulated industrial process. The accuracy of the state space model is compared with FIR and ARX models. It is found that the subspace system identification algorithm provides concise models that are more accurate than the other two models considered. KEYWORDS System Identification, Subspace Methods, Model Predictive Control, State Space Models. INTRODUCTION Model predictive control (MPC) is widely used for the control and monitoring of continuous industrial processes for example on distillery columns in the petrochemical industry and on drying and evaporation processes in the food and beverage industry. MPC uses system identification to obtain a dynamic model; then uses the model to calculate future control inputs and for process condition monitoring. Recently reported subspace methods for system identification have opened the way for new system identification algorithms. These subspace methods use input-output data to identify state space models of multi input multi output (MIMO) systems. The subspace methods have been successfully applied to control low dimension MIMO industrial processes (e.g. glass tubes with 3 inputs and 2 outputs). In this paper we use a subspace algorithm to identify a complex non-linear MIMO system. A simulation of a petrochemical Fluid Catalytic Cracking Unit (FCCU) is used to compare the effectiveness of the subspace method to two industry standard input-output models - a finite impulse response (FIR) model and an autoregressive (ARX) model. It is expected that the subspace methods can provide a valuable alternative to the present methods for system identification. DESCRIPTION OF THE SYSTEM The input-output data used for the analysis comes from a simulation of a model IV Fluid Catalytic Cracking Unit. The simulation contains non-linearities and complex dynamic interactions that mirror many of the challenges in modeling a real plant. A full description of the simulation can be found in
615
McFarlane et al (1993). The FCCU simulation incorporates 10 manipulated variables, 3 disturbance variables and 36 measured output variables. The control objectives are to minimise the use of energy and at the same time maximise the output of valuable hydrocarbons, which means operating the reactor at temperatures close to metallurgical limits and at the same time controlling the oxygen content in the stack gas to setpoint. The process is controlled by manipulating: (1) The rate and combination of fuel supply, and (2) The supply of air to the process through a lift air blower. A diagram of the system in Figure 1 lists the "manipulated" (input) variables and the "controlled" (output) variables. Regenerator Reactor
PRODUCT
• ManiDuiated Variables Diesel Flowrate Total Fresh Feed Flowrate Lift Air Flowrate Controlled Variables Regenerator Temperature. Reactor Temperature. Stack Gas Oxygen. Cone. Wet gas valve position. FUEL
<— Figure 1. A Fluid Catalytic Cracking Unit. A model of the system is based on three "manipulated" (input) variables each affecting the four "controlled" (output) variables. The flowrate of diesel and total fresh feed is manipulated to control the supply of fuel to the process while the speed of the lift air blower controls the supply of oxygen to the process. The aim in general is to maximise the economics of the unit. Experience of the real plant, overriding objectives, insight into the system dynamics and previous modeling experience all provide key pointers for the modeling process. The simulation was simplified by setting disturbance vectors to zero, and other "inputs" to steady state. Three "manipulated" (input) variables and four "controlled" (output) variables were selected to model the process from the total of 49 measured variables available. The input signals (i.e. the "manipulated" variables) used to excite the process were pseudorandom binary signals, generated automatically by the model predictive control package Connoisseur^'^. Step tests were run in Connoisseur^"^ to determine the reaction and settling times of the controlled variables. The PRBS signals were constructed with a minimum period = 25% the slowest settling controlled variable. The entire simulation was run in Connoisseur^*^, then the data transferred to Matlab to do the system identification comparisons. SYSTEM MODELS FIR Models The first modeling method for the FCCU uses a Finite Impulse Response (FIR) model. The system variables are measured at discrete time instants k; k = 1,2,3... The system output y/j^ at time instant k is calculated as a linear function of a fixed number of previous inputs. yk
= S^/"* 616
(1)
where M^ describes the input at time k; m is the number of previous inputs that are incorporated in the model, and e^ is part of a white noise sequence. Ljung (1999) describes this model as an equation error model structure. ARX Models The second model-type, also an equation error model structure, is a first order ARX model. Again the system variables are measured at discrete time instants k\ k = 1,2,3... The system output y,^ at time instant k is calculated as a linear function of the previous output y^_^ and a fixed number of previous inputs. ffl
where i/^ describes the input at time k; m is the number of previous inputs that are incorporated in the model, and e^ is part of a white noise sequence. For model validation, an ARX model is computed recursively using
i.e. only predicted values of the past output are used to model the current output. State Space Models A discrete time state space model is written as a system of first order difference equations using an auxiliary state vector x(A:T),A: = l,2,3..., T is the sampling period. The state space models described in this paper are of the form
Consider an ri^ order MIMO system with m inputs and / outputs, then xeW, ueW^, yeR\ A^W''', B^"^ , CeR^"", DeW"""' VGIR^ W&R\ The sequences w^ and v^;A: = 1,2,3..., are assumed to be zero mean, stationary white noise sequences. The aim is to identify the matrices A,B,C,D. An important property of state space models is that the state basis is changed by any non-singular matrix TeR"^, without affecting the input - output sequences of the system. The model becomes ^*.i =^^k+^^k-^'^k
y,
.5.
=Cx,+Du,+w,
It follows directly from Equation (4) that A=TAT-'; B=TB; C=CT-'
(6)
Subspace methodologies exploit this property of state space models. They deliver estimates of the key parameters that belong to a family of equivalent state spaces - all within a similarity transformation of the actual system; all identifying an identical output for a given input. The input-output relationship remains unchanged by a change in basis, however the choice of basis is important. A balanced 617
realisation (as provided by the subspace algorithm described below) is important when a reduced order model is required. In mechanical systems, the states of the system often have physical meaning (e.g. position and velocity of a spacecraft) however in the chemical systems being investigated here they are simply values within a vector space, SUBSPACE SYSTEM IDENTIFICATION It is beyond the scope of this paper to give a full description of the subspace algorithm used in this paper. A brief sketch of the main core of the algorithm is given below. For a complete description see VanOverschee and DeMoor (1996). Viberg (1995) also provides information on the development of other subspace algorithms and relationships between them. Those wishing to avoid a stream of mathematics should skip to the results section at this point. At the heart of all subspace algorithms is the singular value decomposition (SVD). The SVD allows for the model order best to describe the system to be determined, and also makes it easy for model reduction. Low order models are important from an MPC point of view because they simplify the control problem. The system identification procedure starts with the formation of a special "projection matrix" 9^^, (described the next section), we have 9?^=rx,€lR''^^
(7)
with C CA
(8)
lCA'-\ and (9) / is the number of system outputs, / is the number of block rows used for the initial projection (described below),/ refers to the width of the projection matrix and n refers to the order of the system. X^ is a state sequence of dimension nxj generated according to the system of Eqns. 5. Note that the column space of Eqn. 7 coincides with F,. Also Eqn. 7 is rank deficient when the number of block rows in "^^ is chosen to be larger than the order of the system. The SVD of Eqn. 7 is partitioned to produce
r,x,=(f/,{/i^'
0
(KT\
(10)
V2 J
The first n significant singular values of 5, are used to derive a model of order n. The core dynamics minus measurement and system noise are contained in
Y,x^^u,sy'(
(11)
r, is extracted from Eqn. 9 as •-U,S,
%
(12)
The estimate of T, is now used to calculate the two state sequences X^ and Z^^, (using Eqn. 18 below) and then the least squares method is used to calculate the state space parameters A,B,C,D. This last stage of the algorithm involves a series of algebraic manipulations, fully described in VanOverschee and de Moor (1996). Calculation of the projection Matrix ^\^ Block hankel matrices are drawn up as follows - at time instant k the measurements y,^ and w^ contain vectors of the / system outputs and m system inputs respectively:
Yp =
V, =
yo
yy
yi
yJ-^
y,
yM
y^
yi.j-x
yi
yi
y^
yj
y,.i
y,*i
^,+3
yi.,
>',+3 J'(+4
y:.,.\
yi
y^
y^
y,-\
yi yi^x
'wo
Wj
u^
..
"i
"2
W3
..
"2
u.
u,
..
_w,_i
u,
"M
••
-M'"J.
y.,^i
Yf = y,.i
yi*j-i
yi,-\
y2i
yi,.x
W/J-l
^IJ-O
^.4--?
(13)
yi,.i-i
"y-I" ^.1
.j^m/x,
";.l
(14)
U,=
^^^./-2.
Next define
w„p =
~u~ p _
(15)
7p _
and calculate the following projection
(16)
z. = rfk u'A f)
In Eqn. 16, Z, is the linear combination of U^, Y^ and U^ that is closest (in a least squares sense) to Yj-. Ljung and McKelvey(1996) provide further insight into the nature of this projection. Note also that Eqn. 16 can be written as U„ Z,=^{L,
(17)
L, I3) K^fJ
In the case where the input is sufficiently rich to properly excite the system, and where the input is not correlated with the states and is also uncorrelated with the sequences v^^ and w^, then 619
Z,=Y,X,+HfU,=L,U^ D 0 CB i) //, =1 CAB CB ': '. CA'-^B
+ L,Y^+L,Uf 0 0 D ':'
0 0 0 0 0 0 . 0 D
(18)
(19)
(20) When the three above conditions are satisfied, the state sequence X, in Eqn. 18 converges to an unbiased value - assuming a very large data set » (say) 2000. Note that in the industrial case, the conditions for Eqn. 18 may not met, however good estimates can be obtained as can be seen in the results of this simulation study. Removal of the linear combinations of Uf from Eqn. 18 leads to
^ = {L,
L,)
•T.X.
(21)
where X^ represents a different state sequence to that in Eqn. 18 (interpreted in Van Overschee and De Moor (1994) as different initial conditions for a non steady state Kalman filter). A further weighting is introduced to give better numerical conditioning. This involves projecting Eqn. 21 onto the orthogonal complement of the future inputs, effectively multiplying Eqn. 21 to form
^^=<^[i^-u][u,u]\u^)
(22)
We have now formed 9?^, the initial projection used in Eqn. 1. A Matlab implementation of this algorithm is supplied with Van Overschee and De Moor (1996).
620
RESULTS A data set consisting of 20000 samples was collected. The data was divided equally; the first half used as a training set and the second half used for validating the models. All data was normalised to zero mean and standard deviation = 1 and then run through the identification algorithms. First order ARX models were constructed with the number of input lags = 5, 10, 15 ...100; FIR models were constructed with the number of input lags = 30, 40, 50 ...150; subspace algorithms were constructed v^th the number of block rows used in Eqns 13 & 14 = 2, 3, 4.. .40 and with state space model orders set to 2, 3 and 4. Note that with the subspace identification technique it is easy to obtain a reduced order model the user simply chooses the model order according to the magnitude of the singular values resulting fromtheSVDofEqn?. First Order ARX Model
Subspace (model order > 2)
20
20
15
15
10
10
5
10
IS
20
25
10
15
20
25
30
35
40
10
15
20
10
15
20
25
30
3S
41
L l ~ l
^\
^\——
2^
2 M —
40
60
eO
5
100
15
15
10
10
S
s 40
N
60
80
100
120
140
0.6
0.6
0.4
0.4 0.2
0.2 20
40
E0
80
_
0.02 0.015 0.01
1 30
40
60
80
100
120
1 to
0.02
002
0.015
0.015
0.01
0.01
ARX - number of input lags
Figure 2. Mean Square Error (MSE) of the three models for each of the measured "controlled" variables. TABLE 1 NUMBER OF PARAMETERS IDENTIFIED AND MEAN SQUARE ERROR
1
MODEL
ARX FIR 1 State Space
SIZE
PARAMETERS
50 Lags 70 Lags 2nd Order
604 840 30
621
MSE 14.5 12.2 6.1
1
Subspacs (rr odal order = 2)
15
Subtpac. (nnodal ordar
Subspaca <model ordar
3)
2 .n
1
n/I
4)
_.
5
10
15
20
2S
30
35
40
5
10
15
2 0 2 5 3 0 3 5 4 0
5
10
15
2 0 2 5 3 0 3 5 4 0
u
1"
t ^ S 1 5
r\l J . U S
10
IS
20
25
30
3S
40
5
10
15
20
25
30
35
4
10
15
20
25
30
35
40
15
2 0 2 5 3 0 3 5 4 0
5
10
15
20
25
30
35
12
E
f';
f: .
^
5
10
15
2 0 2 5 3 0 3 5 4 0
h
06
|lx|4-W-^
5°' 5
10
15
20
25
30
35
40
15
20
25
30
35
40
5
10
15
20
25
30
35
40
0015
1 S 001 o
1
_
Figure 3. MSE for 2"^^ 3^^ and 4^ order state space models of the validation data. DISCUSSION Figure 2 shows a comparison of MSE for each of the "controlled" variables of the validation data set. It is clear that the subspace modeling technique has performed better than the ARX and FIR models on all four variables and on the sum MSE. While the subspace model is the best under the conditions of this investigation, it should be noted that the validation data for the ARX model was generated using Eqn 3, i.e. it has been generated recursively using the values y,^ at each step. This is in contrast to an online implementation of an ARX model which uses the previous measured values >'^, and thus performs much better. Eqn. 3 was used for the current work in order to make the comparison of the three different model types "fair" - because using Eqn. 2 would give the ARX model an unfair advantage: it would "see" future values. Further results (not presented here) indicated that an ARX model using measured rather than predicted values resulted in smaller MSE than the models considered here. Table 1 lists the best model configurations for each model type and the number of model parameters required - a measure of the complexity of each of the models. The first order ARX model with 50 time lags was found to give the best results according to total MSE. The FIR model with 70 time lags was found to give the best results of all the FIR models considered. Under the conditions of this experiment, it is clear from Table I that the subspace modeling technique has provided the best model of the system. For control purposes, a simple low order model is highly desirable. Table 1 shows that the subspace methods which produce fiilly paramatized state space models, produce models with fewer parameters than either FIR or ARX. Figure 3 shows a comparison between 2"^ 3"^^ and 4^'^ order state space models. The total MSE performance of the 2"^^ order state space model was found to be as good as the 3^^* and 4^^ order models. The subspace method has captured the system dynamics, and successfully modeled a 3 input - 4 output nonlinear system using a 2"^ order model.
622
CONCLUSIONS Of the models considered, the subspace model performed best on the validation data set. A comparison between the performance of the subspace technique and the FIR model in a modeling situation where the nature of the process determines that the ARX structure is unsuitable, will provide a good indicator as to the suitability of the subspace method for use in industry. The subspace methods deliver fully parameterised state space models that may be exploited by the wealth of control theory that exists for state space models. Further assessment in an MFC context will be useful. ACKNOWLEDGEMENTS Thank you to EPRSC for the funding for the research project. Thank you to CTC Ltd, a Manchester Irmovations Company, (and the associated support of Invensys pic) for sponsorship of the current project. REFERENCES Favoreel W, De Moor B, et al (2000). Subspace State Space System Identification for Industrial Processes. Journal of Process Control 10, 149-155. Ljung L. (1999) System Identification, Prentice Hall PTR, New Jersey. Ljung L. and McKelvey T. (1996) A Least Squares Interpretation of Sub-Space Methods for System Identification. Proceedings of the 35^^ Conference on Decision and Control Kobe, Japan, 335-342. McFarlane R.C. et al. (1993). Dynamic Simulator for a Model IV Fluid Catalytic Cracking Unit Computers Chem. Engng. 17:3,275-300. Van Overschee P. and De Moor B. (1994) N4SID : Subspace Algorithms for the Identification of Combined Deterministic-Stochastic Systems. Automatica. 30:1, 75-93. Van Overschee P. and De Moor B. (1996) Subspace Identification for Linear Systems. Kluwer Academic Publishers, Boston. Verhaegen M. and Dewilde P. (1992) Subspace Model Identification. Part 1. The Output-error StateSpace Model Identification Class of Algorithms. International Journal of Control. 56:5,1187-1210 Verhaegen M. and Dewilde P. (1992) Subspace Model Identification. Part Two: Analysis of the Elementary Output-error State-Space Model Identification Algorithm. International Journal of Control. 56:5,1211-1241, Viberg M. (1995) Subspace-based Methods for the Identification of Linear Time-invariant Systems. Automatica. 31:12,1835-1851.
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Condition Monitoring and Diagnostic Engineering Management Andrew G. Starr and Raj B.K.N. Rao (Editors) © 2001 Elsevier Science Ltd. All rights reserved.
LIFE CYCLE COSTING AS A GLOBAL IMPERATIVE Kenneth J. Culverson, CPL Reliability Engineer Johnson Controls, Inc. Moffett Field, CA, USA
ABSTRACT Until very recently, the only people who discussed life cycle costing were logisticians, a rather shady group of individuals who, it seemed, were out to make the life of the design engineers a living hell. Terms like "reliability" and ''maintainability" and "availability" were bandied about endlessly. Engineers had been trained in the "design-to-cost" school and found the argimients tiresome at best. Of course, management ultimately saw the truth in the engineer's arguments and agreed that the new design would cost less to buy. This caused the logisticians to sulk in the comer and utter dire warnings about how the ultimate cost would go out of sight. If the noise became too great and you did not work in the defense or aerospace industry, the obvious solution to fire the logisticians and build the "less costly" design. Welcome to the new millennium. The stock market is in the tank, business is looking for ways to maximize shareholder return and the cost of doing business is under the microscope again. Now this changing global economy allows less room for short-term solutions. Economics is still the primary driving force in the work we do, and many companies have finally discovered that the "least cost of acquisition" is not necessarily the "least cost of ownership". This new paradigm is impacting traditional ways of designing systems. In fact, the system level is now the only thiQg that matters to most business enterprises. The specific equipment that makes up a system is only of importance in the impact it has on the availability of the system and the ultimate cost of ownership. Those firms and individuals that xmderstand this need and respond are going to be the winners in this century.
KEY WORDS Life Cycle Cost, reliability, maintainability, availability, design-to-cost, logistics
WHAT IS LIFE CYCLE COST? Life cycle cost is a concept has been used to determine the total cost of ownership of systems in the defense and aerospace industry for some time. It includes all costs of design, test, construction, operation and retirement of a system. In the current context, that system may refer to a new manufacturing plant, an office building or a replacement heating system for an existing plant.
625
"Life Cycle Cost" can be thought of as a combination of reliabiKty, maintainability and commonality within a system that enhances the value of ownership over time. Where appropriate, high reliability equipment is procured to increase availability and where high reliability is not cost effective, maintainability of the equq)ment that reduces down-time, also increases availability. Commonality, of course, enhances value by lowering the cost of the parts inventory that has to be carried by the enterprise. All life cycle cost models are normalized to account for the time value of money. In other words, the costs in year ten of a system life have to have the same basis as the costs in year zero. To this end, we use net present value or net future value as a basis for normalizing costs over time. But the real power of the life cycle cost model is that it forces all aspects of ownership to be examined early in the design process and makes decisions on design concepts, technology inq)lementation, system complexity and human needs easier. Not only are the design decisions easier when the costs are visible, but selling the idea of spending a little more to save a lot is vastly simpler with the costs laid out in front of management.
THE LIFE CYCLE COST MODEL At the top level (and unusable) form, all life cycle cost models look the same, covering the cost of design, construction/installation, operation and disposal.
iCC = t[C„+C,+Co,+C^J Where LCC CR C/ Co CD L
(1)
Life Cycle Cost Research & development (Design) cost Investment (Construction & installation) cost Operations & maintenance cost Phase-out & disposal cost Program life (years)
This LCC model was taken from a U.S. Department of Defense (DoD) document and the one I am femiliar with. With small variances in terminology, all LCC models are essentially the same. Of course, each of the major elements need to be expanded to actually calculate the LCC for a given course of action. The summation gets rather large when carried down to each cost element's constituent parts but we are not interested in doing a con^lete LCC analysis for a project of a few hundred thousand dollars, or even a few million dollars. Most of the availability criteria we are interested in inq)act only a few of the many cost elements. This allows tailoring the LCC effort to focus on those areas where potential savings will show up. Research and Developmeot (Design) cost model
Where CRM CRR CRE CRP CRT CRD
Program management cost Advanced R & D cost Engineering design cost Prototype development cost Prototype test cost Engineering data cost
626
Equation (2) detafls the major elements of the design cost model. Of course, since this is primarily aimed at a system development task, some of the elements will not be applicable in a non developmental environment. By the same token, when we look at alternative designs, some of the costs are not impacted enough to justify recalculating them. Investment (construction/installation) cost model
C;=[C,^+C,c+Qj Where QM Cjc CiL
(3)
System/equipment manufacturing cost System construction cost Cost of initial logistic support
While the investment cost element is rather single looking, it covers the major capital costs of system development. Like the research cost, not all of the elements are applicable in a given project, or may have little impact on the total. Operations and maintenance cost model ^O ~ }p00 ••" ^OM "*" ^ON J
Where Coo COM CON
' ^
Cost of system/equipment life-cycle operations Cost of system/equipment life-cycle maintenance Cost of system/equipment modifications
While the investment model may contain most of the big ticket items, it is the operations and maintenance cost area where the most impact occurs. Normally a major project is designed to last a number of years and the cumulative efiFect of those annual costs outweigh the short term design and investment costs. Phase out and disposal cost model Co=[{FclQcAiC^,s-C^c)] Where Fc Condemnation factor QcA Quantity of corrective maintenance actions CDIS Cost of system/equipment disposal CREC Reclamation value
(5)
Phase out & disposal cost model is much more con^plex than shown. While the individual elements cover the range of work that must be done, the underlying complexity of environmental remediation, for example can cause this number to bloom and in a few cases has been the largest contributor to the life cycle cost of a system (after the fact, of course). Note that this is the only model which can have a negative outcome.
APPLICATION TO A REAL DESIGN PROBLEM How does this apply to real (commercial) world and the type of projects that condition monitoring people get involved in? An example of the type of project we encounter at NASA will illustrate how to tailor the LCC equation to simplify the analysis.
627
At NASA Ames Research Center, we normally do not build new researchfecilitiesfor a new mission, we modify an existing facility. To some degree, this is due to the large nimiber offecilitiesthat are protected as historic bxiildings, in part by the lack of funds to build new buildings and tear the old ones down. This latest project involved the modification and upgrade of a 60 year old reinforced concrete building to house a sophisticated flying telescope system with associated support shops, clean rooms, etc. The project entail^ a seismic upgrade to thefecility,removal of interior ofl5ce spaces, construction of new office and shop spaces. The majority of the HVAC equipment was to be removed and replaced and the utility systems repaired and improv^. Given normal operational practices, a program life often years was used to cost out the model. The research team had some very specific requirements for clean rooms and utility availability and levels of air flow, heat and cooling nimibers and routine safety requirements for aircraft maintenance. No other conditions were set but the special clean room requirements pronq)ted the reliability team to strongly consider the availability aspect of the project. We were able to get involved with the project at the original 30% review point and the reliability group had several concerns with the direction the design was going. No attempt had been made to address the availability of any of the equipment and some of the preliminary design elements indicated a maintenance nightmare in the making. Some of the equipment that was being proposed had traditionally shown low reliability, or high maintenance costs. A few of the drawings showed potential access problems with the new equipment. The reliability groupfecedtwo challenges, one of recommending changes to the design and the other to gain acceptance by the design group. To accon:^)lish that end and provide a positive impact on the project, an informal LCC analysis was done based on the numbers available. Given the way projects arefimded,we were able to easily sinaplify much of the LCC equation. Simplified research & development (design) cost model If we look at the research & development (design) cost (CR), we can pick out those cost areas that will be directly afiected by a design change. It was obvious that the only elements that would have any significant impact on cost were design (CRE), and data (CRD). Program management cost is essentially fixed, we were not going to do any advanced research & development and since we were using commercial components to the new irtfi-astructure, there would be no prototype activities. This reduced the design equation to: CR=[CRE^C^]
(6)
Simplified investment (construction/installation) cost model Similarly, the Investment cost was simplified as the only areas directly afifected were construction cost (Cic) and a few of the initial logistic support (CJL). System/equipment manufecturing cost (CIM) had no relevance. This simplified the construction/installation equation to: Q=[Q+Q]
(3)
Simplified operations & maintenance cost model The operations &, maintenance cost was the area with the greatest impact on the process. While all there elements of the cost will remain in use, specific parts will remain constant or not be relevant to the 628
project. At the t o p level, this contract does not change. After discussions with the design group, there was general agreement that looking at the specific equipment to be installed would have at most an insignificant efiFect on the disposal cost. Therefore no additional analysis was done on this element. As a note, the project had already factored in the disposal costs of the demolition of older parts of the building which contained asbestos and lead. This is part of the construction cost and was a constant regardless of the outcome of the equipment changes that were proposed. In general, if construction is involved, it is hazardous to ignore the disposal cost element of the equation.
EXPANDING THE (SIMPLIFIED) EQUATION The research & development (design) element of the LCC equation is a relatively straightforward summation of the various elements. Since we were not involved with this project until the 3 0 % point, we would obviously impact the design engineering part by adding additional effort. Because of concerns about the equipment originally specified, we would add additional cost to the data part. Eqn. (8) represents the variable part of the design element. N
N
(8)
c,=
Expanding the investment (construction and installation) element and simplifying due to items that are not variable or applicable yields Eqn. (9). Here the construction costs have been retained until the i n t a c t of installing the recommended equipment can be assessed. Of the initial logistics support element CIL only those elements having to do with the possible training costs for maintenance personnel have been retained. This item has been historically been included in contracts and the design group had concerns that the proposed changes would significantly influence the total cost. W == \SpICA "*• ^ICB "^ ^ICU "•• ^ICC ) "^ yQsM ATT A^OMTP ) J
(^)
Where QCA Construction labor cost CjcB Construction material cost Cjcu Cost of utilities Cicc Capital equipment cost QsM Quantity of maintenance students TT Duration of training program (weeks) CoMTP Cost of maintenance training ($/student-week) Eqn. (10) shows the pared operations and maintenance elements. While most of the constituent parts were still applicable they were simplified considerably. Since we were dealing with infi-astructure equipment, no operator costs were factored in as the equipment has no "operators" per se. Continuing maintenance training was retained to accoimt for possible follow-on training for maintenance personnel beyond the scope of "on-the-job" training. N o unique support or test equipment was required for any proposed configuration so that element was eliminated. Operational facility cost was retained to examine the effect of utility usage. Of course, spare and repair parts were retained to examine the prospective change in using high reliability equipment.
629
(10)
Co = A^
/=1
Where CPP^ Cv QcA MMHC Cocp CMHC CDC QpA fpt MMHP Copp CMHP COP CA QA CM QM CH QH Csc Con
Cost of operational fecility support ($) Cost of utilities ($) Quantity of corrective maintenance actions {MA), QCA is a fiinction of (ro)(A) Corrective maintenance manhours/MA Corrective maintenance labor cost ($/MMHC) Cost of material handling/corrective M A Cost of corrective maintenance documentation/MA Quantity of preventive maintenance actions ( M A ). QPA relates to fpt Frequency of preventive maintenance actions per operating hour Preventive maintenance manhours/MA Preventive maintenance labor cost (S/M^^HP) Cost of material handling/preventive M A Cost of preventive maintenance documentation/MA Average cost of material purchase order ($/order) Quantity of purchase orders Cost of spare item Quantity of items required or demand Cost of maintaining spare item in the inventory ($/$ value of the inventory) Quantity of items in the iaventory Cost of consumables Cost of system modification
APPLICATION OF THE M O D E L So how did this all turn out. At this writing, the construction is six months behind schedule and counting. Fortunately no one is pointing any fingers at the changes made by the reliability team as responsible for the delays. Rather than buying any LCC software, a spreadsheet was developed to enter all of the numbers and evaluate the cost effect of proposed changes. (Unfortunately, I do not have the rights to the spreadsheet) The initial LCC anatysis was painfiil, requiring signoflf by the design group on every aspect of the change. Until we gained some credibility, we had to justify every entry in the spreadsheet and the calculation behind every number. Each element was discussed imtil concurrence was reached and then the next element was opened for discussionThe research & development nwdel was the first item discussed. We finally reached concurrence that if all of the proposed changes were accepted, we would add two weeks of engineering effort to the design group's work. The reliability team agreed to this as it only represented an increase of just under one percent. The data element was finally agreed to at one percent increase in the cost of equipment (the normal method of ^ p o r t i o n i n g data costs at the time). Of course, the two weeks of additional engineering took place, but the data cost was finally was determined to be no increase in price over the normal package.
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The investment model discussions drew the greatest response from members of the design group and probably the most co-operation of any of the elements. The reliability team had done an analysis of the major design elements to be changed to gain the required availability for the long term in the building. The equipment selected, with the rationale for each major system selection was presented along with the cost and any anticipated construction changes. Part of our concern had been accessibility of certain of the items scheduled for inclusion in the original design. The chiller was of particular concern because of recent problems with core access in another installation. The equipment presented actually simplified the construction (reuse the existing equipment room rather than demolish and rebuild). Utilities use during the construction phase was agreed to have no effect on the total element cost and was not recalculated. The chiller selected was more expensive than the one originally envisioned in the design by approximately five percent but the remaining equipment added less than one percent to the capital cost originally planned. The change in the material and labour costs due to the use of the existing mechanical room took lowered the six percent increase and reduced it to about four percent increase. Still, it would be a difficult sell if money became tight. At this point, it was agreed that the normal training provided with either equipment would not modify the bottom line. Operations & maintenance costs were where the reliability team had to make money. It was more difficult due to the relatively short program life cycle. We used the U.S. Department of Energy (DOE) Motor Master^^ software to analyse the motors specified by the design group and recommended by the reliability team for utility usage. This provided hard numbers that everyone could agree with. We predicted that we would cut utility cost by 15 percent over the motors originally specified. While this was not a huge number, energy usage is a large concern at the centre. Specifying the Lio life of the bearings used in the motors also gave a higher confidence that the motors would have a reasonable life span. Based on experience using the various equipment that was under discussion, we were able to make a rational guess at to the cost of maintenance between the two configurations. Reliability teams equipment selections, based on availability concerns and the lack of redundancy, dictated that major or critical items be put on a condition monitoring program. This led to putting in a first year cost to instrument the chiller and the air handlers used in the system. Since the condition monitoring programs, vibration, motor current signature analysis and infrared were already in place, no additional training was required. Again, based on real e2q)erience with all of the equipment in question, we were sure that the corrective maintenance hours would decrease by 75 percent over the original design and the preventive maintenance cost would increase due to the additional paper work involved with more work orders while the man-hours was essentially the same for either configuration. Spare and repair parts provided another point of savings with the new configuration. With the condition monitoring maintenance philosophy and the increased reliability, and commonality with other existing systems, we were able to cut the cost of spare and repair parts carried by 95 percent. Consumables were considered to be constant and of very little consequence in the LCC equation. By the time each of the elements had gained concurrence, it was agreed that while we had no number to put against the system modification part, everyone agreed that there would be at least one less modification due to the changes 2^eed upon. Thefinalnumbers showed a net saving, at the end of the first year. This is not unusual with this kind of real world problem but had we not broken even at that point, we would have done a net present value analysis for years two through ten. Had the project proceeded as planned at this point, we would have felt good about our efforts. Unfortunately, as the project was going out for bid, the San Francisco Bay area building boom was in fiill swing and the average construction cost per square foot rose by 60 percent. This was a budget buster and at the 90% review, we had to scale back the program to meet reality. Many of us in the 631
meeting felt that all the work we had done was about to unravel. Surprisingly, the key players agreed that even though the equipment was slightly more expensive, the k)ng term benefits outweighed the additional cost. Rather than compromise the analysis work that had been done, refurbishment of several areas of the building which would not be occupied until year two or three of the project were deleted, giving the savings required. These portions of the original plan which were dropped (actually made options) had no effect on the equipment installations we had spent the effort on. Even though this project is behind schedule, the LCC effort has paid dividends for the centre, the design group and the reliability team. The reliability team has been routinely included in planning efforts, the design group has another resource to draw on for future projects, and hopefully, the centre will get systems that are sustainable for their design life and that extra life that is almost automatic with a government project. BIBLIOGRAPHY Blanchard, Benjamin S., (1986), Logistics Engineering and Management, Prentice-Hall, Inc. USA Jones, James V., (1988), Engineering Design, Reliability, Maintainability and Testability, Tab Books, Inc, USA
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Condition Monitoring and Diagnostic Engineering Management Andrew G. Starr and Raj B.K.N. Rao (Editors) © 2001 Elsevier Science Ltd. Allrightsreserved.
SIX SIGMA INITIATIVES IN THE FIELD OF COMADEM Raj B. K. N. Rao COMADEM International, 307 Tiverton Road, Selly Oak, Birmingham B29 6DA, UK. E - mail: [email protected]
ABSTRACT Six sigma process is now used by many as a quantifiable business benefit. It is gaining increasing popularity in UK, USA, Japan and in Europe as a strategic management tool to continuously improve customer retention, companies performance, profit margin, and to retain competitive advantage as a world-class organisation. Six sigma is an ideal candidate to reduce process variability in the field of manufacturing and process engineering. It offers a focused approach to continuously reduce faults/defects in a system thereby improving the inherent quality and reliability of its performance at an optimum cost. Its aim is to improve the total life cycle cost of a system through rigorous application of advanced statistical techniques. In this state-of-the-art review paper, the author will uncover the mysteries of Six Sigma and shred more light to reveal its potential benefits as a strategic Comadem tool of the 21^^ century.
KEYWORDS Six sigma strategy, COMADEM,
INTRODUCTION Dramatic impact on cost reduction, productivity, growth in market share, improvement in customer retention, and reduction in cycle times and defects have been achieved by many manufacturing and process industries world-wide by implementing the total quality management tool of Six Sigma. Six Sigma means near perfection with the ultimate goal of achieving 3.4 defects per million items produced. Generating such a significant change in the culture of any organisations is not easy. The biggest hurdle and challenge faced by those who have implemented Six Sigma is to get the top management on their side and to keep the momentum going. The chief advantages of implementing Six Sigma strategy are:
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• • • • • • • • • • •
It enables manufacturing process to be quantified and predicted at the design stage. It can revolutionise the business by setting targets with the company's overall business strategy. It defines the scope of the projects, gains the confidence of the management and creates an effective management culture within the organisation. Eliminate defects. Significantly reduce costs. Relentlessly pursues perfection. Quantifies process improvements. Continuously add value to the assets. It cuts across all departmental and process barriers. It brings bottom line success and increased customer satisfaction. It is comprehensive and flexible for achieving, sustaining and maximising business success.
WHAT IS SIX SIGMA? It is an integrated, proactive, technology-based business process that allows companies to continuously improve their performance, to add value to their assets and to significantly improve their bottom line by monitoring, diagnosing, prognosing, controlling and predicting everyday business activities in ways that minimise wastages and resources, while improving the quality of life. It is a zero defects technology management strategy, which recreate the process so that defects and errors never arise in the first place. The Six Sigma Strategy broadens the hitherto well-accepted definition of quality to realise 'Value entitlement" for the business and the customer. Businesses that do not adhere to such standards fail to achieve their economic and value entitlement. Mikel Harry and Richard Schrodder brilliantly discuss these aspects in their book on Six Sigma Breakthrough Strategy. In their book entitled The Six Sigma Way, Pande, Neuman and Cavanagh defines Six Sigma as follows: " A comprehensive and flexible system for achieving, sustaining and maximizing business success. Six Sigma is uniquely driven by close understanding of customer needs, disciplined use of facts, data, and statistical analysis, and diligent attention to managing, improving, and reinventing business process". The name Six Sigma originates from the field of statistics in the control of variation during the manufacturing process. A simplified sigma conversion measure is shown below: TABLE 1 Sigma 1 2 3 4 5 6
Defects per Million Opportunities 690,000 308,000 66,800 6,210 320 3.4
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Yield (%) 30.9 69.2 93.3 99.4 99.98 99.9997
While the measure of quaUty is the cornerstone of the Six Sigma process, it's the methodology, tools, techniques and strategies driving process change that differentiate between a quality campaign and a rigorous scientifically based management philosophy. By incorporating the various opportunities for defects in the calculation, the Defects per Million Opportunities (DPMO), can be made more realistic to relate performance across many processes. Six Sigma Strategies consists of three stages, viz, Process Improvement, Process Design/Redesign and Process Management. Process improvement focuses on the solutions to those unsolved business problems. It is synonymous to 'continuous improvement', 'kaizen', or 'incremental improvement'. The objective of process design/redesign is not to fix but to replace an unprofitable process with a new one using Six Sigma Design principles. Process management lays the foundation to the understanding and facilitation of various business processes.
THE DMAIC METHODOLOGY At the heart of the Six Sigma Strategy, there are five fundamental stages summarised by the acronym DMAIC that stands for Define, Measure, Analyse, Improve and Control. See Figure 1. These are briefly discussed hereunder. Define. Clearly identify, define, and clarify problems and requirements related to the business or critical to customer needs. All Critical to Quality (CTQ) factors should be customer focused and correlated with the overall business process. At this stage, all required resources should be clearly identified including employee training and management approvals are sought. Measure. Collect and collate all available data, validate, refine, review and establish base-level measures of inherent defects and customer's 'out of specification' conditions in the existing process. Employee training in this phase should consist of easy to understand and apply basic statistical probability theory and practice, statistical analysis using software, and measurement analysis. Analyse. Explore and exploit underlying root causes of failures by employing the most relevant statistical techniques. Prioritise a list of those influential factors affecting the desired outcome. Develop, identify, assess, validate and refine the analytical process till the desired goal is realised. Fully investigate whether the problem is technology or/and business process related. Improve. Seek the optimal solution(s). Develop, identify, assess, validate, test, standardize and implement a plan of action for implementing and confirming the required soludon. This phase should encompass the Design for Six Sigma (DFSS) as well. Control. It is at this stage that Six Sigma Process leads to sustainable return on investment and payoffs both in terms of quality and financial benefits. Ensure that all desired implemented changes stick. Employ integrated proactive condition monitoring and diagnostic engineering management tools and techniques as the basis for developing and controlling the ongoing measures. Employ knowledgeable workforce to ensure the integrity of the whole business process. 635
Sustain improvements
iL
s
•••*
> a o CI u
9 -^
Define the problem
Measure what you care a b o u ^
Statistically find root causes ^ x
Mobilise change initiative^x^
^ Define
Measure
Analyse
Improve
Control
Methodology Figure 1. DMAIC Methodology vs. ROI
TOOLS AND TECHNIQUES EMPLOYED TO IMPLEMENT SIX SIGMA STRATEGY Table 2 shows some of the tools and techniques used to successfully implement Six Sigma Strategy. TABLE 2 Statistical Process Control Charts: Multiple Charts, X-bar, R Charts, Pareto Charts, Process Capability & Performance Indices, Moving Average/Range Charts, EWMA Charts, Short Run charts, CuSum Charts, Runs Tests, Multiple Process Stream, etc. Process Analysis: Process/ Capability Charts, Ishikawa (Cause & Effect) Diagrams, Gage Repeatability & Reproducibility, Variance Components for Random Effects, Weibull Analyses, Sampling Plans, etc. Design of Experiments: Fractional Factorial Design, Mixture Design, Latin Squares, Residual Analysis & Transformation, Taguchi Design, Central Composite Design, etc. Other Tools and Techniques: Enterprise Resource Planning (ERP), Production Planning System (PPS), Computer Aided Design (CAD), Manufacturing Execution System (MES), Suppliers Assessment Management (SAM), Audit Management (AM), Design and Process FMEA, Risk Assessment, IT Integration, Quality Management Software such as CAQ = QSYS, BabtecCAQ, eVerest, Active Flow, Solution Prosper, Paradigm II, Dataputer, STATISTICA software, Galaxy Metrology software, Quick Serve, CHARTrunner 20QQ, SQCpack for windows, PQRTspy Plus, etc.
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SOME REPORTED ECONOMIC BENEFITS OF IMPLEMENTING SIX SIGMA STRATEGY Six Sigma has achieved outstanding results for companies world-wide. Pioneered by Motorola in the mid 1980s it is no surprise that the global industries have quickly responded to this revolutionary change in management culture. Here are some reported benefits of this highly innovative quality management discipline. "In 1997, General Electric announced that it would save $500 million that year because of Six Sigma and by 1998 the programmes had risen to $1.2billion" - Quality World (1999). 'By completing 5 to 7 projects per year per Black Beh the company will add in excess of US$1 million per Black Belt to it's bottom line' - T. Pyzdek author of The Complete Guide to Six Sigma. " Commonwealth Health Corporation, a 478 bed medical center in Kentucky, began its journey to implement a Six Sigma improvement culture over three years ago. Results have been overwhelming as the medical center reports a reinvigorated and transformed management culture. Within a mere 18 months, errors in one ordering process were reduced over 90%, overall operating expenses had been reduced by $800,000, and employee survey results had improved by 20%. These results were from a single division within the organisation. Now the medical center has realized improvements in excess of $1.5 million and is expanding the program to other areas." - G.T, Lucier and S. Sheshadri, Strategic Finance (May 2001). 'Lockheed Martin saved $64 million on the first 40 projects. Motorola claim dramatic results: Productivity up an average of 12.3% per year; Reduced cost of poor quality by more than 84%; Saved more than $11 billion in manufacturing costs. GE 1995 - 1998 Company side savings of over $1 billion; Estimated annual savings to be $6.6 billion by 2000' - Graeme Knowles, Warwick Manufacturing Group (2000). AUiedSignal Inc implemented Six Sigma Breakthrough Strategy with the goal of increasing productivity of 6% each year in its industrial sectors. These initiatives allowed operating margin in the first quarter of 1999 to grow to a record of 14.1% from 12% one year earlier. Since the CEO implemented the programme in 1994, the cumulative impact of Six Sigma has been a savings in excess of $2 billion in direct costs. - M. Harry & R. Schroeder, Six Sigma (2000). ' ... in fact, a number of prominent companies in industries from financial services to transportation to high-tech are quietly embarking on Six Sigma efforts. They're joining others, ..., including Asea Brown Boveri, Black & Decker, Bombardier, Dupont, Dow Chemical, Federal Express, Johnson & Johnson, Kodak (which had taken $85 million in savings as of early 2000), Navistar, Polaroid, Seagate Technologies' P.S. Pande, R.P. Neuman, R.R. Cavanagh, The Six Sigma Way (2000). Six Sigma is having a significant impact on UK's business. It is now regarded as the intellectual capital of the UK's modem economy and a wealth creation dynamic force. Honeywell Control Systems, Tate and Lyle, Volvo Cars Market Area Europe, David Hutchins International, Glaxo Wellcome, Catalyst Consulting Ltd, Eastman Kodak Company, General Domestic Appliances, Sun Microsystems, Marconi Services ICI EUTECH, DuPontSA have recently reported similar success stories. - Conference
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Proceedings on Practicalities of Introducing Six Sigma into Manufacturing & Process Industries held in London in January 2001.
SIX SIGMA INITIATIVES IN THE FIELD OF COMADEM Industries worldwide employ various assets in their manufacturing/processing activities. Keeping these assets in a fit for use conditions all the time and to make them available when and where they are needed is essential. In real life, systems do fail for one reason or the other. There are ample evidences to show the various modes of failure in engineering/manufacturing/process systems. See Figure 2. Causes for Production (Uptime) Losses
• Chronic Failures: Faster loosening, key wallow, misalignment, oil/air leaks etc. • Sporadic Failures 70%
Figure 2 Drastically reducing the risks of failures/defects in industrial systems not only prolongs its life expectancy, it increases the uptime, enhances the quantity, quality, reliability and availability of the output and significantly improves the life cycle cost and profit margin of the company. Six Sigma Strategy has clearly paved the way to realise zero defect engineering goal in today's consumer oriented, high speed and complex global economy. It is now regarded by many as an effective proactive integrated management strategy and is considered as the main central theme in all major decision making processes. Its sphere of application encompasses pharmaceutical, chemical, electrical, electronic, medical, transportation, service, defence, education/training and many other sectors/industries worldwide. A Design for Quality methodology (Design for Six Sigma - DFSS) based on the utilisation of statistical methods and tools is embedded in a New Product Introduction (NPI) process to develop new gas turbine engines able to meet customer performance expectations ( Bongini, Citti, Mezzedimi and Tosnarelli (2000)). Many industries and engineering disciplines use the Design and Process Failure Modes and Effects Analysis (FMEA) for different purposes and in a variety of ways. For the design engineer, the FMEA is used to anticipate the ways in which a design will fail. In this way, the design can be improved or the effects of the failure avoided. For the Reliability or Maintenance Engineer/Manager, the FMEA is used to allocate
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timely resources to resolve various issues. While in one case (design, reliability/maintenance) the focus of the FMEA is on the failure modes of the components/systems, for a RCM analysis, the focus is on the functions of the system and the w^ays by which the functions can fail. The current thinking in the petroleum industry is to integrate RCM and Six Sigma processes to maximum benefits (Lee Pendleton (2001)). Six Sigma Strategy can be gainfully implemented as an on-going, continuous improvement, proactive maintenance management, and quantitative/predictive methodology in several Best Practices programmes by both large and SMEs. No doubt, with increasing public awareness and understanding of this innovative and revolutionary dynamic process, the nature of manufacturing will change in many drastic ways.
THE ROLE OF SIX SIGMA IN A KNOWLEDGE - BASED ECONOMY There is an urgent need to discover, generate and disseminate this newly acquired knowledge for the benefit of community. A number of organisations are offering education and training programmes in this field. Mike Harry and Richard Schroder have established Six Sigma Academy, Inc. and they can be contacted on www.6sigma.com. We need a constant stream of re-skilled flexible work force to actively contribute to the new wealth creating economy. Here is a unique opportunity for universities, professional institutions and industries to join forces to create innovative, nationally accredited and industrially relevant undergraduate/postgraduate/NVQ programmes. The Governments and the European Community should take proper initiatives to launch suitable knowledge imparting schemes to increase the awareness, public understanding and Partnerships/collaborative/foresight programmes in this world-class campaign. The time is now right to formulate international standards in the training/ accreditation/certification of Six Sigma specialists. Individuals should also take full responsibility to further their own career developments.
CONCLUSIONS Condition Monitoring and Diagnostic Engineering Management is a proactive integrated management interdiscipline. There are well-established/ tried and tested tools, techniques, methodologies and strategies in this field that can be judiciously selected and profitably exploited to maximum benefits. Six Sigma should be of paramount importance to every forward-thinking executive, manager, engineer, technologist, policy maker and service provider determined to make their organisation a world class.
REFERENCES 1.
2.
N.M. Tichy & S. Sherman (1993). Control Your Destiny or Someone Else Will: Lessons in Mastering Change - from the principles Jack Welch Is Using to Revolutionize GE. Harper Business. New York. M.J. Harry & J.R. Lawson (1994). Six Sigma Producibility Analysis and Process Characterization. Addison-Wesley.
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3.
W.J. Kolarik (1995). Creating Quality: Concepts, Systems, Strategies and Tools. McGraw-Hill. 4. H. Mikel (1999). Six Sigma: The Management Breakthrough Strategy Revolutionizing the World's Top Corporations. Bantom Doubleday Dell. 5. P.S. Pande, R.P. Neuman & R.R. Cavanagh (2000). The Six Sigma Way. McGraw-Hill. 6. M. Harry & R. Schroeder (2000). Six Sigma: The Breakthrough Management Strategy Revolutionizing the World's Top Corporations. Currency. 7. wsvw.sixsigmaexchange.com. The independent on-line information gateway for Six Sigma professionals world-wide. 8. D. Bongini, P. Citti, V. Mezzedimi & L. Tognarelli (2000). New Product Introduction and Design for Six Sigma Processes Integration in Gas Turbine Design. Proceedings of the 3^^ International Conference on Quality, Reliability and Maintenance. Professional Engineering Publishing Ltd. London. 9. K. Young (2000). Process Control, Variability Reduction & Six Sigma Performance. Proceedings of a one-day seminar held at the University of Warwick in November. 10. G.T. Lucier & S. Sheshadri (2001). GE Takes Six Sigma Beyond the Bottom Line. Strategic Finance. May Issue. 11. L. Pendleton (2001). The Application of RCM2 to Equipment used to Manufacture Water Chillers. Proceedings of the Maintenance and Reliability Conference MARCON 2001. Organised by the University of Tennessee in May 2001.
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Condition Monitoring and Diagnostic Engineering Management Andrew G. Starr and Raj B.K.N. Rao (Editors) © 2001 Elsevier Science Ltd. Ail rights reserved.
MONITORING EXHAUST VALVE LEAKS AND MISFIRE IN MARINE DIESEL ENGINES A. Friis-Hansen' and T. L. Fog^ ' Department of Mechanical Engineering, Building 101E Technical University of Denmark, DK-2800 Lyngby ^ Research & Development, MAN B & W Diesel A/S, Teglholmsgade 41, DK-2450 Copenhagen SV
ABSTRACT The objective of the present study is to identify efficient classifiers for detection of two different failure modes in marine diesel engines, namely exhaust valve leaks and defective injection (misfire). The classification is performed on the basis of structure-borne stress waves from recorded RMS Acoustic Emission (AE) data. These were obtained by experiments with a two-stroke four cylinder 500 mm bore low-speed marine diesel engine with an approximate output of 10.000 BHP. The study may be seen as a step towards a diagnostic method, which can assist in the interpretation of measurements and detection of causes of abnormalities during management of the engine. The dataset is analysed for identification of the failure modes: Normal exhaust valve (i.e. no leak), small leak and large leak. Measurements from the compression stroke are found to hold more information about exhaust valve leaks than other subsets analysed. By using a simple equation relating the cylinder pressure to the crankshaft angle as well as a data-fitting scheme, it is possible to arrive at a very efficient classifier (no misclassification). This classifier is flexible because it is based on a physical principle and should therefore be relatively easy to adapt to other engines and sampling frequencies. Defective injection (or misfire) may be detected by exploiting symmetry properties of the above mentioned equation and compare it to the measured data. The results are in general very encouraging and the solution may be seen as a hybrid approach combining domain knowledge and data processing.
KEYWORDS Condition monitoring, Exhaust valve leaks, Acoustic emission, Diesel engines, Classification
INTRODUCTION The purpose of the exhaust valve is to seal the combustion chamber from the surroundings during compression, thus securing maximum pressure in the cylinder during the combustion event. This again is necessary for achieving maximum engine performance in terms of output power. Exhaust valve leaks (or bum-through) are usually caused by dent marks on the sealing face of the valve. Due to hot corrosion, the cross-sectional area of the leak will increase rapidly and the engine output performance will decrease 641
correspondingly. If the leak is detected early, reconditioning of the damaged valve may be possible. But more often the valve has to be replaced. In both cases the repair and replacement is a time consuming process that may cause costly transport delays. It is therefore of great importance to be able to monitor the degradation of the exhaust valve so that the associated maintenance work can be properly scheduled and thereby secure continuous operation of the vessel. Experiments were carried out on the Research Engine of MAN B&W Diesel A/S (see Fog (1998)). Analysis of the obtained dataset has been reported in Fog (1998) and Fog et al. (1999) in which the classification task was performed using an ensemble of neural networks which outputted posterior probabilities of the valve condition. Other studies of detection systems for exhaust valve leaks have been reported, for instance in Bardou & Sidahmed (1994).
EXPERIMENTS AND DATASET The Research Engine is a two-stroke, four-cylinder low-speed diesel engine with a 500 mm bore. From the series of experiments reported in Fog (1998), it was concluded that acoustic emission (AE) signals were better indicators of mechanical events than the other investigated detection methods (temperature of exhaust gas, cylinder pressure and vibration measurements by accelerometers). AE sensors measure structure-borne stress waves released as energy when deformation of the material occurs. AE has proved superior to the other measurements obtained, indicating sensitivity to both mechanical and fluidmechanical events, where acceleration for instance is sensitive only to the mechanical activity. In this study, the only signal type investigated is the AE signals. Two identical AE sensors were mounted in two different positions on the exhaust valve housing as shown in Figure 1.
Temperature
HB^K^
AE Sensor I I ^ ^ ^ ^ ^ ^ H p
Accelerometer
A£ Sensor U
Figure 1: Approximate positions of the two AE sensors on the outside surface of the valve housing. The accelerometer and the thermocouple are not used in this study. See also Fog (1999). During operation of the engine, AE signals were recorded from each of the sensors. In addition, a shafttiming signal with a resolution of 2048 angle-specific pulses per revolution and a Top Dead Centre (TDC) signal were recorded. As a pre-processing step the AE signals were synchronised with the TDC of the piston so that each data series comprises exactly one revolution starting at the TDC. Furthermore, the signal is trigger-resampled into 2048 points per revolution, so that one data series comprises 2048 angleequidistant values. The experiment was performed for four different engine load cases: 25%, 50%, 75% and 100%. For each engine load case, three failure modes were investigated: No exhaust valve leak (normal operation), small leak (approx. 4 mm^ cross-section), and large leak (approx. 20 mm^ crosssection). When the engine is used for propulsion of a ship with a fixed pitch propeller, the load is regulated by the engine speed. In this situation, the engine is said to work on the propeller curve. In this study only data obtained from the propeller curve is analysed. A series of experiments was performed without fuel injection in order to simulate a defective injector, also called misfire. The experiments 642
comprised a total of 367 datasets (engine revolutions), of which 21 cases have misfire. As an example, a dataset for a large leak at 50% load (no misfire) is shown in Figure 2.
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Figure 2: Measurements for an entire revolution, 50% load, propeller curve. Large leak. PRELIMINARY DATA ANALYSIS In Figure 2 it is illustrated that a relatively low vibration level is observed in the beginning of the series (part I - data point 1 to approximately 700). In the middle (part n - data point 700 to approximately 1400), the data shows a very irregular pattern and towards the end (part HI), a more regular pattern is resumed. The datasets may thus be partitioned into three qualitatively distinct parts. The physical explanation is that the valve is closed for the first part (combustion), open for the intermediate part (exhaust and scavenge) and closed in the last part (compression). It seems reasonable to detect exhaust valve leaks from part I and part in where the exhaust valve is closed. Hence, the portion of the dataset where the valve is open is neglected. If the valve is closed and has a leak, the pressure difference between the inside and the outside will cause gas to escape through the leak and emit a "hissing noise". The pressure reaches its maximum during combustion, but in order to avoid disturbance from injection noise and noise from the combustion process itself, the datasets from the compression stroke are expected to contain more consistent information about leaks than the other parts of the series do. In Fog (1998) the injection timing as well as the timing of the opening and closure of the exhaust valve is given for different operating conditions. The latest closure angle is 211.1° and the earhest injection angle is 352°. To eliminate clutter from closure of the valve and avoid injection noise, the analysis is restricted to comprise the portion of the data from the crankshaft angle 300° - 350°, corresponding to data points 1706-1992. Examples of such measurements are given in Figure 3. It is seen that the noise level increases significantly with the leak size.
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Figure 3: Measurements and mean level for compression stroke, 50% load, propeller curve. Upper left panel: normal condition, upper right panel: small leak, lower panel: large leak.
As a first attempt to gain knowledge of the dataset, the second order statistics are computed. The mean noise level \i of the dataset distinguishes perfectly between no leak and small leak. The distinction between small and large leak is associated with a misclassification rate of 4.4%. However, when conditioning on the load case the classification is 100%.
CLASSIFIER BASED ON DATA FITTING OF A SIMPLE PHYSICAL MODEL The basic assumption of this section is that if gas is escaping through a leak, then the general noise level is proportional to the pressure difference between the inside and the outside of the compression chamber. We further assume that the AE signal s(v) measures the general level of the hissing noise as a function of the crankshaft angle v. The following model then applies: s{v)=k,{P,„-P^J
(1)
where k] is a constant. Pin is the pressure in the combustion chamber and Pout is the pressure on the outside of the cylinder. Pout is assumed to be much lower than P,„ and is therefore neglected. Based on the indications of the preliminary data analysis it is assumed that the noise level is dependent on the leak size so that the level of degradation can be estimated. This dependence relation is unknown and therefore not accounted for in the model (Eqn. 1) but the assumption is confirmed later. A general expression for the 644
pressure in the cylinder as a function of the crankshaft angle a is searched for. In Figure 4 a schematic drawing of the cylinder, piston, piston rod and crankshaft is shown.
Figure 4: Geometry of cylinder, piston position and crankshaft angle. Geometry considerations yield x - r - a zo'^a - h cosy^ - k. By considering the top dead centre (TDC) position of the piston, we also see that r^m+k+b+a and by insertion X=m + a(l - cos «) + Z?(l - cos )^)
(2)
Assuming an adiabatic process for an ideal gas in a confined space at constant temperature, PiV\=P2V2, where P is the pressure and V is the volume. The volume of the cylindrical combustion chamber is given by V=xA where A is the cross-sectional area of the cyUnder. During the compression stroke, all valves are closed so the mass of the gas in the combustion chamber is constant, and the pressure P2 may be computed from (3) ^
V2
^
A(m + a(l - cos Cir) + ^ ( l - c o s )^))
The angle )5 is ranging from zero to /3max given by sin j3max= a/b- If we assume b»a, the expression for P2 may be written
c,
then cos J3 = 1 and
(4)
C2 -COS a
in which Ci=PiVi/(Aa) and C2-l+m/a. Although the model is based on many idealisations, a simple model for the pressure as a function of the crankshaft angle a has now been established. For the noise level to be proportional to the pressure, a number of further assumptions are needed. First, that the leaks are too small to decrease the pressure considerably in the combustion chamber compared to normal operation. Secondly, that the transmission of the stress waves through the structure as well as the amplification of the sensor signal are linear. The exact nature of transmission and amplification is unknown, but Eqn. 1 is general enough to take into account a proportionality factor which in a primitive way accounts for the model uncertainty. Hence, the general model can be formulated s{a) =
0, 62-cos a
(5)
where Oj = k2Ci and ft :•C2' As an empirical justification of the model, good agreement with the dataset is shown in Figure 5.
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Figure 5: Curve fitted to data by a non-linear optimisation algorithm. Large leak, ft = 506 and 62 -L06. The model contains only two parameters to be estimated, but unfortunately, linear models are not applicable because the problem cannot be formulated as the general linear model: Y=xfe4-£,
(6)
where 0 - (61 , 61 f is the column vector of parameters to be estimated, x is a known matrix solely dependent on the independent variable (here the sampled time steps t e {1706, ... , 1992} and Y is a column vector of the measured data points. Et is the vector of error terms. Instead, a non-linear optimisation algorithm (Levenberg-Marquardt, see e.g Ljung (1987)) is used. This is more timeconsuming and convergence to the global minimum cannot be guaranteed. However, the global minimum of the objective function is relatively well defined in the case where a leak is present, because the minimum value of the fitted function is given by s'(amin)= Oi /(02'Cos(300°)) and the maximum is governed by s'(amax)= 6i/{62-cos(350°)). Both of these have limited possible range, and the minimum of the objective function should therefore be well defined and facilitate convergence. In cases where no leaks are present, the general noise level does not change much during compression, see Figure 3 (top left). The signal is thus stationary around its mean value, and the algorithm searches for values for which the cosine term of Eqn. 5 becomes negligible and the ratio O1/O2 expresses the mean noise level. The algorithm does not stop until the tolerance limit has been reached. RESULTS A number of different classifiers based on the optimisation result Oj and ft are tested. It is found that the following quantities are poor classifiers: the residual norm, ft , s'iOmin), s'{amax) and ft/ ft. Using 7/ft aS a classifier it is possible to discriminate completely between the leak sizes but an even more effective classifier can be formed by the ratio: ^i=-
s\(X^)
^2~cos(300')
^'(O^min)
<92-COS(350°)
It is possible to discriminate completely between the leak sizes on the basis of the following intervals: Rie [1.00, 1.00] <=» No leak 646
(7)
Rj e [2.87, 5.17] Small leak Rj E [5.48, 8.52] <=> Large leak There is a large gap between the intervals corresponding to no leak and small leak. The gap between the intervals of small leak and large leak is relatively small. The detection of a leak is thus very efficient whereas the determination of its size is somewhat harder. Since there is no overlap between the intervals, no misclassification is possible. To detect a leak it is only to be checked if/?/=l .00 or not.
COMMENTS The clear indication of a leak means that the classifiers are very well suited for early warning purposes. Furthermore, using a ratio such as R] (Eqn. 1) has a better generalisation ability than using absolute values such as J/02- The classifier is based on data fitting where the functional model is derived from physical principles. It is therefore expected that the method is robust and has good generalisation abilities to other engine types. The crucial parameter of the classifier is $2- The idealised expression O2 = (l+m/a) explicitly determines 62 as a function of m/a, which is closely related to the compression ratio r=(m+2a)/m. The fact that the data fitting yields different values of ft in the cases of large and small leak indicates that ft is not only determined by the geometry but also influenced by the leak size. Despite the unknown nature of this influence, the classification works very efficiently. When a diesel engine is operated for propulsion the normal condition is that the exhaust valve is intact. In this case, the signal is stationary and it may seem inefficient to use a non-linear optimisation algorithm to identify the mean level of the noise. However, the optimisation is necessary for detection of leaks, and when the mean noise level is known, a reasonable starting point for the optimisation may be given. As a suggestion, ft may be chosen to be 100 // and ft to be 100 where ju is the mean noise level. If there is no leak, the cosine term of Eqn. 5 is negligible and the sensitivity of the residual to changes in 0 is small, and the algorithm soon reaches its tolerance limit. If there is a leak, the global minimum is well defined and will be found in very few iteration steps even if the initial guess was far off. It may seem that the applied non-linear optimisation algorithm is a somewhat clumsy way of identifying the ratio between the maximum and the minimum noise level. Since the shape of the fitted function is rather simple, a satisfactory fit might be achieved by using a polynomial model. In this case, the normal equation is valid, and an explicit expression for the parameters is thus available. Computation time would be saved and identification of the global minimum be ensured. However, use of general polynomials does not yield a satisfactory classification because for higher degrees than one they are not monotonically increasing, and they often fail to identify the signal levels of amm and amax correctly. This corrupts the classification. When no leak is present the signal is close to being stationary around the mean. The non-linear optimisation scheme identifies the mean level with very small fluctuations whereas polynomials of higher order fail to do so. Hence, it is concluded that no polynomial model can replace the "true" model of Eqn. 5 and the need for non-linear optimisation algorithms remains. It has also been attempted to fit a sine function to the data, but again the problem arises that it is not necessarily monotonic in the domain.
MISFIRE The dataset comprised 21 cases where the injection was blocked. The 21 cases were distributed on the load cases of 25% and 50%, the three leak sizes and the two sensors. The purpose of this section is to find a way of detecting misfire from the recorded signals. If the combustion process runs properly, the pressure in the combustion chamber is higher during the combustion stroke than during the compression stroke. If there is no combustion, the pressure will decrease after the TDC in the same manner as it increased during the compression stroke. This is reflected in the symmetry property of Eqn. 5 around a=0. 647
In order to detect misfire (and various degrees of misfire) we have to quantify the increase in pressure after the TDC compared to before the TDC. As shown above it is reasonable to consider the AE signal proportional to the pressure if a leak is present. The simplest measure is to compare the mean value of the data points from symmetric portions of the data series before and after the TDC. Results The classifier is established by computing the ratio between the mean value from the data portion after the TDC and the mean value of the data portion before the TDC. To ensure comparability the data portions on either side must be of the same length, i.e. we consider the fractions of a revolution of 300° - 345° and 15° - 60° to avoid disturbance from injection and opening of the exhaust valve (at 12° and 104°, respectively). R.=
Rafter
(g)
H'hiijon'
This ratio can easily discriminate between cases where misfire is present and where it is not, without misclassification and by use of the following intervals: R2 e [0.64, 0.82] <=> Misfire R2 e [1.24, 3.29] <=> Normal combustion In practice, it is enough to test if the ratio is below or above unity. It is worth mentioning that the approach of using a ratio instead of an absolute value eliminates problems related to the attenuation of the signals from sensor 1 to sensor 2. Admittedly, these conclusions are based on sparse data, but the results are very encouraging.
CONCLUSIONS This study has identified an efficient classifier which can discriminate completely between leak sizes in the exhaust valve of a diesel engine based on recorded RMS acoustic emission signals. The classifier is based on a physical principle, namely the pressure conditions in the cylinder during operation, and it is therefore robust and general. Moreover, an efficient classifier for detection of misfire has been developed. The classifiers are not only able to detect a deviation from the normal condition, but can also predict the type of failure and the level of degradation. The results are very encouraging and it should be noted that the described methods might as well be used in land-based facilities such as power plants.
REFERENCES Bardou O. and Sidahmed M. (1994). Early Detection of Leakages in the Exhaust and Discharge Systems of Reciprocation Machines by Vibration Analysis. Mechanical Systems and Signal Processing 8, 551-570. Fog T. L. (1998). Condition Monitoring and Fault Diagnosis in Marine Diesel Engines, PhD thesis, IMM, Technical University of Denmark, Lyngby, Denmark Fog T. L. and Hansen L. K. and Larsen J. and Hansen H. S. and Madsen L. B. and Sorensen P. and Hansen E. R. and Pedersen P. S. (1999). On Condition Monitoring of Exhaust Valves in Marine Diesel Engines. Proceedings of the 1999 IEEE Workshop on Neural Networks for Signal Processing, 554-563. Ljung L. (1987). System Identification: Theory for the user, Prentice-Hall. 648
Condition Monitoring and Diagnostic Engineering Management Andrew G. Starr and Raj B.K.N. Rao (Editors) © 2001 Elsevier Science Ltd. All rights reserved.
COMBINING VIBRATIONS AND ACOUSTICS FOR THE FAULT DETECTION OF MARINE DIESEL ENGINES USING NEURAL NETWORKS AND WAVELETS N. G. Pantelelis\ A. E. Kanarachos\ N. D. Gotzias', N. Papandreou' and F.Gu^ ^ Department of Mechanical Engineering, National Technical University of Athens, P.O.BOX 64078, 157 10 Athens, Greece. [email protected]. gr ^Manchester School of Engineering, University of Manchester, Simon Building, Oxford Road, Manchester, Ml3 9PL, UK. fgu(a)fsl .eng.man.ac.uk
ABSTRACT Attempts to solve the predictive maintenance problem using numerical simulations have been already presented with some promising results. Furthermore, Neural Network methods have been used extensively on the condition monitoring either alone or through the identification of dynamic models but in a simplified manner. A basic characteristic of these methods is the assumption of the existence of accurate, either experimental or theoretical, simulations for the production of massive data with various faults for the learning purposes of the Neural Networks. On the other hand little work has been performed on the in-situ predictive maintenance of marine diesel engines especially with respect to acoustic condition monitoring. At the present paper a new method is presented for the complete modelling of the main faults of large diesel marine engines. This method combines measurements of both vibration and acoustic signals recorded at or near the operating engine and uses Wavelets as a Feature Extraction technique in order to feed a Neural Network classification system towards marine diesel engines' fauh detection. KEYWORDS Fault detection. Marine Diesel Engines, vibration, acoustics, wavelets, neural networks, fault classification, condition monitoring. INTRODUCTION The early detection of combustion condition in a marine diesel engine is of high importance for engine operating condition. A rapid growth of an insipient fault can become extremely severe and can lead to unexpectedly engine breakdown that could hind navigation safety and require expensive repairs. Until 649
recently only skillful technicians and expert engineers could provide the necessary knowledge for the condition monitoring of reciprocating internal combustion engines. On the other hand, marine engineers use mainly indicator diagrams (such as the cylinder pressure signature) of the diesel engines at scheduled time intervals and by comparison to the ideal diagrams provided by the manufacturer of the specific diesel engine can provide useful information of the engine status. Although in modem ships the cylinder pressure monitoring has become an automated task, the identification of combustion quality is time consuming, and still relies on engineers' expertise and experience. Thus, developing an intelligent system based on experience, advanced signal processing and modelling techniques will be able to discover faults in a more systematic way and probably in a more incipient stage. The development of this system will monitor the cylinder combustion performance at every engine cycle via acceleration and noise measurements and using wavelets for the signal processing and Artificial Neural Networks for the identification of appropriate deviations from normal operating condition. Artificial Neural Network (ANN) methods have been used extensively on condition monitoring of structural and rotating machinery either alone or through the identification of dynamic models Pantelelis et al (1999, 2000). In the case of reciprocating machinery, Sharkey et al (1996) have used Neural Networks to identify normal operation and two faulty (retarded and advanced fuel injection) combustion conditions from simulated cylinder pressure and exhaust gas temperature data. Until now in practice, no sensor exist to measure cylinder exhaust gas temperatures versus crankshaft angle and as authors argued this drawback can be overcome, if temperature data will be calculated from pressure measurements by means of the application of thermodynamic equations. Wilson et al (1992) have shown that an approximate spark timing value can be obtained using cylinder pressure data as input to a neural network system. As mentioned above, in-cylinder pressure data provide the most valuable information about the quality of the combustion process and in conjunction with volume data can be used to calculate or estimate, among others, air to fuel ratio (Gassenfeit et al (1989)), Indicated Mean Effective Pressure and injection timing. On the other hand, the process of acquiring and processing cylinder pressure versus crank angle is not perfectly applicable. Diesel engines generate extreme temperatures in the cylinder chamber and direct pressure sensors (transducer mounted inside a cylinder like flame front sensors) are until now very sensitive at these high temperature values. Likewise pressure sensors are very expensive, fragile, they process too slow the measured data and require considerable engine modifications for their installation. Piezoelectric strain washer sensors whish are mounted beneath the cylinder head bolt may have longer lives but still some installation inconvenience exist and moreover their output is not a direct measurement of cylinder pressure. These problems make in-cylinder pressure measurements inconvenient for routine condition monitoring of cylinder performance and obligate marine engineers to collect engine pressure signatures at rare time intervals of approximately 2 or 3 months. Only recently, the well-known method of vibration analysis for condition monitoring of rotating machinery has been applied in the area of reciprocating engines. Chandroth et al (1999) have combined pressure data and vibration measurements at cylinder heads for the condition monitoring of cylinder combustion. At the same time research efforts i.e. Kimura et al (1998), Ball et al (1998 1-2) have been focused to detect abnormalities in diesel engines by measuring the sound radiated by the engine. Gu, Jacob and Ball (1999, 1-2) tried to estimate cylinder pressure by the measured output engine torque. In their study they examined the application of non-parametric models, like an RBF network, to simulate in-cylinder pressure in order to monitor engine performance and condition. In the present paper the combination of sound and vibration measurement for the fault diagnosis of marine diesel engines will be examined when in-cylinder pressure has not been acquired because of the above-discussed problems of the pressure transducers. The main idea in all the above-mentioned studies is the application of Neural Networks either explicitly in the diagnosis procedure or implicitly as a non-parametric identification model. A test rig or a mathematical model always exists in order to construct the appropriate data sets for healthy engine operation. Several commonly occurring faults are then induced in the engine (or the model) in order to acquire appropriate data sets for the abnormal engine operation. Afterwards healthy and faulty 650
measured or simulated raw data have to be processed in order to extract valuable features (Feature Extraction, FE) that will help NN to infer the functional mapping from measured values (pressure, vibration, sound) to the class (operation condition) to which they belong. However no literacy exists for in-situ fauh diagnosis of a marine diesei engine where no faults can be induced in purpose and knowledge about normal and abnormal operation, from the measured data signatures, does not exist a priori. Feature extraction and how can be related to normal and faulty operation classes is a very complicated task and in consequence NN training can become extremely difficult. It is the scope of this paper to report the basic tasks of a NN based, non-destructive fault diagnosis system of reciprocating machinery and the features that can be extracted from its application in a marine diesei engine working in a real operating environment. Sound and vibration data that have been acquired from engine's cylinder heads will be presented along with some representative features that were identified from the vibration signatures. CONDITION MONITORING BASED ON SOUND AND VIBRATIONS Radiated sound is transient elastic waves generated by the release of energy within a material or by a process and is based upon high frequency stress waves produced at a microscopic level by the failure of materials. A typical high frequency range of sound emissions is from 50 kHz to 1 MHz. Stress waves can also be produced by several phenomena that exist during engine operation, like impacts, friction and turbulence. The ability of dual channel FFT analysers and other analysers using digital filters to compute fast and accurately the cross spectrum between two microphone signals has been the basis for the very rapid growth in using acoustical intensity measurements to determine the sound power radiated by machines. So Acoustic Emission Transducers (AET), like microphone probes, can be used to record engine radiation sound and detect machinery operation abnormalities. The usual measurement procedure is to surround the machine with a fixed array of dual microphone probes or a traverse set-up that sweeps over an area surrounding the machine. If the machine is located on a freefield, non-reflecting environment then the sound intensity is determined by the Total Radiated Power:
However, the sound measurements of an engine at the operation site include significant noise from the environment that it is difficuh to isolate. Furthermore, measuring the Total Radiated Power is just an indication and if the equipment does not pass the acceptable levels then further tests should be performed in order to identify the exact source of noise. As already mentioned vibration analysis has been extensively used for the condition monitoring and fauh diagnosis and isolation of rotating machinery during the last two decades. However vibration analysis of diesei engines and generally reciprocating engines is a far more difficult task, since several phenomena like gas pressure, impacts, air-fuel mixtures, movement of rotating and reciprocating parts, and imbalance, all contribute with their own frequency and time content in the measured vibration signal. The measured signal is extremely complex and a frequency transformation will produce a signature consisting of several harmonics and sub-harmonics of rotational crankshaft speed along with resonant frequencies of the structure. In addition a diesei engine usually operates in a wide range of speed and load and energy cycles are not identical, even for the same operation conditions, because of the non-linear dynamics of turbulence in the cylinder chamber and the uncertainties of the combustion process. Consequently evaluation techniques for machinery condition such as trend analysis, Fourier and Short Fourier transform that were incorporated in modem machinery diagnostics, seems to be impractical for real time condition monitoring of internal combustion engines (Chandroth et al 1999) making FE a very complicated task. However zooming in on the raw data at the area of combustion i.e. just before and after Top Dead Center (TDC) can improve significantly the combustion condition diagnosis procedure (Chandroth et al 1999) but no information can arise about valve timings and condition as about crankshaft and journal bearings operation. On the other hand it is well known that 651
engine events are predetermined to occur at specific crank angles and this information can be treated appropriately in the FE process to distinguish events from each other. So any vibration or acoustic signal acquired from the engine has to be time-logged with a known engine event; i.e. the acquisition process will start when piston at cylinder 1 will be in the TDC. For this reason a sensor is needed to trigger the data acquisition procedure when the selected event occurs and to encode crankshaft rotation speed. The knowledge of crank angle value is of high importance and once this task has been accomplished Wavelet Transform (WT) can be the perfect choice (Chandford and Staszewski 1999) in order to extract valuable features capable to relate measured data with classes of faults. WT decomposes the raw signal at predetermined levels depending on the signal frequency content providing also the necessary information about the time (crank angle) of a specific event. Besides the power of WT lies in the fact that wavelet decomposition enables a full characterisation of local as long as larger features in a given signal. After WT a NN classifier can recognise the mechanical event (combustion, exhaust valve opening etc.) from time information and the condition of the event from the frequency generated and the corresponding peak amplitude. In addition NN classifier will be able to recognize actual valve, combustion and injection timings in comparison with normal values.
TEST FIELD AND INSTRUMENTATION SETUP In order to study the proposed approach, vibration and sound of a large diesel main engine of an ordinary ship in sailing conditions were recorded. At the ship's engine room two large four-stroke Vshaped 16-cylinder diesel engines with maximum output of 9600 PS at 520 RPM operate simultaneously. Accelerometers were mounted at the top of the cylinders of one of the engines and measurements were taken at a sampling rate of 20000 samples/s for 10 crankshaft cycles i.e. 5 complete engine cycles. Sound was recorded using two microphones over the diesel engines positioned on the outer and at the centre between the two engines as can be depicted in Figure 1. One Digital Magnetic Pickup (DMP), mounted at the flywheel was used to record crankshaft angular velocity and another DMP provides the timing reference point at Top Dead Center (TDC) of cylinder No 1. This last device was also used to record actual crankshaft rotation speed. All data were recorded simultaneously by a PC-based system using a NI-DAQ 16-channel 16-bit A/D card. A Lab View® based Data Acquisition Software (DAS, Figure 2) was developed in order to acquire and record the measured data. The DAS Graphical User Interface graphically presents measured signals, calculates a basic Power Spectrum of the raw data and checks measured values with predetermined alarm and limit levels. If any signal distortion or out of limit data are found DAS automatically will record new data in order to improve acquisition accuracy. More detailed data processing can be accomplished off line at the laboratory, in order to achieve more insight of the engine's operation characteristics. Another program, the Data Processing Software (DPS) using Matlab® environment, has already been developed in order to apply several data processing techniques to the measured values. Once the appropriate methodology of feature extraction techniques is concluded, a fault diagnosis system is introduced in DAS, capable for the online diagnosis and fault isolation of engine performance. With respect to the signal processing of the recorded data FFT with 4096 lines using either no filtering, or a Butterworth Low Pass Filter at 10 kHz was used. Figure 3 presents sound emission and Averaged Fast Fourier Transform - time series FFT from the 5 engine's operation cycles has to be averaged to present the FFT of one operating cycle recorded by the microphone sited above the engine (Figure 3.a) and between the two engines (Figure 3.b) (please refer to Figure 1 for microphone topology). The noise levels at Figure 3 are extremely high (up to 92 db, approximately) and it is very difficult to figure out faults under generation and moreover which is the origin of the fault i.e. which cylinder has a problem and what problem is this. On the other hand, vibration measurements present more features when they are analysed in Figure 4 for cylinders No 4 and 13. In principal, it is easier to compare two cylinders and to study their 652
variations in the frequency domain. Hence, in Figure 4 the averaged frequency analysis of vibration at cylinder No 4 present significant difference in the maximum peak level at approximately 2.5 kHz as well as a higher frequency range between 3.5 and 4 kHz. On the other hand, three low level peaks near 0.4, 1.2 and 2 kHz exist in both cylinders. This high frequency activity cannot be easily explained looking at the time series acceleration measurements, where the amplitudes and the shape at both cylinders during combustion seem similar and the only significant difference can be observed at the measured vibration during air and exhaust valve closing. In the above-mentioned observations it is clear that FFT has a significant drawback: no time information exists in the frequency signature and for a more detail fault classification time series have to coupled with FFT. At the same time in a following implementation, if engine's operation speed slightly changes a new not-comparable frequency signature will be produced, due to lateral shifting of spectral peaks and alterations of the engine's activated resonance.
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8
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Figure 1. Microphones' topology at the Figure 2. DAS GUI, Power Spectrum Acceleration of engine room. cylinder No 1. Since noise levels are extremely high and measured signals are very complex, a more advanced signal processing technique like wavelet transform seems to be the only way forward. From frequency domain signatures (Figure 3 & Figure 4) it is very difficult to distinguish the faulty condition and moreover the source of the fault. In Figure 5 Continuous Wavelet Transform on the vibration of cylinders 4 and 13 is depicted. Orthogonal 7^'^ order Daubechies filter was used and a scale range from 1 to 16 was investigated. Due to space lack it is not possible to introduce here the basic theory of wavelet transform (Mallat 1998), so only the results obtained will be summarised. From Figure 5 it is clear some dissimilarity for each engine's operating cycle. Averaging CWT based on crank angle information the Averaged Continuous Wavelet Transform (ACWT) of Figure 6 can be produced. ACWT produces a more detail image of the examined signal by representation of a full engine operating cycle eliminating at the same time, in some degree, the uncertainties of engine dynamics occurring at each individual cycle. Another important feature that can arise from ACWT operation is that wavelet coefficients are globally (by angle and by scale) normalised in order to produce the values depicted in Figure 6. Thus it is possible to use different fauh classification systems (NN systems) for each engine's function of concern (i.e. combustion, compression, air intake, gas exhaust, valve opening and closing), overlapping at the same time information from one function to the other; from one classification system to the other. This way individual knowledge based on each autonomous NN system along with overall criteria based in decision fusion will strengthen the accuracy of the final assessment. In addition each NN system will have less number of inputs (a short range of crank angle values) and classification outputs, which in turn denotes simpler NN architecture, less training epochs and better 653
generalization. As long as NN evaluation time is short (once learning has been concluded) and computational effort of the ACWT is not dramatically long the proposed approach seems to be very promising for an on-line fault classification system. • • 1 • Ml«i«ph«ii« Ovular
MB 1 • MIcra^haB* I i
O.a
0.4 Fra<|ii»acy (Hi)
Figure 3. Recorded sound signal and FFT (a) over the engine, (b) between two engines. d FFT-Ut ME • Cytodv 4 & 13
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Figure 4. Vibration levels (a) and averaged FFT (b) at cylinders No 4 and No 13. A Multi Layer Perceptron Neural Network was used to classify the ACWT data obtained from vibration measurements of cylinders 4 and 13 to engine combustion condition. The classification target used is zero for data belonging to cylinder 4 and one for those belong to cylinder 13. For both cylinders a set of 50 vibration signatures has been recorded from 10 ship trips; 25 representing cylinder 4 and the remaining 25 representing cylinder 13. For each set 6480 (16x405) data points have computed from wavelet transform, each belonging to a data sample; 16 for the observed scales and 405 for a crank angle range of 30 degrees before and after cylinder TDC with a step angle of 3/20 degrees. In order to simplify NN architecture and achieve better system generalization some data manipulation has to be performed. One first approach was to interpolate crank angle information at each scale considering a crank angle step of 1 degree. In this case the number of NN input layer neurons was 906 and after the training process using a randomly selected train set with the 60% from the available samples, NN was able to classify data with an error of 9.5x10"^. Unfortunately NN could not perform well in the remaining 40% of test samples where the 15% was not appropriate classified. Even using 70 or 80% of the available data set for training, NN could never perform a 100% classification when tested. Because of space lack it is not possible to present results from the above approach.
654
Averaged CWT- Left ME - Cylinder4 & 13
CWT - Left ME - Cylinder 4 & 13
0
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1500
2000
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Figure 5. Vibration CWT of cylinders 4 and 13 over Figure 6. Averaged CWT of cylinders 4 and 13, the entire signal range. representing one engine full operating cycle. Performance is 9.99757e-005, Goal Is 0.0001
NN Evaluation PerfOTmance O Target 1 4 NN Output 1
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Figure 7. NN training convergence.
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Figure 8. NN classification efficiency with test data.
A simple Principal Component Analysis (PCA) was then performed. Calculating the mean and standard deviation from the ACWT samples at each observed scale, a new data set with 50 samples belonging in two classes and each sample consisting of 32 (16x2) points was produced. An MLP NN with one hidden layer consisting of 20 neurons and an input and output layer with 32 and 1 neurons respectively was trained using the gradient descent backpropagation with adaptive and momentum learning algorithm. A training data set consisting with the 60% from the available samples was used and NN performed an error of 9.99e-005 after 2309 epochs (Figure 7). Evaluating NN with the remaining 20 samples, NN was able to classify input data with 100% success. In Figure 8 the NN evaluation capability is depicted. Because classification can only take values of 0 and 1, if NN outputs are rounded towards nearest integers the correct classes for every sample number will be calculated. CONCLUSIONS The initial steps of an automatic fault diagnosis of large diesel engines using sound and vibration signals has been presented. The problem is complicated and the combination of both methods is essential in order to give reliable diagnosis of fauhs in their initiation. For such complex mechanical systems the need for reference data for comparative analysis between a healthy state and the later state 655
of the engine is of vital importance. Using FFT and wavelets combined with Artificial Neural Networks can lead to the automatic diagnosis of a fauh at the early stages where repairing costs are reduced and repairements can be scheduled much more easily considering that stopping a ship (either a ferryboat or a carriage) for repairs can cost much more than the repairement cost itself In the present case the need for on-site accurate measurements taking into consideration the specific characteristics of the engine room is more likely to result in accurate fault diagnosis. Furthermore, some first results indicated that special developed signal analysis tools are required in order to avoid the high noise level of the engine room. ACKNOWLEDGEMENTS This research was funded by the Greek Secretary of Research and Technology and the Maritime Company of Lesvos under PAVE 97-BE-121. REFERENCES Ball A.D., Gu F., Li W. and Leung A.Y.T. (1998). The condition monitoring of diesel engines using acoustic measurements. Parti : Acoustic modelling of the engine and representation of the acoustic characteristics. SAE Technical Paper OOPC-199. Ball A.D., Gu F., and Li W. (1998). The condition monitoring of diesel engines using acoustic measurements. Part2 : Fault detection and Diagnosis. SAE Technical Paper OOP-277. Chandroth G. O. and Staszewski W. J., (1999). Fault detection in internal combustion engines using wavelet analysis. Proceedings 'Comadem 99', 7-15. Chandroth G.O., Sharkey A.J.C. and Sharkey N.E., (1999). Cylinder pressures and vibration in internal combustion engine condition monitoring. Proceedings 'Comadem 99', 141-152. Gassenfeit E.H. and Powell J.D. (1989). Algorithms for air-fuel ratio estimation using internal combustion engine cylinder pressure. SAE Technical report 890300. Gu F., Jacob P.J. and Ball A.D., (1999). Non-parametric models in the monitoring of engine performance and condition - Part 2: non-intrusive estimation of diesel engine cylinder pressure and its use in fault detection. Journal of Automobile Engineering, Proc. Instn. Mech. Engrs., Part D, 213, 135143.
Jacob P.J., Gu F., and Ball A.D. (1999). Non-parametric models in the monitoring of engine performance and condition. Part 1 : modelling of non-linear engine processes. Journal of Automobile Engineering, Proc. Instn. Mech. Engrs., Part D, 213, 73-80. Malat S., (1998). A Wavelet Tour of Signal Processing. Academic Press, San Diego, California. Pantelelis N., Kanarachos A. and Gotzias N. (2000). Neural Networks and simple models for the fault diagnosis of naval turbochargers. Mathematics and Computers in Simulation 51, 387-397. Pantelelis N., Kanarachos A. and Gotzias N. (1999). Condition monitoring of naval turbochargers with journal bearings using model based and system identification tools. Proceedings of COMADEM'99, 499- 508. Pantelelis N., Kanarachos A. and Gotzias N. (2000). Diagnostic maintenance of naval turbochargers. Journal of Measurement and Control 34:4, 101 -102 Ryuichi Kimura, Noboru Nakai and Tomonori Kishimoto. (1998). Abnormal Sound detection by Neural Networks in the Diesel Engine. Bulletin of the M.E.S.J. 26:1, 24-31. Sharkey A.J.C, Sharkey N.E. and Gopinath O.C, (1996). Diverse neural nets solutions to a fault diagnosis problem. Neural Computing and Applications, 4, 218-227.
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Condition Monitoring and Diagnostic Engineering Management Andrew G. Starr and Raj B.K.N. Rao (Editors) © 2001 Elsevier Science Ltd. All rights reserved.
CONDITION DIAGNOSIS OF RECIPROCATING MACHINERY USING INFORMATION THEORY T. Toyota^ T. Niho\ P. Chen' and H. Komura^ ' Kyushu Institute of Technology, Kawazu 680-4, lizuka City, Fukuoka 820 -8502, Japan ^RION Company, Higashi motomachi 3-20-4l,Kokubunji City, Tokyo 185-8533, Japan
ABSTRACT The conventional condition diagnosis methods by vibration analysis is generally performed based on the probability density function analysis in time domain and power spectrum analysis in frequency domain. How^ever, these methods has serious drawbacks that are insensitive to the signal pulse phase shifting or small local change in amplitude, so it is difficult to fmd the faults of valves and other important elements in the reciprocating machines such as reciprocating compressors and internal combustion engines etc. In this paper, we propose a new method that can detect the signal pulse phase shifting and small local change in amplitude by introducing rotating angle density function and its normalized power density function (NPDF). By transforming this NPDF to Kullback-Leibler information function, It become possible for us to fmd and evaluate the faults in the reciprocating machinery very precisely. KEYWORDS Failure diagnosis, Reciprocating machinery, Information theory. Rotational angle density iimction
INTRODUCTION For the condition monitoring and diagnosis of rotating machinery, condition variables such as vibration, sound, pressure, rotating speed fluctuation, electrical current in electrical machines etc have been effectively used. The basic condition variables of reciprocating machinery monitoring and diagnosis are (1) frame vibration, (2) rod drop, (3) rod run-out, (4) cross-head vibration, (5) main bearing vibration, (6) valve temperature, (7) frame vibration. They say the most important vibration parameter of a successful monitoring program is frame vibration, and monitoring this parameter can help prevent catastrophic failures. As well-known, for the on line monitoring and the precise diagnosis of fault in the reciprocating 657
machines and intermittent operating machine, phase shifting and local small change in the vibration signal are essentially important. Typical conventional analysis methods for feature extraction of these signals are (1) Time domain analysis (probability density function analysis) (2) Frequency domain analysis (power spectrum analysis) (3) Space domain analysis (orbit analysis) But, for the diagnosis of the machine with reciprocating mechanism such as reciprocating compressors, conventional analysis methods have the serious draw-backs that is insensitive to the timing or phase shifting and local small changes in the signal of vibration. To overcome these defects, we propose the new method that can detect the time shifting and local shape changes in the signal with high sensitivity. For this purpose, we introduce the angle density function and information waveform of the condition variables such as vibration, sound, pressure signals.
BASIC THEORY OF PROPOSED METHOD Conventional Methods As shown Figure 1, For condition diagnosis of rotating machines, probability density function p{x) of condition variables such as vibration or pressure signal have been generally used. Generally next two feature parameters of the signal using density function p(x) are used for condition diagnosis (l)Skewness
r x'p{x)dx (2) Kurtosis
f x'p{x)dx
But we can hardly extract the symptom of pulse position shift or local small change in amplitude of signal for the reciprocating machines or intermittent operating mode machines by these kind of conventional methods. Definition of the Rotating Angle Power Density Function (RAPDF) To detect the pulse position shifting and local small change in amplitude of condition variables precisely, we propose the new method, namely rotating angle power density function (RAPDF) and feature extraction by information theory. When the condition variables such as vibration or pressure signal be measured synchronously with rotating shaft, let consider the condition variable such as vibration or pressure signal (from now, simply say vibration signal) ftmction of rotating angle 6, x(6). (1) Transform the original time series x{t) to the function of rotating shaft angle x{0), Q<0 <2n.
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Figure 1: probability density function and rotating angle power density function (3)
T (2) Transform x(p) to the normalized power density function P\6) by Eqn. 4.
p{e)-.
^{e)
(4)
P^ 2
£
^'WB
Using normalized power density function P(0) gotten above, We can extract the pulse position shifting or local small changes for the diagnosis of reciprocating machines by introducing information theory. Definition of the Information Divergence (ID) and Information Function ID(d) Let reference normalized angle power density function (for good condition) P^ (0), and measured normalized angle power (for unknown condition) i^(^),then KuUback-Leibler information/a and Kullback-Leibler divergence ID can be written as (1) Kullback-Leibler information XL
KL=J^PMog
de
(5)
(2) Kullback-Leibler divergence fD
/D=j^{p,(e)-p,(e)}iog||j^e
(6)
Information parameters KL introduced by Eqns. 5 and 6 are very sensitive to the pulse phase shifting and local small pulse height change shown in Figure 2. For precise diagnosis of reciprocating machines, the shaft angle 6 is the very important parameter to know the fault element, so we here define function ID{d) by removing integral from Eqn. 6.
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3.00
0.06
0.10
Time [sec] (a) Pulse train in good condition
K)
0.05
0.10
Time [sec] (b) Pulse train phase shifting
0.05
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Time [sec] (c) Single pulse missing
0.00
0.05
0.10
0.15
Time [sec] (d) Single pulse phase shifting
Figure 2: Simulated vibration signal for good and fault condition of the machine shown by Eqn. 7 and show this function ID{6) is very useful to detect the timing of the fault event occurrence. We call this new defined function as "information function of signal", which is very sensitive to the pulse position shifting and local small pulse height change of vibration signal.
P.ie) iDie)={pie)-p,{e)YogW namely function ID{6) is defined by instantaneous information divergence ID expectation of the information of difference of reference P^[d) and test signal Pt[9).
(7)
and mean the
SIMULATION To verify effectiveness of the proposed method, let simulate the vibration signal from the various condition of the reciprocating machine using the pulse signal simulated for vibration signal shown in Figure 2. You can confirm the simulated the pulse train for good condition, pulse train phase shifting, single pulse missing and single pulse phase shifting in Figure 2. 660
TABLE 1 FEATURE PARAMETERS CORRESPONDING TO THE SPECIFIC VIBRATION WAVEFORMS
eood condition pulse train phase single pulse missing single pulse phase
Skewness /3i 0.1330 0.1786 0.0650 0.1048
Kurtosis P2 5.8136 5.9528 5.7690 5.7034
Simulation of the Conventional Methods (Skewness and Kurtosis Values) At first we confirm the conventional methods in which usually use the probability density analysis of vibration signal using the skewness and kurtosis as feature parameters. Watching Table 1, we can clearly confirm that the conventional feature parameters such as skewness p^ and kurtosis P2 ^^^ insensitive to the pulse phase shifting and missing of the single pulse in pulse train well seen in reciprocating machinery diagnosis. Simulation of the Proposed Methods (Information Divergence ID and Information Function ID(6)) Next we confirm the effectiveness of the proposed method using the same simulated pulse train signal shown in Figure 2. Using Eqns. 3 and 4, angle density function P(0) can be calculated and from this, the information divergence ID and the information function ll>{d), which is defined by Eqn. 7, can be calculated and shown in Figure 3. We can clearly confirm the high sensitivity of information function ID{6 ) to the local change of pulse signal in phase shifting and small amplitude changes. Referring to Figure 3, we can clearly confirm that Information function ID(O) has high sensitivity to the pulse phase shifting and local change in amplitude, namely (1) For the regular pulse train (for good condition), I D ( ^ ) is small and uniform, (2) For the train phase shifting, ID(0 ) show twice number of pulse (3) For the single pulse missing, single pulse appear in missing position of IDyd) (4) For the single pulse phase shifting, two pulse appear as seen in Figure 3 (d) We can conclude that the information function ID(0 ) is small and uniform for regular pulse train corresponding to good condition and show the specific waveform corresponding to the pulse waveform changes such as the pulse train phase shifting, single pulse missing and single pulse phase shifting. And watching Table 2, we can quantitatively confirm that the very high sensitivities of this method to the pulse little waveform change in phase and amplitude by calculating the Kullback-Leibler information KL using Eqn. 5 and Kullback-Leibler divergence ID using Eqn. 6. We can confirm the effectiveness of the proposed methods to detect the phase shifting of pulse and small change in amplitude of vibration signal, witch is essentially important for the reciprocating machinery condition monitoring and diagnosis.
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2.00
4.00
Angle [rad] (a) 10(0) for regular pulse train 15.O1
5.0|
Ik
ililikilMliikii 2.00
4.00
6.00
Angle [rad] (b) 10(0) for the pulse train phase shifting •
15.0|
I
•
'
'
'
I
^10.0| 5.0|
bhfiihiiiitkrtiiyiiiAiiiliiiiiiiiihili l i l i ^ 0.00
2.00
4.00
Angle [rad] (c) 10(0) for the single pulse missing 15.0F
2.00
4.00 Angle [rad]
(d) 10(0) for the single pulse phase shifting Figure 3: Information function lT>{d) for specific pulse waveform. TABLE 2: KULLBACK-LEIBLER INFORMATION KL AND KULLBACK-LEIBLER DIVERGENCE ID FOR THE SPECIFIC PULSE WAVEFORMS
1
KL 0.8632 3.2739 L0249 L0747
regular Dulse train pulse train phase shifting single pulse missing single pulse phase shifting |
ID 0.7499 6.5754 2.0036 2.1409
CONCLUSION We proposed the new method useful for the condition monitoring and diagnosis of reciprocating machine or intermittent operating machine, in which we introduced the two new function, namely (1) angle density function P[6) and (2) information function lD[d) of the condition variable such as vibration signal, sound, or pressure signal. Using simulated pulse train signal, we confirmed the effectiveness of this method clearly in Figure 3 and Table 2. Reference Bloch H. P. (1995), yl Practical Guide to Compressor Technology, McGraw-Hill. KuUback S. (1959), Information Theory and Statistics, John Willy & Sons, Inc. 662
Condition Monitoring and Diagnostic Engineering Management Andrew G. Starr and Raj B.K.N. Rao (Editors) © 2001 Elsevier Science Ltd. All rights reserved.
EXPERIMENTAL RESULTS IN SIMULTANEOUS IDENTIFICATION OF MULTIPLE FAULTS IN ROTOR SYSTEMS N. Bachschmid, P. Pennacchi, A. Vania Dipartimento di Meccanica, Politecnico di Milano Via La Masa 34,1-20158 Milano, Italy
ABSTRACT The topic of the fault identification in rotor systems has been deeply analysed in literature both by theoretical studies and field results. However usually they consider one fault only, while the presence of multiple simultaneous faults is not taken into consideration. The method proposed in this paper is a model based identification method that requires the definition of the model of the system (rotor, bearings and supporting structure) and of the faults. It handles simultaneous multiple faults by means of a least square identification in frequency domain. The method is here vahdated experimentally using the test-rig (two coupled rotors, four oil film bearings and flexible foundation) buih by Politecnico di Milano. By this way some known faults can be appHed and the results of the simultaneous identification can be verified. KEYWORDS Rotor dynamics. Identification, Diagnostics, Multiple faults, Model validation. Experimental results. INTRODUCTION During the monitoring of a rotating machine, generally it is suggested to activate a diagnostic process when the vibration vector goes off the acceptability region; the vector differences with respect to the reference situation are then used for the identification procedure. These vibrations are generally due to a single fault, since the effect of the residual original unbalances and bows has been subtracted. Many recent contributions are available in literature about single fault identification: Kreuzinger-Janik & Irretier (2000) use a modal expansion of the frequency response function of the system, on both numerical model and experimental results, to identify the unbalance distribution on a rotor. Markert et al. (2000) and Platz et al. (2000) present a model in which equivalent loads due to the faults (rubbing and unbalances) are virtual forces and moments acting on the linear undamaged system model to generate a dynamic behaviour identical to the measured one of the damaged system. The identification is then performed by least square fitting in the time domain. Edwards et al. (2000) employ a model based identification in the frequency domain to identify an unbalance on a test-rig. A more comprehensive approach, able to identify several different types of faults and to discriminate among
663
faults which generate similar harmonic components, has been introduced by Bachschmid & Pennacchi (2000). This method has been experimentally validated on different test-rigs and real machines, as reported in Bachschmid et al. (2000), for many types of faults, such as unbalances, rotor permanent bows, rotor rubs, coupling misalignments, cracks, journal ovalization and rotor stiffness asymmetries. However in some cases a second fault can be generated as an effect of the first fault: a thermal extended bow, or an increasing unbalance due to erosion or deposit of particles, can cause a rub in a seal and the consequence is the an arising local bow. Sometimes the reference situation is not available, so the multiple fault identification method could enable to identify both the original unbalance or bow and the arising fault. The model based identification method proposed in this paper is a generalization of the method proposed by Bachschmid & Pennacchi (2000) and requires the definition of the model of the system (rotor, bearings and supporting structure) and of the faults. It handles simultaneous multiple faults by means of a least square identification in frequency domain. IDENTinCATION OF MULTIPLE FAULTS IN FREQUENCY DOMAIN Denoting by x the change in the vibration vector due to the fault(s), the general equation that models the behaviour of system, can be written as: M x + Dx + K x = F^(0
(1)
where F//) is a system of equivalent external forces, which force the fault-free system, represented by M (mass matrix), D (damping matrix) and K (stiffness matrix). Otherwise, if a reference case is not available, the vibration vector x represents the total vibration due to both the original unbalance or bow and the arising fault(s). This way, the problem of fault(s) identification is then reduced to a force identification procedure with known system parameters. Keeping in mind that the final goal is the identification of faults, this approach is preferred since only few elements of the unknown fault forcing vector are in reality different from zero, which reduces significantly the number of unknowns to be identified. In fact, the forces that model each fault are considered to be applied in not more than two different nodes along the rotor. Nowadays, experimental vibration data of real machines are often collected by CM systems and are available for many rotating speeds, typically those of the run-down transient that, in large turbogenerators of power plants, occurs with slowly changing speed, due to the high inertia of the system, so that the transient can be considered as a series of different steady state conditions. This allows to use these data in the frequency domain. Assuming linearity of the system and applying the harmonic balance criteria from Eqn. (1), we get, for each harmonic component: [-(/2Q)'M + //2QD + K]X„=F^^(Q)
(2)
where the force vector F^ , has to be identified. This force vector could be function of the rotating speed Q or not depending on the type of the fault. It is worth to stress that if the presence of several faults (fi. m faults) is considered, then the force vector F^^ is composed by several vectors Fj.|\ l?(2)
T^(m) .
Few spectral components in the fi-equency domain (generally not more than 3, in absence of rolling bearings and gears) X„, measured in correspondence of the bearings, represent completely the periodical vibration time history.
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Moreover, the ^* fault acts on few d.o.f. of the system, so that the vector F]-*^ is not a full-element vector which is convenient to be represented by: F}*^(Q) = [Ff^]A^*^(Q)
(4)
where [F|*^] is the localisation vector which has all null-elements except for the d.o.f. to which the forcing system is applied and A^*^(0) is the complex vector of the identified defects. The vector [F{*^] does not give just the assumed position of the defect but also expresses the link between the force fault system and the modulus and phase of the identified fault that produce it. Eqn. (2) can be rewritten, for each harmonic component, in the following way: [ E ( « Q ) ] X„=J;F]/^(Q) = F^^^(Q)
(5)
where [ E ( « Q ) ] is the system dynamical stiffiiess matrix for the speed Q and for the n^^ harmonic component. The identification method can be applied for a set of p rotating speeds that can be organized as a vector: (6) Then matrix and vectors of Eqn. (6) have to be expanded:
IF<:>(Q,) E{nQ,)
0
0
0
0
E{na^)
0
0
'
1=1 m
[E(WQ)] X„ =
£F<;>(n,) /=l
0
0
= F/.(Q)
(7)
0 E(«QJ
Under a formal point of view, it is unimportant to consider one or p rotating speeds in the identification. The fault vector is the sum of all the faults that affect the rotor as stated in Eqn. (3). Matrix [ E ( « Q ) ] can be inverted and Eqn. (5) becomes X, = [ E ( « Q ) ] - ^ F ^ (Q)=a„(Q).F^^(Q)
(8)
where a„(Q) is the inverse of [ E ( « Q ) ] . Reordering in a suitable way the lines in Eqn. (8), by partitioning the inverse of the system dynamical stif&ess matrix, and omitting from «„ and F^ the possible dependence on Q for conciseness, we obtain:
(9)
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where X^
is the complex amplitude vector representing the measured absolute vibrations in
correspondence of the measuring sections and X^ is the vector of the remaining d.o.f of the rotor system model. Using the first set of Eqns. (9), the differences 6„, between calculated vibrations X^ and measured vibrations X^„, can be defined, for each harmonic component, as: d„=X,,-X„„,_=a,,-F,,-X,„^
(10)
The number of equations «£ (number of measured d.o.f) is lower than the number ny (number of d.o.f of the complete system model) which is also the number of elements of F^ . But, as said before, F,^ becomes a vector with many null-elements, even if the fault is not one only, so that the number of unknown elements of F^ is smaller than the number of equations. The system therefore has not a single solution for all the equations and we have to use the least square approach in order to find the solution (identified fault vector) that minimises the differences which are calculated for all the different rotating speeds which are taken into consideration. A scalar relative residual may be defined by the root of the ratio of the squared 8„, divided by the sum of the squared measured vibration amplitudes Xg„, :
S. =
[a./F,^-X,,J »
Y
[a,^-F,,-X,,J T
(11)
Y
V
By means of the hypothesis of localisation of the fault, the residual is calculated for each possible node of application of each defect. This fact implies that, if we indicate with Zk the abscissa along the rotor in correspondence to the J^^ fault among m fauhs, the relative residual in Eqn. (11) is a surface in a V"^' space, in other terms: : / ( z , , Z2,...,z,,..., z^)
(12)
Where the residual reaches its minimum, i.e. the minimum of the surface in Eqn. (12), there is the most probable position of the fault. Figure 1 shows an example of the residual surface. The corresponding values of F^ give the modulus and the phase of the idenfified faults. The relative residual gives also an estimate of the quality of the identificafion, since it results the closer to zero the better the identified fault corresponds to the actual one; this follows easily from the analysis of Eqn. (11). EXPERIMENTAL VALIDATION The proposed method has been also tested by means of experimental results obtained on the MODIAROT (Brite Euram Contract BRPR-CT95-0022 MOdel based DIAgnostics in ROTating machines) test-rig designed by the Politecnico di Milano for analysing the effects of different malfunctions on the dynamic behaviour of rotors. The test-rig, shown in Figure 2, is composed of two rigidly coupled rotors driven by a variable speed electric motor and supported on four elliptical shaped oil film bearings. The rotors have three critical speeds within the operating speed range of 0-6000 rpm. The rotor system is mounted on a flexible steel foundation, with several natural frequencies in the operating speed range. In this case the foundation has been modelled by means of a modal representation. Two proximity probes in each bearing measure the relative shaft displacements, or the journal orbits; two accelerometers on each bearing housing measure its vibrations, and two force sensors on each bearing housing measure the forces which are transmitted to the foundation. 666
Relative residual for the Identification of 2 faults (1 x rev. component): 1 Unbalance & 1 Local Bow
100
120 100 Rotor nodes
Rotor nodes
Residifaf surface mmimum Residual (overall): 0.000 Node: 78 (1st fault Unbalance) 93 ^st fault Local Bow)
0 0 Module: 4.006-^000 [kgni] (1st fault Unbalance) Phase: 20.0 H (1st f^ult Unbalance) 7.00e+006 [Nm] (2nd fault Local Bov/) 50.0 H (2nd fault Local Bow)
Figure 1: Residual surface in case of simultaneous identification of two faults. The location of the faults is in the minimum of the surface.
Figure 2: MODIAROT test rig of Folitecnico di Milano.
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The absolute vibration of the shaft is calculated by adding the relative displacement measured by the proximitors to the absolute bearing housing displacement, which is obtained integrating twice the acceleration measured by the accelerometers. The force measurements were not used in this case. A first run-down test was performed in order to obtain a reference vibration data due to the weight and the unknown unbalance force and unbalance moment. Then, in order to simulate two faults, two masses were added to the rotor on the 2"^ and 7^^ balancing plane (see Figure 2) and a second run-down was performed and the total vibration obtained. Then, the first measurements were subtracted from the last ones in order to obtain the vibration vector x due to the faults. These difference vibration vectors for bearings U2 and #3 are reported in Figure 3 and Figure 4 . The frequency response diagrams need some comments: i) the first natural frequency of the long shaft are about 950 rpm in vertical and 1150rpm in horizontal direction; ii) the very high peak in vertical direction corresponds to the third mode of the foundation (2256 rpm), while the others at 1350 and 2000 rpm are horizontal modes of the foundation; iii) even if the test-rig is built to operate in the range 0-6000 rpm, the rotating speed at which the measurements are taken is limited to the range 550-2700 rpm, the upper limit is due to the fact that system non-linearities become more significant over 2700 rpm and the model fitting is not so good as for lower rotating speeds. .|g«
Measuring Plane 2 - Vertical & Horizontal Directions - 1x Rev. Component
800
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1400 1600 1800 Rotating Speed [rpm]
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2200
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Measuring Plane 3 - Vertical & Horizontal Directions - 1x Rev. Component
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Nvv—^Hf^'^^'VV^^
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J Villi
rsii~\
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2200
Figure 3: Experimental vibration differences in Figure 4: Experimental vibration differences in bearing #2. bearing #3. Since experimental data are used, some caution should be taken into account performing the identification procedure. Generally the use of data close to the resonance peaks leads to a poor identification, also in the case of a fault only. Anyway a first attempt has been done without taking into account this fact by choosing the measures corresponding to a set of 13 equally distributed rotating speeds within the available range. The results of the identification are reported in TABLE 1. TABLE 1 Identification results in the range 550-2700 rpm 550-2700 rpm Type Node Module Phase Residual Node Module Unbalance 10 3.6e-4 kgm -90° 4.2e-4 kgm 8 0.579 Unbalance 35 3.6e-4 kgm -90° 3.81e-4kgm 36
668
Phase -82.8° -89.7°
Error on module 17% 6%
Error on phase 4%
0.2%^ J
Even if the relative residual of the identification in rather high, the location of the identified fauhs can be considered fairly good since they are on the same flywheel masses of the actual faults. The error on the phase is reduced, but this fact is quite common in least square identification, as so as that on the module. Similar resuhs are obtained with different set of rotating speeds in the range 550-2700 rpm. The comparison between the theoretical response of the model to the identified faults and the experimental data is reported in Figure 5 for bearing #2 and in Figure 6 for bearing #3. ^ .|g6 Measuring Plane 3 - Vertical & Horizontal Directions - 1x Rev. Component
Measuring Plane 2 - Vertical & Horizontal Directions - 1x Rev. Component I —Experimental vertical —Analytical vertical ••"Experimental horizontal I ---Analytical horizontal
QI l l * ' V l f l ^ l l l l l l 600 800 1000
Ijilj
t'- 1
*v»
Vv--
1200 1400 Rotating
::V :
2200
g
::5 =:: • !^ :•:: 5:: j i l l J ^ / ^'%••. -. ?. ^Vy^ :si:s:j ':: :: : E ^-4f.^<;L. S t\ fi) ^siiiliU '.••
600
1800 2000 d [rpm]
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Qi!^--!
v^
lit^ 1 1
.J-'^^^TT 2000 2200
1400 1600 1800 Rotating Speed [rpm]
2400
1W 2400
2600
Figure 6: Comparison between experimental and analytical results using the rotating speeds in the range 550-2700 rpm, bearing #3.
Figure 5: Comparison between experimental and analytical results using the rotating speeds in the range 550-2700 rpm, bearing #2.
Even if the previous identification can be considered as quite good, it has been looked for improving the resuh. The first analysis that has been performed was the running of the identification of the two simultaneous faults by considering a rotating speed at once. This is shown in Figure 7 and Figure 8, where the relative residual, the identified nodes, modules and phases are plotted as a function of the rotating speed. The best results, as expected, are obtained in the speed range between 1400 and 1900 rpm where the identification of both faults is accurate in position, module and phase. In fact this speed range is between the rotor critical speed and does not include higher speeds and lower speeds where the fitting of the model is not so accurate. So a good strategy, in this case, is to choose the rotating speeds one at time in the range 1400-1900 rpm or a set of them in the same range. An example of identification with a rotating speed only (1729 rpm) is shown in TABLE 2. The relative residual value is very good if we consider that we are dealing with experimental data and identified module and phase have low errors. The localization of the first fault on node 9 instead of 10 has to be considered by checking the f e. model of the rotor, where the nodes are very close each other (20 mm). TABLE 2 reports the identification results, obtained by using all the available rotating speeds between 1400 and 1900 rpm. They can be considered as good also in this case even if the relative residual value has increased and the module and phase of the faults have errors comparable to the case of the full speed range. However the localization of the fault is correct. Anyway, if the results of the simulation in Figure 5 and Figure 6, where all the available speed range was used, are considered, it can be seen that some aspects of the vibrational behaviour cannot be reproduced by the model. This consideration suggests, in field applications, when it is difficult to evaluate the validity range of the system models, the use of quality indexes of the identification that allow for including or excluding some rotating speeds from the identification procedure as described in Pennacchi & Vania (2001). 669
Identified module per rotating speed
Relative residual per rotating speed
Actual unbalance 1 5 2 module 3 6 10 kgm j.biUKgm
BOO
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1200
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Identified phase per rotating speed
Identified nodes per rotating speed
800
1200
r^
2200
2400
2600
180 600
800
1000
1200
1400 1600 1800 Rotating speed [rpm]
2000
Figure 8: Identified modules and phase using a rotating speed at once.
Figure 7: Relative residual and identified nodes using a rotating speed at once.
TABLE 2. Identification results. 1729 rpm Type I\ode Module Phase Residual Node Module Unbalance 9 3.6e-4 kgm -90° 3.25e-4 kgm 9 0.225 Unbalance 35 3.6e-4 kgm -90° 3.68e-4 kgm 35 1400-1900 rpm 4.16e-4kgm 10 0.398 3.7e-4 kgm 35
Phase -93.5° -84.7°
Error on module -10% 2%
-ll.T -77.9°
16% 3%
Error on 1 phase 1 -1.9% 2.9% 6.8% 6.7%
Anyway, if the results of the simulation in Figure 5 and Figure 6, where all the available speed range was used, are considered, it can be seen that some aspects of the vibrational behaviour cannot be reproduced by the model. This consideration suggests, in field applications, when it is difficult to evaluate the validity range of the system models, the use of quality indexes of the identification that allow for including or excluding some rotating speeds from the identification procedure as described in Pennacchi & Vania (2001). CONCLUSIONS A general method for the identification of muhiple faults of different types is presented in this paper, by means of a model based identification in the frequency domain. The method has been tested by means of experimental results, obtained on a test-rig, and has shown its effectiveness in identifying simultaneous faults in both position, module and phase. ACKNOWLEDGEMENTS This work is partially funded by the MURST (Italian Ministry for the University and Scientific Research) Cofinanziamento "IDENTIFICAZIONE DI MALFUNZIONAMENTI IN SISTEMI MECCANICI" for the
year 1999.
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REFERENCES Bachschmid N. and Pennacchi P. (2000). Model Based Malfunction Identification from Bearing Measurements. MechE-f^ Int. Conf. on Vibrations in Rotating Machinery, 12-14 September 2000, University of Nottingham, UK, 571-580. Bachschmid N., Pennacchi P., Tanzi E. and Vania A. (2000). Accuracy of Modelling and Identification of Malfunctions in Rotor Systems: Experimental Results. Journal of the Brazilian Society of Mechanical Sciences, XXII:3, 423-442. Edwards S., Lees A.W. and Friswell M.I. (2000). Estimating Rotor Unbalance from a Single Run-down. IMechE-f^ Int. Conf on Vibrations in Rotating Machinery, 12-14 September 2000, University of Nottingham, UK, 323-334. Kreuzinger-Janik T. and Irretier H. (2000). Unbalance Identification of Flexible Rotors Based on Experimental Modal Analysis. IMechE-f^ Int. Conf on Vibrations in Rotating Machinery, 12-14 September 2000, University of Nottingham, UK, 335-346. Markert R., Platz R. and Siedler M. (2000). Model Based Fault Identification in Rotor Systems by Least Squares Fitting. ISROMAC-8 Conference, 26-30 March 2000, Honolulu, Hawaii, 901-915. Pennacchi, P. and Vania, A. (2001). Measures of Accuracy of Model Based Diagnosis of Faults in Rotormachinery. COMADEM 2001 14^^ Intl. Congress and Exhibition on Condition Monitoring and Diagnostic Engineering Management, 4-6 September 2001, University of Manchester, UK. Platz R., Markert R. and Seidler M. (2000). Validation of Online Diagnostics of Malfunctions in Rotor Systems. IMechE-7^^ Int. Conf on Vibrations in Rotating Machinery, 12-14 September 2000, University of Nottingham, UK, 581-590.
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Condition Monitoring and Diagnostic Engineering Management Andrew G. Starr and Raj B.K.N. Rao (Editors) © 2001 Published by Elsevier Science Ltd.
THERMODYNAMIC DIAGNOSIS AT STEAM TURBINES Paul Girbig Siemens Power Generation Industrial Turbines and Power Plants Research and Development 91050 Erlangen, P.O.Box. 3220, Germany
ABSTRACT An approach is presented to turbine engine gas path analysis and monitoring, which permits the isolation of single or simultaneous multiple engine faults, with a quantitive assessment of their relative severity. The focus is on thermodynamic diagnosis at steam turbines with overheated steam and nozzel group valves in the inlet chest. The software approach is described, showing features of its mathematical development and thermodynamic justification. Measureable turbine parameters are treated as dependent variables, changes in which are mathematically interrelated to changes in component performance brought about by physical turbine faults. Typical results are presented from real programs, wherein turbine data were analyzed to provide meaningful and verified diagnoses of single and multiple turbine faults. This approach is like storing a finger print of the thermodynamic behavior of a turbine and comparing the actual turbine condition with the stored finger print. Details of the turbine construction are not necessary, because the finger print is developted on the typical measurements of the turbine and includes a sensor fault elimination before comparing stored with the actual condition.
KEYWORDS Diagnosis, State and Sensor Faults, Modeling, Steam Turbine(s), Gas-Path-Analysis 1
INTRODUCTION
Steam turbines are used in power generation plants and industrial processes. The owner of a turbine like to increase the operating time and to reduce the shutdown time for service. Abnormal changes in a steam turbine may occure a shut down of the turbine and create production losses. Maintenace and operations want to know what the fault is and its serverity before a decision is made whether to shut the machine down or let it run. There are existing several products for indicating vibrations or mechanical deviations. An important source to calculate the availibity and profitableness of a steam turbine is the knowledge of the conditions of all parts in the gas containment path in a turbine, for example the condition of the nozzel group valves, turbine blades and exhaust. The deposits of salt and silica are justly feared because they can have a very serious effect indeed on the running of the turbine. In addition to reducing the power output, however, the deposits can also cause mechanical
673
disturbances such an increase in the axial thrust, which can overload the turbine thrust bearing, and sizing in control devices and emergency stop valves. There are also mechanical effects which can result in corrosion of blade materials. In the course of its useful life the gas containment path of a turbine is susceptible to encountering a wide variety of physical problems. These inculde such things as: • erosion, corrosion • fouling, built up dirt • foreign object damage • worn seals, • excessive tip clearances, • burned or wraped turbine stator or rotor blades. The target is to have a high quality information about the condition of all parts in an steam turbine, which are in touch with the steam. To indicate any changes inside the turbine, it is important to • indentify clearly faults as faults, • quantify the size of the faults, • localize the faults • and to follow the fault development related to the time A qualified and an effective diagnosis needs a most accurate information on the fault free state of the turbine - the so called reference state. The knowledge about the deviations from the reference state allows to decide about the necesserary actions, which have to be taken. 2 MODELING 2.1 Preliminaries A steam turbine could be subdivided into different sections • the inlet section including the inlet chest with nozzel group valves and stop valve • the single-row control stage • the admission section including exhaust section Inlet chest with n o z ^ l group valves and stop valve
admission section including exhaust secti<»i single-row control stage
Figure 1: Sections of a typical steam turbine
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The inlet chest with nozzel group valves and stop valve allows to change the geometry of the gas containment path. This section is followed by the single-row control stage. The control state itself and the admission section including exhaust section are normaly not equipped with features, which allow to change the geometry. This is important to know for diagnosis, because the influence of an geometrical change in a turbine done by an valve has to seperated from the influence which is a result of a developing fauh. The typical measurements located at a steam turbine are the thermodynamic measurements of steam pressure, temperature and flow at the inlet, the position of the nozzel group valves, the steam pressure and temperature behind the single row control stage, normaly located at the steam entrance to the admission, • and the steam pressure, temperature at the exhaust. The generator supervision offers a power output measurement, which allows to verify the efficiency.
single row control stage
admission
generator
ITT power output
Figure 2: Typical measurements at a turboset A diagnosis depends on the accuracy of the measured values. For the diagnosis of gas turbines a procedure is outlined which eliminates (detects and compensates) systematic sensor faults. This procedure was transfered to steam turbines. In steam plants the typical faults are at the steam flow measurement. This could be detected by measuring of the power output at the generator and parallel measuring the change of steam flow indication compared to the position change of the nozzel valve group. Both allow to recalculated the st^am flow quantitiy. Typical steam flow measurements creating faults in part load condition. This has to do with the calibration to nominal load. Other typical fauhs in steam plants are at the steam temperature measurements. The reason for this faults is the delay in the temperature sensor itself and the influence of the casing temperature. For example, this could be detected behind the single row control stage, where pressure changes due to different nozzel valve positions could be detected , but nearly now changes in temperature measurement could be seen. The real temperatur has to calculated on the physics of the steam process. In both fault situations it is necessary to eliminate the sensor faults by using the procedures, already developed for gas turbines [Lunderstaedt, Hillemann93].
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2.2
Procedure
The knowledge of the actual measurements and also the calculation of the characteristic values like efficiency 'X\ and steam flow rate (p provides the operator only with an information about the actual operating point. For comparison the actual condition of the turbine to a fault free state, an accurate model of the thermodynamic behavior in the gas containment path is required. Diagnosis systems for gas turbines/jet engines basing on the Gas-Path-Analysis (GPA) are a powerful tool in many applications of civil and military airplanes and stationary gas turbines as well [Limderstaedt, Fiedler85]. The GPA is based on the mathematical model of the turbine, which describes the gas condition and flow in the gas containment path. The thermodynamic condition of a turbine could be described by using the physical characteristics of rotating speed code Mu und pressure code number \|/ with the condition characteristics of the efficiency r| r] = i{\ifM,)
(1)
(p = f{\l/Mu)^
(2)
and steam flow rate (p
The rotating speed code Mu is defined with
M=-
"
"
ylK'P^V
(3)
and the pressure code number \|/ 2
K
yr=—-—-.p,.v, fc-l
u
1-1^ (4)
On closer examination of a turbine section is u the rotating speed, fc{p,T ) the isentrop exponent, p^ pressure and v^ (p,r ) the specific volume at the inlet and p^ the pressure at the outlet of the turbine section. The rotating speed // is defined by steam turbine with (5) where -^ is the middle radius of the ring area steam flows through and the revoltions // per minutes of the turbine rotor .
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rotating speed code Mu
pressure code number
|_ ^ ^
Module of a turbine section 1
1
^
r
efficiency
steam flow rate
^
1
Figure 3: Relation between condition values and the physical characteristics Near to an operating point it is allowed to linearsize the equations (1 ) and ( 2 ) to
^ ^ A
'
'/
f{i/r,Mj
^
di/r
^
^
"
'•
f{y/,Mj
~
•
dM^
^ ^ • •
^rj J
(6)
The above described modeling is only correct, when there is dry steam (single gas state) and no changes in the geometrical form of the turbine, like the nozzel group valves. Steam turbines are normaly equipped with nozzel group valves, stop valve and the single-row control stage in the inlet chest. So further terms have to be added to the equations above. 2.3 Procedure for variable geometry In view of the circumstances, that it is nearly impossible to measure the steam condition between the regulations valves and the single row control stage (see figure 4), the change of the geometry caused by the regulation have to be considered. Meastitti^ of steagn coiultttom h&tmt single row contml stage
Fig 4: It is impossible to measure the steam condition between the nozzel group valves and the single row control stage
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In the following the position s of the nozzel group valves will be used like an further characteristic term for determination of the operating point. The thermodynamic condition of a turbine with a variable geometry could be described by using the physical characteristics rotating speed code Mu, pressure code number \\f and valve position s with the condition characteristics of the efficiency r\ (7) and steam flow rate (p
(p = f{\l/Mu^s)
(8)
A qualified and an effective diagnosis needs a most accurate information on the fault free state of the turbine - the so called reference state. During the operation of the turbine the diagnosis is carried out with the model of reference. So near to an operating point it is allowed to set the equations linear, so that the modeling for no fauh condition is A/„ ^/(^,M„5) ¥ df{y/.M^,s) A^.
A^-
AA/.,
^^A
A^-
AA/„
As
As
(9) Adding the valve position s to the equations means, that now the complete section from the inlet flange up to the single row control stage is observed.
rotating speed code Mb
1 position of nozzle valve group
1
s
pressure code number
U
If
n Module of a turbine stage i
1 1 >'
Efficiency
>' Steamflowratel
Fig 5: The position s of the nozzel group valves will be added to describe the operating point of the inlet chest.
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2.4
Experience with using the GPA at steam turbines and diagnosis results
The first experiment with this modeUing and procedure was done at a backpressure turbine 35 MW installed in an power station and. The observation started in 1997 and is till today in service. The very interesting information was, that the position of nozzel group valves have a great influence to the efficiency of the complete section of the inlet chest (see figure 6). The reason is, that if there is a valve in the nozzel group not fully opend, the losses at the valve are increasing. For seperating the changes in the thermynamic steam state of the inlet chest, it is necessery to get a very accurate measurement of the nozzel group valves position. In figure 6 it is possible to see, which differences in the efficiency are depending from the positions s of the nozzel group valves.
Fig 6: The efficiency of inlet chest is depending from the positions s of the nozzel group valves At this observed turbine it has been investigated the inlet chest section and the admission section for a period of 3 years. The technical programm did compare the actual datas with the reference state. After 2 years small changes have been detected. The results of the observation did show a small change in the efficieny and steam flow rate. TABEL 1 Diagnosis results of a specific turbine 35 MW backpressure Where? Location of the fault
How much? Quantity of the fauh
} }
Inlet chest and admission section mostly in inlet chest
Inlet chest:
efficiency approx steam flow rate approx admission sectionl: efficiency approx steam flow rate approx
When? Detected chaces related to the time
In the time range of 7 month great chance in the between Dez. 1998-Jan 1999
Why? The chace is typical for
Behaviour typical for fouling, built up dirt based on low steam quality
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- 1,5 - 0,5 - 0,3 ± 0
percentage percentage percentage percentage
The results have a high quality and exactness. This was aproved by several different measurements. The reason for this deviation from the reference state is due to salt and silicia deposits in the steam turbine. The thickness of the deposit and the detailled location only will be seen at the next revision. The actual datas show still a small deviation from the reference values, so that a revision or cleaning procedure of the observed turbine is not yet necessary. 3 CONCLUSION AND OUTLOOK The Gas path analysis for multiple fault isolation provides a powerful tool which, when used in conjunction with the better known techniques for accessory and mechanical component diagnosis, can lead to significant benefits to turbine users in terms of reduced maintenance, overhaul and operating costs brought about by timely, exact knowledge of turbine status. The technique is applicable to all turbine types with oveiiieated steam and in practice is customized to the particular turbine installation, instrumentation and operational history. It is based on relative shifts rather than absolute measurements and hence is primarily influenced by instrumentation repeatability, which is always better than absolute accuracy. It is valid for all multiple combinations of sought for faults, with isolation to specific modules. Execution requires minimal computer memory, involving only muhiplication and addition to solve a simple set of linear equations. In any installation, the informational yield from any given set of measurements is greater using gas path analysis than known competitive technique. Further investigations are running for steam turbines with condensation.
4 REFERENCES [Lunderstaedt, Fiedler85] Limderstaedt,R.;Fiedler,K.: Zur systemtheoretischen Diagnose von Stahltriebwerken. Automatisierungstechnik at 33, pages.272-279 and 313-317, 1985. [Lunderstaedt, Hillemann93] Lunderstaedt, R. und Hillemann, Th.: Sensor Fault Detection for Gas Turbines with Knowlegde Based Methods. Proceedings 12. IFAC KongreB, Sydney, 1993.
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Condition Monitoring and Diagnostic Engineering Management Andrew G. Starr and Raj B.K.N. Rao (Editors) © 2001 Elsevier Science Ltd. All rights reserved.
ON-LINE VIBRATION MONITORING FOR DETECTING FAN BLADE DAMAGE P.S. Heyns and W.G. Smit Dynamic Systems Group, Department of Mechanical and Aeronautical Engineering University of Pretoria, Pretoria, 0002, South Africa
ABSTRACT The use of on-line vibration monitoring to detect and classify damage levels on fan blades is investigated. It is shown that output only data can be used to determine modal parameters such as natural frequencies with sufficient accuracy to be used as damage indicators. A finite element model is used to study the feasibility of using frequency shifts as damage indicators on rotating fan blades. These results indicate that certain mode shapes are more sensitive to damage at the root of the blade. An experimental fan blade damage simulator is then used to verify these findings experimentally. A hypothesis that the differences in fi-equency shifb between the finite element model and the measured frequencies, due to damage, is the result of global structural behaviour, is verified by using an extended fmite element model. Local mode shapes make it possible to classify damage levels on individual blades, using one sensor per blade only. Damage levels of as low as 10% could be detected experimentally. KEYWORDS Condition monitoring, damage detection, fan blades, on-line monitoring. INTRODUCTION Continuous monitoring of the condition of critical equipment is becoming increasingly important in industry. Failure of such equipment may have serious consequences for a whole process. The forced and induced draught fans used on power station boilers, are examples of such critical equipment. Vibration measurement is often utilised to identify the presence of bearing faults on such equipment. Conventional vibration monitoring approaches are however not suitable for the identification of localised damage on the fan blades. This is traditionally the domain of non-destructive damage detection methods such as ultrasonic scanning, xray diffraction, or dye penetrant inspection methods. These techniques can however not be implemented during the operation of fan blades and exploit the fact that operational loads should emphasize the existence of damage. This leads to the question if alternative approaches to vibration monitoring cannot perhaps be applied for the detection of fan blade damage during operation. Vibration monitoring of structures has received considerable attention in the past few decades. Doebling et al (1996) have presented an extensive survey of this field. The ability of these methods to do global monitoring of a structure using relatively few sensors at fixed measurement positions during normal operation of the machine, as opposed to methods such as x-ray inspection that require scanning of the entire structure under stationary conditions, is a huge advantage.
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It is usually difficult to find a vibration related characteristic that change significantly with increasing levels of damage. Friswell and Penny (1997) noted that algorithms should be tailored to a specific application, as it is unlikely that a single best method for all applications will ever emerge. Many investigators have considered the use of changes in natural frequency as damage indicator. The observation that changes in structural properties cause changes in system naturalfiiequencies,was indeed the stimulus for using modal methods for damage identification and health monitoring. However, researchers soon found that fi^uency changes have significant practical limitations for structures such as bridges, offshore oil platforms and other large civil engineering structures. The low sensitivity of frequency shifts to damage required either very precise measurements, or high levels of damage. However, Silva and Gomes (1994) found that the level of damage on a simple cantilever beam could be determined accurately using shifts in natural frequencies. If more than onefi*equencywere used the location could also be determined. This suggests the possibility of using blade vibration frequency measurements to identify the presence of damage on fan blades, especially on axial flow fans witii blades that resemble cantilever beams. Wolff and March (1989) investigated the detection of cracks in a centrifugal scrubber fan. They used both finite element models and experimental measurements to identify the effects of artificially induced cracks on a scrubber fan blade. Experimental modal analysis was performed on the stationary fan, using an electromagnetic exciter attached to a blade through a stinger. Some success was attained, although it is clear from this work that frequency based damage detection is difficult for complicated geometries such as shrouded centrifugal fans. These experiences do suggest that a feasible on-line vibration based monitoring technique might be developed that can identify blade damage from measured natural fi^uencies, determined continuously from the operational excitation that is always present under normal operating conditions. This is particularly so for axial flow fans with cantileverlike blades where the damage is likely to be caused by fatigue close to the blade root. This work investigates the use of such an on-line vibration monitoring approach for axial fan blade damage detection. The investigation entails a fmite element sensitivity study, followed by an experimental investigation on a four bladed experimental fan blade damage simulator, developed for this purpose. Further finite element studies are done to explain some discrepancies found between the finite element and experimentally determined results. FINITE ELEMENT INVESTIGATION OF DAMAGE SENSITIVITY After guidelines for the accurate modelling of bladelike structures have been established, using simplified fmite element (FE) models, a model of one of the blades of the four bladed experimental fan blade damage simulator was constructed, using a stiflftiess matrix-updating scheme to account for the effects of centrifugal forces on the blade. To avoid the need for material properties which have to be experimentally determined, it was decided to restrict crack modelling to simple methods that did not require any special material properties to be known. Local damage, as was to be induced on the simulator, was simulated by untying the nodes. The percentage shift in natural frequency with increasing levels of damage (more nodes untied) can be seen in Figure 1. (Damage percentage corresponds to the percentage of cross section that is assumed to have cracked through.) As can be expected, the frequency shift increased significantly as more damage was introduced. It is clear that some modes (for example mode 3 at 280 Hz) are much more sensitive than others to the blade root damage scenario that was considered. Such modes are obvious candidates for monitoring damage. FAN BLADE DAMAGE SIMULATOR The effect of blade damage was experimentally investigated using a fan blade damage simulator depicted in Figure 2. Two piezoelectric strain gauges and one miniature piezoelectric accelerometer was used to measure
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the dynamic behaviour of the blade with increasing levels of damage. Damage was simulated by using a fine hack saw to cut into the root of the blade. An additional accelerometer was attached to another blade to test the ability of the damage detection method to identify damage levels on individual blades. The FE model provided valuable insight regarding the placement of sensors at a position of maximum strain or acceleration, depending on which sensor was used. During preliminary measurements the 4*^ (first torsional) natural fi-equency was found to be very well defined. Although the predicted sensitivity of this natural frequency was not as high as the 3^^ natural fi-equency, special care was taken to position the sensors to measure this fi-equency as well.
20
30
Damage level (%j
Figure 1 Sensitivity of natural frequencies to damage levels
20 - Channel slip ring unit
1.5 kW, 3 - phase motor
Connection point to Siglab
Figure 2 Fan blade damage simulator FAN BLADE DAMAGE mENTIFICATION An AutoRegressive Moving Average with eXogenous signal (ARMAX) model was used to obtain a curve fit to the measured Power Spectral Density plots. Ify(t) and u(t) are scalar signals and eft) is a white noise sequence, the model structure is: A(q-')y{t) = Biq-')u(t) + C{g-' )e(t)
683
(1)
A typical ARMAX fit to accelerometer data of a fan rotating at 750 r/min can be seen in Figure 3. The most apparent feature of this PSD and the resulting curve fit, is the blade pass frequency harmonics due to the six stationary support beams of the simulator. This necessitates the use of much higher order ARMAX models than would otherwise have been necessary. The speed controller also causes a high level of electrical noise to be present. Natural frequencies can be seen at around 200 Hz, 280 Hz, 440 Hz, 560 Hz, 1100 Hz, 1340 Hz and 1800 Hz. This corresponds very well with the predicted natural frequencies of the FE model.
0
500
1000
1500
2000
2500
3000
Frequency (Hz)
Figure 3: ARMAX fit to measured acceleration PSD Since the shifts in natural frequencies due to damage are very small, it is important that natural frequencies of the structure should be identified with an even smaller band of uncertainty. Since long time signals combined with a high order ARMAX model result in high computational times, it is important to minimize the number of measurements and the measurement time. By using different record lengths and numbers of measurements, it was found that the two fi^uencies that showed most promise as damage indicators could be detected with a 99.5% confidence interval while still keeping computer time within reasonable limits. If twelve 4 s measurements are used, the mean frequency estimate will fall in the range of 279.88 < Hx < 280.44 for the third natural frequency and 446.95 < ^x < 447.20 for the fourth natural frequency. This translates into a maximum error of 0.32% for the third and 0.06% for the fourth natural frequency due to measurement errors. All subsequent experimental results are the average of twelve 4 s response records. EXPERIMENTAL RESULTS After initial measurements with no structural damage were taken, damage of 10%, 20%, 30% and 40% were induced by cutting into the root of a blade instrumented by two piezoelectric strain gauges and a micro accelerometer. A lightweight accelerometer was also mounted on an undamaged blade to test the ability of the technique to detect damage levels on individual blades. Figure 4 depicts the variation in natural frequency with increasing damage. While the general trend of the shifts with increasing levels of damage for the 3^^ natural frequency is the same for the FE model results and the experimental results, the FE model predicted a much more significant shift. The natural fi^quency corresponding to the 4* mode shape however showed much better correlation. The 3"* natural frequency also shifted on the undamaged blade, while the 4* natural frequency did not. This phenomenon will be dealt with in the next section. From these examples of measured data, it is apparent that the fan blade damage simulator experienced a measurable fi^uency shift with increasing levels of damage. The measurements sampled at 500 Hz tend to
684
give more consistent results than measurements sampled at higher frequencies. This could be expected since a higher order ARMAX model is necessary and more blade pass frequency harmonics are present in the resulting spectrum. The ARMAX curve fit is more accurate when only a few well-defined peaks and valleys are present in the PSDs. Because of the stress distribution found in most fan blades, the maximum stress will nearly always be found at the root of the blade. When this is the case, resultsfi-omthe experimental fan blade damage simulator showed that damage levels almost down to 10%.could be detected using output only data andfi-equencyshifts.
^
-6
en
&-1Q c
(U
tu
^•20 -25 ID 20 30 Damage level [%]
1D 20 30 Damage level [%]
Figure 4: (a) Frequency shift for mode 3
(b) Frequency shift for mode 4
GLOBAL MODES Due to the discrepancies found between the frequency shifts of some mode that were predicted by the FE model and the actual measured shifts, fiirther investigation was deemed necessary. It was postulated that the most likely cause for this phenomenon is that the measured frequencies corresponded to global frequencies of the entire structure, and that the tacit assumption of a blade acting like a single cantilever beam is not accurate. This means that thefi*equencyshift might be less than predicted by what would be a local mode shape of a single blade modelled by the FE model. While a readily measurable shift of the frequency of mode 3 (around 280 Hz) was experienced with increasing levels of damage, the shift was nowhere near as much as expected. At around 40 % damage, the shift was found to be 6% compared to the predicted shift of 25%. The reason for these results can be seen in Figure 5. While four discemable peaks exist around 270 Hz, the ARMAX model fitted a curve through the average of these peaks.
Amplttude
—
ARMAcLPififl MeasufEd PSD
Figure 5: Curve fits for modes 3 and 4 This behaviour is caused by the global nature of the system modes. The other three fan blades thus also participate in the system motion at slightly different local fi-equencies. As a result these local modes are
685
superimposed on each other to give a much more complex behaviour than would be expected from a single blade. Further proof for this hypothesis can be found in the fact that an instrumented, undamaged blade also showed frequency shifts at most of the natural frequencies with increasing levels of damage on the damaged blade. In contrast to the 3"^ natural frequency, the 4* natural frequency produced a well-defined peak at one frequency only. This peak also stayed at virtually the same position on the undamaged blade witfi increasing levels of damage. The peak at 360 Hz is the 7* harmonic of the blade pass frequency at 450 r/min. To gain a better understanding of the behaviour of the complete system, a simplified FE model of the hub and blade interface was created. The nature of the model is clear from Figure 6 which depicts the four blade mode shapes observed in the region of the 3"^ natural frequency.
Figure 6: Four blade mode shapes found in the region of the 3"* natural frequency (about 290 Hz) While it is not an exact representation of the fan blade damage simulator blade, it adequately explained the unexpectedly smaller shifts of frequency observed during measurements. The numerical values of the different mode shapes in the region of 280 Hz can be seen in Table 1. TABLE 1 GLOBAL MODAL FREQUENCIES AROUND MODE 3 Undamaged case 294.1Hz 282.0 Hz. 292.8 Hz 294.8 Hz
25 % Damage case 293.7 Hz 267.4 Hz 286.7 Hz 294.8 Hz 686
Similarly, the modal frequencies in the region of 440 Hz can be found in Table 2. Clearly, these modes (which are torsional modes) are far less influenced by other blades in the structure and are almost local in nature. TABLE 2 GLOBAL MODAL FREQUENCIES AROUND MODE 4
25 % Damage case 429.3 Hz 433.5 Hz 433.6 Hz 433.9 Hz
Undamaged case 433.4 Hz 433.6 Hz. 433.8 Hz 434.0 Hz
Global mode shapes provide global damage level indicators of a structure. This means that the shift in frequency may be smaller than predicted by a local model due to the fact that the damage is less relative to the whole structure, than predicted by a local model. CONCLUSIONS This paper presents an approach to on-line damage detection in fan blades based on the measurement of frequency shifts. Damage usually occurs very close to the root of a blade due to the stress distribution found in a rotating structure unless some external influence, such as a contaminated working fluid, is present. A finite element model shows that certain mode shapes are more sensitive to damage at the root of a blade than others. Measurements taken at bearings or on other parts of a fan structure does not contain enough information about the dynamic behaviour of the blades. Sensors mounted directly on the blade, with the exact location carefiilly chosen to maximize the sensitivity to chosen mode shapes, do however provide useftil information on the damage level of the blade. Due to rapid progress in computer technology and software, it is now possible to use powerfiil finite element packages on desktop systems. This makes it possible to study the effect of damage on the dynamics of a structure such as a blade prior to construction of a test structure. Although it is easy to simplify symmetric structures such as fans, carefiil consideration has to be given to global system dynamics. Some mode shapes are local and are very valuable when individual blades have to be monitored, but most mode shapes are global. REFERENCES Doebling S.W., Farrar C.R., Prime M.B., Shevitz D.W. (1996). Damage identification and health monitoring of structural and mechanical systems from changes in their vibration characteristics: A literature review, Los Alamos National Laboratory Report LA-13070-MS. Friswell M.L, Penny J.E.T. (1997). Is damage location using vibration measurements practical? Structural Damage Assessment using Advanced Signal Processing Procedures Proceedings ofDAMAS '97, University of Sheffield, 30 Jun - 2 Jul, 351 -262. Silva, J.M.M., Gomes, A.J.M.A. (1994). Crack Identification of simple structural elements through the use of natural frequency variations: the inverse problem. Proceedings of the 12^^ IMAC, 2, 31 Jan - 3 Feb, 1728-1735. Wolff, P.J., March, P.A., (1989). Experimental and finite element investigation of blade cracking in a scrubber fan model. Proceedings of the f^ IMAC, 1, 30 Jan - 2 Feb, 119-123.
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Condition Monitoring and Diagnostic Engineering Management Andrew G. Starr and Raj B.K.N. Rao (Editors) © 2001 Published by Elsevier Science Ltd.
A HYBRID KNOWLEDGE-BASED EXPERT SYSTEM FOR ROTATING MACHINERY Y. B. Lee, T. W. Lee, S. J. Kim, C. H. Kim^ and Y, C. Rhim^ ^Tribology Research Center, Korea Institute of Science and Technology, Seoul, Republic of Korea, 136-791 ^Department of Mechanics and Electronics, Yonsei University, Seoul, Republic of Korea, 120-749
ABSTRACT In order to develop a superior fault diagnosis system for rotating machinery, this paper suggests a knowledge-based expert algorithm, which is basically the combination of aframe-basedmethod using Sohre's chart and a rule-based method. The former covers wide vibration causes, relating the probability of the occurrence of symptoms to an underlying cause, while the latter based on IFTHEN clauses gives relatively specified diagnosis results. Thus, the combined algorithm can guarantee high accuracy of fault diagnosis and also be easily extended by adding new causes or symptoms. Some examples using experimental data show the good feasibility of the proposed algorithm for condition monitoring and diagnosis of industrial rotating machinery.
KEYWORDS Condition Monitoring, Rotating Machinery, Knowledge Base, Fault Diagnosis, Expert System
INTRODUCTION The primary technique used to perform rotatmg machine diagnostics and predictive maintenance is vibration analysis, because there is a direct correlation between machine vibration pattern and the actual and potential machine defects. Analyzing vibration data, however, is a difficult task that usually requires experts. Experts in vibration analysis are scarce and may be unavailable on site in case of emergency. Thus it is necessary to develop on-line monitoring systems that assist in the diagnosis of rotating machinery defects. Among them, knowledge-based expert system and artificial neural network (ANN) are mostly used. Although the ANN has capabilities of association.
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memorization, error-tolerance, self-adaptation and multiple complex pattern processing, it cannot explain it's own reasoning behavior and cannot diagnose new faults which were not previously trained [Jang, 1997]. Compared with this, expert systems can explain their reasoning behavior and diagnose new faults using the knowledge bases. Expert systems are comprised of three basic modules [Taylor, 1989]: the knowledge base, the inference mechanism, and the user interface. The knowledge base contains the human expertise, which is often expressed in terms of rules and fects. Rules are conditional statements that state what action occurs if a specific condition is satisfied. The inference mechanism is the controlling mechanism which determines how the knowledge in the knowledge base should be accessed and used. The inference mechanism may be thought of as the main control software and the knowledge base may be regarded as the data that is controlled. In this paper, to implement a feuh diagnosis system for vibration cause identification in rotating machines, two knowledge bases are used: the Sohre's chart and a rule base. The former covers wide vibration causes, relating the probability of the occurrence of symptoms to an underlying cause, while the latter based on IF-THEN clauses gives relatively specified diagnosis results. Adopting this point of view, this paper presents a combined algorithm of Sohre's chart and rule base so that it can guarantee high accuracy of fault diagnosis. In addition, it can be easily extended by adding new causes or symptoms. Figure 1 shows the configuration of the proposed expert system. In the rest of this paper, a brief introduction to knowledge bases is given. Next, the proposed expert system is described, concentrating on the inference method. Two case studies using experimental data are then provided, demonstrating the success of implementing system in solving real problems.
Data Input Data Acquisition System Monitoring
Abnormality Alarm
Fault Diagnosis
Display
Diagnosis Results
Decision Making
Knowledge Base Sohre's Chart
User Interface
Figure 1: Configuration of the proposed expert system
690
Rule Base
DIAGNOSIS ALGORITHMS Frame-based Diagnosis TABLE 1 shows the correlation between vibration frequency symptoms and potential causes, which is a subsection of the Sohre's chart described in a handbook [Sawyer, 1980]. The fiill chart covers about 90 dififerent possible symptoms grouped into 10 categories and over 40 different possible causes. Thus, although performing fault diagnosis by directly examining the chart may be laborsome and tedious, it provides a way to relate the probability of the occurrence of a symptom to a cause. For example, when unbalance is found to be the cause of a vibration problem, the probabilities of the occurrence of the IX, 2X, and very high predominant frequencies are 0.9, 0.05 and 0, respectively. Owing to its popularity and wide coverage of vibration causes, Sohre's chart is used to serve as the basis upon which to conclude the potential operating problems. When using this chart, one should note that vibration severity criteria depends on machine size as well as other variables pertaining to loading and the operation environment. Thus the thresholds of each symptoms should be carefiilly determined, considering the machine's own operating condition. The consultant process based on Sohre's chart is as follows; first, the probabilities of the occurrence of all causes are calculated by categories, based on the detected symptoms, as
PM) =
^{probabilities
of detected symptoms) (1)
2 {probabilities of all symptoms)
where Pi{A) means the probability of the cause A in the category number /. Next, the fmal results are obtained as the geometric average of the probabilities calculated above.
TABLE 1 Relationship between vibration causes and symptoms (part of Sohre's chart) Causes of
1
Vibration
§ sr
Unbalance
5
Casing distorting
8
It
I
><
X
-
-
90
5
5
10
10
10
60
20
10
Seal rub
10
10
10
20
10
10
Bearing damage
20
20
20
40
20
10
Coupling damage
10
20
10
20
30
10
Eccentricity
60
-
60
-
5
10
-
40
Misalignment
30
60
10
Bearing damage
20
20
20
40
20
Critical speed
100
-
Oil whirl
-
80
-
100
-
-
-
691
fe ^ -
-
10
5
la
O fc
-
-
-]
10
10
10
-
-
20
-
80
10 20
-
P(A) = \T\P„{A)\
"(D"-'
(2)
Here, n is the number of the categories considered in the inference and P{A) can be defined as a certainty fector of the hypothesis that the cause A occurred. As well known, compared with arithmetic average, the geometric average gives lower probability, which results in weighting the probability that the cause A does not occur. It is intended to clarify the possible causes. For example, let us suppose that the following synq^toms were detected; Predominant frequency: Direction of predominant amplitude: Location of predominant amplitude: Amplitude response to speed increase: Amplitude response to speed decrease:
Ix (high) vertical shaft increase decrease
Then, for the cause, 'unbalance', the probability is calculated in each category by using Eqn. 1 as Piiunbalance) = 90 / (5+90+5+5) = 0.86 P2(unbalance) = 40 / (40+50+10) = 0.4 Psiunhalance) = 90 / (90+10) = 0.9 Piiunbalance) = 1 Psiunbalance) = 1 From Eqn. 2, the certainty factor of the cause A is P{unbalance)^ VO.86x0.4x0.9x 1 x 1 =0.79 In similar way, the certainty factors for another causes can be also calculated and, in the order of their magnitudes, the possible causes are selected. Like in this example, categories in which available symptoms are not detected can be excluded from the inference. In other words, this knowledge base can be easily extended by inserting new category. Rule-based Diagnosis Rules produced mainly from human experts as a result of a lengthy knowledge engineering process, are probably the most common form of knowledge representation methods. It is because they allow the mcorporation of multiple clauses, enabling the use of confidence measures and modular systems to be built, and most of all, a lot of human reasoning can be expressed as rules. On the other hand, rule-based systems have also some disadvantages that may place upper Hmits on their reliability and applicability: the process of knowledge acquisition is very time consuming and prone to errors, and rules are brittle when presented with noisy or incomplete data. For example consider the following
IF: predominant frequency component is IX
and the direction of vibration is radial and the location of predominant vibration is rotor THEN: problem is unbalance with belief 0.7 From this rule, a clear diagnosis result can be directly obtained. However, noise in the measured data or any novel combination of input data may cause incorrect diagnosis. Thus in order to cope with the complexities of real-world data a practical rule-based system must have enough rules to account for the most common cases that should occur. And also, the normal level of vibration and the thresholds should be carefiiUy set. The rules used in this paper were built for compressor problems in University of Virginia [Nahar, 1989]. The number of rules is 78 and they are classified into 11 categories: unbalance, mechanical looseness, misalignment, gear problems, aerodynamic problems, coupling problems, thrust bearing problems, sub-harmonic resonance, harmonic resonance, electrical problems and instability problems. Note that the beliefs expressed in these rules can be interpreted as certainty factors. To deal with the certaintyfectorsgivenfromthe rules, Dempster-Shafer method is adopted [Nahar, 1989]. This method replaces probabilities by the concept of evidential support. That is, the focus is moved from whether the hypotheses is true, to whether the evidence means that the hypothesis is true. In this theory, a set of all hypotheses is represented by 0 and the belief in a hypothesis is assigned into a value between 0 and 1, which means a certain piece of evidence. Note here that in case of compressor diagnosis 0 would be the set of possible causes and their combinations. The effect of each piece of evidence on the subset ^ of 0 is defined as a basic probability, m(A). Then, m satisfies w(0)=O and Y,^(A) = \
(3)
where 0 is null space. The evidence-gathering process for a diagnosis requires a method for combining support for a hypothesis based on accumulated evidence. Given two basic probabilities, mi and w^, from different hypotheses, Dempster's combination rule computes a new basic probability which represents the effect of the combined evidence. Y,m,iX)m,(Y) 1-
2^m,iX)m^(Y) XnY=fi>
Dempster's rule states that the combination of ml and m2 is the total amount of belief among the subsets of 0 by assigning the product, m^{X)m2(Y), to the set interaction of X and Y. Using multiplication ensures that the results are the same regardless of the ordering of the evidence. For example, let's suppose that rules 1 and 2 give the beliefs, 0.4 and 0.2, respectively in misalignment and rules 3 and 4 give the beliefs, 0.6 and 0.5, respectively in unbalance. mimisalignment) = 1 - (1 - 0.4 )(1 - 0.2 ) = 0.52 m(unbalance) = 1 - (1 - 0.6 )(1 - 0.5 ) = 0.8
693
(5) (6)
Then, thefinalcertainty factors in unbalance and misalignment can be combined with each other as [w, *m2\(misalignment) = ———r-—r-— = 0.178 1-0.52x0.8 1.
L 7
N (1 - 0.52) X 0.8
(7)
^ .„
(8) [m, * mAiunhalance) = -^^ = 0.657 ^ ' ^^^ ^ 1-0.52x0.8 Here, note that unlike Eqns. (5) and (6), the certainty factors are normalized because the basic probability assignment of null space is not 0 but 0.416(= 0.52 x 0.8). Hybrid Knowledge-based Diagnosis Based on Sohre's chart and the rules described above, a hybrid diagnosis algorithm is proposed. The main reason for building the hybrid system is to reduce the brittleness of the rule based algorithm by incorporating the reliable consultationframeprovided by Sohre's chart, leading to high diagnosis ability. The hybrid technique has a parallel structure of two methods. That is, diagnosis results are independently calculated from each method using same input data, and combined with each other to generate the final certainty factor. For the combination, the Dempster-Shafer method is used again as shown in Eqns. (5) and (6). That is. [rrisc *rnj^]{A) = 1 -{1 -msciA)}{\-mj^{A)}
(9)
where m^^ and w^jg are certainty factors obtained from the Sohre's chart and the rule base, respectively. In this case, the normalization is not needed because the same subset of 0 is considered. By using this inference structure, we can improve the reliability and correctness of the diagnosis results.
THE DIAGNOSTIC EXPERT SYSTEM The proposed expert system was implemented in the diagnosis module of iCMS software programmed by Visual C++ 6.0. Figure 2 shows the display window and Figure 3 is the structure of iCMS. The iCMS software consists of vibration data acquisition module, display module of various plots such as time history, spectrum, waterfall plot, bode plot, orbit, etc. and diagnosis module. For data acquisition. National Instrument (NI) board having 16 channels is used and its maximum samplingfrequencyis 100 kHz. For data storage, MS-SQL server 7.0 is used, where trend data is stored every
Figure 2: Display window of iCMS diagnosis module.
694
^^^B ^^^^B
Figure 3: Structure of iCMS minute. On the other hand, the diagnosis module includes the user interface windo\\[ that is for changing the diagnosis condition according to operation environment. Diagnosis Test Results In order to test the proposed hybrid diagnosis system, two kinds of faults, i.e. oi| whirl and imbalance, are intentionally made in a rotor kit provided by Bently Nevada Co.. The obtained vibration spectrums are plotted in Figures 4 and 5. Actually the more symptoms are considered, the more correct results could be obtained. However we used only vibration analysis par^ of Sohre's chart, which include the categories of predominant frequencies, direction and location of predominant ampHtude, amplitude response of speed variation during starting up and coasting down. Detected symptoms: vibrationfrequency= 40-50% high location of vibration = shaft. Then, the diagnosis results are given as Sohre's Rule chart base Oil whirl 0.89 0.54 Coupling damage 0.34 Bearing damage 0.32 0.23 Rotor axial rub 0.31 Thrust bearing damage 0.29
Hybrid 0.95 0.34 0.48 0.31 0.29
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< Case 2: Unbalance > Detected symptoms: vibrationfrequency= IX location of vibration = shaft. Then, the diagnosis results are given as Sohre's Rule chart base 0.87 Unbalance 0.54 Casing distortion 0.67 Eccentricity 0.47 Bearing damage 0.44 0.23 1 Rotor axial rub 0.39
Hybrid 0.94 0.67 0.47 0.57 0.39 !
Figure 5: Unbalance spectrum
In above two cases, only five causes selected by Sohre's chart were listed in the order of magnitudes, while the rule-based diagnosis gave two possible causes. The most probable cause is the same in two methods, and amplified in the hybrid diagnosis method, which makes the results more evident. Through the tests, it is noticed that the hybrid method can be efficiently used for diagnosis of rotating machines.
CONCLUSION A hybrid expert system was built for fault diagnosis in general rotating machines, based on Sohre's chart and a rule base, so that it can guarantee high accuracy of fault diagnosis. In the system, the diagnoses in the two knowledge-based algorithm are performed independently, and after that, inferred by Dempster-Shafer method. The mference method is also used in the rule-based algorithm to combine the probabilities produced by the rules. This hybrid expert system is capable of wide coverage of possible causes and symptoms, improving the brittleness of the rule-based diagnosis, and being easily extended by inserting new module. A software, iCMS, where these diagnosis module as well as data acquisitk)n and monitoring modules is implemented, was introduced. Using the software, two diagnosis tests were tried. The results show the feasibility of the proposed expert system for condition monitoring and diagnosis of industrial rotating machinery.
REFERENCE Jang, Mizutani and Sun. (1997). Neuro Fuzzy and Soft Computing, Prentice Hall, USA. Nahar R. (1989). An Expert System for Vibration Fault Diagnosis of Turbomachinery, Master Thesis, Department of Mechanical and Aerospace Engineering, University of Virginia. Sawyer J. (1980). Sawyer's Turbomachinery Maintenance Handbook, Turbomachinery International Publications. Taylor T. and Lubkeman D. (1989). Applications of Knowledge-based Programming to Power Engineering Problems. IEEE Transactions on Power Systems 4:1, 345-352.
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Condition Monitoring and Diagnostic Engineering Management Andrew G. Starr and Raj B.K.N. Rao (Editors) © 2001 Elsevier Science Ltd. All rights reserved.
MONITORING THE INTEGRITY OF LOW-SPEED ROTATING MACHINES D.Mba^andL.Hall^ ^School of Engineering, Cranfield University, Cranfield, Beds. MK43 OAL
ABSTRACT Monitoring the mechanical integrity of rotating machines has usually been successfully undertaken with vibration analysis. Equipment rotating at slow rotational speeds are particularly difficult to monitor since conventional vibration measuring equipment is not capable of measuring the fundamental frequency of operation. This paper presents a study of high frequency acoustic emissions as a tool for detecting early stages of loss of mechanical integrity in low-speed rotating machines. Furthermore, it examines the use of time series analysis techniques to aid in defect source location. Investigations were centred on a test-rig that rotated at 1.1 rpm. The mechanism of acoustic emission generation was the relative movement between mating components experiencing loss of mechanical integrity, for instance, the loss of tightening torque between clamped components. KEYWORDS Acoustic emissions, auto-regressive coefficients, dendrograms, Kmeans clustering, Nielson source test, rotating biological contactor, low-speed rotating machinery. INTRODUCTION This work originated from mechanical difficulties experienced on slow-speed rotating machinery, in this instance. Rotating Biological Contactors (Mba et. al [1999]), see figure 1. The units consist of a collection of plastic panels held in position by a steel structure and supporting rods. A typical mechanical defect is the loss of bolt tightening torque between clamped components, as illustrated infigure2.
697
Figure 1
RBC unit
Figure 2
Mechanical deficiencies resulting in relative movement between mating components
Monitoring low-speed machines make great demands on both the analyst and vibration diagnostic instrumentation. This is primarily because standard predictive maintenance measuring instruments are inappropriate for low-speeds. Typically, lowspeed machines are massive in size and consequently when mechanical defects begin to occur the resulting vibration is often very low and serious faults can go undetected. The main problems with vibration analysis of low-speed machinery have been documented (Robinson et al [1996], Berry [1992], Mba et al [1999]). There have been a few attempts to develop systems for monitoring bearings at speeds between 1 to lOrpm, though with limited success (Robinson et al [1996], Kuboyama, Canada et al [1995]). For this particular investigation, mechanical deficiencies such as relative movement between mating components cannot be accomplished by vibration analysis. However, they lend themselves to monitoring with acoustic emission analysis.
698
ACOUSTIC EMISSIONS (AE) Acoustic emissions as described in this paper, refer to surface waves generated entirely by the rubbing action of failed components. The formation, deformation, and fracture of surface irregularities or asperities, which is associated with friction of metals (Green [1955]), will resuk in the generation of AE's. The application of the AE technique in research and industry is well-documented (Weavser [1996], Beattie [1983], Mathews [1983]) and one of its main attractions is its the high frequency content, overcoming audible operational background noise. Studies in the application of AE to tribology are relevant to the current investigation on rubbing between clamped components. Research (Sarychev [1991], Boness [1990], Linard [1989]) into wear between loaded metal surfaces in relative motion has concluded that AE can be used to determine the onset and the rate of wear between components. The source of AE activity was attributed to the breaking of surface asperities and the formation and destruction of the friction contact. It may be concluded that the process of rubbing between contact faces of an RBC will generate AE activity. SIGNAL PROCESSING It is reasonable to assume that relative movements between loose components will remain fairly constant during the brief period of data acquisition. Therefore, the AE's generated from these movements will have very similar source characteristics and resulting signature patterns; the latter is attributed to the uniqueness of the transmission path. Whilst this source mechanism may vary with time in the long term, it is assumed that during acquisition these changes will be insignificant to the signature shape. This implies that AE signatures from different sources having different transmission paths will be of unique shape/pattern. Furthermore, this unique pattern can be represented by a few auto-regressive (AR) coefficients (Oksa et al [1995]). This makes signature classification easier due to the significant reduction in data size. Kay et al (1981) and Makhoul (1975) have detailed auto-regressive modelling and the computation of AR coefficients is derived from linear prediction (Haykin [1984]). To aid fault/source identification, a clustering process known as Kmeans (Everitt [1974]) grouped the AR coefficients associated with each AE signature. This is a non-hierarchical technique that measures the Euclidean distances between the centroid value of the AR coefficients associated with each signature. The results were displayed on dendrograms (Everitt [1974]) with the nearest distances clustered together. EXPERIMENTAL PROCEDURE AND APPARATUS A test-rig was built to simulate some of the mechanical defects, see figure 3. The faults simulated were: i Structural looseness, a resuh of loss of tightening torque ii Rubbing of a broken support rod The test-rig is equivalent to a pie section of an RBC unit having four radial arms, together with four support rods onto which the plastic panels rested. The rig size was equivalent to a smaller RBC unit and was constructed of mild steel. A motor/gear box unit provided a rotational speed of 1.12rpm.
699
I
I
90
Media S»PP«rtr«d^ J-A
counter-balance weigths
2 metres Figures
View A-A
The Test-rig
Whilst it is standard practice to place the transducer on a non-rotating member of the machine, usually its bearing housing, the sensor was placed on the flat end of the stub shaft. To test the effectiveness of AR coefficients as a classification tool, the Neilson source test was applied to the test-rig. This was selected due to the simplicity and repeatability in generating AE signals and involved pressing lead, 0.5mm 2H, obliquely against the surface at predetermined positions until fracture, seefigure4. A total of six signatures were taken from each position. The computation of AR coefficients was applied on averaged signatures generated from the pre-determined positions. As the lead input is not exactly repeatable, due to variations in break angle, lead contact and break force, this technique was thought to provide an adequate test of robustness.
700
Shaft-FbsitionsPI andF2; Shaft damp-PositionsF3 andF4 Radial arms-FbdtionsFS to P12; SuHwrt rods-Positions P13 to P18
Figure 4
Pre-determined positions on test-rig
SIMULATION OF MECHANICAL DEFECTS For all mechanical fault simulations, AE signatures were recorded whilst the rig was rotating with a mass loading equivalent to actual operation. STRUCTURAL LOOSENESS Structural looseness was simulated by a reduction of the tightening torque securing the radial arms onto the shaft clamp plate, thereby creating relative movement between the mating parts. This was achieved by loosening the bolts by approximately a quarter of a revolution from the initial tightened position. Looseness of a single radial arm and all radial arms were undertaken respectively. BROKEN SUPPORT ROD RUBBING Cutting the cross-section of a support rod into a cup/cone shape, which is typical of a fatigue fracture, simulated this mechanical defect. The two halves were held together by means of a squirrel cage, as illustrated in figure 5. Placing weights on either side of the cracked assembly simulated loading of the unit. The cage assembly was put into one of the four outer support rod positions. During rotation the cut faces of the support rods were forced to rub against each other. Inspection of the cut faces after simulation showed evidence of wear, therefore, AE detected during simulation were attributed to the mechanism of friction and wear.
701
Figure 5
Squirrel cage
EXPERIMENTAL SET-UP AND APPARATUS A schematic diagram of the data acquisition system used throughout all experimental tests is shown in figure 6. A conmiercially available piezo-electric type sensor, with an operating frequency range between lOOKHz to lOOOKHz was used. The preamplifier used on all experimental tests was a PAC (Physical Acoustics Corporation) type 1220A, specifically designed for acoustic emission measurements. A dual channel, 8-bit analogue to digital converter, R2000 Rapid systems, was used for data acquisition. The electronic noise level on the ADC system, with 60dB amplification, had a peak voltage of 30mV. The sampling rate used was lOMHz. Acoustic emission sensor, lOOkHz to IMHz
COMPUTER Post processing
Figure 6
Pre-amplifler, 60 dB gain
Analogue-to-digital converter (ADC)
I Post-amplifier, 0 dB gain Power source for pre-amplifier
Data acquisition set-up
EXPERIMENTAL RESULTS NIELSON SOURCE TEST Each AE signature was represented by a 20^ order AR model. The calculated AR coefficients were passed through the cluster algorithm and the results are displayed in figure 7. Signatures from the support rods, radial arms and shaft/shaft clamps were successfully grouped. It is also interesting to note that, in some instances, within each
702
cluster group, signatures that shared similar transmission paths were clustered together, e.g., P6 and PIO or P5 and P9 (radial arms). Dendrogram of AR coefficients associated with AE signatures from the Nieison source test Legend P - Position on test-rig
h S o 5!
P8 P12 P7 P11 P5 P9 P6 PIO P13 P16 P15 P17 P14 P18 PI P3 P2 P4
Position on test-rig
Figure 7
Classification based on AR coefficients associated with Nieison source test
The successful classification of acoustic emission signatures into source groups with AR coefficients suggests that the shape of signatures associated with the same source were distinct. Figure 7 displays three signatures from different positions on the testrig. RESULTS OF SIMULATIONS ARISING FROM MECHANICAL FAULTS TO THE TEST-RIG All signatures were obtained with the receiving transducer placed on the stub shaft end. Acoustic emission signatures for all fault simulations were obtained as a result of rubbing/sliding between the radial arms and shaft clamps, and the cut faces of the support rods. The signatures shown in figure 9 were typical for a loose radial arm and rubbing of a support rod.
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AE signatures from three different positions on ttie test-rig Position PI
|||l»i4lHiMi|i>ll»iiii|w 2000
4000
6000
8000
10000
12000
14000
16000
2000
4000
6000
8000
10000
12000
14000
16000
Figure 8
Bursts generated at three different positions on the test-rig.
Typicai AE rul>bing signature of a loose frame 1
I
I
2000
4000
6000
r
I
I
I
8000
10000
12000
14000
2
0
-2 -3 0
Data points Typical AE rubl)ing signature of a broicen support rod
Figure 9
Typical A£ response to fault simulations.
704
16000
CLASSIFICATION OF SIMULATIONS ARISING FROM SEEDED MECHANICAL DEFECTS TO THE TEST-RIG Acoustic emission signatures used for classification consisted of ten support rod fault signatures and ten structural looseness signatures. The latter was a mixture of single and entire radial arm looseness. From the clustering of results, displayed in figure 10. Signatures of structural looseness (group B) were clearly distinguishable from support rod distress signatures (group A). Dendrogram of AR coefficients associated with AE signatures from fault simulations Legend S • Structural looseness R - Fractured rod
Cluster 'B'
Cluster 'A'
n5 R1 R2 R3 R4 R5 R6 R7 R8 R9 R10S1 S2 S3 S4 85 S6 S7 S8 S9 SIO
AE signature Figure 10
Classification of simulated mechanical faults on the test-rig
DISCUSSIONS The best position for placement of the transducer was on the stub shaft. During mechanical deficiency simulations on the test-rig the transducer cable had to be unwound after ten revolutions, this was because twisting of the cable triggered the acquisition system. Acoustic emissions generated on the test-rig were attributed to the relative movements between mating components. However, the rate of wear and relative movement between mating components could not be controlled, therefore, no relationship between severity and acoustic emission activity was established. Furthermore, during experimental tests for structural looseness, it was observed that further reductions in tightening torque resulted in a reduction of acoustic emission activity. Analysis of defect AE signatures showed a frequency range of between lOOKHz and 750KHz. Fundamental to the philosophy behind the condition monitoring system proposed is that at the time of data acquisition, relative movements between any loose components will probably remain fairly constant. These movements will generate AE*s of similar pattern from the position/s of such looseness. Although slight
705
variations in amplitude and duration of the distress signature can be expected, these variations were assumed insignificant to the classification algorithms employed. The classification of these distinct acoustic emissions offers the opportunity to diagnose the health of an RBC unit. Furthermore, a decision on the healtti of an RBC unit can be reached without prior base-line data or knowledge of any characteristics, as long as the transmission path to the stub shaft is guaranteed. CONCLUSIONS Investigations into the application of the acoustic emission technique to condition monitoring of low-speed rotating machines have been successful. Results of the seeded mechanical faults on the test-rig showed that acoustic emissions generated from rubbing of mating components were of complex pattern, indicative of their different transmission paths. Auto-regressive (AR) coefficients associated with each acoustic emission provided an efficient parameter for classification and diagnosis. This particular technique has its strength in the ability to represent the shape of an acoustic emission by a few AR coefficients.
REFERENCES Beattie, A. G. (1983). Acoustic emission, principles and instrumentation. Journal of Acoustic Emission. Vol. 2, no. 1 / 2pp 95-128. Berry, J. E., (1992). Required vibration analysis techniques and instrumentation on low speed machines (particularly 30 to 3(X) RPM machinery ), Technical Associates of Charlotte Inc. y Advanced Vibration Diagnostic and Reduction Techniques. Boness, R.J., McBride, S.L., and Sobczyk, M. (1990). Wear studies using acoustic emission techniques. Tribology International. Vol, 23, No. 5. pp 291-295. Canada, R.G., and Robinson, J.C, (1995). Vibration measurements on slow speed machinery. Predictive Maintenance Technology National Conference (P/PM Technology), Vol. 8, no. 6. pp 33-37, Indianapolis, Indiana. Everitt, B. (1974). Cluster analysis. Pubhshed on behalf of the Social Science Research Council by Heinemann Educational Books New York: Halsted Press. ISBN 0 435 82297 7. Green, A. P. (1955). Friction between unlubricated metals: a theoretical analysis of the junction model. In Proc. Of the Royal Society of London. A, Vol. 228, pp 191204. Haykin, S. (1984). Introduction to adaptive filters. Macmillan Publishing Company, New York. ISBN 0 - 02 - 949460 - 5. Kay, S.M, and Marple, S.L Jr. (1981). Spectrum analysis - A modem perspective. Proceedings of the IEEE. Vol. 69, No. 11. pp 1380-1419. Kuboyama, K., Development of Low Speed Bearing Diagnosis Technique, NKK Fukuyama Works, Fukuyama City, Hiroshima, Japan. Linard, S. and Ng, K.K. (1989). An investigation of Acoustic emission in sliding friction and wear of metals. Wear 130, pp 367-379. Makhoul, J. (1975). Linear prediction: A tutorial review. In Proc. Of the IEEE Vol. 63, No. 4. pp 561-580. Mathews, J. R. (1983). Acoustic emission, Gordon and Breach Science Publishers Inc., New York. ISSN 0730-7152
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Mba, D., Bannister, R.H., and Findlay, G.E. (1999). Condition monitoring of lowspeed rotating machinery using stress waves: Part I. Proceedings of the Instn Mech Eng., Vol. 213, Part E. pp 153-170. Oksa, G. and Bahna, J. (1995). Matched predictive filter enhancement recognition of bursts. Proceedings on the Symposium on Nuclear Reactor surveillance and diagnostics. Session 10, Avignon, France. Robinson, J.C, Canada, R.G., and Piety, R.G. (1996). Vibration Monitoring on Slow speed Machinery: New Methodologies covering Machineryfrom0.5 to 600rpm. Proc. 5th International Conference on Profitable Condition Monitoring - Fluids and Machinery Performance Monitoring, pp 169-182, brf Group Ltd., PubUcation 22, Harrogate, UK. Sarychev, G.A., and ShchaveUn, V.M. (1991). Acoustic emission method for research and control of friction pairs. Tribology International. Vol. 24, No. 1. pp 11-16. Weavser, M., (1996). Fundamentals of Acoustic Emission, In Proc. 22nd European Conference on Acoustic Emission Testing, EWGAE, pp 1-11, The Robert Gordon University Press, Aberdeen, UK.
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Condition Monitoring and Diagnostic Engineering Management Andrew G. Starr and Raj B.K.N. Rao (Editors) © 2001 Elsevier Science Ltd. Allrightsreserved.
DETECTING AND DIAGNOSING FAULTS IN VARIABLE SPEED MACHINES C.K. Mechefske^ and L. Liu^ ^Department of Mechanical Engineering, Queen's University Kingston, Ontario, Canada, K7L 3N6 ^Department of Mechanical & Materials Engineering, University of Western Ontario London, Ontario, Canada, N6A 5B9
ABSTRACT In this paper simulated vibration signals are used to explore the behaviour of the auto-regressive (AR) model based spectral estimation method in regard to optimum model order and the signal length when analyzing vibration signals from varying speed machines. Conditions representing various speed ranges, different rates of change and different signal-to-noise ratios in the vibration signals are considered. A comparison of AR model based results with FFT based spectra is discussed. The method is also applied to vibration signals recorded from a rolling element bearing with and without an outer race fault. The results indicate that when applying the AR model based method to varying speed machines both the range of speed and the rate of change of the speed are important. Criteria and guidelines are recommended for the successful application of the AR model based method. This investigation shows that the AR model based spectral estimation procedure can generate clear and useful spectra for fault detection and diagnosis from relatively short vibration signals taken from machines operating under varying speeds. KEYWORDS Vibration signal analysis, condition monitoring and diagnostics, varying speed machines, autoregressive model based spectral estimation. INTRODUCTION Vibration based machine condition monitoring (MCM) is used successfully for fault detection, fault diagnosis and machinery integrity prognosis. It is also becoming popular in efforts to optimize product quality control. Fault detection using vibration signals involves detecting and quantifying changes in the vibration signal that correspond to deterioration of the machinery. While the vibration signal in the time domain contains a weath of information in regard to the machinery condition, it is often too complex to allow direct detailed analysis. Therefore, the time-history of the signal is usually processed using an FFT algorithm and presented in the frequency domain as a frequency spectrum. The spectrum can then be inspected for clues that may indicate the existence of a particular type of fault in the
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machinery. There are two main approaches to frequency domain analysis; the visual inspection of the spectra generated from the recorded time series, and the trending of parameters calculated based on the frequency spectra. These two methods are often combined in order to maximize confidence when identifying useful information in the spectra. In this work only the first approach (visual inspection of the spectra) is used. As indicated above, the method to be used in this research is based on frequency analysis of the vibration signal. Although the FFT is the preferred technique for doing this in machinery condition monitoring and diagnostics applications, this method may exhibit poor diagnostic capability in some cases, such as when signals contain non-steady state phenomena. For example, variable speed machines operate over a range of speeds rather than at one fixed speed. This results in a change in the defect related vibration frequencies that are generated during operation. The vibration frequencies of interest for fault detection and diagnosis will then shift up and down in frequency as the speed changes, making machinery fault detection and diagnosis difficult. Most of the signal processing techniques used to analyze vibrations in the frequency domain are based on the assumption that the signal is sampled during steady state machinery operating conditions. FFT based frequency spectral estimation has a long history of successful application in the field of machine fault detection and diagnosis. Most of these applications involve high to moderate speed rotating machines where large amounts of vibration data can be sampled quickly. Previous work [1-4] has shown that when the FFT method is used to analyze short lengths of vibration data, the method yields relatively poor spectral results. The reason for this is that FFT based spectral estimates require lengthy data sets. Collecting large amounts of vibration data from high or moderate speed machines in a short time is not a problem because a high sampling rate can be used. This data represents many repeated operating cycles. However, in some situations only short length vibration signals are available. Such is the case for varying speed machines when data is sampled during a relatively short steady state period or a short signal is used for analysis in order to limit to range of operating states contained in the signal. An alternative method is needed in these situations. The auto-regressive (AR) model based spectral estimation procedure is a tool recently adopted for processing short duration machinery vibration signals [1,4]. The improved spectral resolution available when using the auto-regressive model based technique is one key improvement. SPECTRAL ESTIMATION FFT based spectral estimation FFT based fi-equency spectral estimation is one of the most popular methods for dynamic signal analysis because it allows the calculation of the discrete Fourier transform of a time series with great speed. In many cases real time analyzers can provide a spectral estimate as quickly as the data is sampled. However, regardless of its popularity the method of using the FFT to obtain a power spectral estimate has limitations. For example, the procedure generates acceptable results only when a large amount of data is available for analysis. When it is used to analyze short lengths of vibration data the FFT based method yields poor results. Because only short lengths of vibration signals are available in some situations an alternative method of generating useful frequency spectra is needed. Auto-regressive model based spectral estimation Parametric model based spectral estimation techniques have been used with success to generate frequency spectra from vibration signals originating from low speed rolling element bearings [1,4]. These techniques are known to allow detailed frequency spectra to be generated using short length data sets [5]. Parametric model based spectral estimation is a three-step procedure. The first step is to select
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a model type. The second step is to estimate the parameters of the model using the available data. The third step is to obtain the spectral estimate using the model parameters. For this work the auto-regressive (AR) model is selected. In this method, the vibration signal is used to define an AR model of the signal generating process. The AR model based frequency spectral estimation method is based on a model of the sample data where the present value of a time series (vibration signal) is expressed as a weighted sum of the past values plus a noise term [2,3]. In equation form this is X(t) = ai X (t-1) + a2 X (t-2) + as x (t-3) + ... + n (t) (1) where; X(t) is the present data value, X(t-l),X(t-2),... are the previous data values, ai, a2, a3,... are the model parameters, and n(t) is a noise term. An AR model consists of a group of parameters which multiply a set of previous data values to arrive at a prediction of a current data value. The number of parameters in a model is referred to as the model order. The method for calculating the model parameters in this work was chosen as the forward and backward least square approach. To obtain optimum estimates of the AR parameters the average of the estimated forward and backward prediction error powers is minimized. Details fo the AR model based spectral estimation procedure are available in previous publications [1-4] and will not be repeated here. The following equation is used to calculate the power spectral estimate,
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where; /^XO is the AR power spectral density estimation, a^ are the AR model coefficients, p is the model order and cr^ is the variance. NUMERICAL SIMULATION OF VARYING SPEED CONDITIONS For this study, a signal in a sinusoidal wave format was generated with frequency f varying linearly between lOHz and 14Hz in zero mean random noise. The average frequency for this signal is 12Hz, and the frequency variation is ±17% about this average frequency. Mathematically, the signal can be expressed in the following equation, 7 = ^ sin(2;^0 + B random (/) (3) Where A (= 1) is the amplitude of the signal, f is the frequency of the signal, which varies linearly with time between lOHz and 14Hz, B (= 1.5) is the amplitude of the random noise, random(t) is the random noise frmction, and t is the time duration, which was selected as 5 seconds in this example. While the results for only this test are presented here, many other tests at different speeds, with different ranges of speed variation and with different speed variation rates were also conducted. The results are all similar and support the conclusions reached later in this paper For comparison, the frequency spectra are generated by both the AR model based method and the FFT based method for the same signal. For the AR spectrum, the model order is selected as 60. Figure 1 shows the time history of the frmction generated from equation (3). Figure 2 shows the corresponding FFT spectrum and Figure 3 shows the AR model spectrum for the same signal. The AR model spectrum of Figure 3 shows a dominant peak response at about 12Hz. The 12Hz frequency is the average frequency between the minimum (lOHz) and the maximum (14Hz) frequencies of the signal. The FFT spectra in Figure 2 shows a spectrum that is generally noisy and the
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response is also widely smeared around the average frequency of 12Hz. The clear response peak at the characteristic frequency in the AR spectrum is a sign of its superior ability at extracting usefril information from short length varying frequency vibration signals. Appropriate selection of the model order allows for a spectrum with sufficient resolution to show the responses of interest without showing extraneous or phantom information.
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EXPERIMENTAL APPARATUS AND METHOD Experimental Apparatus and Instrumentation A diagram of the experimental set-up is shown in Figure 4. The apparatus consists of an electric motor, a speed controller and a bearing module mounted on a riser. The bearing is beh driven by the motor. There are two rolling element bearing modules. One has a defect in the outer race and the other has no faults of any kind. Accelerometers were mounted on the bearing houseing at the fault location. The speed of the motor was varied by adjusting the controller. A stroboscope was used to measure the bearing rotational speed. A PCB080A30 accelerometer was used to measure the vibration signals. All signals were stored on DAT tapes. The tapes were played back and selected sample data was stored on computer disks using a HP35670 dynamic signal analyzer. The data was then analysed using programs written in MATLAB.
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Figure 4: The bearing test rig. Test Conditions The test data used in this analysis are Usted in Table 1. The test numbers, the corresponding range of speed variation and the expected defect characteristicfrequenciesare all given in the table. The rate of change of the speed, (how fast the speed was changing during each test) is also given. The two main groups of tests conducted used a variable speed bearing module with a outer race fault. Seven tests were carried out for each group. The test conditions for the first group (from Trac6028 to Trac6088) correspond to the second group (from Trac7028 to Trac7088) respectively Table 1. Test conditions Group I: Speed variation ±40 rpm, speed varying rate 20 rpm per second (with outer race fault). Speed Fault Ave. Range I.D.# Freq. Freq. (rpm) (Hz) 260 15.56 Trac6028 300 19 17.95 340 20.34 380 22.74 Trac6038 27 420 25.13 460 27.53 500 29.92 31 Trac6048 540 32.31 580 34.71 680 40.69 Trac6058 43 720 43.08 760 45.48 820 49.07 Trac6068 860 51 51.46 900 53.85 57.44 960 Trac6078 60 1000 59.84 1040 62.23 64.62 1080 Trac6088 67 1120 67.02 1160 69.41
Group II: Speed variation ±40 rpm, speed varying rate 40 rpm per second (with outer race fault). Ave. Fauh Speed Freq. Freq. I.D.# Range (Hz) (rpm) 15.56 260 19 Trac7028 17.95 300 20.34 340 22.74 380 26 25.13 Trac7038 420 27.53 460 29.92 500 32 32.31 Trac7048 540 34.71 580 40.69 680 43 Trac7058 43.08 720 45.48 760 49.07 820 51 51.46 Trac7068 860 53.85 900 57.44 960 60 Trac7078 59.84 1000 62.23 1040 64.62 1080 68 67.02 Trac7088 1120 69.41 1160
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Group IV: Speed variation ±200 rpm, speed varying rate >200 rpm per second (with fault). Fault Speed Ave. Freq. Freq. Range LD.# (Hz) (rpm) 31.12 520 Trac8401 51 43.08 720 55.05 920
Group III: Speed variation ±40 rpm, speed varying rate 20 rpm per second (without fault). Speed Ave. Fault I.D. # Range (rpm) Freq. Freq. (Hz) 680 N/A Trac3058 No 720 N/A 760 N/A
The only difference between the two groups is the rate of speed variation. Tests with the name of Trac6~ have a speed varying rate of 20 rpm per second, and tests with the name Trac7~ have a speed varying rate of 40 rpm per second. The sampling rate is the same for all tests at 512Hz, and the sampling period is 8 seconds. For comparison, a few more tests were conducted at test conditions corresponding to test Trac6058 and test Trac7058. Test Trac3058 used a bearing which was in good condition with no faults of any kind. Test Trac8401 used a large motor speed varying range of ±200 rpm and a high motor speed varying rate of more than 200 rpm per second. RESULTS AND DISCUSSION All results fall in the expected frequency range and in most cases they are very close to the average characteristic defect frequencies corresponding to the base motor speeds. For most of the analysis the AR model order was selected at 20. Only for cases with very low motor speeds was the AR model order seleted at 60. With low motor speed the signal is both relatively weak and slow-changing. A higher resolution is needed to pick up the useful information. A group of sample plots from the analysis are given in Figures 5, 6 and 7 for test Trac6058 with a base rotational speed 720 rpm in a speed range of ±40 rpm and a speed changing rate about 20 rpm per second. The estimated outer race fault characteristic frequencies at this speed range are between 40.7Hz and 45.5Hz with the mean value at about 43.1Hz. Figure 5 is the time history of the signal. Figure 6 shows the corresponding FFT frequency spectrum and Figure 7 shows the corresponding AR frequency spectrum for the same signal. Figure 7 shows dominant peaks at about 43Hz (the outer race fault characteristic frequency) and its first harmonic of 86Hz. The FFT spectrum of Figure 6 also shows peaks around these frequencies, but the spectrum is clearly smeared.
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For comparison. Figure 8 shows the AR spectrum for the same test conditions without any fault. Clearly, the 2 dominant peaks at the outer race fault defect characteristic frequency and its first harmonic are not present. For test Trac6058, the 8 seconds of data collected represents 96 machine cycles. Regulary there are situations where only data representing a few machine cycles are available. The test data collected was shortened to smaller data lengths to simulate the above situation and see the effect of shorter data lengths on the AR spectra. Figure 9 shows the AR spectra for Trac6058 at the data lengths of 4 seconds (48 cycles), 2 seconds (24 cycles) and 1 second (12 cycles). The corresponding first peaks are located between 42Hz and 44Hz, within the expected frequency range listed in Table 1.
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At the low speed variation rate used in this test (20 rpm per second) the 1 second data sample represents only part of the frequency variation. Depending on where the data used to calculate the AR spectrum was picked from the original 8 second sample, it could represent any part of the fi-equency range under consideration. This explains the difference in the peak locationfiromthe 1 second data spectrum compared to the spectra calculatedfiromthe larger data sets. Figures 10 and 11 show the FFT and AR spectra for test Trac7058, which has the same test condition as test Trac6058 except with a higher speed varying rate of 40 rpm per second. The AR spectrum in Figure 11 shows dominant peaks at aroundfi*equency43Hz and its first harmonic, 86Hz. In contrast, Figure 10 shows a smeared FFT frequency spectrum around the characteristic frequencies. Because of
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the higher frequency varying rate compared to test Trac6058, the FFT spectrum indicates a somewhat wider smeared frequency range relative to Figure 6.
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Figure 11: AR spectrum for test Trac7058. CONCLUSIONS In summary, the AR model based spectral estimation procedure shows potential for use m the extraction of reliable information for use in fault detection and diagnosis from vibration signals in varying speed machines. The AR model order should be selected based on the speed varying rate. For high rates of change in speed a model order between 10 and 30 is needed. For slower rates of change in speed a higher model order (close to 60) is recommended. When applying the AR method to varying speed machines not only is the range of speed variation important, but the rate of change of the speed is equally important. Limits on both the speed variation range and speed variation rate are needed for the AR method to be successful. In the current investigation signal lengths between 1 second and 8 seconds were used for spectral estimation. Further investigation is needed to see how the data length requirement relates to tiie limits on the range of speed variation and the rate of speed variation. ACKNOWLEDGEMENTS This work was made possible throught a grant from the Natural Sciences and Engineering Research Council of Canada. REFERENCES 1. Mechefske C.K. and J. Mathew, "Fault Detection and Diagnosis in Low Speed Rolling Element Bearings: Preliminary Investigations", Mechanical Systems and Signal Processing, Vol. 7, No. 1, pl-12, 1993. 2. Kay, SM and S.L. Marple, "Spectrum Analysis: A modem Perspective", Proceddings of the IEEE, Vol. 69, No. I l , p l 3 8 0 - 1 4 1 9 , Nov. 1981. 3. Akaike, H, "Fitting Autoregressive Models for Prediction", Annuals of the Institute of Statistics and Mathematics, Vol. 21, p243 - 247,1969. 4. Mechefske, CK and J. Mathew, "Fault Detection and Diagnosis in Low Speed Rolling Element Bearing, Part 1: The Use of Parametric Spectra", Mechanical Systems and Signal Processing, Vol. 6,No.4,p297-307,1992. 5. Mechefske, C.K., "Parametric Spectral Estimation for use in Machine Condition Monitoring, Part I: The Optimum Vibration Signal Length", British Journal ofNon Destructive Testing, Vol. 35, Issue 9, p503-507, September 1993.
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Condition Monitoring and Diagnostic Engineering Management Andrew G. Starr and Raj B.K.N. Rao (Editors) © 2001 Elsevier Science Ltd. All rights reserved.
ARMADA^^^ . ADVANCED ROTATING MACHINES DIAGNOSTICS ANALYSIS TOOL FOR ADDED SERVICE PRODUCTIVITY J. Toukonen^ M. Orkisz^, M. Wnek^, K. Saarinen^ Z. Korendo^ ^ ABB Corporate Research, Virtaviiva 16 D, FIN-65101 Vaasa, Finland email: [email protected] ^ ABB Sp. z O.O., Corporate Research, Starowislna 13 A, 31-038 Krakow, Poland email: [email protected] http://www.abb.com/research
ABSTRACT The authors describe a condition-monitoring concept that is based on the integration of different techniques; a modular solution based on commercial software and add-ons like automatic analysis and reporting. The system supports rotating machines: electrical motors (9 different types) and centrifugal pumps. Modules exist also for frequency converters, electrical mains and installations. KEYWORDS bearing, automated analysis, acceleration, fuzzy, motor, availability, condition monitoring, vibration, electrical measurements, pump, drive MOTIVATION Observing the maintenance routines in most companies, the reality we face is such that one single machine is maintained by different teams, generally with completely independent approaches. Another fact is that each of these teams counts on a variety of tools and techniques that are used on a daily basis to perform their activities. A good example is an electric motor: one finds the electrical team performing tasks such as current and voltage measurements, while the motor is under operation conditions; and dielectric tests in the windings to evaluate the insulation condition, while the motor is stopped or even during an undergoing overhaul in a workshop. On the other side, one finds the mechanical team performing tasks that can vary from simple visual inspection to spectral vibration analysis. Generally, each and every data retrieved from these activities are stored in different places, sometimes using different criteria to define the condition of the machine. If adding to these usual techniques other more recently used such as infrared thermography and lubricant condition monitoring (contaminant analysis) the situation becomes even more complicated.
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There are often situations when the knowledge about the monitored target is scanty. Then we need tools that can help us even in those situations. ARMADA^'^^ CONCEPT The ARMADA^"^^ concept is the integration of different techniques, a modular solution based on the commercial condition monitoring software (Entek Odyssey®) and existing dataloggers (like SKF CMVAxx® and Entek dataPAC®). See Figure 1 for the data structure that allows easy additions of modules containing intelligence exploiting the data saved in the database.
Database
^^BSffPM Figure 1: ARMADA^'^^ data management structure LAYER "Off-the-shelf products or existing hardware": This layer should consist of well-positioned products - an open database platform in order to enable external access via standard software tools and to eliminate update risk. Besides, the hardware platform can be updated or exchanged as well (for instance memory volume, new sensing devices etc.) LAYER "User interface platform": It handles the user interface and defines the database structure. We decided to make a frame agreement with an experienced software provider also in order to reduce the maintenance risk but mainly to speed up the implementation process. One of the preconditions was that as little as possible customised developments would be ordered. We should cope with an off-shelf product on this level as well. LAYER "ABB know-how": This layer defines exact response to ABB needs. At this layer we placed our A R M A D A ' ^ ^ ^ functions (Automated Rotating MAchines Diagnostics and Analysis). To this layer we have developed modules that utilise data mining techniques to bring intelligence and ease of use to the system. These modules are: - Fault classification - Automated reporting - Common data structure for sharing the data between ABB sites all over the world
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Targets supported by ARMADA^^^ Today ARMADA^'^^'s features support the following rotating machines: electrical motors (9 different types) and centrifugal pumps. Modules exist also for frequency converters, electrical mains and installations. There is an additional base of classically supported machines within the standard condition monitoring techniques provided by the Entek Odyssey®. ARMADA^'^^ MODULES These modules have been developed within ABB to create additional functions above "standard" condition monitoring system like Entek Odyssey® The first goal for ARM AD A^'*^^ is to be capable to support a variety of techniques, see Figure 2. On top of the commercial solution we developed and embedded a comprehensive range of additional functions, especially concerning electric motors and the systems associated to them. Visual inspection, operational parameters such as currents and temperatures, drive system analysis, vibration and dielectric measurements are in the same database, allowing for a complete diagnostic of the machine. We also take advantage of integration of other techniques that are already provided by the database software, such as thermal pictures and oil analysis. Basically all analysis processed by our procedures are returned to the user by means of a "traffic light" code, internal function of Odyssey software, meaning that no burdensome training is required to perform the tests nor understanding the results.
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Figure 2: ARMADA^"^^ integrates several Condition Monitoring techniques in the same database Common data structure By using the common measurement templates, one also creates a database following the company standard, which allows us to share comparable data, experience and expertise around the globe. The same training is applicable to all users.
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Fault classification Today the following fault classification modules exist for ARMADA^'^^: - Mechanical analysis of electrical motors (AC & DC) and centrifugal pumps - Broken rotor bar detection for AC motors - Off-line insulation analysis for motors - In-use electrical analysis for drive systems Fault classification foundations A good foundation for any classifier is reliable, informative and accurate input data set that describes the characteristics of the object to be classified. Current and vibration measurements result vectors with thousands of numerical values for one motor. Thence it is clear that before the fault classification can be approach at all, the essential information must be projected into lower dimensional space. Through them waveforms are presented more economically and at the same time properties important to correct classification can be enhanced. Sometimes non-linear pre-treatment of the signals before projection may be advantageous. The forces that cause vibration in the rotating machine most often change direction or amplitude in accordance with the rotation speed. Thus vibration measurement of a machine with mechanical fault yield periodical signals, whose shape depend on machine structure and fault type. These periodical signals x'\n\ , j e {a,h,v,nd) can be effectively represented by orthogonal expansion of sinusoidal signals (i.e. by Fourier series) M
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where f.A^^, ^^ are rotation frequency, amplitude and phase, respectively, n is the number of the sample and superscript set {a,h,v,nd} means that measurement was made in axial, horizontal, vertical and non-driven end radial direction, respectively. Traditionally Fourier-transform based method (periodogram) has been applied to sinusoidal parameter estimation mainly due to its low computational complexity and lack of a priori assumptions on the signal. However, the Fourier method suffer from low resolution and high variance. The low resolution implies the use of long measurements. To improve the statistical properties of the periodogram an averaged periodograms can be used, but then even longer measurements are needed. The accuracy of the Fourier-methods is limited by the fact that during the long measurement period the rotating frequency can change significantly. To solve the problem of low resolution, high-resolution parametric methods have been developed, Kay (1988) & Marple (1987). of which the maximumlikelihood method is considered to be optional. We have developed a maximum-likelihood timedomain method, Saarinen & Orkisz (2001), to estimate accurately the rotating frequency from short measurement signal. After the rotating frequency is estimated, the amplitudes .4„' and phases ^,/, can be estimated easily by least square method , Kay (1988). The important characteristics with respect to classification of the faults are the mutual ratios of the velocity amplitudes Fj =Ai/(2mnf). (2) Before classification we form a so-called feature vector by applying non-linear transformation and normalisation to the velocity amplitudes.
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The classification consists of statistical orthogonal expansion (Principal Component Analysis , Jolliffe (1986)) followed by cluster analysis, Saarinen & Korendo (2001). The obvious aim of the before mentioned data pre-treatment is to fmd out mapping which maximises the between-cluster variation and minimises the within-cluster variation. Mechanical status evaluation Vibration based condition assessment is illustrated in Figure 3.
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Figure 3: Vibration based condition assessment in ARMADA^ '^''' The bearing condition evaluation is based on a time domain shock pulse analysis, Toukonen & Makkonen(1999). Electrical status evaluation In-use electrical status evaluation consists of side band identification to find out broken rotor bars (the comparison of the slip frequency sidebands in the current spectra to the line frequency related peak in the same spectra) and separate measurements (voltage, current) to check things like common mode currents, total harmonics distortion and bearing currents. Measurements are done according to ARMADA^"^^ templates and resuhs are provided automatically to the end user. Insulation status evaluation This test was developed by Pinto (2000) and it is based on the work of Goffaux (1978). The analysis is targeted to detect the presence of contamination in electrical machines by measuring the "Insulation resistance" and the "Polarization Index" of the machine. Measurements are commonly done to the machine in off-line state with a hand-driven or a motor-driven DC generator or a regulated electronic DC power source, commonly known as a "megger". Measurement values are then fitted to model parameters and then an interpretation is done with fuzzy rules. Output to the ARMADA^^^ user is: measurement quality - indicating the quality of measurement, general winding status, contamination (conductive (carbon, carbonised oil, soot), non721
conductive (oil), moisture), ageing, depolymerization, defects of insulation between corona shield and core, lack of cure, erosion of corona protection shield, ingress of contaminant into the winding, cracks at slot ends or looseness of coils, Paithankar (2000). Reporting ARMADA^*^^ has an "1-button" reporting where it is possible to use the pre-defined types of automatic reports, see Figure 4. If required, customised reports can be created and added to the existing library, and these new reports can be shared within the organisation. Reporting is a proprietary development of ABB based on the Odyssey database and standardised reports - reflects directly the company requirements in the field.
Figure 4: 1-button reporting (operation diagram)
FIELD TEST RESULTS Before letting the ARMADA^'^^ system to the ABB wide distribution we made testing. Controlled lab testing is the important issue when checking the functionality of the algorithms. In real life there are always enough interferences (mechanical & electrical) present that the testing of the functionality of any condition monitoring system must be done at the real environments. Totally we have evaluated vibration and bearings status for more than 150 motors (from 15 to 630 kW) and 90 pumps. The most of the tested equipment were on the factory floor (pulp & paper mills, steel plants, and power plants). We tried to validate results as well as possible either with the repair shop results or with the other condition monitoring systems. One of the main resuhs for vibration and bearings was that no false alarms were encountered. If ARMADA^^^ informed about some problem there always existed one, even if not exactly the one that ARMADA^"^^ pinpointed. If there was any fault that ARMADA^ "^"^ was not able to notice we cannot be 100% sure. But those faults were also impossible to llnd with any other tools available. The insulation tests have been done before the concept of ARMADA^*^^ was invented. Hundreds of motors have been measured during the last ten years.
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Our goal was to have three quarters of the fault cases classified automatically and according to results it seems we have reached it. The speed measurement from the vibration signal has proved to be reliable and accurate when the initial information provided has been reasonable (given nominal speed within ± 20% of the operating speed, number of poles of the motor). The error of the speed compared to the value of the tachometer is below one per mille. But there are still some problems in ARMADA'"'^'^ to notice when the initial information is misguided. INTEGRATION TO ABB'S WORLD WIDE MAINTENANCE BUSINESS ARMADA^"^^ is the result of intense co-operation between ABB Corporate Research Centers in Poland and Finland, ABB Lenzohm Service India and several ABB Service companies around the world. ABB's service business has created a structure to maximise the added value of the ARMADA ^ \ The common measurement templates enable the possibility of data and information sharing and immediate analysing help via Lotus Notes database, see Figure 5.
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EXPERIENCES FROM THE FACTORY FLOORS There are now over twenty installations of ARMADA^^^ in several ABB locations. The first installations were done a year ago. One third of the installations have been in the "real use". The response to integrated and modular concept of ARMADA^^^ has been good. Traditionally there have been too many different tools. Automatic report generation that produces standard Microsoft Word files is valued high. The main source of dissatisfaction has been the amount of data needed to be stored in the data collector. ARMADA^^^ requires saved time domain data for automatic analysis (instead of spectrum data) and that demands a lot of memory in data collectors. Also transferring that data from the collector takes longer than in typical low-resolution spectrum based measurements. And finally there is still one shortcoming in ARMADA^"^^: Utilising it effectively requires training. But we feel the need is less than with other competitive systems.
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CONCLUSION It is possible to build condition-monitoring tools that help the end user to concentrate on the cases that need special attention. The fast classification of machines to sound ones and "easy cases" frees human capacity to where it is really needed. ACKNOWLEDGEMENTS Our project team is thankful to many organisations and individuals that have made these developments possible. We wish to thank our customers: Mr. Amaldo Spiller from ABB Maintenance-TV and all the people working in the area of service within ABB. All trademarks are properties of their owners.
REFERENCES Goffaux R. (1978). On The Nature Of Dielectric Loss In High Voltage Insulation. IEEE Trans. Elect. Insul. Ei: 13, 8-11 Jolliffe I.T. (1986). Principal Component Analysis, Springer Verlag, USA Kay S.M. (1988). Modern Spectral Estimation: Theory and Application, Prentice-Hall, UK Marple Jr. S.J. (1987), Digital Spectral Analysis with Applications, Prentice-Hall, UK Paithankar A. and Pinto C. (2000). Fractal Analysis, A Novel Approach for Residual Life Assessment of Electrical Rotating Machines. Comadem 2000 proceedings Pinto C , Paithankar A. and Wnek M. (2000). Diagnosis of insulation health of rotating machines. CWIEME'2000 conference proceedings Saarinen K. and Orkisz M. (2001). Finnish Patent 20000646 Saarinen K. and Korendo Z. (2001). Polish Patent 338286 Toukonen J. and Makkonen A. (1999). Bearing Analyzer For Condition Monitoring Of Rolling Element Bearings Using Local Intelligence And Comprehensible User Interface. Comadem 1999 proceedings, Coxmoor, Oxford, UK
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Condition Monitoring and Diagnostic Engineering Management Andrew G. Starr and Raj B.K.N. Rao (Editors) © 2001 Elsevier Science Ltd. All rights reserved.
CONDITION MONITORING AND DIAGNOSIS OF ROTATING MACHINERY BY ORTHOGONAL EXPANSION OF VIBRATION SIGNAL T. Toyota', T. Niho\ P. Chen' and H. Komura^ 'Kyushu Institute of Technology, Kawazu 680-4, lizuka City, Fukuoka 820 -8502, Japan ^RION Company, Higashi motomachi 3-20-41,Kokubunji City, Tokyo 185-8533, Japan
ABSTRACT Here we present the new robust condition monitoring and diagnosis method based on the statistical hypothesis on the vibration characteristics of the rotating machines in good condition. The hypothesis is that if the machine is in good condition, its probability density function of the vibration signal follows the normal distribution in time domain. This method can lead to the robust failure diagnosis without any prior knowledge concerning to the vibration characteristics corresponding to specific failure to be detected. KEYWORDS Condition monitoring, Condition diagnostics. Vibration signal. Orthogonal expansion. Normalized power density function INTRODUCTION One of the widely used methods for failure detection and diagnosis of the rotating machines is one by vibration analysis. In this method, failure detection and diagnostic procedure can be roughly divided into four steps, namely; vibration measurement, signal processing, feature extraction, and feature recognition. So if we have little knowledge for vibration feature corresponding to the specific failures to be detected, it become difficult for us to detect and discriminate the malfunction or failure in the rotating machines. To solve this difficult problem, we set the one hypothesis on vibration characteristics of rotating machines in good condition. The hypothesis is that if the machine is in good condition, its density function of vibration signal follows the normal distribution in time domain. In this report, we propose the time domain analysis based on the hypothesis that if machine is in healthy condition, the density function of the vibration signal is distributed normally. 725
SURVEY OF THE CONVENTIONAL TECHNIQUES In condition monitoring and diagnosis by vibration analysis, as well known, time and frequency domain analysis techniques has been used. In the time domain analysis, we use the probability density function p{x) of vibration waveform to extract the features. Useful feature parameters in time domain analysis is skewness P^ and kurtosis 132, witch is defined by Eqns. 1 and 2. ^i-f_^{x-ixfp(x)dx
(1)
I32=j^jx-fifp(x)dx
(2)
Above two feature parameters, namely the skewness fi^ and the kurtosis ^^2 in time domain are very useful to extract the features of vibration signal for condition diagnosis. Typical research examples of the application of kurtosis P2 ^^ ^^e deterioration trend control in rolling element bearing. BASIC CONCEPT OF PROPOSED METHOD They show that kurtosis p^ ^^ ^^^Y indicative parameter of bearing's deterioration and its value is close to 3 if bearing is in good condition, and kurtosis P2 increase according to progress in deterioration. This fact suggest the proposed hypothesis is true, namely bearing is in good condition, its density function of vibration signal show that kurtosis value isP2 =3 and its density function is distributed normally. R.M. Stewart with Southampton University in U.K insist on that if bearing is in good condition, kurtosis value/32 show 3, and according to the progress of deterioration of bearing, P2 value increase and deviated from 3. This mean statistically that if bearing is in healthy condition, density function is normally distributed, and if condition become worse, its density function deviated from normal distribution. Author strongly insist that this hypothesis is true not only for vibration signal of bearing but also vibration signal of another rotating machines except the special structured rotating machines just as shown later section. Typical example for the first hypothesis are shown in Figure 1. Figure 1 (a) show the standardized vibration waveform of rolling element bearing in good condition. As you see clearly its density function is very close to normal density function. Figure 1 (b) show the standardized vibration waveform of rolling element bearing with local defects in outer-race. As you see clearly its density function is getting sharp and deviate largely from normal one. Figure 1 (c) show the standardized vibration waveform of rotating machines with unbalance. As you see clearly its wave form become sinusoidal and the density function is having two peak at both side. Basic concept of the proposed method is that measured pdf (probability density function) can be expanded into normal distribution component (good component) and residual components (bad component) by Gram-Charlier orthogonal expansion theory. By the Gram-Charlier orthogonal expansion theory (denoted by GC expansion in short), arbitrary pdf f{x) can be expanded into normal component q9(x)and its residual components. 726
0
0.2 pdf
Time
0.4
(a) Good condition
rft H
•o O.OS
H
ff
S -5.0 0 0.5 1 1.5 2 2.5 pdf
Time
(b) Local defect in outer-race t r
E
: :
MlWlWii^
CO
^
Measured pdf Normal pdf
O.OJ
ffi -5.0 0
0.2
Time
0.4 pdf
0.6
(c) Unbalanced condition Figure 1: Vibration waveforms and its density functions with different condition
f{x)==q{x){l+c^H^{x)+C2H2(x)+.. .+c„if„(x)+..} (3)
Here, q)(x) is density function of standardized normal distribution
cp(x)--
1
(4)
and Hfj (x) is the Hermite function of n-th degree. Expansion coefficient c^ can be shown by Hermite function //„ (x) c,=-f
f{x)H,Xx)dx
(5)
If pdf is standardized, C(, = l,c, ^c^ ^ 0, so Eqn. 6 reduce to f{x) = (p{x) + cp{x){c^H^{x) + c,H, {x) (6) = (p(x) + r{x)
727
Here r{x) is the deviation component from the normal distribution component
(7)
This mean that if the machine is in healthy condition then (p{x) = \,r{x)=0 unhelthy, it become r{x)^0 .
and if the machine get
CALCULATION METHOD OF THE EXPANSION COEFFICIENTS As well known, Hermite polynomial function have the orthogonalitiy. [y{x)H,{x)H^{x)dx
=
0
(8)
a^j)
so expansion coefficients c,, Cj (/ ^ j) show the unrelated components of vibration signal and its strength depend on different failure in the machinery. For vibration signal density function/(jc), let the mean //, = 3c ,and the variance a', r-th moments of about of the mean /u,,
A el=^i^h-'^) c,=-M, C4=^(<"4-6>«2+3)
120
}
(/^i-lO/i,)
(9)
^6 = —(>"6-15/i4+45/i2-15)
1 ( / i , - 2 1 / ^ 3 + IO5//3) 5040 1 (M, - 28/i, + 210/i, - 420/^, +105)
40320
J
As seen in Eqn. 9, coefficients C3is equivalent to the skewness >^, in Eqn. 1 and coefficients c^ is equivalent to the kurtosis p^ ^^ E^^- 2. For density function of vibration / ( x ) , let the mean //, = 3c ,and the variance cr^, r-th moments of about of the mean /u^ Advantages of GC expansion coefficients compared with conventional one are summarized bellow. 1) Measured pdf can be separated into normal components ^(x), that mean good component, and residual components r(x) ,that mean bad components, and values of coefficients c, is adjusted by /! automatically just seen in Eqn. 9. 2) Meaning of GC expansion coefficients c, are clear by Hermite polynomial, and by looking the value of c,, we can estimate the type of failures. 728
3) GC coefficients c. are orthogonal and its each coefficient is corresponding to different information of vibration signal, no overlapping in information.
ALGORITHM FOR THE FAILURE DETECTION AND IDENTIHCATION We can establish the failure detection and identification algorithm using GC expansion coefficients. Next algorithm can be used for failure detection and identification. for i s 3 for failure detection if V(Ci=0) then good condition if
3(Ci^0)
then bad condition
for failure identification if
Cj ^ 0
thencj type failure
if
Cj ?i 0 a n d Cj ^
0 then Cjj type failure
CRITERION FOR THE GC COEFFICIENTS AND DEFINITION OF THE SEVERITY FACTOR From the orthoganality of Hermite polymomial .- {f{x)-
^_^cj
(10)
replacing variable C/ = c / li\ and stop the expansion at n-th term, we can introduce severity factor T^ as bellow
'--L
{f{x)-q>{x)f (p(x)
n
dx
(11)
Criterion Values of Individual Coefficient C, Individual coefficient C; follows the chi-square distribution with degree of freedom 1, so criterion value is
Cf^x\n-a)
(12)
where 1 - a is confidence level. Criterion Value for Severity Factor Severity factor follow the chi-square distribution, so we get next criterion. r^ = J c f > x ' ( n ~ 2 , l > a ) /=3
729
(13)
-S
4.0|
I
2.0
I
0.0
1 -20MAMMJA' I -4.0 0.1
0.3
0.2
Time [sec] (a) Good condition l0.08
0.1 Oi
0.08 Q>
^0.06
0.06h
O
0.04
"5
0.04h
:ro
0.02
i0.02
0.00'
0.00
Coefficients of Gram-Charlier expansion (b) GC coefficients and T^ Figure 2: Vibration waveform and its GC coefficients for rolling element bearing in good condition
VERIFICATION OF THE EFFECTIVENESS BY EXPERIMENT Figure 2 show the vibration waveform measured from the bearing in good condition. Figure 3 (a) show the vibration waveform measured on rolling element bearing with small local defect on inner race, and by Figure 3 (b), we can confirm that all individual expansion coefficient C^ and severity factor T^ grow large and exceed the criterion values. Figure 4 (a) and (b) show the vibration waveform measured on rolling bearing with local defect on outer race. Also we can confirm that the individual expansion coefficient C/" and the severity factor grow very large and exceed the criterion values. APPLICATION TO THE DIAGNOSIS OF MACHINES FOR WHICH THE HYPOTHESIS IS NOT IN FORCE For some types of rotating machines, its density function of the vibration in good condition do not follow the normal distribution. Typical machines of this type are 1) Bladed machines Vibration signal of bladed machine such as compressors, pumps has blade passing frequency components, so its density function do not follow the normal distribution precisely. 2) Reciprocating machines Reciprocating machines has inherently unbalance component and its vibration signal have rotating frequency components and its harmonics. 3) Another special structured machines Machines such as the high seed ones with flexible shaft have the sinusoidal vibration waveforms and do not follow the normal distribution, in good condition.
730
0.1
0.2 Time [secj (a) Vibration waveform lO.OOi
15.00
10.00 X(3, 0.95)
5.00
0.00
Coefficients of Gram-Charlier expansion (b) GC coefficients and its T^ Figure 3: Vibration waveform and GC coefficients of bearing in initial failure condition
10.0]
•"I
Li.
: 11 rt
Lii
rt
0.0
S-10.0J
0.1
^'t]
0.3
0.2 Time [sec]
CO
(a) Vibration waveform 25.00I
i30.00
^ 20.00
i#?^
i 15.00I
20.00
o *5 10.001 ^
5.001
X(h 0.95)
10.00 ]X(3>0.95)
f^-l . ...
0.00
0.00' C^
C2
C3
C4
C5
T
Coefficients of Gram-Ciiarlier expansion
(b) GC coefficients and its J^ Figure 4: Vibration waveform and its GC coefficients T^ for dangerous condition For diagnosis of this kind of machinery, we can apply the presented method with a little modification of GC expansion coefficient. At first step, calculate GC expansion coefficients c^' of the vibration signal in good condition. Next step, make difference between measured GC coefficients c. and prepared coefficients c / , c^ " = Q -c^. ', this new coefficient c^ " can be used to diagnose the condition of these machines and have same advantage as original GC coefficients.
731
CONCLUSION We proposed the new method to detect and diagnose the rotating machinery condition based on the hypothesis that if the machine is in good condition, its density function of the vibration signal is distributed normally. 1) If machine is in good condition, all individual components q are zero 2) Individual coefficients c^ are corresponding to the specific failure type, so we can estimate the failure types by investigating c, that exceed the pre-decided criterion values. Another advantages of this method is 1) Criterion value for individual coefficients Q can be theoretically decide and its criterion value is always constant 2) Severity factor T^ that show the severity of failure can be defined theoretically and its criterion values are determined statistically. References Stuart A. and Ord K. (1994), Kendall's Advanced Theory of Statistics, Volume 1, Griffin. Stewart R. M. (1979), Machinery Health Monitoring Group Technical Brochure, University of Southampton. Stewart R. M. (1977), Some Useful Data Analysis Techniques for Gear Box Diagnostics, Institute of Sound and Vibration, University of Southampton.
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Condition Monitoring and Diagnostic Engineering Management Andrew G. Starr and Raj B.K.N. Rao (Editors) © 2001 Elsevier Science Ltd. All rights reserved.
COMPARISON OF APPROACHES TO PROCESS AND SENSOR FAULT DETECTION A. Adgar. Control Systems Centre, University of Sunderland, School of Computing, Engineering & Technology, Edinburgh Building, Chester Road, Sunderland, SR13SD,UK ABSTRACT Many process industries are under continual commercial and environmental pressure to produce higher quality products at lower cost. Improved process control through the introduction of new technologies is an area of prime importance in the drive towards these goals. With many industrial processes becoming increasingly more complex, highly instrumented and using more advance control strategies, it is necessary to increase investment in process and sensor fault detection schemes to ensure the process may be monitored and thus controlled in a safe and efficient manner. The water industry is not immune to any of the problems mentioned earlier, and indeed it has many problems specific to itself The reduction in staff levels has made routine maintenance of the sensors required for control of the process impractical. A condition based approach is now necessary. The techniques to be described exploit a range of techniques utilising statistical models, neural network models and fuzzy models to represent knowledge about the process. This can be captured in terms of the normal variance of a particular sensor and its relationship to other sensors under a set of operating conditions. Faulty sensor measurements, once detected, can be reconstructed based upon the fault detection models described earher. The fault analysis process will continue to function and by using the other measurements can produce an accurate estimate of the corrupted sensor measurement. Particular emphasis will be placed on the pre-processing of data sets, and the best ways in which to introduce examples of fault scenarios into the data used for model development. Computational time required for the different model types will be considered as well as their overall performance and drawbacks. The effectiveness of these new techniques will be demonstrated using data sets collected from a real treatment works, with fault information superimposed upon it.
KEYWORDS Artificial neural networks, multivariate statistics.
fuzzy modelling,
fault detection,
733
input selection,
water treatment,
INTRODUCTION The majority of the drinking water consumed in the UK is treated at surface water treatment works where raw water is abstracted from rivers and reservoirs. The type of treatment it then undergoes depends on the source and the quahty of its water. In general, the poorer the qualities of the raw water the more expensive it is to treat. The operation of water treatment plants is significantly different from other 'manufacturing' industrial operations because the raw water sources are usually subject to natural perturbations. This is especially true during periods of flood and drought that significantly affect the characteristics of the water abstracted into the treatment works. Prior to privatisation the majority of water treatment processes were under manual control. Since privatisation, the water industry has been seeking ways, especially via the increased use of automatic control [Bevan, 1999], to produce high quality water at reduced cost whilst at the same time 'downsizing' its work force. The water treatment process consists of a complex group of interconnected physical and chemical systems. It is not immediately obvious how each one relates with its neighbour, however, it is well known that a problem with one process, if not addressed, will quickly result in a much larger problem in one or more of the subsequent stages. Water treatment plant operators now have a wealth of modem instruments available as aids for monitoring the performance of chemical coagulants. Such instruments include zeta-potential meters, streaming current detectors and coagulant residual analysers. The instruments, however are always at the mercy of sensor failures and this causes problems for control schemes which act upon the sensed variables. Sensor failures may occur due to several physical reasons including: bubble formation on electrodes, fouling of electrodes by solids or chemical species in the water and also clogging of instrument sample lines and chambers.
OVERVIEW OF WATER TREATMENT The purification of water for domestic consumption involves several stages of treatment of the raw water to remove suspended solids, colour and bacteria before entering the distribution network. The individual treatment processes include clarification, disinfection, pH adjustment, filtration and taste and odour removal. Some of these are presented in a typical treatment scenario in Figure 1.
Coagulant
Figure 1: Water treatment process schematic diagram.
734
Raw water is often stored prior to treatment to improve the water quality (by settlement, bleaching by UV light and oxidation) and also to ensure adequate supplies at periods of high demand. The storage units may also attenuate the quality variations in the raw water. Screening may also be performed. Here, the inlet water is passed through a grid of bars (for coarse screening) or through floating booms or air bubble curtains (for finer screening.). Clarification can be roughly divided into a two-stage process, comprising of coagulation and flocculation. In the coagulation stage, a coagulant chemical (a salt of a highly charged metal ion) is added to the water in a mixing vessel, often with mechanical agitation to ensure uniform distribution of the chemical. The highly charged metal ion destabilises the negatively charged impurities in the water. The species combine together to form larger particles called floes. Flocculation involves the combination, by collision, of small particles, under natural turbulence, into larger particles. A flocculation aid (usually a natural starch or a polymer solution) may also be added to assist floe formation. The final stage of the clarification process is the separation of the large floes formed by coagulation and flocculation from the water, usually by settlement. Water flows upwards in a tank, which may be of uniform or increasing cross section. The balance between the settling velocity of the floes and the up-flow rate of the water allows the floes to be held in a 'blanket' of sludge in the tank. This 'blanket' traps other floes and becomes more concentrated. Clean water is decanted fi*om the top of the tank over weirs. The amount of sludge present may be controlled by periodic removal from collection hoppers in the blanket. Filtration is used to remove small grade suspended matter from the water. The water is passed through layers of sand, gravel and anthracite. Pipes buried in the layers of sand collect the filtered water. Particles trapped by the layers of sand must be periodically removed by 'back-washing' (forcing air and/or water under pressure back through the bed, temporarily fluidising it and removing trapped impurities.) Filtered water usually contains some remaining bacteria and pathogenic viruses. These are removed before the water goes to supply, by disinfection using ozonation, chlorination or UV radiation. Li the UK chlorination is the method usually applied mainly because chlorine is easy to add to water, is highly soluble and cheap. Water treatment processes, such as clarification and filtration produce considerable amounts of waste sludge in the form of slurry. This is usually held in holding tanks or lagoons to allow settlement and then further treated in filter presses. The concentrated sludge is disposed at landfill sites and the supernatant water may be returned to the inlet of the works for re-treatment.
FACTORS LIMITING SUCCESSFUL PROCESS CONTROL SCHEMES Early applications of automatic control in the water industry were compromised by the poor quality of instrumentation. Improved sensor technology enabled successful regulation of variables such as pH and chlorine concentration. However it is the control of the clarification and filtration stages, which is fundamental to the efficient operation of a treatment plant. This remains to be successfully accomplished at many treatment works. The design of effective feedback control schemes for the latter systems is difficult for two primary reasons. Firstly, instrumentation measuring the performance of the units is only now beginning to emerge. Their reliability and accuracy, however is not always of sufficient standard to achieve quahty objectives and the plant has long time constants, has ill defined, non-linear characteristics and varying process dead-times. These factors render the application of control extremely demanding. Nevertheless, with the advent of low cost modem computing power, coupled with advanced modelling and control techniques, it has now become feasible to reduce many of the sensor limitations and process difficulties.
735
More recently the use of streaming current detectors for simple clarification feedback control has resulted in major cost savings on a typical water treatment plant [Bishop, 1992; Dentel et al, 1989]. Considerable benefits have been obtained, but the instruments are far from reliable and a high level of maintenance is required. Current water treatment plant monitoring and control methodologies are site dependent, somewhat antiquated and still labour intensive (even after considerable downsizing). As described in the introduction, this paper outlines some of the research carried out at the University of Sunderland to resolve some of the problems relating to sensor failure scenarios. A PLANT MONITORING AND DIAGNOSIS STRATEGY Previous work by Adgar & Cox [1997] and Bohme et al [1998] has suggested possible strategies to detect and identify sensors that have failed or are beginning to fail. These range from simple univariate statistical models through more complex multivariate statistical models and finally to advanced techniques such as the auto-associative neural network. Fault Detection Faults occurring on sensors are detected by using specific previously determined fault detection models. In this research models have been constructed via linear regression, fiizzy logic and neural network approaches. The model development has been performed in each case by taking representative trains of process operating data and superimposing fault characteristics on the data by the injection of noise into the process variables. In each case, training, validation and test data sets were utilised in a ratio of 40%, 40% and 20% respectively. In each case, local sensor fault detection modules are developed to estimate *true' values of specific process variables even when faults may be present in other signals. Simulink interface One of the difficulties encountered when researching this type of problem is that the data is difficult to handle. The faults introduced into the operating data have been simulated, but we need a method by which we can examine the performance of the sensor fault detection schemes under a wide range of condidons. The different model types should ideally be tested on a selection of operational data sets, the fault types and magnitudes should be varied, as should the time of introduction of the faults. This requires a sophisticated and flexible development environment not ideally suited to conventional programming techniques. The author has found the Simulink environment to be suitable for these purposes. The simulations can be set up in a drag-and-drop model structure (see Figure 2) to allow rapid changes to be made to the model test schedules.
736
RBSWTWI Eie £ *
yiew Simulation Form^ Tads Help
n Sensor Drift
•*€>
•t^
MATLAB Function
m
•te-
->€>
- ^
CE3H "B
Neural Netwoik
ipixedStepDiscrele
Rea*;;
Figure 2: Screenshot of the Simulink development environment.
RESULTS The summary of performance of the three model types are shown in Table 1. As can be observed, the model accuracy increases (the sum-squared-error decreases) with model structure complexity, as expected. Table 1: Model errors for training, validation and test data sets. Model Linear Fuzzy Logic Neural Network
Training 0.5309 0.5144 0.3843
Validation 0.5166 0.4951 0.4238
Test 0.4871 0.4654 0.3855
The residual errors of the three models are shown in Figure 3 in histogram form. Again this reveals the smaller error variance attributed to the more sophisticated models. An example of the detection scheme results in practice is shown in Figure 4, with the linear fault detection model being used. Here we have simulated a sensor drift added to the first process variable at sample time 220. The faulty (measured) signal, true signal and on-line estimate are shown in the top graph. The estimate can be seen to be very close to the true signal at most times. The lower graph in Figure 4 shows the simulated fault, the error between the on-line estimate and the measured signal and a filtered version of this error. As the fault magnitude increases, the corresponding error increases and this can be used as a fault detection mechanism.
737
400 r a) Linear Regression Model
400 b) Fuzzy Logic Model
200
400 c) Neural Network Model
200
^
Error
Figure 3: Histogram of model errors for the three model types. 400 300
IV'-"
•§•200 O
100
\\k iiilAi^^^iAtfGtJH/^pU k iM'^'"
0
50
100
150
200
250
300
350
400
450
350
400
450
Sample 300 200
I 100 ,-
( : Jj^
0
^^.^j>.-a,i^:K.,^^^^>^-
-100 0
50
100
150
200
250
300
Sample
Figure 4: Time series traces for the process variables and the model estimation errors.
738
The proposed sensor validation scheme is fairly sensitive to fault impacts on each sensor variable and is ideal for detecting 'soft' failures. However, the network does not always work on the same variable fault magnitudes. The levels of fault which can be detected depend upon the levels of noise used when pre-processing the data. High levels of noise will give good fault detection performance, but may reduce signal reconstruction accuracy. One major advantage of this data-driven technique is that there is no need to have detailed knowledge about the system.
CONCLUSIONS The introduction of increasingly stringent regulations at water treatment works emphasises the need for a high degree of reliability in the operation of the individual unit operations. The response of the industry has been the estabUshment of a range of quality or conformance standards since their satisfaction offers potential benefits in terms of production consistency, reduced operational costs and improved safety. However, these improvements can only be realised if the process control and plant management policies are well structured and designed. This paper has identified a number of situations where an ANN has been used to help provide a solution that has contributed to an improvement in the overall plant performance. Control strategies are always at the mercy of sensor failures or malfunction. Any means by which these can be dealt with effectively are of great value. Benefits include the security of treatment operations and cost effective performance. Methodologies which identify sensor failure and aid signal reconstruction have been described. The results suggest that the vahdation technique is able to adequately identify sensor failure whilst also providing reasonable estimates of "corrupted measurements" for monitoring and control purposes.
ACKNOWLEDGEMENTS The authors would like to thank University of Sunderland and Northumbrian-Lyonnaise for the technical and financial support for this work.
REFERENCES Adgar, A. & Cox, C.S. (1997) Improving Data Reliability using Statistical and Artificial Network Strategies, COMADEM'97, 10th Int. Congress and Exhibition on Condition Monitoring and Diagnostic Engineering Management - Espoo, Finland, 2, 94-101, 9-11 June, 1997. Bevan, D. (1999) Monitoring, Control and reporting strategies for surface water treatment plants M.Phil. Thesis, University of Sunderland. Bishop, S. (1992) Use of the streaming current detector at Langsett Water Treatment Works. Journal of the Institution of Water & Environment Management, 6:1, 1-9. Bohme, T.J. et al (1998) Sensor failure detection and signal reconstruction using auto-associative neural networks. Int. ICSC/IFAC Symp. Neural Computation, Vienna, Austria, Sept. 1998. Dentel, S.K. et al. (1989) Evaluation of the streaming current detector, I. Use in jar tests. Water Research, 23:4, 413-421.
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Condition Monitoring and Diagnostic Engineering Management Andrew G. Starr and Raj B.K.N. Rao (Editors) © 2001 Elsevier Science Ltd. All rights reserved.
THE NEURAL NETWORK PREDICTION OF DIESEL ENGINE SMOKE EMISSION FROM ROUTINE ENGINE OPERATING PARAMETERS OF AN OPERATING ROAD VEHICLE E.Berry, J.Wright, P.Kukla, F.Gu and A.D.Bali Maintenance Engineering Research Group The University of Manchester Oxford Road, Manchester, United Kingdom, Ml3 9PL Email: [email protected] Phone:+44 (0)161 275 4407 Web: www.maintenanceengineering.com
ABSTRACT Accurate measurement of diesel exhaust smoke is a primary phase in meeting the ever-stricter EC regulations on emission levels and a fundamental step towards the improvement of many factors including fuel economy, atmospheric pollution levels and more importantly, human health, with the additional aim of automatic engine management systems and condition based maintenance. However, it is often difficult to measure smoke levels directly on a vehicle in transit, therefore this paper documents a study into the feasibility of diesel exhaust smoke prediction based upon the engine operating parameters of exhaust temperature (°C), engine speed (rpm) and road speed (mph) using a hybrid neural model. The results show that the smoke can be predicted by indirect measurements with good accuracy. KEYWORDS Diesel particulates, neural networks, emission prediction, routine operating parameters. INTRODUCTION It is well understood that the monitoring and analysis of exhaust smoke generated by a diesel engine can provide powerftil information about a range of factors, from the condition of top-end engine components, through the human health implications of the emission, to the environmental impact that the operation of the engine is having. The simplest basis for such an assessment is merely quantitative. More complex analyses target, amongst other things, the chemical composition of the exhaust particulate and the distribution of particulate size. Direct measurement of diesel engine exhaust smoke output is difficult to perform for an engine operating in a vehicle because of the equipment involved
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and access restrictions. For this reason the possibilities for direct measurement are generally restricted to test-bay usage. The ability to infer smoke emission level from routinely monitored engine operating parameters, without the need for specialist or cumbersome instrumentation, would bring with it the possibility of mapping quantitative particulate emission for an engine in a vehicle operating under normal on-road conditions. This, in turn, would provide the basis for a whole spectrum of possible benefits, from the optimisation of fleet vehicle deployment according to the duty cycles of specific routes, to the automated real-time adjustment of engine operating parameters based upon predicted pollutant emission. This paper outlines the next step in the work carried out by Berry et al (May 2001), which involved recording data from a test bed engine. The work documents the progression to a vehicle on the road. A correlation between easily measurable engine parameters and the exhaust emission levels by using them in a neural network model is investigated. Previous work in thisfieldincludes estimating diesel engine pollutants using a regression model based on engine speed, air-flow andftiel-flowby Ouenou-Gamo et al (1998). MEASUREMENTS AND DATA SETS The test data was recorded from a 10.5 litre, V8 Perkins 640 diesel engine powering a Dennis fire appliance. The raw data acquired during the test is illustrated in Figure 1 below. The vehicle was driven over varying road gradients in order to achieve afiiUrange of typical loads and speeds. The exhaust temperature, engine speed and road speed were recorded throughout the test. This data was used as the input parameters for the prediction. Along with the engine operating conditions, the smoke emission in Filter Smoke Number (FSN) was measured simultaneously.
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Figure 2 shows the basic trends of the engine emission with exhaust temperature, engine speed and road speed. It indicates, with exhaust temperature in particular, that the emission increases as the value of the measured parameter increases. However, the emission is correlated to the measured parameters in a non-linear way, and hence a neural network is employed to model this relationship.
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NEURAL NETWORK MODEL (NNM) A generalised regression neural network (GRNN), Demouthe & Beale (1997) & Wasserman (1993) was used to model the non-linear relationship between emission and operating parameter. The GRNN used had two layers of neurons, with the first layer being radial basis functions and the second layer comprised of linear neurons. The data set consisted of 152 samples, so with half of the data being used for network training and the other half for testing, the first layer had 76 radial neurons, and correspondingly the second layer also had 76 neurons. The spread of radial basis functions in a GRNN isfixedby a spread parameter. This controls the tradeoff between over-fitting and under-fitting. Each different configuration of GRNN was optimised to find the spread parameter giving the least error. Figure 3 below shows how the error between the predicted and raw data varies with a varying spread parameter. GRNN optimisation
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Figure 3: Network optimisation Legend o: 1-input network using exhaust temperature x: 2-input network using exhaust temperature and engine speed *:: 2-input network using exhaust temperature and rroad speed 0: 3-input network using exhaust temperature, road speed speed;and engine speed It can be seen clearly in Figure 3 that the more inputs the network uses, the lower the average error produced. Seven predictions have been carried out, i.e. every combination of the three engine parameters recorded. The first was performed using all three engine parameters, namely exhaust temperature (°C), engine speed (rpm) and road speed (mph). The network was then 'pruned' to omit either one or two of the parameters and a prediction carried out again using the same raw data.
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RESULTS AND DISCUSSION Figure 4 gives an indication of how well the NNM predicts the smoke emission using all three parameters. It can be seen that the network produces good prediction results for both the training data set and test data set. This means that the network has not only fitted well to the training data but that it also has a good generalisation capability.
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Figure 5: Error analysis using: (a) exhaust temperature, engine speed and road speed as input parameters, (b) exhaust temperature and road speed as input parameters, (c) exhaust temperature and engine speed as input parameters and (d) exhaust temperature as the only input parameter. Figures 5 (b) and (c) show the network test error of a two-input NNM after having omitted the engine speed then the road speed respectively. Figure 5 (d) shows the error of a single-input NNM after both engine and road speeds were omitted. It can be seen that the average network test errors do not vary much compared to the un-pruned network shown in Figure 5 (a), nevertheless the error does increase.
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CONCLUSIONS AND FUTURE WORK The work in this paper has demonstrated that routine engine operating data can be fitted by a neural network, and that this can then be used to give a close prediction of exhaust smoke output. It has also shown that a two input network displays a fairly equal capability when predicting emission levels as when all three parameters were used. Although at an early stage, this work highlights the feasibility of indirect measurement of engine emissions on a moving vehicle with a view to automatic engine management and condition-based maintenance. The next stage of the work will involve advanced optimisation of a network using the least amount of inputs for easy application to a road vehicle. Investigations are currently being undertaken into the effect of using signals from more recent engine sensors such as manifold absolute pressure and throttle position for use in the NNM. In some ways, this work can be considered better than test bed experiments, due to the exposure of the test vehicle to erratic 'real world' conditions, for example humidity and atmospheric temperature and pressure. REFERENCES Berry E., Wright J., Kukla P., Gu F., Ball A. (May 2001). The Prediction of Diesel Engine Smoke EmissionfromRoutine Engine Operating Parameters, and its Implicationsfi)rEngine Health Monitoring, MARCON Conference, Gatlinburg, TN, USA Demouthe H. and Beale M. (1997). Neural Network Toolbox User's Guide, MathWorks Ouenou-Gamon S., Ouladsine M., Rachid A. (1998). Measurement and prediction ofdiesel engine exhaust emissions, Elsevier Wasserman P.D. (1993). Advanced Methods in Neural Computing, Van Nostrand Reinhold
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Condition Monitoring and Diagnostic Engineering Management Andrew G. Starr and Raj B.K.N. Rao (Editors) © 2001 Elsevier Science Ltd. All rights reserved.
EARLY DETECTION OF LEAKAGE IN RECIPROCATING COMPRESSOR VALVES USING VIBRATION AND ACOUSTIC CONTINUOUS WAVELET FEATURES M. Elhaj, F. Gu, A. D. Ball, Z. Shi, J. Wright Maintenance Engineering Research Group, University of Manchester Oxford Road, Manchester, Ml3 9PL, UK www.maintenance.org.uk Phone+44 (0)161-275 4541
ABSTRACT Valve leakage is a major source of failure in reciprocating compressors and greatly influences the operation and performance. Valve impact and non-stationary airflow induction are two primary sources of vibration and acoustics. Therefore, this paper investigates the early detection of valve leakage based on vibration and acoustic measurements. It was revealed that the conventional analysis in either the time or frequency domain can not resolve the detection information from the acoustic signal due to noise contamination. The joint time-frequency analysis of the Continuous Wavelet Transform (CWT), however, can extract fault detection features successfully. These acoustic and vibration CWT features were developed for small leakage in both the suction and discharge valves of the compressor. Compared with vibration monitoring, acoustic monitoring needs more signal processing but its implementation can be carried out remotely. KEYWORDS Compressor Valves, Vibration Monitoring, Acoustic Monitoring, Continuous Wavelet Transform, Fault detection. INTRODUCTION The most common failure in reciprocating compressors is leaky valves. Not only the fault reduces performance of compressor but also causes secondary damage to other parts of the compressor. In [6, 9], valve problems were concerned as the primary cause for reciprocating compressor shutdown. Figure 1 illustrates that such damage accounts for 31% of complete breakdowns on this type of compressor and represents the biggest source of failure [6]. The leakage of valve results in a loss in efficiency because air is simply being pushed forth and back across the valve. Figure 2 gives the performance graph of a compressor obtained by measuring the discharge timing at different discharge 749
pressures. The discharge time needed for different valve leakage is longer than that of normal, which indicates that the compressor has to w^ork harder [2,3]. Leaky valves can result from damaged channels or valve seats or warped channels due to high process temperatures. In addition, carbon build-up can occur because of the high temperatures that can be reached at discharge which (sometimes over 200 C ) tends to bum the oil that accumulates on the channel and valve seat. Leaks tend to occur more often in the discharge valve because of the higher impact velocities and temperatures that are reached there. Also, leakage of the discharge valve is more severe than that of the inlet valve for the following reasons: the opening time of the discharge valve is shorter than that of the inlet valve, hence, the leakage time is greater: the average sealing pressure is higher: and the heat strain reduces sealing ability [2,3]. Due to the valve leakage, the temperature inside cylinder and valve system will increase further and hence accelerate the deterioration of whole system. Therefore, many techniques have been investigated to detect valve leakage. Although vibration analysis has been widely used for the monitoring of machines such as bearings, gearboxes and gas turbines, it still proves difficult to successftilly apply this to reciprocating compressors because they are naturally noisy, heavily vibrating machines. Problems are also encountered due to the non-stationary characteristics of the vibration signals encountered in this type of machine [7]. Discharge and suction valve leakage can reduce compressor efficiency.
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Figure 1: Failure rates in reciprocating compressors [6] This paper investigates the early detection of leakage and more generally the problem of predictive diagnosis in the discharge and suction valves of reciprocating compressors. Analytical and experimental studies of the vibration response of a reciprocating compressor have led to the development of a procedure for the detection and diagnosis of valve faults. In particular, we have found that timing and strength of the valve impacts are directly related to the severity of valve leakage. In reciprocating compressor valve operation generates transient vibrations and sounds which are so broad in frequency that vibration spectra tend to be very complicated, a fact which can render conventional vibration monitoring practically useless for detailed fauU diagnosis of valves. Therefore, the joint time and frequency analysis of the continuous wavelet transform (CWT) representations offers a solution and provide diagnostic information for valve faults [1].
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Figure 2. Influence of leaky valves on compressor performance VIBRATION AND ACOUSTIC SOURCES OF A RECIPROCATING COMPRESSOR Many studies have shown that the major source of vibration and noise are the mechanical impacts between valve plate and its seats. In addition, the sequences of airflow and non-flow processes, which constitute a cycle in the compressor, generate gas pulsation in the system. This non-stationery airflow also causes vibration and noise [5]. I-Mechanical impacts of Compressor Valve
^ 1 Generally, the suction and discharge valves in a reciprocating compressor are of similar design, containing a valve plate, a spring and a pneumatic chamber. As illustrated in figure (3), a compressor valve can be modelled with a mass, a spring and a damper. The movement of the valve plate is thus governed by a non-linear process [1,4] and forms a sequence of events: the rapid opening of the valve, linear move of the valve and impacts between the valve and seats. The amplitude of vibration and noise response measured close to the valve can be characterised as follows: - Higher discharge pressure [Pa ] produces higher amplitudes; - Higher cylinder pressure [Pc] produces higher amplitudes; - Smaller mass [m] of the valve plate produces higher amplitudes; - Lower stiffness [k] of the valve spring produces higher amplitudes; - Lower damping [C] of the valve chamber produces higher amplitudes. //- Non-stationary Airflow
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P'^ure 4: Airflow path within reciprocating compressor valve
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Figure 4 shows the airflow inside a reciprocating compressor valve. The airflow starts from the cylinder chamber, travels through the cylinder port and valve chamber and finally reaches to the delivery tube. For the suction process, the airflow path is the exact opposite to that of the discharge process. As the velocity and pressure of this airflow is varying with time, it may be concerned as secondary sources of vibration and sound. The sound generation may be quite similar to the way humans form recognisable speech from a bit of air pressure and tissue motion [1,8]. This indicates that the sound and vibration produced from the airflow is not only determined by the pressure of airflow, but also influenced by the parameters of the airflow paths. Therefore, the changes in the chambers, valve plates, springs will lead to a variation in measured sound and vibrations. In general the vibration and sound from reciprocation compressor are a complicated combination of mechanical impact and non-stationery airflow. CHARACTERISTICS OF THE VIBRATION & ACOUSTICS IN A RECIPROCATING COMPRESSOR A two-stage (Broom-Wade Reciprocating Compressor -TS9) was used for the study of valve fault detection using vibration and acoustic methods. An accelerometer was installed upon the cylinder head. As the temperature of the cylinder head can reach as high as 120°C and limited space is available; a transducer adapter is glued to the cylinder to reduce the heat transferring to the accelerometer. In addition, the temperature of the cylinder surface was controlled at around 90**C to reduce temperature-varying errors on the accelerometer. In contrast, no treatment is required when a microphone is installed 10cm away from the cylinder. In order to validate previous analysis, this study of fault detection was carried out using a leaky discharge and suction valves plates on the first and second stage cylinders. Figure 5a shows the raw vibration signals. There are four significant transient vibration responses within one rotation period of the compressor cycle. These four transient responses are well consistent with valve impact events: Suction Open (SO), Suction Close (SC), Discharge Open (DO) and Discharge Closing (DC). When the top dead centre (TDC) of the piston in the first stage cylinder is taken as the reference position, the sequence of the four events for the second stage will be identified as show in the figures. In addition, the discharge event related vibration is higher than that of suction events. This is due to that the vibration transducer is closer to the discharge valve. However, due to low frequency noise influences, the four events can not be observed fully from acoustic signals shown in Figure 5bl. Only the opening of the suction valve of the first stage can be seen around 50° with higher amplitudes.
FAULT FEATURE IN TIME DOMAIN To study the vibration and acoustic fault features, a Imm-diameter hole is drilled in valve plates to simulate the valve leakage. As indicated by the performance plot in Figure 2, the influences of these leakage for the second stage on the performance is very small (less than 8% at the maximum discharge pressure). Without any post-processing, the vibration signals measured on the compressor cylinder head in are shown in Figure 5a. Some difference can be seen between the faulty and normal cases. From the variation of crank angle position of the impact events, the leak causes the advanced opening of the discharge valve and the reduced opening impact strength. The reason for this is that high pressure air flows back though the discharge leak and raises the pressure in the cylinder above that which would 752
normally exist. As a result, the pressure needed to open the valve is reached sooner, though the impact severity changes slightly. The suction valve is also altered because the higher cylinder pressure. It delays the time at which the pressure is low enough to open the valve. Also, the final closing impact for the suction valve occurs slightly earlier.
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When the leakage occurs in a suction valve, the suction valve will open earlier due to the clearance volume of gas expanding on the downward stroke of the piston. There will be less pressure than normal during this stroke and the pressure differential across the suction valve will become sufficient to open the valve at an earlier point in time. In addition, this delays the closure of the suction valve as shown infig6b. Also figures 6c, and 6d shown the four events are not reflected directly in the time-domain of the acoustic signal of suction valve leaky, high noise content, and the fault is not clear as shown in vibration signal. FAULT FEATURES IN THE FREQUENCY DOMAIN Fig 6a and 6b present the spectrum results of both vibration and acoustic signals for discharge and suction valve signals. Comparing the spectra between healthy and faulty cases, the vibration spectrum shows enough differences for the detection of the faults. Although the acoustic spectrum also shows some difference but not as distinct as that of vibration. Either time or frequency method along reveals a limited amount of information from acoustic signals. This calls for an alternative analysis technique.
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CONCLUSIONS AND FURTHER WORK This paper investigates the early detection of valve leakage based on vibration and acoustic measurements. To obtain the signatures for both the detection and diagnosis, the joint time-frequency analysis of the Continuous Wavelet Transform (CWT) has to be applied to the acoustic signals. Acoustic feature revealed by CWT is as clear as thosefromvibration signals and can be summaries as follows: • For the leakage of the discharge valve, the discharge opening and the suction closing are advanced while the discharge closing and the suction opening are delayed. The amplitude of the faulty vibration is also larger. • For the leakage of the suction valve, the suction closing and the suction opening are advanced while the discharge events has less variation in time. As the surface temperature of the cylinder head is high and the transducer mounting space is compact, the acoustic monitoring thus can be taken an alternative to the vibration monitoring. REFERENCES [1]. B. Liang, F.Gu, A.D. Ball. (1996). Valve Fault Diagnosis in Reciprocating Compressors, Maintenance, Volume 11, Number 2, 3-8. [2]. Daniel, J. (1995). Dynamic Modeling of A reciprocating Air Compressor for use in Predicting Fault Signatures. Second International Conference on Acoustical and Vibratory Surveillance Methods and Diagnostic Techniques. Paris. 1-11. [3]. Daniel, J. (1997). Prognostics for A reciprocating Air Compressor. NOISE'CON97.Pennsylvania State University, 195-200. [4]. Fleming, J. (1989). A theoretical and Experimental Investigation of the Flow of Gas Through Reciprocating Compressor Valves, University ofStrathclyde, Glasgow, 117-119. [5]. J.Maclaren. (1975). Vibration and Noise in Pump, Fan, and Compressor Installation, 51-62 [6]. O. Bardou. (1994). Mechanical Systems and Signal Processing. CETIM-Senlis.Fvanc, 551-570. [7]. Shiyuan, L. A D. Ball. F,Gu.(2000). Vibration Monitoring of Diesel Engines Vsing Wavelet Packet Analysis and Image Processing. Research Report, University Manchester. 1-11. [8]. Titze, Ingo, R. (1994). "Principles of Voice Production", Prentice-Hall, Englewood Cliffs, NJ. [9].htt://204.168.68.51/e-tech/tp020/tp020prt.htm
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Condition Monitoring and Diagnostic Engineering Management Andrew G. Starr and Raj B.K.N. Rao (Editors) © 2001 Elsevier Science Ltd. Allrightsreserved.
INERTIAL SENSORS ERROR MODELLING AND DATA CORRECTION FOR THE POSITION MEASUREMENT OF PARALLEL KINEMATICS MACHINES Jian Gao, Phil.Webb and Nabil.Gindy School of Mechanical, Material, Manufacturing Engineering and Management The University of Nottingham University Park, Nottingham, NG7 2RD, UK
ABSTRACT Inertial systems are inherently inaccurate due to a variety of error sources and the interdependent characteristics of inertial variables. These systematic error terms, such as scale factor error and bias uncertainty are usually invariant with respect to time and thus predictable and hence can be compensated by a proper error model. However, an error model cannot completely remove all of the involved errors, the residual errors caused by the unpredictable error component and random error will always be present and restrict the accuracy inertial system performance which cannot be solved by inertial data alone. Therefore, data correction by external measurements is necessary for the inertial system to improve its position accuracy. In this paper an error model to correct the systematic errors, and a data correction method are described which update the random errors in the inertial system. The error model is applied to a simple axis and is used to correct the linear measurement obtained from the accelerometer. The external measurements are obtained through the machine's encoder which are used to update the random errors of the inertial data. The experimental result shows that the position accuracy by the accelerometer was improved by 55% through the error model and external data correction.
KEYWORDS Inertial Sensors, Accelerometers, Error Model, Data correction, Error Compensation, Parallel Kinematic Machine (PKM)
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INTRODUCTION The key to the positional accuracy of a Parallel Kinematic Machine (PKM) is the precision to which the parallel link (or leg) length can be measured. In most parallel kinematic machines, each leg's length is measured by a rotary encoder which is mounted on the leg. However, the method suffers some problems. For example, it cannot measure the deformation of the mechanical structure caused by mechanical effects such as backlash, wear, and thermal expansion caused by the fraction of ballscrew. To compensate for these errors and achieve accurate platform position, elaborate measurements with comparative measuring devices such as laser interferometers are needed, these are very expensive[l]. To greatly reduce this cost, a low-cost solid-state inertial sensor based measurement system is proposed in this paper to directly measure the actual position of the PKM. Inertial sensors have been widely used in aerospace for guidance and navigation applications, but because of the inherent errors within inertial sensors and application environment effects, inertial measurement systems cannot provide high accuracy position information for most positioning systems. These systematic errors are predictable and therefore can be maximally compensated by developing reasonably detailed models of the inertial platform, but random error cannot be corrected in this way. In order to bound random drift error and residual systematic error, external measurements from different sources can be used to periodically correct the inertial data. Under the correction of error modelling techniques and external measurement, the inertial positioning system can provide valuable information for positioning applications.
INERTIAL SENSOR ERROR ANALYSIS All inertial sensors are subject to a variety of errors which limit the accuracy of inertial system. Inertial sensor errors are generally due to mechanical imperfections in the sensors and electrical imperfections in the associated instrumentation. The dominant sources of error present in accelerometers are listed as follows [2]: a) Measurement noise: random error added to the measurement. It includes electrical noise and environmental noise which depends on vibration amplitude and could have some frequency components higher than others. b) Bias error: is a bias or displacement from zero on the measurement when the applied acceleration is zero. The size of the bias is independent of any motion to which the sensor may be subjected. c) Scale factor error: errors in the ratio of a change in the output signal to a change in the input which is to be measured. Scale factor errors may be expressed as percentages of the measured full scale quantity or simply as a ratio; d) Scale non-linearity: deviation from the desired linear input/output relationship. e) Cross-axis coupling error: which give rise to a measurement bias under certain conditions when the sensor is subject to vibration along the sensitive and pendulum axes simultaneously. Cross coupling is often expressed as a percentage of the applied acceleration. f) Accelerometer minimum threshold: a lower limit below which small changes in input cannot be detected by sensors. Threshold can be regarded as a dead band around null which effects the output of small acceleration or angular rate input. Bias Error Among the error items described above, biases of inertial sensors are the main sources which cause drifts in the velocity, position and attitude information. These drifts are determined from inertial sensors through a transformation and integration process and such integration processes result in unbounded growth in the position and velocity errors.
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When no acceleration is applied to an accelerometer, the output voltage of the accelerometer is referred to as the zero-g bias level. The bias error will shift the sample mean value away from the true mean value of the measured variable by a fixed amount. Accelerometer bias accumulates into displacement error according to the following equation
where b^ is the bias acceleration, Sd is the displacement error through the travelling time /. The kinematic equation shows that an uncompensated accelerometer bias builds up displacement error as the time's square. For the application of PKM positioning system, an uncompensated accelerometer bias error of Img in a 100mm movement lasting 2 second (supposing the initial velocity is zero) gives a distance error 20mm. Furthermore, zero-g bias of an accelerometer is highly temperature sensitive. It may also slowly change over time, perhaps from the ageing of internal components. As variations occur in the ambient temperature, zero bias will exhibit some temperature drift. When the accelerometer used to measure low g levels over wide temperature ranges, the zero-g drift can become large in proportion to the signal amplitude. On the other hand, a gyro bias will cause the angle error by integrating over time which works as a time-varying misalignment and cross-couples into the accelerometer in 3-axes inertial system causing an error in position. In this paper, the determination of PKM position (not contain orientation) is the main focus, and gyro bias will not be described here. Scale factor error The scale factor is the ratio between a change in the output signal and the change in input. Scale factor drift defines the amount by which an accelerometer's sensitivity of measurement varies as ambient condition change. These conditions could be the temperature or frequency of the measured motion. Thermal effects on bias and scale factor errors can be very significant and are often difficult to model accurately. This is because in some sensors, temperature gradients within the sensors can alter the performance of many of its components. As variations occur in the ambient temperature as well as the internal components, the scale factor will change the value by a function of temperature. Considering the accelerometer in this work, the sensitivity is lOOOmV/g under the standard ambient conditions of temperature, frequency, etc. With the calibrated information specified by the sensor supplier, the scale factor drift with temperature is specified as K^,^ = 0.02%F5'/°C (reference temperature is 22°C). So the corresponding scale factor drift e^p under the measure temperature (T°C) can be calculated by the expression of e^^ = AT,, • (r°C - 22°C). Cross-coupling errors Since an accelerometer is a directional device, ideally, it should only be sensitive to its axial motion. However, the actual sensing direction of the accelerometer is not just the direction along its primary sensing axis, ft also senses the motion in a plane that is perpendicular to the primary sensing axis. This sensitivity to orthogonal acceleration components is called cross-axis or transverse sensitivity which primarily arises as a result of manufacturing imperfections even in well designed transducers. Crosscoupling or cross-axis sensitivity is often expressed as a percentage of the applied acceleration and is generally less than 5 percent. But for highly demanding applications, the error caused by the cross-axis sensitivity will seriously contaminate the experimental data [3].
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SYSTEM ERROR ANALYSIS Besides these errors of inertial sensor, some other errors occur in inertial systems such as initial misahgnment and numerical integration process. In addition, it should be noted that the dynamic environment affects the way in which these error sources propagate into inertial system errors. Therefore, vibration error analysis for the application is performed because of the hostile environment and severe vibration experienced in machine tool applications. Alignment Errors Inertial sensors attached to the platform will resolve their measurements relative to inertial space along the sensitive axes of the instruments. In most systems, the instrument-sensitive axes are nominally aligned with the platform axes. Perfect alignment with their assumed directions is not possible under realistic conditions. To visualise the problem, assume that an accelerometer was mounted on a platform to measure its horizontal motion. In theory, the accelerometer shouldn't measure any acceleration along its sensing axis in a steady state. However, the platform may tilt a small angle along the axis under realistic condition, so the accelerometer will sense an acceleration component of gravity. Since the tilt won't disappear during its movement, the gravity component contained in the output of the accelerometer will be treated as a part of acceleration, which will build up the position error (actual as time t^. Assume the misalignment angle along sensitive the sensitive axis is p shown in figure 1, then the axis) gravity component in accelerometer data at the initial position can be expressed as
Figure 1: Gravity component contained in acceleration data due to initial alignment error
(2)
gsinfi
Therefore, the velocity and position error due to the misalignment are derived by time integral: V^=g-smj3't
(3)
P.=\^/=\g'^^^J3'^'
(4)
Integration Errors According to the error analysis, the output of the inertial sensors in one direction can be written as (5) 0)^ = co-i. -\r5co- 0), + 5(0^ + w
(6)
where, a^.co^ are the measured acceleration and angular rate, and the a-i^co, are the true acceleration and true angular rate that should be measured by the accelerometer and gyroscopes. Sa^,5co^ represent the other errors accept the random.
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The incremental velocity and position is then obtained by integrating the Equations (5) and (6): F = Fy. + Saj + \wdt P = Pj.+-Saf + \jwdt 2 And e = O.J. + Scoj -f jwdt
(7) W (^)
In these equations from (5) to (9), the terms of Sa^, Sco^, cause errors in velocity and attitude growing linearly with time, while the error in position grows quadratically with time. Therefore, integration will introduce important errors to the derived inertial parameters because of the noise or errors contaminated in the measured data. This mainly causes an offset in the derived velocity and drifting error in the double integrated position. Vibration Dependent Errors The inaccurate characteristics of inertial systems is mainly due to the above discussed errors, which occur in every inertial system. However, the dynamic environment of the inertial application also affects the result of system accuracy. For the machine tool application, the platform vibration is a severe source of errors for the inertial system. Owing to limited sensor bandwidth, dynamic mismatch between sensors and insufficient computational speed, which prevent the system from interpreting such motion correctly, vibration or oscillatory motion can cause inertial system errors. The effects of sculling known as the combination of angular and translational motion can be particularly detrimental to system performance. In the presence of such motion, if the inertial system fails to detect the motion and accurately process the inertial measurements, then significant system errors can arise. The sculling errors [4] will cause an acceleration bias through failure to take account of the rapid changes of attitude occuring between successive acceleration resolutions.
SYSTEMATIC ERROR CORRECTION Because of errors (systematic errors and random errors) contained in a measurement system, the accuracy of the measurement can never be certain. The importance of a measurement error is that it obscures the ability to ascertain the desired information: the true value of the variable measured. Therefore, an error model is required to correct the effects of the predictable systematic errors on the accuracy of inertial sensors (random errors cannot be compensated by this way). In the error model, the predictable error components and their coefficient can be represented by an equation and hence modelled mathematically. These predictable error components can be estimated from observations of performance and used in the opposite sense to correct or compensate for the imperfections in the sensor performance. Based on the error analysis described in previous sections, the output of an accelerometer along the x axis may be expressed in terms of an applied acceleration and sensor error coefficients and system error coefficients as follows:
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1
(11)
where ^^.^ represents the true acceleration applied in the direction of the sensing axis, which should be measured by the accelerometer; a,„^ represents the measured acceleration, K^j is the scale factor of the accelerometer, a^ and a, are the accelerations applied perpendicular to the sensitive axis x, 5^., and S.^ represents the cros-coupling factors, b^ is zero bias error (or offset error), e^P is scale factor error caused by temperature's change, e^^ represents the misalignment error in initial position, w represents random noise. Generally, these coefficients of zero-g bias, cross-coupling and scale factor error can be measured and correction can be therefore applied to compensate the repeatable components of these errors. However, some errors are not constant all the time, such as the biases are temperature dependent errors and have switch-on to switch-on variations. Scale factor errors are also temperature dependent errors. The random bias and random scale factor error are always present, random noise and vibration are also variant with the dynamic environment. These errors are less predictable and therefore cannot be easily compensated.
RANDOM ERROR CORRECTION Once the acceleration data was corrected through the error model, the observable systematic errors are compensated, but as described above, it still contains random errors, such as white noise, offset drift, random scale factor errors and random transverse sensitivity. These errors will build up with time, and cause drifting errors in the derived velocity data and final position. Because these errors are timedependent and the inertial variables are inter-relative, it is impossible to correct the errors by inertial sensors alone, external measurements must be used to update periodically the inertial data. In this paper, the exact reference data provided by the motor encoder are used to correct the accelerometer data in which the systematic errors has been partly removed through the error model. Based on the equation of (11), there is
where QJ^ (/) represents the true acceleration that the accelerometer should measure, a^^(r) is the measured acceleration after removing the bias error, C| is the system scale factor, CQ is a constant which combines the rest of the systematic errors and random en*or. The equation (12) can be restated by equation (13)
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a„,{t) = —a,{t) + -^ = K,a^,{t)^K, C,
(13)
C,
Suppose the initial velocity and initial position are zero, then the measured displacement can be derived by double integration from above equation as: '2
>
Xt^
K
y{t) = K,x \dt, \dt, xa.,. (t) + -^—0
0
(14)
^
The true (error free) displacement x(t) is expressed as I
'2
x(t)= Idt^ldt^xa^j-iO 0
(15)
0
When the error free displacement x, ,^2 are available at Tj ,^2, then
yi =K,xx,+
^ ' ,y^ =K,xx^+
-^ -
(16)
Thus the two coefficients K^, Kj can be obtained by solving this system linear equations 2{y^xx^-y^xx,) ^1 =
~TZ
y2'^tl ~ ;T~'^2 =
-fZ
y.xt;) 'pT
(17)
Therefore the corrected displacement can be obtained by the following expression: .^ 1 . .X 40 = —x(y(0
^2X^\
'-y-)
(18)
The performance of the error model and data correction method introduced above was validated through experimental data. The experiment was carried on a PKM leg testbed, a capacitive accelerometer was mounted on the leg to measure the acceleration of the linear movement and a laser interferometer was used to measure the real displacement as a standard reference. Figure2 shows the RMS values of the displacement results from raw data, error model modified data and external data corrected data. Due to the noises contaminated in the measured data, the RMS of the displacement is 13.mm for 100mm linear movement. But the error can be reduced about 18% through the error model compensation. And further error reduction of 37% can be achieved by the external two points correction.
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lOOnmlioear Movem^
RawData
ErrorModel
KKcorrect
Figure2. Performance of the error model and the data correction.
CONCLUSION Systematic errors and random errors contaminated in inertial system restrict the achievable accuracy. Systematic errors may be compensated by the application of error modelling technique which is dependent on precisely how the coefficients in the error model represent the actual sensor errors. On the other hand, it can be noted that the random errors also cause severely drifting error in velocity and position, especially for the long-term tracking applications. In order to curb error growth, an external reference system has to be used. If two exact reference points are available, then the correction can be made by equation (18) to update the measurements of accelerometer. Therefore, with the error model and data correction by external measurement, coupled with the proper signal processing, both systematic error and random error in inertial system can be corrected.
REFERENCES 1. Whittingham, B. Capabilities of Parallel Link Machine Tools: Preliminary Investigations of the Variax Hexacenterd. in ASME International Mechanical Engineering Congress and Exposition. 1998. Anaheim, California, USA,. Lawrence, A., Modern Inertial Technology: Navigation, Guidance, and Control. 1998, New York: Springer. McConnell, K.G., Vibration Testing: Theory and Practice. 1995: John Wiley & Sons, Inc. Titterton, D.H. and J.L. Weston, Strapdown inertial navigation technology, ed. P.B. E.D.R.Shearman. 1997: Peter Peregrinus Ltd.
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Condition Monitoring and Diagnostic Engineering Management Andrew G. Starr and Raj B.K.N. Rao (Editors) © 2001 Elsevier Science Ltd. All rights reserved.
ON-LINE SENSOR CALIBRATION VERIFICATION "A SURVEY" J. W. Hines, A. Gribok, and B. Rasmussen The Maintenance and Reliability Center The University of Tennessee Knoxville, Tennessee 37996-2300
ABSTRACT On-line, real-time, sensor calibration verification techniques have been under study for almost two decades. Several techniques have been developed that use significantly different data-based modeling algorithms. Three of these techniques: the Multiple State Estimation Technique originally developed by Argonne National Laboratory, the Autoassociative Neural Network technique currently employed by Halden Reactor Project's PEANO system, and the Non-Linear Partial Least Squares technique currently used by The University of Tennessee's researchers, have progressed to become viable options for sensor calibration monitoring. These three techniques are compared on the basis of several performance considerations including development effort, scalability, consistency, non-linear modeling capabilities, and the ability for the system to adapt to new operating conditions. In addition to these performance attributes, we also compare their availability on the commercial market and their experience base.
KEY WORDS Sensor Calibration, Neural Networks, MSET, Inferential Sensing, Non-linear Modeling, Fault Detection and Isolation.
INTRODUCTION As companies move towards condition-based maintenance philosophies, new technologies are being developed to ascertain the condition of plant equipment. This paper looks at three methods used to monitor the condition of sensors and their associated instrument chains. Currently, the most common method used to assure sensors are operating correctly is periodic manual calibrations. This technique is not optimal in that sensor conditions are only checked periodically; therefore, faulty sensors can continue to
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operate for periods up to the calibration frequency. Operating a plant with faulty sensors can cause poor product quality, poor economic performance, and unsafe conditions. Periodic techniques also cause the unnecessary calibration of instruments that are not faulted which can result in damaged equipment, plant downtime, and improper calibration under non-service conditions. It is obvious that a technology that can accurately predict the condition of an instrument during operation can increase product quality, plant efficiency, safety, and profitability. University of Tennessee (UT) researchers have been pioneers in the development of online sensor calibration verification systems. Dr. Belle Upadhyaya was one of the original investigators in the 1980's [Upadhyaya 1895, 1989], through a Department of Energy funded research project to investigate the application of artificial intelligence techniques to nuclear power plants. Researchers at Argonne National Laboratory continued with similar research throughout the late 1980's and 1990's [Mott 1987] in which they developed the Multivariable State Estimation System (MSET) which has gained wide interest in the US Nuclear Industry. Chicago based SmartSignal Inc. licensed the MSET technology for application to other industries [Wegerich 2001]. Several other US companies such as Pavillion Technologies, ASPEN IQ, and Performance Consulting Services [Griebenow 1995] have also developed sensor validation products. The major European player in this area is the Halden Research Project where Dr. Paolo Fantoni and his multi-national research team have developed a system termed Plant Evaluation and Analysis by Neural Operators (PEANO) [Fantoni 1998] and applied it to the monitoring of nuclear power plant sensors. Many other researchers have been involved with inferential sensing on a limited scale and also with the verification of redundant sensors.; these techniques are not evaluated in this paper. The objective of this paper is to review techniques for plant wide sensor monitoring. Also note that the techniques surveyed do not include analytical redundancy based fault detection and isolation (FDI) techniques that are based on physical models. Several articles surveying FDI techniques already exist [Isermann 1984, Frank 1987, Gertler 1988]. Numerous data-based technologies have been used by major researchers in the field including autoassociative neural networks [Fantoni 1998, Hines 1998, Upadhyaya 1992], fuzzy logic [Hines 1997], principal component analysis [Qin 1999], non-linear partial least squares [Qin 1992, Rasmussen 2000a], and kernel based techniques such as MSET [Singer 1997] and the Advanced Calibration Monitor (ACM) [Hansen 1994]. This paper will survey three technologies used in the Nuclear Power Industry that use different databased prediction methods: a kernel based method (MSET), a neural network based method (PEANO), and a transformation method (NLPLS). First, a brief review of the theory will be given, then the methods will be compared using several performance indicators; and finally, example applications will be presented.
METHODOLOGIES This section will present concise descriptions of the three prediction methodologies surveyed in this paper. For a more complete description of the theory, additional
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references are given. All of these techniques use historical plant data that is assumed to be error free and cover the operating space of the system or process. From this data, predictive models are constructed which are used to predict sensor values for previously unseen data. Figure 1 is a block diagram of a basic sensor calibration monitoring system in which a vector of sensor measurements (x) are input to a prediction model which calculates the best estimates of the sensors (x'). The estimates are compared to the measured values forming differences called residuals (r). A decision logic module determines if the residuals are statistically different from zero and establishes the health or status (s) of each sensor. Measured Signal Values ues ^1 X'^i
,. "
Signal Predictions
^ Prediction Model 1 Z
Prediction Residuals
Sensor Status
1*
x' Comparison Module
s Decision Logic
J Figure 1. Sensor Calibration Monitoring System Diagram
There are several methods used to perform the decision logic including limit checking, statistical process control charts, and the sequential probability ratio test (SPRT) originally developed by Wald [1945]. These methods are not the focus of this survey, but can be investigated further in papers by Gross [1992] of Argonne or Yu [2001] from the University of Cincinnati who is currently working with Argonne. Multivariate State Estimation Technique (MSET) Non-parametric regression methods such as kemel regression [Cherkassky, 1998] or MSET, which proves to be a kemel regression method in disguise [Zavaljevski, 1999], have been used for sensor calibration verification. MSET is a non-linear kemel regression technique that utilizes a similarity operator to compare a set of new measurements to a set of prototypical measurements or states [Gross et al. 1998]. This comparison process generates a weight vector that is used to calculate a weighted sum of the prototype vectors to provide an estimate of the true process values. The similarity function uses one of two proprietary similarity/distance operators [Singer 1996]. MSET functions as an autoassociative model, reproducing an estimate of each of a set of measured signals that are provided as inputs to the model. The presented derivation of the MSET algorithm comes from Black [1998] but originated in Singer [1996]. Single underlined symbols represent vectors while double underlines represent matrices.
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MSET is similar in many regards to multiple linear regression. In linear regression, we let A, referred to as the prototype matrix, represent a matrix assembled from selected column-wise measurement vectors, and let w represent a vector of weights for averaging A to provide the estimated state ]C_ as follows: x^=Aw
(1)
The column-wise measurement vectors which make up the prototype matrix 4 ^re selected by a proprietary technique that is carefully performed to provide a compact, yet representative, subset of a large database of measurements spanning the full dynamic range of the system of interest. If e represents the difference between an observed state x and the estimated state x^, then the following relations may be constructed: e^ = x-x^ = x^-Aw
(2)
The least squares solution to the minimization of e yields the following expression for w, (where the left hand factor of the matrix product is known as the recognition matrix): }V = (A''
'^~'
'{A''
-x)
(3)
A chief liability of this linear method is that linear interrelationships between state vectors in A result in conditioning difficulties associated with the inversion of the recognition matrix. This shortcoming is avoided by the MSET by applying nonlinear operators in lieu of the matrix multiplication. These operators generally result in betterconditioned recognition matrices and more meaningful inverses of the recognition matrices. MSET extends the multiple regression equations to include a non-linear operator as follows:
W^IA" eAy(A'' ®x)
(4)
The ' 0 ' symbol represents an appropriate similarity operator which is also termed a kernel operator in non-parametric regression [Cherkassky 1998]. A typical kernel operator is the gaussian operator: K(u)=(27ia)'^^*exp(-u^/2a^). An estimate of the plant states can then be given as:
x_'=4'"y'" e^j''^"" ©^)
(5)
Note that the estimate is dependent on a matrix of prototype states A, the current state x, and the choice of kernel operator and its spread constant a. The most mathematical intensive operation in this equation is the matrix inversion which can be done off-line and stored since it does not depend on current input values. This allows MSET to operate in real time.
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Non-Linear Partial Least Squares (NLPLS) The Non-Linear Partial Least Squares (NNPLS) based system consists of a set of inferential models that, when combined, form an autoassociative design. Each parameter to be monitored requires a separate inferential model. An inferential model infers a prediction of a specific sensor's measurement based on the values of correlated sensors. The sensor values that are used as inputs to each inferential model include all sensor values except for the specific sensor being modeled. A schematic of a NNPLS inferential model is provided in Figure 2.
Inputs X
t Z^^^<^t7^
Output •IANN2I
•«
^^"--^
X'
Figure 2. NNPLS inferential unit schematic. The theoretical basis for Partial Least Squares (PLS) is explained in detail in many publications and texts (Geladi 1986, Hoskuldsson 1988], PLS is an iterative algorithm that sequentially decomposes the input data set into orthogonal score vectors. The output data set is also decomposed into a new set of vectors, though orthogonality is not a constraint. The transformations of the input data set and output data set to their respective score vectors, t and u, are derived such that the first set of score vectors explains the maximum covariance between the input data set (X) and output data set (Y). The standard PLS algorithm then prescribes a linear regression of the input score vector onto the output score vector. The variability in the input data set explained by the first input score is then subtracted from the input data set, resulting in an input residual. Similarly, the variability in the output data set explained by the first output score is subtracted form the output data set, resulting in an output residual. The input and output residuals represent the remaining variability, from which further information about the covariance between the input and output data sets can be extracted. The second set of score vectors is then determined such that the maximum variance in the input residual related to the output residual is captured, with the additional constraint of orthogonality with the previous input score vector. After the second set of score vectors is obtained, the input and output residuals are again reduced by the variability explained by the second set of score vectors. This iterative process continues until the information left in the residual matrices is negligible, and can be attributed to noise, or unrelated variation with respect to the desired output. Based on the desired accuracy of the model, and the level of noise present in the data, the number of input score vectors to be included in the model can be determined.
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To extend the PLS algorithm to incorporate non-linear mapping abihties, the linear regression between each pair of score vectors is replaced by Single Input Single Output (SISO) Artificial Neural Networks (ANNs) [Qin 1992]. Each simple SISO contains a single hidden layer with two sigmoidal activation functions, and a single linear output neuron. The number of SISO neural networks required for a given inferential NLPLS model is equal to the number of orthogonal input score vectors retained in the model. It is significantly less than the number of sensors provided at the input and is determined through the evaluation of the prediction errors of a validation data set. Cross-validation training of the simple ANNs is used to prevent the detrimental effects incurred when overtraining an ill-posed problem. A complete derivation of the NLPLS algorithm is not given in this paper due to space restrictions but can be found in Rasmussen [2000a]. Auto-Associative Neural Networks (AANN) The neural network based system uses the Auto-Associative Neural Network architecture consisting of an input layer, 3 hidden layers, and an output layer as recommended by Kramer [1992]. As shown in Figure 3, the first of the hidden layers is the mapping layer with dimension greater than the number of input/outputs. The second hidden layer is called the bottleneck layer. The dimension (number of neurons) of this layer is the smallest in the network. The third hidden layer, called the demapping layer, has the same dimension as the mapping layer. Kramer points out that five layers are necessary for such networks in order to correctly model non-linear processes.
Input Layer
Mapping Layer
Bottle-Neck De-Mapping Layer Layer
Output Layer
Figure 3. Architecture of a Five-Layer Bottlenecked AANN.
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The mapping-bottleneck-demapping combination forces the network to develop a compact representation of the training data that better models the underlying system parameters. Essentially, the bottleneck layer functions like a non-linear principal component analysis filter that gives a richer representation of the data. The non-linear activation function of the three hidden layers are sigmoidal functions. The network uses a linear output layer and is usually trained with a conjugate gradients algorithm. A more detailed description of AANN development for sensor calibration monitoring can be found in Hines [1998].
COMPARISONS In this section the three prediction techniques will be compared and contrasted with respect to several performance considerations necessary to have a system that is accurate, reliable, and easy to use. Specifically, we will look at system development effort, the ability for the system to scale up to large (high dimensional) problems, the consistency of the solutions, non-linear modeling capabilities, and the ability for the system to adapt to new operating conditions. In addition to these performance attributes, we will also compare their availability on the commercial market and their experience base.
Development Time and Effort Each system must be developed using a data set that is assumed to contain error-free observations from all expected operating states. The time and oversight required to design and train each type of system vary significantly. It is desired to have a system that requires little or no expert oversight and is trained rapidly. MSET training is a single-pass (i.e., is not an iterative process) operation, and consists of little more than the operations involved in a single matrix multiplication and an inversion or decomposition. Data selection plays an important role, as the number of operations (and processor time) required per recall is proportional to the product of the number of prototype measurements and the dimensionality of the measurements. Therefore, as a function of the number of signals to be monitored, and the data availability rate, there is an upper limit upon the number of pattems that may be included in the prototype measurement matrix. Since the purpose of the prototype matrix is to compactly represent the entire dynamic range of previously observed system states, the pattems that are included must be carefully chosen. Both the data set selection and the model training can be automated and performed efficiently. In this method the spread constants of the similarity relation must be carefully chosen. This too can be automated, but no methods related to sensor calibration monitoring have been published in the open literature. The computer time necessary for the automated construction of a system is on the order of minutes. The Non-Linear Partial Least Squares (NLPLS) algorithm constructs an inferential model for each sensor and combines them into an autoassociative framework. Each inferential model may have a different number of latent variables included. The optimal number is
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chosen through a cross-validation process using a validation data set. Each latent variable has a simple neural network non-liner processor. The training of these networks are iterative processes and use a regularized training algorithm. All of the development is automated and the construction time is on the order of a few hours [Rasmussen 2000a]. The AANN development is the most involved of the three methods. The architecture, which is defined by the number of hidden neurons in each of the three hidden layers, must be properly chosen. It varies with the number of sensors to be monitored, the redundant information contained in the data set, and the training error goal. Although heuristics can be used to estimate the optimal architecture, no techniques have been proven to be consistent. Training AANNs is a time consuming task that can result in a sub-par performing network due to local minima that occur in the iterative training process. Although research has been performed to automate the system [Xu 2000], the development and training of these models is expected to require some oversight and may take a few days.
Scalability Scalability is defined as the ability of the system to operate in high dimensional (multiple sensor) spaces. The MSET has no limit on the dimensionality of the input space although better performance can be attained if separate systems are developed for uncorrelated subsets of data. Since kernel regression is based on the kernel density estimation technique [Cherkassky 1998], for high dimensional data, estimation may become inconsistent due to the curse of dimensionality and is very sensitive to the kernel selection. Kernel regression can be made consistent by using regularization techniques similar to those used in ridge regression. This will be discussed in more detail in the next section. The NLPLS algorithm has no dimensionality limit and its implementation removes uncorrelated inputs from each inferential model. This prevents system degradation from uncorrelated inputs [Rasmussen 2000a]. The AANN based system does not have a theoretical limit on the number of network inputs but it does have a practical limit. This limit is of the order of 30 and no research results have been presented with significantly larger numbers of sensor inputs. When monitoring plants with many sensors, the set of sensors must be divided into smaller correlated groups. Although automated methods of grouping inputs have been devised [Xu 2000], they are very complex and have not been implemented on actual plants.
Consistency of Results (Robustness) The data sets used for sensor calibration verification must contain redundant information to predict one sensor value from the other sensor values. This trait shows up as strong correlations. One concern when using highly correlated data is the repeatability and
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consistence of the results. It is widely known that problems containing highly correlated data, also termed collinear data, may be ill-posed. That is, data based prediction methods will have solutions that are not stable or smooth under small perturbations of the data. Solutions can be made repeatable and stable with regularization methods. A study of regularization methods for inferential sensing has been published [Hines 1999]. Regularization methods have been successfully applied to MSET [Zavaljevski 1999, Gribok 2000]. The results of these studies show that without proper regularization MSET and other statistical techniques are sensitive to minor variations in the data. MSET can be regularized using two different methods: the proper choice of the kernel width (similarity function width) regularizes the solution and the use of a regularization parameter in the matrix inversion also regularizes the problem. NLPLS is a transformation method that transforms the predictor data into a new space where the latent variables are not correlated, but orthogonal. Because of this, NLPLS is not adversely affected by collinear data. AANNs are the most difficult method to regularize. There are several methods that "help" regularize the network, but none give totally repeatable results. These methods include training with jitter (noise), Levenberg Marquardt training, weight decay, neuron pruning, cross validation, and Bayesian Regularization [Hines 2000]. When using AANN, the user must realize the difficulties inherent in predicting from collinear data and must take steps to reduce their effects. Another cause of AANN inconsistent results is the random initialization of weights and biases. Because of local minima inherent in neural network training, several different networks can be constructed that produce similar results on training data but may produce quite different results on test or validation data.
Non-Linear Modeling Capabilities MSET, being a kernel regression technique, is termed a universal function approximator. This means it can model any non-linear function to any desired degree of accuracy, but does not prescribe the required state matrix. Neural networks have also been proven to be universal function approximators but there is no theory that prescribes the correct architecture or the weights and bias necessary to produce the desired solution. Both techniques are extremely accurate at modeling non-linear functions, but the free parameters that give the systems the non-linear abilities also increase the repeatability problems that regularization methods address. NLPLS is a non-linear mapping agent but the original transformation is based on a linear transformation developed to maximize the linear correlation between the latent variable and the signal to be modeled. If the relationship is highly non-linear, the transformation will not be optimal and the prediction accuracy will be poor. To combat the problem of modeling highly non-linear relationships, a modular fuzzy neural design can be used.
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The data is clustered into several overlapping operating regions and models are developed for each region. This design is shown in Figure 4. It is assumed that the relationship may be non-linear between operating conditions but only slightly non-linear within each operating condition. These slight non-linearites can be modeled by the NLPLS system due to the embedded SISO neural networks. This modular design was developed by the Halden Group for use in the PEANO system [Fantoni 1998], but has also been applied to the NLPLS based system.
Training Data
AANN 1 AANN2
Training Data
Clustering Algorithm
AANN 3
I Test Data
Membership Grade Evaluation
Trained networks
.NJk-. AANN 1 AANN 2
Output Processing
Output
AANN 3
Membership Values of Test Data
Figure 4. Modular Design It should be stated that the relationships encountered in sensor calibration monitoring have been mostly linear, with the amount of non-linearity not sufficient to require the modular design for the NLPLS based system.
Retraining When input data is outside the operating region of the training data, the non-linear modeling systems' predictions are not reliable. This has been reported for MSET [Singer 1996], NLPLS [Rasmussen 2000b] and AANN [Fantoni 1998, Xu 1999]. Because of this, all three calibration monitoring systems have incorporated modules to monitor the plant operating region. The PEANO system has a reliability assessment module that outputs an estimation confidence [Fantoni 1998]. When several predictions are poor and/or the inputs are outside of the range of the training data, the reliability is assessed to be low. Poor predictions and different operating conditions can be caused by different process line-ups, different operating conditions, plant faults, etc. The identification of operating condition changes will alert the operator to verify correct operations, and if the operation is correct, to retrain or retune the system to operate in the new region.
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The MSET system incorporates retraining by adding new prototype vectors to the prototype matrix. This is a simple way to extend the model to encompass new operating conditions. Newer techniques, which incorporate expert system rules to determine if the system is faulted or in a new operating condition, have been devised by Smartsignal and are reported in Wagerich [2001]. The NLPLS based system can be retrained using new training data. This task is similar in complexity to the original system development. Other techniques that have recently been explored include using the modular design and simply adding a new model covering the new operating condition. This technique eliminates the requirement of saving historical training data since the relationships are stored in the NLPLS model. It reduces the saving requirement to only saving the models developed for each operating condition. We have stated that training AANNs is a difficult, time consuming task. The retraining of an AANN would be less difficult if the old architecture and parameters were used as the starting point. Extremely fast re-tuning has been performed by simply performing a Singular Value Decomposition based regression solution of the linear output layer using new training data [Xu 1999]. The modular design discussed above could also be used for AANN techniques.
Commercialization Level An important consideration when choosing a sensor calibration monitoring system is the level of commercialization of the product. We feel that this is true since the techniques are not mature products and will require timely support due to their complexity. The MSET technique has been developed by Argonne National laboratory and commercialized at a fairly low level. SmartSignal has also developed a commercial MSET-based package and has installed applications into several industrial plants. The NLPLS technique has been demonstrated at a fossil power plant by researchers at the University of Tennessee (UT), but UT is not in the business of developing commercial software applications. PEANO has been developed by a research team with the skills of commercial software developers, but to date, has not been installed in a commercial application.
Experience Base Another consideration is the experience base of the employed technology. MSET has been investigated and used for over a decade. This technique has an extensive experience base. The neural network technique has also been around for over a decade, although their application is more limited than the MSET techniques. The NLPLS technique is the newest of the three techniques and was developed to combat the deficiencies the other techniques have to collinear data. The NLPLS techniques have only been in operation for the past two years. We will now present a brief application for each of these techniques.
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MSET Probably the first and best-known sensor calibration monitoring case study was a 1996 Department of Energy funded project applying several techniques to data from Florida Power Corporation's (FPC) Crystal River Nuclear Power Plant. In this study, that only spanned six months, Argonne National Laboratory's MSET system, UT's AANN based system, and Sandia National Laboratory's Probabilistic Neural Network system competed in a head to head competition. Although formal testing and assessment was never performed due to funding issues, MSET was clearly the most advanced system at that time. Because of this, research applying the MSET system continued and the MSET system was reported to perform well on FPC operational data [Gross 1997]. New applications of MSET by SmartSignal include monitoring a steel plant, chemical plant monitoring, and the monitoring of jet engines in the airline industry. These recent projects have had several successes in detecting degrading equipment before failure. AANN The AANN architecture has been employed by The University of Tennessee at Tennessee Valley Authority's Kingston Fossil Power Plant in the late 1990's [Xu 1999], but a more commercialized application was that of Haldon's PEANO system [Fantoni 1999]. This system was employed to monitor 29 sensors of the Halden Boiling Water Reactor. Data covering a year of operation was classified into five overlapping clusters and used to train the five AANNs that were integrated into the modular PEANO design (Figure 4). This was the first time that PEANO was connected to real-time application. The system was able to detect several measurement anomalies and artificial sensor drifts. This system was found to have a superb user interface with a well-designed client server architecture. NLPLS The NLPLS system has been field tested at Tennessee Valley Authority's Kingston Fossil Power Plant over the last 12 months by researchers at The University of Tennessee. The system has been used to monitor 84 sensors with an average estimation error o f - 1 % of the measured value, and out-of-calibration alarm levels at -2.5% drift for the boiler system, and - 1 % for the turbine system. During this time period several instrumentation anomalies have been detected, but a formal assessment of its operation has not been completed. Several issues, including data communication problems and plant outages have slowed the formal assessment. An informal assessment shows that the NLPLS technique is easier to develop and maintain than the previous AANN based system.
SUMMARY Table 1 is a summary of each technique's performance comparison. The grades given are on a scale from 1-5 with 1 being the best and 5 being the worst. The authors
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acknowledge that this is a very subjective quantification but feel that it provides a concise, useful summary. Table 1. Summary of System Performance Comparisons Development Scalability Consistency NonRetraining Commercialization Linear \' MSET 1 1 2^ 2 1' 2 4 3 AANN 3 4 1 3 NLPLS 2 1 2 1 2 A few explanatory comments are necessary to properly interpret this summary table. 1. The consistency and scalability values of the MSET system assumes it uses a regularization technique as presented in Zavaljevski [1999]. 2. The commercialization grade for MSET is for the systems developed by SmartSignal which is a purely commercial enterprise. The Argonne system would be rated a 2.
CONCLUSIONS Three data-based prediction modeling techniques were compared and contrasted with respect to their ability to perform real-time, on-line, sensor calibration monitoring. All three techniques have performed adequately on case studies but each have notable advantages and/or disadvantages. Price was not a factor used in this study but conunercialization and experience levels were discussed.
References Black,, C.L., R.E. Uhrig, and TW. Hines (1998), "System Modeling and Instrument Calibration Verification with a Non-linear State Estimation Technique", published in the proceedings of the Maintenance and Reliability Conference (MARCON 98), Knoxville, TN, May 12-14. Cherkassky V. and F. Mulier (1998), Learning From Data, John Wiley & Sons, ISBN 0471-15493-8. Fantoni, P., S. Figedy, A. Racz, (1998), "A Neuro-Fuzzy Model Applied to Full Range Signal Validation of PWR Nuclear Power Plant Data", FLINS-98, Antwerpen, Belgium. Fantoin, P., (1999), "On-Line Calibration Monitoring of Process Instrumentation in Power Plants", EPRI Plant Maintenance Conference, Atlanta, Georgia, June 21, 1999. Frank, P.M. (1987), "Fault Detection in Dynamic Systems via State Estimation -A Survey", in Tzafestas, S., M. Singh and G. Schmidt (Eds.), System Fault Diagnostics, Reliability and Related Knowledge-Based Approaches, Reidel, Dordrecht, Vol, 1, 35-98. Geldi, P. and B. Kowalski (1986), "Partial Least Squares Regression: A Tutorial", Analytica Chemica Acta, 185, pp. 1 -17.
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Gertler, J. (1988), "Survey of Model Based Failure Detection and Isolation in Complex Plants", IEEE Control Systems Magazine, pp.3-11, (1988). Gribok, A.V., J.W. Hines and R.E. Uhrig (2000), "Use of Kernel Based Techniques for Sensor Validation in Nuclear Power Plants", American Nuclear Society International Topical Meeting on Nuclear Plant Instrumentation, Controls, and Human-Machine Interface Technologies (NPIC&HMIT 2000), Washington, DC, November, 2000. Griebenow, R.D., A.L. Sudduth (1995), "Applied Pattern Recognition for Plant Monitoring and Data Validation", The 1995 ISA POWID Conference. Gorss, K.C. (1992), "Spectrum-Transformed Sequential Testing Method for Signal Validation Applications", 8^*" Power Plant Dynamics, Control & Testing Symp., Knoxville, Tennessee, Vol. I, May, 1992, pp. 36.01-36.12. Gross, K.C, R.M. Singer, S.W. Wagerich, J.P. Herzog, R. VanAlstine, and F. Bockhorst (1997), "Application of a Model-based Fault Detection System to Nuclear Plant Signals", Proceedings, Intelligent System Applications to Power Systems, (ISAP (&), Seoul, Korea, July 6-10, pp. 66-70. Gross, K.C, S. W. Wagerich, R. M. Singer, and J. E. Mott (1998), "Industrial Process Surveillance System", US Patent #5,764,509. Hansen, E.J., and M.B. Caudill (1994), "Similarity Based Regression: Applied Advanced Pattern Recognition for Power Plant Analysis"; E.J. Hansen, M.B. Caudill; 1994 EPRIASME Heat Rate Improvement Conference; Baltimore, Maryland. Hines, J.W., and D.J. Wrest (1997), "Signal Validation Using an Adaptive Neural Fuzzy Inference System", Nuclear Technology, August, pp. 181-193. Hines, J.W., and R.E. Uhrig, (1998), "Use of Autoassociative Neural Networks for Signal Validation", Journal of Intelligent and Robotic Systems, Kluwer Academic Press, February, pp. 143-154. Hines, J.W., A.V. Gribok, I. Attieh, and R.E. Uhrig (1999), "Regularization Methods for Inferential Sensing in Nuclear Power Plants", Fuzzy Systems and Soft Computing in Nuclear Engineering, Ed. Da Ruan, Springer, 1999. Hines, J.W., A.V. Gribok, I. Attieh, and R.E. Uhrig (2000), "Neural Network Regularization Techniques for a Sensor Validation System", American Nuclear Society Annual Meeting, San Diego, California, June 4-8, 2000. Hoskuldsson, A. (1988), "PLS Regression Methods", Journal of Chemometrics. Vol. 2, pp.211-228. Isermann, R. (1984), "Process Fault Detection Based on Modeling and Estimation Methods-A Survey", Automatica. Vol. 20, No. 4, pp. 381-404. Kramer, M.A. (1992), "Autoassociative Neural Networks," Computers in Chemical Engineering, Vol 16, No. 4, pp. 313-328. Mott, Young, and R.W. King, "Pattern Recognition Software for Plant Surveillance", US DOE Report, 1987. Qin, S.J., and T.J. McAvoy (1992), "Nonlinear PLS Modeling Using Neural Networks," Computers in Chemical Engineering, vol. 16, n. 4, pp. 379-391. Qin, S.J and W. Li (1999), "Detection, identification and reconstruction of faulty sensors with maximized sensitivity", AIChEJ., 45(9).
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Rasmussen, B., J.W. Hines, and R.E. Uhrig (2000a), "Nonlinear Partial Least Squares Modeling for Instrument Surveillance and Calibration Verification", by published in the proceedings of the Maintenance and Reliability Conference (MARCON 2000), Knoxville,TN, May 7-10. Rasmussen, B., J.W. Hines, and R.E. Uhrig (2000b), "A Novel Approach to Process Modeling for Instrument Surveillance and Calibration Verification", Proceedings of The Third American Nuclear Society International Topical Meeting on Nuclear Plant Instrumentation and Control and Human-Machine Interface Technologies, Washington DC, November 13-17, 2000. Singer, R.M,, K.C. Gross, and R.W. King, (1994), "Analytical Enhancement of Automotive Sensory System Reliability," Presented at the World Congress on Neural Networks, San Diego, California. Singer, R.M., K.C. Gross, J.P. Herzog, R.W. King, and S.W. Wegerich (1996), "ModelBased Nuclear Power Plant Monitoring and Fault Detection: Theoretical Foundations," Proc. 9th Intl. Conf on Intelligent Systems Applications to Power Systems, Seoul, Korea. Upadhyaya, B.R., (1985), "Sensor Failure Detection and Estimation", Nuclear Safety. Upadhyaya , B.R., and K. Holbert (1989), "Development and Testing of an Integrated Signal Validation System for Nuclear Power Plants", DOE Contract DE-AC0286NE37959. Upadhyaya, B.R., and E. Eryurek (1992), "Application of Neural Networks for Sensor Validation and Plant Monitoring," Nuclear Technology, vol. 97, pp. 170-176, February, 1992. Wald, A. (1945), "Sequential Tests of Statistical Hypothesis," Ann. Math. Statist., Vol. 16, pp.117-186. Wegerich, S, R. Singer, J. Herzog, and A. Wilks (2001), "Challenges Facing Equipment Condition Monitoring Systems", MARCON 2001, Gatlinburg TN, May 6-9. Xu, X., J.W. Hines, and R.E. Uhrig (1999), "Sensor Validation and Fault Detection Using Neural Networks", published in the proceedings of the Maintenance and Reliability Conference (MARCON 99), Gatlinburg, TN, May 10-12, 1999. Xu, X. (2000), "" PhD dissertation, The University of Tennessee, Nuclear Engineering Department, Knoxville, TN. Yu, C , and B. Su, "A Nonparametric Sequential Probability Ratio Test for Signal Validation", presented in the proceedings of the The 2001 ANS/HPS Student Conference, Texas A&M University, March 29 - April 1, 2001. Zavaljevski, N., K. Gross and S. Wegerich (1999), Regularization Methods for the Multivariate State Estimation Technique (MSET), Proc. of the Int. Conf on Mathematics and Computation, Reactor Physics and Environmental Analysis in Nuclear Applications, September 27-30, Madrid, Spain, pp. 720-729.
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Condition Monitoring and Diagnostic Engineering Management Andrew G. Starr and Raj B.K.N. Rao (Editors) © 2001 Elsevier Science Ltd. All rights reserved.
THE APPLICABILITY OF VARIOUS INDIRECT MONITORING METHODS TO TOOL CONDITION MONITORING IN DRILLING Erkki Jantunen Technical Research Centre of Finland, Manufacturing Technology P.O.Box 1702, FIN-02044 VTT, Finland
ABSTRACT Condition monitoring of cutting tools is important for a number of reasons. The unmanned use of flexible manufacturing systems is not possible without a reliable system for tool condition monitoring. Tool wear affects the surface quality of processed workpieces. Tools cannot be optimally used based on tool change policy which relies on time and which easily leads to too frequent change of tools from which it follows that valuable production time is lost and the tool cost becomes high. There is great variation in how well different monitoring methods work in tool condition monitoring. It is well known and accepted that cutting forces increase as a ftmction of tool wear and consequently thrust force and torque are often monitored in drilling. Feed drive and spindle current actually also measure the same thing as feed force and torque transducers although through a longer measuring chain. Tool wear also changes the dynamics of cutting processes and consequently drift forces, vibration and sound have been used for tool wear monitoring. Cutting dynamics change also at higher frequencies i.e. ultra sonic vibrations and acoustic emission are also used for tool wear and failure monitoring. In the paper some physical reasons behind the use of various indirect monitoring methods of tool condition in drilling are presented and the benefits and drawbacks of each method are discussed. KEYWORDS Drill wear monitoring, drill failure monitoring, thrust force, torque, drift force, spindle power, vibration, sound, acoustic emission INTRODUCTION Many research projects have been carried out in the field of tool wear and failure monitoring. There are a number of reasons for this interest among the research society and industry. Probably the most important reason is that manufacturing technology has changed towards process industry in the sense that today production equipment are capable to work in production cells which can be fiiUy automated. However, in order to automate the production a way of tool condition monitoring is needed because a wom or broken tool could cause a lot of damage either to the workpiece or workpieces or, in the worst case, to the machine tool itself hi case of drilling this is rather apparent because if the tool is broken there might not be a hole
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wiiere the next tool e.g. thread tap is used. A less radical consequence is that with a worn tool the surface finish and dimensions are not as good as they should be. If tools are changed based on the time they have been used the economical life time of these tools cannot be benefitedfi*ombecause there is great variation in tool life. An other factor related to this conservative way of defining the tool change is that valuable production time is lost because of unnecessary tool changes. Tool condition monitoring can be based either on direct or indirect methods. The monitoring methods are considered direct if they actually measure the amount of wear and correspondingly indirect if the change of the measured parameter is a consequence of wear i.e. such as the increase of cutting force or vibration. This paper covers only indirect methods mainly because the direct methods still seem to be much more expensive methods to use and in many cases they also cause restrictions to the manufacturing process as such. DRILL WEAR MODEL Based on a series of drilling tests following relations based on physical models in drilling cast iron have been observed / 27 /: torque (M)
= ai HB d^ f + a2 HB d^ r + as HB d^ W
thrust (T)
= 34 HB d f + as HB d w + 36 HB d r + ay HB d^
HB d f w r ai... a?
= Brinell hardness of work material =diameter of the drill = feed per revolution = average flank wear = radius at the cutting edge = constants
(1) (2)
where
The terms in Eqn. 1 are comingfi-omthree contact zones 111 I namely: a) The rake face of the tool which contacts the chip and transmits most of the force necessary to perform the cutting action, b) The nonzero radius of the tool cutting edge (the transition surface between the rake and flank faces) which contacts the work material at the point where the chip and work separate. This edge radius causes an indenting force. (The nonzero intercept observed for zero feed on cutting force versus feed rate plots may be attributed to this effect.) c) An area on the flank face having 0 deg clearance, known as the flank wear land which rubs against the work surface. The shear stress between the flank and the workpiece has been determined to be approximately equal to the work material yield shear stress. The shear force caused by the flank wear is termed to be the third force component. From the above given relationships it is apparent that there is a strong dependency of workpiece hardness which actually means that tool life varies remarkably as a fiinction of this / 27 /. Consequently, cutting of a few random workpieces of large hardness may influence the drill life much more than a large number of workpieces of low hardness. Hence, in an industrial operation, drills may fail very early or after a long time, depending on the occurrence of these few workpieces of high hardness. This could explain the large variation in drill life observed in industrial conditions. The workpiece hardness also influences the thrust forces and torque occurring in a drilling operation. If the variation in thrust force, on account of changes in flank wear, is to be significant, the variation in workpiece hardness has to be held within 5 percent of the mean hardness value. This is very difficult to achieve in industrial castings. Hence, torque or thrust measurements for monitoring drill wear should be attempted only after a very close tolerance has been obtained in the workpiece hardness. Another observation in ref / 27 / is the speed in which drill wear takes place at the end of the drill life.
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Process parameters The approach developed in ref. I 211 has been fijrther studied in ref. / 17 / putting more emphasis on the cutting parameters when drilling copper alloys. In the test series thrust force and torque have been recorded at three different flank wear states, three cutting speeds, three feed rates and three drill diameters. It has been concluded that the relationships between the cutting force signals and drill wear as well as other cutting parameters including spindle rotational speed, feed rate and drill diameter were established. Tool wear can then be estimated using these relationships. It is also shown that the tool wear can be estimated knowing the thrust force signal, feed per revolution and drill diameter. Based on the studies conducted, the following conclusions are drawn / 17 /. 1) The effects of feed per revolution, depth of cut and tool wear on cutting force signals are significant, while the effect of cutting speed on the cutting force signals is relatively insignificant in the cases studied. 2) Both the thrust and the torque increase as the flank wear increases. 3) Thrust and torque can be well represented as functions of tool wear, drill diameter and feed per revolution. 4) Tool wear can be properly estimated knowing the thrust force and other cutting parameters, especially for larger tool wear. Drill geometry Due to production variations, a drill is typically slightly asymmetric / 6 /. Accordingly, the two comers of the drill point wear gradually while maximum wear alternates from one cutting edge to the other / 3 /, / 6 /. This alternating process continues until both lips have zero clearance at the margin. The drill then adheres to the workpiece and breaks if the cutting process is not stopped in time. The described phenomena is the explanation why drift forces can also be used as an indicator of tool wear /14 / together with feed force and torque. MONITORING METHODS Quite a number of indirect monitoring methods have been tested for drill wear and failure detection. The most popular methods reported in literature have been feed force, torque, drifl forces, spindle motor and feed drive current, vibration, sound, ultrasound vibration and acoustic emission. A summary of how popular each of these methods have been is shown in Figure 1 based on all references cited in this paper. Cutting speed and feed rate have also often been measured although they are not really used for tool wear monitoring. Since the other measured parameters are influenced by the cutting speed and feed rate they are also needed in a monitoring system or in adaptive control systems e.g. /11 /. In the subsequent paragraphs the monitoring methods are divided into two groups i.e. those that are related to measuring forces and those that are related to measuring vibration. The force measurements cover measurement of thrust (=feed) force, torque and drift forces together with measurement of spindle motor current and feed drive current. The spindle motor current actually corresponds to the measurement of torque although through a longer measuring chain and similarly feed drive current corresponds to the measurement of thrust force. The vibration related methods consist of mechanical vibration, sound, ultrasonic vibration and acoustic emission. Mechanical vibration is normally considered to take place from 1 Hz to about 10 kHz or 20 kHz. Airbome sound is often measured in the frequency range from 20 Hz to 20 kHz. Ultrasonic vibration starts from where mechanical vibration ends i.e. from about 10 kHz to about 80 kHz which then is the lower limit for acoustic emission which goes to as highfrequenciesas 1 MHz. Drilling forces The equations presented in previous chapter drill wear model actually explain why drilling forces i.e. feed force and torque have so widely been tested for drill wear monitoring. From the equations it becomes clear that these forces increase with increasing tool wear. Drift forces also indicate tool wear because of the
783
asymmetry of drills and also dynamics of the drilling process. Spindle motor current is an indicator of torque and feed drive current an indicator of feed force although through a longer measuring chain than the forces.
5
10
15
20
25
Number of references that report tests with the specific monitoring method
Figure 1: The popularity of measuring methods in drill condition monitoring. Feedforce Feed force has been tested or is used as an indicator of tool wear and failure in references / 2 /, / 3 /, / 5 /, / 8 /, / 10 / - / 15 /, / 17 / - / 21 /, / 23 / - / 32 / i.e. it is the most popular method for tool wear and failure monitoring in the cited literature. However, in many of the references the reported test material is very limited e.g. / 2 / and in some of the material the results are not encouraging i.e. the correlation between thrust force and wear has been found weak e.g. / 3 /. At the same time some other researchers have been successful m incorporating thrust force and torque into diagnostic approaches e.g. /15 /, /17 / and /18 /. In references / 19 / and / 20 / the thrust force has been tested and used together with vibration with good success. The increase of dynamic variation of thrust force and torque as a sequence of drill wear has been verified to correlate with the surface quality of composite material in ref / 23 /. An approach that can detect severe drill damage just before tool breakage occurs based on thrust force measurement has been proposed in ref / 28 / and I 29 I. An other kind of approach for the same task i.e. detection of severe damage before the drill actually breaks, has also been developed in ref / 30 /. In practise the measurement of feed force is rather a demanding task when it is done close to the tool i.e. it is difficult to find a way to measure force without causing some kind of problems. For example it is not very convenient to use an additional force transducer between the spindle and tool holder. Another place to position the force transducer is naturally between the workpiece and the table or between the table and the guideways. In the tests reported in ref / 1 0 / the feed force was ranked the second best measuring method after horizontal vibration when measured using a force transducer between the tool and spindle. The use of force sensor in the quideways was not as successful because in this kind of installation the force was actually influenced by the location of the hole in the workpiece, see Figure 2. However, it should be noted that at the end of drill life the indication of tool wear can be seen even with this rather poor signal.
784
300
Figure 2: Normalised RMS-value of feed force measured from the guideways for a 10.2 mm twist drill. All the results shown in this paper are based on measurements made with a horizontal machining centre. Four different kind of tools (shank end mill, end mill, twist drill and tread tap) were tested. The measurement system included force sensors, torque transducer, various equipment for the definition of voltage, current and electrical power consumption, accelerometers, acoustic emission sensors and microphones. The test and analysis program has been reported in more detail in ref. /10 /. Torque In most of the references where feed force has been tested also torque has been tested for the same purpose / 3 /, / 4 /, / 5 /, / 8 /, /10 /, /11 /, /12 /, /14 /, /15 /, /17 /, / 18 /, / 21 /, / 23 /, / 25 / - / 27 / and / 31 /. The conclusion of / 3 / does not give support to using torque as an indicator of drill wear. As could be expected on the basis of what has been presented about tool wear in the previous chapter the general experience with testing torque is more or less the same as with thrust force. One drawback of measuring cutting forces and torque has been the difficulty and, consequently, the expense of the measuring arrangement for practical applications. A new cheaper arrangement for measuring torque based on eddy current has been presented in ref / 4 / with good experience. Driftforces Drift force measurement is covered here in the same context as the other forces though it rather belongs to the same group as vibration measurements i.e. drifl forces are a result of asymmetry in the drill and drilling process as explained earlier in this paper. Although the reported correlation with thrust force and torque with respect to tool wear was not good the results with drift force have been considered encouraging / 3 /. The result has been considered to give support to the theory of asymmetric wear of drills described in the drill geometry paragraph of this paper. However there seems to be a problem related to this type of measurement i.e. it has been indicated the parameters calculated from drift force seem to form a sort of an s-type trend index. At first the index increases when one side of the drill is wearing, and then it decreases and starts to increase again and so on. This makes it somewhat difficult to define when the drill is actually wom. Also the findings reported in ref / 14 / support the theory of asymmetric wear of drills. It is suggested that tool life criterion could occur when the RMS of the drifting force achieves a minimum close to that of the sharp drill. In this paper all the figures that are shown and most of the findings in referenced papers are based on statistical parameters calculated from time domain signal. In most of these papers one or many of the tested methods have proved to be suitable for drill wear monitoring. In the studies reported in ref / 21 /the conclusion for torque, feed and drift force measurements is simply that the signals in time domain do not show any correlation with drill wear. With more sophisticated signal analysis based on the use Fast Fourier Transform (FTT) the influence in the measured signals has been seen. The conclusion was that torque, feed 785
force and drift force in x-direction showed good correlation with drill wear but the correlation of drift force in Y-direction was not as good. In ref. / 31 / after using torque, feed and drift forces in the test it has been suggested that the power spectrum of drift force could serve as an index to monitor the onset of tool failure. Spindle motor current Spindle motor current (or power depending what sort of measuring arrangement is used) is especially interesting as a measure of drill wear because it is so easy to monitor. In principle it can be expected that the same phenomena's as with torque should be possible to see in this signal. In ref. 716/ spindle motor together with feed drive current has been tested for drill breakage detection. Also in ref / 24 / good experience with diagnosis of drill wear based on spindle motor and feed drive current combined with diagnosis of failure v^th feed force is reported although no examples of actual signals are given in this reference. The experience reported in ref. / 25 / was not encouraging but the same also applies to feed force and torque measurements. In ref. / 27 / the reported spindle motor power and torque curves as a fiinction of drill wear are very similar both indicating a very rapid increase in the signals at the end of drill life. In the tests reported in ref /10 / the analysis of spindle motor current or power was not successftil as can be seen in Figure 3. It should be noted that there has been remarkable variation in the tests reported in ref. / 10 / but overall the results with electrical power and current measurements were below average level of the other signals. This could possibly be explained by the type of machine tool i.e. motors and also possibly into some extent to sensitivity of the measuring equipment used.
150 Sample (time)
Figure 3: Normalised root mean square value of spindle motor power for a 10.2 mm twist drill. Feed drive current The measurement of feed drive current has been tested in many of the same references as spindle motor current or power / 7 /, /10 /, /16 /, / 24 / and / 25 /. It is somewhat surprising that not that many researchers have tried to use feed drive current since it should be able to give similar information as the feed drive force. Actually one could expect that the amount of noise in feed drive current would be less pronounced than is the case with spindle motor current. Quite similarly as with the spindle motor current drill breakage has also been detected with feed drive current in ref. /16 /. An example of the root mean square value of feed drive servo motor current is given in Figure 4 based on the tests reported in ref. / 10 /. The indication of tool wear is not as evident as with some other measuring techniques although the level increases at the end of the tool life. Apparently some disturbance has taken place in the beginning of the test so that one measurement value clearly is not as it should be. Vibration It is very logical to measure vibration as an indicator of tool wear and failure. In principle, when the cutting forces increase due to wear also vibration and sound emitted by the structure in question increase. The 786
increase of vibration at higher frequencies can also be expected when forces increase. Another logical reason for vibration monitoring to work is that when the tool becomes worn the cutting process tends to get somewhat more unstable i.e. the dynamic nature of the process becomes more apparent and vibration increases. This is also the reason why parameters that indicate the variation of the measuring signal (higher order terms like standard deviation and kurtosis) actually show some difference. It should be noted that vibration related measuring signals tend to be easier to use in practise than most of the force related methods since an accelerometer or microphone can be mstalled a bit further away from the tool and workpiece but in order to work really well force and torque should be measured between the motors that provide the forces and the tool which is somewhat more complicated or demanding.
Figure 4: Normalised RMS-value of feed drive servo motor current for a 10.2 mm twist drill. Mechanical vibration Mechanical vibration has been studied by quite a number of researchers I\IJ2IJ6IJ1IJ9IJ\^IJ\?> /, /19 /, / 20 / and / 26 /. This is not surprising because of the previously described reasons and also the fact that vibration is the most widely used method in condition monitoring of machinery in general. The way some of the different types of drill wear i.e. chisel, outer comer, flank and margin is seen in the spectrum of vibration signal has been studied in ref / 6 / with artificially produced wear. It has been concluded that monitoring vibration has been proved to be a useful method in predicting drill wear and failure. It should be noted that in the spectrums the most dominating increase of vibration due to wear has taken place at high frequencies ( 3 - 6 kHz) close to the natural modes of the tool and tool holder. The proposed analysis methods i.e. Kurtosis value together with cepstrum analysis, power spectrum and a statistical triggering parameter (ratio of absolute mean value) woiUd seem to try to focus on the change of the dynamics of the signal. In tests reported in ref 7 10/ vibration (acceleration) was the best indicator of all of the tested methods including force, motor current, acoustic emission etc.. An example of vibration signals is given in Figure 5. Thrust force and vibration have been tested in references / 19 / and / 20 / simultaneously. It has been concluded that either of these could be used for on-line classification of drill wear. However, integrating both signals yields better results. Sound The use of sound measurements has only been tested in a few references I2IJ 3 I and /10 /. In / 3 / it has been considered striking that the curves of two completely different physical values i.e. drift force and sound have been very similar. However, it could be debated whether the result could actually be anticipated since airbome sound actually is a result of the mechanical vibration of the parts of the machine tool, tool and work piece and the vibration of these is a function of the dynamic forces present in the drilling process. Actually it could be claimed that all the same information that is available in mechanical vibration signal should be available in the sound signal. However the problem with sound signal is that it is very diffused i.e. it is reflected from various surfaces in various directions. The real benefits in using sound measurements for tool wear and failure detection are in the ease of installation of the transducer since a microphone can very easily 787
be installed rather close to the tool and the price of a microphone compared to an accelerometer is really low. An other factor which one could think of that would have encouraged the use of sound measurements in drill wear monitoring is the fact that the machine tool operators often rely on their hearing when they define whether the tool is worn i.e. the sound the tool produces changes with tool wear. The results reported in ref / 10 / can be considered promising, hi Figure 6 a sound signal curve is shown for the same drill for which also vibration curve has been shown. Although the standard deviation value of sound signal is not quite as clear an indicator as the low pass filtered RMS-value of vibration signal it tells the same story. 16
1^ =? 2 (0 «
12
8
,„-,„„_-,..,,.^ 50
150
100
200
250
300
Sample (time)
Figure 5: Normalised RMS-value of low pass filtered vertical vibration for a 10.2 mm twist drill.
•o 12
c 3 O ^ 10 ^
8
P
6
/J\JUJW^/u^^^-AAMvA^^^ 50
150
100
200
250
300
Sample
Figure 6: Normalized standard deviation of sound signal for a 10.2 mm twist drill. Ultrasonic vibration Some researcher have tested ultrasonic vibration / 9 /, / 13 /, / 26 / for drill wear monitoring and breakage detection. However, in some cases vibration measurement below 80 kHz has also been called acoustic emission because it does not seem to be commonly accepted how vibration at these higher fi-equencies should be called. In ref / 9 / vibration infi-equencyrange from 20 kHz to 80 kHz is defined as ultrasonic vibration and the same definition is used in this paper. The use of ultrasonic vibration as an indicator of tool wear is explained and compared to other methods in the following way in ref / 9 /. Acoustic emission is considered to suffer firom severe attenuation and multi-path distortion caused by bolted joints commonly found in machine tool structures. It has especially been noted that ultrasonic vibration does not suffer as much because it takes place at lower frequencies and consequently the transducer can be placed fairly far 788
from the chip forming zone. When compared to lower frequency vibration, ultrasonic vibration is considered better in the sense that the structural modes of vibration do not affect it because the structural modes in this range are so closely spaced that they form a pseudo-continuum. In ref I 26 I ultrasonic vibration has been tested together with torque, feed and drift force measurements. In tests ultrasonic vibration has been the most effective method both for wear and failure monitoring especially when the signal analysis of ultrasonic vibration is based on band-pass filtering (10 kHz bands with 10 kHz steps in frequency range from 10 to 70 kHz. Acoustic emission Acoustic emission takes place when a small surface displacement of material surface is produced / 22 /. It is considered that acoustic emission can be used to monitor crack growth, sudden impacts and rubbing of material against another which all cause vibration of the structure at very high frequencies (from 80 kHz to 1 MHz). Monitoring of acoustic emission has been rather popular in turning but surprisingly it does not seem to be that popular in drill wear and failure monitoring. One possible explanation to this is that in turning the AE transducer can be positioned closer to the tool than is the case in drilling. One of the benefits of acoustic emission is that since it takes place at very high frequencies it does not travel very far i.e. noise from other sources such as electrical motors do not travel to the tool in turning due to damping. The same feature is actually very easily a drawback in drilling since in practise the transducer has to be positioned rather far away from the tool and there might be a number of joints on the way where the AE needs to travel from one part to another which is very disadvantageous to the signal. The above explanation is probably the reason why in the tests reported in ref /10 / acoustic emission was not found to be one of the best methods for tool wear monitoring in drilling. The normalized root mean square value of acoustic emission (200 kHz centre frequency) is shown in Figure 7. In the example some disturbances are SQQn in the early part of the signal. Unfortunately it is rather typical that something, which destroys the measuring signal, happens during the machining process. There are a number of possibilities that could be the cause of this type of jump in the analysed signal e.g. something has hit the transducer or cable or some outside source has caused high vibration noise. It is possible to try to avoid wrong conclusion if a number of signals from various transducers are used in the diagnosis as the basis for defining whether the tool is worn or not. In ref / 22 / acoustic emission has been tested with drill with and without TiN (Titanium Nitride) coatings. It has been suggested that AE works best when certain phases of drilling process are analysed because there are peaks in the signal in the beginning and end of the process of drilling a hole.
3.2 CO
(0
E .2
t
(0 S
150
100
200
250
300
Sample (time)
Figure 7: Normalized root mean square value of acoustic emission for a 10.2 twist drill. CONCLUSION Condition monitoring of cutting tools is important for a number of reasons. The unmanned use of flexible manufacturing systems is not possible without a reliable system for tool condition monitoring. Tool wear affects the surface quality of processed workpieces. Tools cannot be optimally used based on tool change 789
policy which relies on time and which easily leads to too frequent change of tools from which it follows that valuable production time is lost and the tool cost becomes high. Based on the reports of a number of researchers it can be claimed that there is great variation in how well different monitoring methods work in tool condition monitoring. It is well known and accepted and also mathematical models have been developed which show that cutting forces increase as a function of tool wear and consequently thrust force and torque are often monitored in drilling. Feed drive and spindle current actually also measure the same thing as feed force and torque transducers although through a longer measuring chain and therefore also they can be used for tool wear and failure monitoring. Tool wear also changes the dynamics of cutting processes and consequently drift forces, vibration and sound have been used for tool wear monitoring. Cutting dynamics change also at higher frequencies i.e. ultra sonic vibrations and acoustic emission are also used for tool wear and failure monitoring. Based on the reported test material in the literature and the tests reported in this paper it would seem that thrust force, torque, drift forces, mechanical vibration, sound and ultrasonic vibration are all potential monitoring methods for drill wear monitoring although not all of the experience gained is as good. The result is not surprising since all of these methods are linked and those methods that have not been as successful simply suffer from a longer measuring chain that dampens the signals and introduces noise. REFERENCES / 1 / Barker R.W., Kluthe G.A. and Hinich M.J., 1993, Monitoring Rotating Tool Wear Using HigherOrder Spectral Features, Journal of Engineering for Industry, Vol. 115, Transactions of the ASME, 2329 121 Braun S. and Lenz E., 1986, Machine Tool Wear Monitoring, Mechanical Signature Analysis, Theory and Applications, Academic Press Ltd, 321-342 / 3 / Braun S., Lenz E. and Wu C.L., 1982, Signature Analysis Applied to Drilling, Journal Mechanical Design, Vol. 104, Transactions of the ASME, 268-276 / 4 / Brinksmeier E., 1990, Prediction of Tool Fracture in Drilling, Annals of the CIRP, Vol. 39, No. 1, 97-100 / 5 / Christoffel K. and Jung W., 1981, Uberwachungseinheit fur die Bohrbearbeitung, Industrie Anzeigner, Vol. 103 (62), 198-199 / 6 / El-Wardany T.I., Gao D. and Elbestawi M.A., 1996, Tool Conditon Monitoring in Drilling Using Vibration Signature Analysis, International Journal of Machine Tools & Manufacture, Vol. 36, 6, Pergamon Press Inc., 687-711 111 Erdelyi F. and Santha C , 1986, Monitoring Tasks on Boring and Milling Production Cells, Computers in Industry, Vol 7, Elsevier Science B.V., 65-71 / 8 / Govekar E. and Grabec I., 1994, Self-Organizing Neural Network Application to Drill Wear Classification, Journal of Engineering for Industry, Vol. 116, No. 3, Transactions of the ASME, 233238 / 9 / Hayashi S.R., Thomas C.E. and Wildes D.G., 1988, Tool Break Detection by Monitoring Ultrasonic Vibrations, Annals of the CIRP, Vol. 37, No. 1, 61-64 /10 / Jantunen E. and Jokinen H., 1996, Automated On-line Diagnosis of Cutting Tool Condition (Second version), International Journal of Flexible Automation and Integrated Manufacturing, 4 (3&4), Begell House Inc., 273-287 /11 / Kavaratzis Y. and Maiden J.D., 1989, System for Real Time Process Monitoring and Adaptive Control during CNC Deep Hole Drilling, Proceedings of Comadem, '89, Kogan Page, London, UK, 148-152 7 12/ Konig W. and Christoffel K., 1980, Sensoren fiir die Bohrbearbeitung, Industrie Anzeigner, Vol. 103 (100), 29-33
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7 1 3 / Kutzner K. and Schehl U., 1988, Werkzeugiiberwachung von Bohrem kleinen Durchmessers mit Korperschallsensoren, Industrie Anzeigner, Vol. 110 (82), 32-33 /14 / Lenz E., Mayer J.E. and Lee D.G., 1978, Investigation in Drilling, Annals of the CIRP, Vol. 27, No. 1,49-53 /15 / Li P.G. and Wu S.M., 1988, Monitoring Drilling Wear States by a Fuzzy Pattern Recognition Technique, Journal of Engineering for Industry, Vol. 110, No. 2, Transactions of the ASME, 297-300 /16 / Li X., 1999, On-Line Detection of the Breakage of Small Diameter Drills Using Current Signature Wavelet Transform, International Journal of Machine Tools & Manufacture, Vol. 39, Issue 1, 157-164 /17 / Lin S.C. and Ting C.J., 1995, Tool Wear Monitoring in Drilling Using Force Signals, Wear, 180 1-2, Elsevier Science S.A., 53-60 /18 / Liu T.I. and Anantharaman K.S., 1994, Intelligent Classification and Measurement of Drill Wear, Journal of Engineering for Industry, Vol. 116, Transactions of the ASME, 392-397 719/ Liu T.I. and Ko E.J., 1990, On-Line Recognition of Drill Wear via Artificial Neural Networks, Monitoring and Control for Manufacturing Processes, PED. Vol. 44, ASME, Dallas, TX, 101-110 / 20 / Liu T.I. and Wu S.M., 1990, On-line Detection of Drill Wear, Journal of Engineering for Industry, Vol. 112, Transactions of the ASME, 299-302 / 21 / Noori-Khajavi A,, 1992, Frequency and Time Domain Analyses of Sensor Signals in a Drilling Process and Their Correlation with Drill Wear, Ph.D. Thesis, Oklahoma State University, Stillwater, OK, / 22 / Quadro A.L. and Branco J.R.T., 1997, Analysis of the Acoustic Emission during Drilling Test, Surface & Coating Technology, Vol. 94-95, No.1-3, Elsevier Science S.A., 691-695 / 23 / Radhakrishnan T. and Wu S.M., 1981, On-Line Hole Quality Evaluation for Drilling Composite Material Using Dynamic Data, Journal of Engineering for Industry, Vol. 103, Transactions of the ASME, 119-125 / 24 / Ramamurthi K. and Hough C.L. Jr., 1993, Intelligent Real-Time Predictive Diagnostics for Cutting Tools and Supervisory Control of Machining Operations, Journal of Engineering for Industry, Vol. 115, Transactions of the ASME, 268-277 / 25 / Routio M. and Saynatjoki M., 1995, Tool Wear and Failure in the Drilling of Stainless Steel, Journal of Materials Processing Technology, Volume 52, Issue 1, Elsevier Science B.V., 35-43 / 26 / Schehl U., 1991, Werkzeugiiberwachung mit Acoustic-Emission beim Drehen, Frasen und Bohren, Aachen, / 27 / Subramanian K. and Cook N.H., 1977, Sensing of Drill Wear and Prediction of Drill Life (I), Journal of Engineering for Industry, Vol. 101 (or 103), Transactions of the ASME, 295-301 / 28 / Tansel I.N., Mekdeci C , Rodriguez O. and Uragun B., 1993, Monitoring Drill Conditions with Wavelet Based Encoding and Neural Network, International Journal of Machine Tools & Manufacture, Vol. 33, No. 4, Pergamon Press Ltd, 559-575 / 29 / Tansel I.N., Rodriguez O. and Mekdeci C , 1992, Detection of Tool Breakage in Microdrilling Operation with RCE Neural Networks, PED, Vol. 47, No. 1, ASME, 83-88 / 30 / Thangaraj A. and Wright P.K., 1988, Computer-asisted Prediction of Drill-failure Using Inprocess Measurements of Thrust Force, Journal of Engineering for Industry, Vol. 110, Transactions of the ASME, 192-200 7 3 1 / Valikhani M. and Chandrashekhar S., 1987, An Experimental Investigation into the Comparison of the Performance Characteristics of TiN an ZrN Coatings on Split Point Drill Using the Static and Stochastic Models of the Force System as a Signature, T / 32 / Von NedeB C. und Himburg T., 1986, Automatisierte Uberwachung des Bohrens, VDI-Z, Bd. 128, Nr. 17,651-657
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Condition Monitoring and Diagnostic Engineering Management Andrew G. Starr and Raj B.K.N. Rao (Editors) © 2001 Elsevier Science Ltd. All rights reserved.
A PALM SIZE VTORATION VISUALIZING INSTRUMENT FOR SURVEY DIAGNOSIS BY USING A HAND-HELD TYPE TRIAXIAL PICKUP Hidemichi Komura, Kazuo Shibata, and Kazuhiro Shimomura Machine Diagnosis Technology Department, RION Co., Ltd. 3-20-41 Higashimotomachi Kokubunji, Tokyo, 185-8533, Japan komura@rion,co.jp, [email protected], [email protected]
ABSTRACT Various types of machine faults such as unbalance, misalignment, looseness, and eccentricity are found in the category of fault of structure. The unbalance includes static unbalance, couple unbalance, quasistatic unbalance and dynamic unbalance. The misahgnment includes parallel misalignment, angular misaUgnment and compound misalignment. As the directional characteristics of vibration change with the type of machine fault, it is extremely difficult for conventional unidirectional type of vibrometer to diagnose the fault of structure. We can observe the movement of the structure using orthogonal three directional vibration measurements, and it will be a great help to diagnose the fault of structure. But large and complex operations are necessary to realize it. We have developed a palm size vibrometer, which can measure vibrations in three orthogonal directions simultaneously and visualize the movement of the measuring point on LCD screen only by pressing the sensor onto a surface of machine. We call it the orbit visualizing vibrometer. The structure of the sensor is very unique. Three piezoelectric accelerometers are arranged at equal intervals around the sensor center axis with the sensitive axes of piezoelectric accelerometers facing the sensor tip. The principle of operation is quite different from conventional triaxial vibration pickups. A detailed operation principle of the new handheld type triaxial vibration pickup is reported, and applications of the orbit visuahzing vibrometer are described.
KEYWORDS fault of structure, orbit visualizing vibrometer, rotating machinery, triaxial vibration pickup
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INTRODUCTION In rotating machinery, most of the faults are attributed to unbalance, misalignment, looseness, bearing fault, or gear fault. Among them unbalance, misalignment, and looseness are classified into the fault of structure. In any fault of structure, vibrations both perpendicular and parallel to the rotation shaft occur. Therefore, the orthogonal three directional measurements must be required. Nevertheless in most of the conventional types of vibrometers they can measure only one directional vibratioa In order to measure vibrations in three directions they have to measure three times. Those three measurements provide the amplitude of the vibration and no other information. On the other hand, use of a triaxial type vibration pickup cuts the measuring time to one third by allowing simultaneous measurement. More importantly, it provides phase information on the three directional vibrations whereby we can visualize the movement of the measuring point and this makes diagnosis extremely easy. Thus, for easier measurement the development of a hand-held type triaxial vibration pickup was called for. With conventional orthogonal triaxial vibration pickups, when pressed onto a surface the stifihess parallel to the surface is too low to provide a contact resonance frequency to high and this makes the measurement in that direction impossible. Herein, we report on the operating principle of the handheld type vibration pickup we have developed that allows the orthogonal triaxial vibration measurements simultaneously, as well as, the vibration characteristics of a prototype pickup. In addition, we discuss the orbit visualizing vibrometer, which is provided with the prototype pickup.
PRINCIPLE OF OPERATION Detection of Horizontal
Vibration
When a bar is pressed onto a horizontally moving surface, Fig.l shows the motion of the bar. Assume that, in the plane that contains the centerline of the bar, point N and point E are respectively located at equal distances from point B, which is located in the centerline of the bar. Further, assuming that the point at which a straight line passing through point N, intersecting the centerhne of the bar at angle 0 is expressed as Q, a straight line connecting point E and point Q also intersects the centerline of the bar at angle 9. Vibration sensor elements having sensitive axes QN and QE are attached to the points N and E. When the bar's tip, U, is pressed onto a surface, the u point N and the point E simultaneously vibrate in the Figure 1: Motion of a bar same direction and perform a rocking motion around horizontally moving surface point B as the centerline inclines pivoting on point O. Each of the vibration sensor elements at point N and point E detects the combined vibration of the horizontal motion and the rocking motion, in the QN and QE directions. The components of each vibration in the QN and QE directions are separated geometrically from the signals that are detected by the sensors attached at point N and point E. Figure 2 shows the operating principle of the hand-held type triaxial vibration pickup, which has been
794
newly designed according to the method described in the Fig. 1. Assuming that every point as shown in Fig.l has no distortion in the measurementfrequencyrange, the motions at point N and point E are symmetrical and in Fig. 2 we will discuss on the motion at point E. In the Fig.l we have discussed the bar having an acute angled tip but hereinafter we treat the bar as a box case for a vibration pickup. In the Fig. 2 point O expresses the origin of the coordinates, the z-axis extends in the OU direction, and the>'-axis is orthogonal to the z-axis. The coordinates of points B, Q and U on the z-axis are expressed as b, q, and u respectively and the coordinate of point E on y-zxis is expressed as e. When the point U tip of the bar vibrates in the direction parallel with the j^-axis as a function of time, r, that is, yu \t), the sensor attached to point E simultaneously detects components of the vibration in the j'-direction, ;;^(/), and a rocking vibration in the pickup, ^^(r), in the QE direction, respectively. Therefore the detection signal pg(r) is expressed as; /'£W=>'£{Osin +z^{Ocos
(1)
Assuming that the deviation angle of the origin O, at which the tip of the vibration pickup is moving in a j;-direction, is (0, its geometrical relations to ^^^(O, ^^{O* and (t) are obtainedfromthe following equations respectively;
(2)
Substituting Eqa2 into Eqn.l, the signal for the QE direction that will be detected at point E is; PE[t)-{f)sm +ecos )sin {t)
BiO,b) E(e,b)
(3)
From b - g-ecos /sin , the above Eqn.3 develops into; p^{t)=qsm
sin (/)
(4)
Similarly to Eqn.2,fromthe geometrical relation; sin (t}=yu(t)/u
-*-yi/(t) Figure 2: Operating principle of the hand-held type triaxial pickup
(5)
Substituting Eqn.5 into Eqn.4, it is found that the signal detected at point E is proportioned to the horizontal motion, y^jit), of the surface which the pickup is in contact with, and after elimination of any components attributed to the rocking motion, the following equation is obtained; /'£W=>^t/W^/«)sin
(6)
In Eqn.6, the signal /^^(O* which is detected by the sensor positioned at point E, is expressed by a function for the intersection angle , produced by sensitive axis QE and the centerline, and the ratio of the distance OQ to OU. The intersection angle can be structurally determined but the origin O is a pivot for the pickup motion that depends on the vibratingfrequencyof the contact point U. To make the signal p^it) independent of the position of the origin O, the geometrical structure of the pickup must be q=u. That is, when the intersection point Q coincides with the contact point U, the signal detected by the sensor positioned at point E, becomes;
795
P£(0=>'(y(Osin
(7)
The detected signal obtained from Eqn.7 above reveals that it is identical to the signal obtained by a conventional vibration pickup that isrigidlyattached to point U. Detection of Horizontal Vibration and Vertical Vibration The signal detected by a sensor, while the contact point U is moving vertically (in the z direction) at Zu(t), is determined exclusively by the geometrical relation Zu(t)cos . Therefore, when the contact point U, is moving horizontally and vertically, the signal detected by the sensor is expressed as; P£(0=3^o(^)sm -\-z^.{t)cos
(8)
The signal detected by the sensor positioned at point N is identical to the signal detected by the sensor at the point E, as far as vertical motion is concerned, and it shows the opposite phase of horizontal motion. As for the signal Pj^ (t), detected at point N, the sign in the first term of therightside of Eqn.8 isreversedas follows;
pAf)=-yu(fh^
+^o(^)cos
(9)
Eqn.8 and Eqn.9 are for signals which are detected by the two sensors attached to points E and N respectively, while the contact point U is moving, andfromthe sum of the equations or their difference, the motion components in vertical direction and horizontal direction can be obtained. The vertical component is expressed as;
,^.{,)=Mh£M
(10)
2 COS
and the horizontal component is expressed as;
(/)=MizMl 2sm
(11)
Detection of Two Orthogonal Horizontal Vibrations and Vertical Vibration In the above discussion only one of the horizontal dimensions has been discussed but for horizontal motion two orthogonal dimensions must be detected. To detect the two dimensions of horizontal motion, position three sensors horizontally around the z-axis at interval , = 2 /3 as shown in Fig. 3, and simply compensate for the above equation with the geometrical position of sensors according to the method as shown below. Signals /?i(f), P2(t) and p^(t) which are detected by sensors 1, 2 and 3 while the contact point U is moving vertically (in the z-direction) at Zu{t)y and horizontally (in the ydirection) atyu(t), and also horizontally (in the jr-direction) at Xu(t), which is orthogonal to the y direction, are obtained as follows; pM=yu(f)^^ +zt/(0cos P2(t)=-Xij(t)sin sin +yy(t)cos sin +Zij{t)cos Pi(t)=Xij{t)sm sin +yy(t)cos sin +Zu(t)cos
796
(12) (13) (14)
By substituting =2 / 3 , the vibration components Xy(t), yyit), and Zy(t) are obtained from the signals p,(0, JD2(0 and /^jl?) as follows;
M-.PAt)-P2Kt) 2 sin sin
(15)
y^{^y'^PAthPi[t)-pAt) 3 sin
(16)
2^{^)^PM±PJM±PM.
(17)
3 cos Sensor3
z Leaf Spring k Sensorl
J
Sensor!
7
Figure 3: Structural design of the hand-held type triaxial pickup STRUCTURE AND CHARACTERISTICS OF THE HAND-HELD TYPE TRIAXIAL PICKUP We have developed the hand-held type triaxial pickup based on the operating principles described in the previous section and measured its frequency responses and directional characteristics. Figure 3 shows the structural design and Fig. 4 shows the appearance of prototype. Eqn.15, Eqn.l6 and Eqn.l7, indicate that the amplitudes of the three orthogonal components depend on the intersection angle of the sensitive axes. In order to make the output levels identical in each direction, the optimal intersection angle must be /4. However, this value necessitates a larger diameter for the pickup. Since the pickup is used for machinery diagnosis, it is desirable that an operator can grip it Figure 4: Appearance of prototype pickup tightly. We decided the diameter of the pickup to 30mm and the intersection angle to 20 degrees, which is lower than the optimum value. The three piezoelectric accelerometers are pressed into fixing holes, which are prepared at 120 degree intervals around a circle on block in Fig.3. The sensitive axes cross the centerline at the tip of the pickup with 20 degrees respectively. The three combined elements can be regarded as a rigid body within the frequency range up to IkHz. The sensitive axes as pickup itself consist of the z-axis, which extends along with the centerline, and the >^-axis, a direction of one of the sensitive elements in a plane orthogonal to the z-axis, and the x-axis, which is orthogonal to the z-axis and the j^-axis.
797
»
1«
-2«
fc
2k
iK
«i
Figure 6: Directional characteristics of the Y-axis
Figure 5: Frequency responses
Figure 5 shows the frequency responses of the prototype pickup in the z-axis and the ^^-axis. Both the z-axis and >'-axis are ahnost flat up to IkHz. The contact resonance determines the maximum frequency of the pickup. Generally it is desirable that the contact resonance is three times higher than the maximum frequency. In conventional triaxial vibration pickups, the horizontal contact resonance frequency is one third of the vertical contact resonance frequency. The contact resonance frequency depends on the pickup mass and the stiffness at the contact surface. From the difference shown in the contact resonance frequencies it is expected that the stiffness in the shearing direction is lower than that in compression direction. For the pickup we have developed however, the contact resonance frequencies of the horizontal x- andjv-axes are 5.2kHz respectively, and the vertical z-axis is 3.5kHz. That is, the horizontal direction is higher than the vertical direction. This is because the stiffness at the contact surface is the same but the dynamic effective mass of the pickup is smaller. The dynamic effective mass is a quantity that is proportional to the force, which is required to make motion. The center of gravity of the present pickup is located away from its tip, which allows it to cause a motion in the horizontal direction with a smaller force. The horizontal effective mass is smaller than the actual mass, but in the vertical direction the actual mass becomes the dynamic effective mass. Figure 6 shows the directional characteristics of the ^-axis when the prototype pickup is pressed onto a surface vibrating horizontally at 160Hz, and then rotated around the z-axis. In a plane orthogonal to the z-axis, the characteristics coincide with a cosine curve in a range of ±70 degrees from the >'-axis, with tolerance ±5%. The lateral sensitivity at 90 degree is 8 %. It is not as good as conventional unidirectional pickup whose lateral sensitivity is 5%, but it is not a significant problem for practical
ORBIT VISUALIZING VIBROMETER We have developed a vibrometer, which is equipped with a hand-held type triaxial pickup whose frequency response is almost flat from lOHz to IkHz and the full-scale range is lOOnmi/s.
798
Figure 7: Orbit visualizing vibrometer
Figure 7 shows the appearance of the vibrometer. We adopted a 128x64 dots LCD which displays measurement data. The display can be switched between numerical value mode and orbit mode. Figure 8 shows an example of numerical value mode. This mode displays four items, vibration velocities in three directions and a composite value of them. The composite value is a scalar value that does not depend on the attached direction of the vibration pickup. In orbit mode the vibrometer samples 1024 points of waveform data of velocity with 2,56kHz sampling rate in three directions and displays them after converting to displacement. Figure 9 displays an example of an unbalanced state. The unbalance is caused by centrifugal force with an uneven rotating mass and it shows an orbit of close to circularity in a plane orthogonal to the rotation axis. Figures 10 and 11 show examples of misalignment. These orbits indicate parallel misalignment where the shafts are coupled by a miniature flexible coupling. The both hubs of the coupling have two connection arms as shown Fig. 12. Figure 10 is the orbit on a plane orthogonal to the rotation axis and Fig. 11 is the orbit on a horizontal surface. You will find in Fig. 10 that the axis moves to the same position after rotating four times. This is caused by the 4th order harmonics. Thus the present vibrometer allows us to visualize the movement of attached point and provides information that is very useful for machinery diagnosis. The present vibrometer can store 100 sets of numerical value mode and 8 sets of orbit mode. In the orbit mode one data set includes vibration waveforms in three directions and their reference signal, for which a waveform or rotating pulse signal is used. The stored vibration waveforms can be transferred to personal computers and used in animated displays of multiple points.
Measure Ux 3.6mm/sl U^ 14.9mm/sH Uz 31.6mm/sl Us 35. Imm/'sl Figure 8: Example of numerical value mode
Figure 9: Orbit of unbalanced state 1 -irwa ORBIT
Y-Z DISP
^'^'^^
^1^ a\
...L...11..
Oc
•f""/V
^
lOtxin
Figure 10: Orbit of misalignment orthogonal to the rotation axis
Figurell: Orbit of misalignment on horizontal surface
Conventionally, most diagnosis of fault of structure is carried out based on frequency data, but by using the orbit vibrometer further detailed information on time data as well as phase data can be obtained that will significantly improve the accuracy of diagnosis. Figure 12: The coupling we examined
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\
CONCLUSIONS Herein we have presented and discussed a pickup based on a new principle that allows the three orthogonal vibration measurements simultaneously by pressing it onto a machine surface. The purpose of the pickiq) is to perfomi accurate diagnosis of fault of structure. For that purpose, the pickup is designed for use up to 1 kHz, which is defined as the vibration severity. We have made a prototype pickup and demonstrated its characteristics. It has been proved that the prototype has sufficient perfomiance for practical use. We have also made the orbit visualizing vibrometer, which is provided with the pickiq). It displays the orbit of the measured point on its screen. Using the present vibrometer, we introduced actual examples of fault of structure and showed that the displayed orbits could provide very useful information for diagnosis. Using personal computers and the vibrometer that is able to store waveforms, any phenomenon that results from combined waveforms at multiple points can be displayed, and allows further visualization of the movement of a structure as a whole. We believe the field of multi-dimensional diagnosis technology will be established in the near future and the present technology will contribute to diagnosing rotating machinery.
REFERENCES [1] Yokota A., Komura H., and Tokita Y. (1993). Study on the Hand-held Type 3D Vibration Pickiq). Proc. Spring Meet. Acoustical Society of Japan, Vol.1,421-422 [2] Yokota A., Komura H., and Tokita Y. (1994). Study on the Hand-held Type 3D Vibration Pickiq) Part II - Estimation of effective mass of horizontal axis direction. Proc. Autumn Meet. Acoustical Society of J^an, Vol.1, 547-548 [3] Yokota A., Komura H., and Tokita Y. (1996). Development of Hand-held Type 3D Vibration Pickup. The Journal of the Acoustical Society of J£q)an 52:
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Condition Monitoring and Diagnostic Engineering Management Andrew G. Starr and Raj B.K.N. Rao (Editors) © 2001 Elsevier Science Ltd. All rights reserved.
DEVELOPMENT OF AN ON-LINE REACTOR INTERNALS VIBRATION MONITORING SYSTEM(RIDS) J.-H. Park', J.-B. Park^ C.-H. Hwang\ E.-S. Choi^^ ' Department of Advanced Reactor Technology, Korea Atomic Energy Research Institute, 150 Duck-Jin Dong, Taejon, Korea ^ Korea Electric Power Research Institute, Mun-Ji Dong, Taejon Korea ^ Giga Communications & Energy Inc., Gal-Ma Dong, Taejon, Korea
ABSTRACT An on-line reactor internals vibration monitoring system called by RlDS(Reacor Internals Diagnostic System) has been developed for monitoring the vibrational modes of a core support barrel in a nuclear reactor under full power operation. The core support barrel is a key structure which supports and protects nuclear fuel assemblies and suffers flow induced vibration due to the high pressure primary coolant. This system acquires the noise signals from the ex-core neutron detectors and performs real time basis time and spectral analyses to diagnose the natural vibration modes of the core support barrel using the real time mode separation algorithm. It is comprised of signal isolators & signal conditioners, digital signal processor module, and a host computer system with PCI interface. Also the application software has been developed to implement data storage, time and spectral domain signal analyses and improve diagnostic functions compared to the conventional reactor internals vibration monitoring systems. This system will be installed in Korean nuclear power plants in which it has not been equipped yet, for the early detection of potential problems related to the structural integrity of the reactor internals. The neutron noise signal analyses using RIDS have been implemented for the Yonggwang unit 1 to build baseline data. It reveals that the 1^^ beam mode and the 1^^ shell mode natural frequencies of the core support barrel are about 7.5 Hz and 21 Hz, respectively.
KEYWORDS Vibration, Monitoring, Reactor Internals, Core Support Barrel, Digital Signal Processor, Neutron Noise, Mode Separation, Beam Mode, Shell Mode.
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INTRODUCTION The reactor internal structures which consist of many complex components are subjected to flowinduced vibration due to the high pressure reactor coolant flow. The flow-induced vibration may cause degradation of the structural integrity of the reactor internals such as degradation or failure of the preload condition of the hold down ring in the upper part of the reactor vessel, and result in loosing the mechanical binding components which might impact other equipment and component or cause flow blockage. The above phenomena may cause significant core damage and cannot be detected in advance by conventional detection methods and even detected, after serious damage occurred on these components. In order to prevent this kind of problem the integrated on-line monitoring system for the reactor intemals(called by IVMS; Internal Vibration Monitoring System) has been developed by using the reactor noise techniques(ABB-CE, 1994; Y.-S. Joo et al, 1995; J.-H. Park et al, 1999). The reactor noise is defined as the fluctuations of measured instrumentation signals during plant fullpower operation, which have information on the reactor system dynamics such as neutron kinetics, thermal-hydraulics, and structural dynamics. These noise signals can be obtamed directly from the existing instrumentations of the reactor system without any effect on the reactor operation. It is well known that the structural integrity of the reactor internals can be monitored and diagnosed by using the structural dynamic components of the ex-core neutron noise measured by the neutron detectors located around the periphery of the reactor pressure vessel(J.A. Thie, 1979; EPRl, 1987; ASME/ANSl 0Mb, 1989; J.-H. Park etal, 1999). In Korea, there are still several nuclear power plants in which the IVMS has not been equipped yet. Thus a state-of-the-art reactor internals vibration monitoring system(RIDS; Reactor Internals Diagnostic System) has been developed for installing to those plants which have been operated for over 10 years.
SYSTEM DESCRIPTION RIDS system has been developed to comply with ASME/ANSI 0Mb Part 5, "Inservice Monitoring of Core Support Barrel Axial Preload in Pressurized Water Reactors". The system configuration is illustrated in Fig. 1. This system has various kinds of high performance capabilities such as simultaneous signal isolation and conditioning, 8 channel real time FFT(Fast Fourier Transform) with a maximum sampling frequency range of 100 kHz per channel, and a real time display of each natural mode power spectra, etc. And it can easily be expanded to incorporate other additional functions; for example, fuel assembly vibration and loose part signal monitoring. Hardware The schematic of the system hardware is shown in Fig. 2. The hardware system consists of Signal Isolators, Signal Conditioners(one per each input channel), DSP module, and a Host Computer. The Signal Isolator is provided for protecting the plant instrumentation from being affected by the unexpected connection of the input signals. Each Signal Conditioner has a programmable signal amplifier and an anti-aliasing filter. Thus the incoming raw neutron signals through the Signal Isolators can be filtered and amplified automatically through the application software setup options. The DSP module has 2 DSP boards, 8 A/D converters(16 bit, 4 per each DSP), 2 digital signal
802
processor chip(TMS320C31), and its own memory(4 Mbytes/board). The data digitized from A/D converters are processed for computing user-defined functions in the DSP chip, and then the results are stored in DSP memory on a real time basis. The stored data are again transferred into the main computer memory by way of PCI interface for utilizing in the application software. The Host computer is a type of industrial PC(Pentium III, Intel 500 MHz, Windows NT) which meets the specifications of US military standards against high temperature and shock vibration. It is equipped with several PCI interface slots for communication with various kinds of signal processing units, and for expanding further DSP boards. Also a high capacity hard disk driver and printing device are attached for analyses data storage and hard copy outputs. Software The system software consists of a DSP driver, a PCI interface program, and a host software as shown in Fig. 3. The DSP driver has two major function programs. One is the main routine which initiates and tests the DSP boards, the other subroutine controls the A/D conversion process directed by the main routine, executes basic function processing including the real time FFT, and transfers the results into the host computer's memory through the PCI interface. The PCI interface program is a kind of utility program that makes it possible to communicate between Windows operating system(Win32 device driver and PCI API DLL) and PCI 1/0 accelerator of the DSP board as shown in Fig. 3. A variety of time and spectral domain signal analyses are implemented in the host software. The digitized time domain data transferred from the DSP board will be used for the real time display of the input signals and computation of the normalized RMS(Root Mean Square), normalized auto and cross PSD(Power Spectral Density), mode separated PSD, and coherence functions, etc(J.-H. park et al, 1999). Microsoft C++ software has been used for the development of the signal analyses and user interface programs in a Windows NT environment. The major functions including database menu are depicted in Fig. 4. The RIDS software also performs system calibration automatically before system operation.
SIGNAL ANALYSIS FOR MONITORING REACTOR INTERNALS A series of ex-core neutron signal analyses using RIDS have been implemented for Yonggwang unit 1 nuclear power plant in order to build baseline data. The real time displays obtained from the Real-Time Data menu are illustrated in Fig. 5 and Fig. 6. Fig. 5 shows the acquiring time signals of the total ex-core neutron detectors of Yonggwang unit 1 and Fig. 6 displays the mode separated PSDs of the core support barrel assembly in real time. The normalized auto PSD(NAPSD)s and the corresponding mode separated PSD(MPSD)s using the lower plane excore neutron signals are summarized in Fig. 7 and Fig. 8. It reveals that the beam mode frequency of the core barrel assembly is 7.5 Hz and that of the shell mode 21 Hz. The detailed technical informations and the related analyses results are described in the reference(KEPRI, 2001). In case abnormal condition occurred, the status informations screen for the whole channels and the
803
related vibration modes are displayed as shown in Fig. 9.
CONCLUSION An On-line Reactor Internals Vibration Monitoring System called as RlDS(Reactor Internals Diagnostic System) has been developed to monitor and diagnose the degradation of axial preload of core support barrel and the vibration modes of the structure by using ex-core neutron noise signals. It provides state-of-the-art tools for monitoring and diagnosing the integrity of the reactor intemal structures on a real time and operator-friendly basis. The neutron noise signal analyses using RIDS have been implemented for the Yonggwang unit 1 to build baseline data. It reveals that the 1^^ beam mode and the P^ shell mode natural frequencies of the core support barrel is about 7.5 Hz and 21 Hz, respectively. This system will be installed in Korean nuclear power plants in which it has not been equipped yet, for the early detection of potential problems related to the structural integrity of the reactor internals.
ACKNOWLEDGMENTS The support of Korea Ministry of Science and Technology is greatly appreciated.
REFERENCES ABB-CE, NIMS-Intemals Vibration Monitoring System, 1994. ANSI/ASME 0Mb PartS, Inservice Monitoring of Core Support Barrel Axial Preload in Pressurized Water Reactors, 1989 EPRl NP-4970, Utility Guidelines for Reactor Noise Analysis, 1987. Jinho Park et al,"Development of Fault Diagnostic Technique using Reactor Noise Analysis," KAERl research report, KAERI/RR-1908/98, 1999. Jinho Park et al,"Dynamic Characteristics of Yonggwang 3&4 Reactor Internals," Proceedings of the Korean Nuclear Society Autumn Annual Meeting, Oct., 1999. KEPRI report, "A Development of Diagnostic System for Reactor Intemal Structure using Neutron Noise," KEPRI/99-C16, 2001. Y.S. Joo et al, "Development of Reactor Internals Vibration Monitoring ystem(RIVMOS) using Excore Neutron Noise," SMORN VII, Avignon, 1995. T.R. Kim et al,"A Study on the Fault Diagnostic Techniques for Reactor Intehial Structures using Neutron Noise Analysis, KAERI/RR/1386/94, 1994.
804
Figure 1: RIDS
Figure 2: Hardware configuration
Figure 3: Software Structure
805
LR!5ssw3yC]r«i^«e^ HO*M
['n»i*jj>«twtfp»tt-
't^<*j»^iaiwicM»j D»t« Store : DB, Btcktip
Figure 4: Main Menu Structure
Figure 5: Real Time Display of Neutron Signals in Time Domain
Figure 6: Real Time Display of Neutron Signals in Frequency Domain
806
Q c
-50-]
-60 4
V
LW1 LW2 LW3 LW4
1 •^°" Q
2
< Z
-eo-J -90-100-110-
is •
1
10
-120-
•
1
'
20
!
•
T"^
30
•
40
Figure 7: NAPSD of Ex-core Neutron Signals
B .;
IQ
1
^
Global Beami Beam2 Shell
o'
1 + r '" 1 30
40
Figure 8: Mode Separated PSD of Ex-core Neutron Signals
Figure 9: Status Information Screen
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Condition Monitoring and Diagnostic Engineering Management Andrew G. Starr and Raj B.K.N. Rao (Editors) © 2001 Elsevier Science Ltd. Allrightsreserved.
TRUNCATION MECHANISM IN A SEQUENTIAL LIFE TESTING APPROACH WITH AN UNDERLYING TWO-PARAMETER INVERSE WEIBULL MODEL Daniel I. De Souza Jr., PhD. Fluminense Federal University, Civil Engineering Dept., Graduate Program, Niteroi, RJ, Brazil, & Industrial Engineering Lab., North Fluminense State University, Campos, RJ, Brazil; e-mail:[email protected]
ABSTRACT The sequential life testing approach is an attractive alternative to that of predetermined, fixed sample size hypothesis testing because of the fewer observations required for its use. The two-parameter Inverse Weibull distribution was derived by Pascoal Erto (1982). It is rich in shape and has been used in Bayesian reliability estimation to represent the information available about the shape parameter of an underlying Weibull sampling distribution Erto (1982), De Souza & Lamberson (1995). It has a scale (6) and a shape (P) parameters. It happens that even with the use of a sequential life testing approach, sometimes the number of items necessary to reach a decision about accepting or rejecting a null hypothesis is quite large De Souza (2000). In situations like that the development of a truncation mechanism is essential to secure the major advantage of using sequential life testing; that is, small sample sizes. In this work, we will develop a truncation mechanism for a sequential life testing approach in which the underlying sampling distribution is the two-parameter Inverse Weibull model. In determining an expression for the expected sample size E(n) of a sequential life testing, we will apply to the two-parameter Inverse Weibull model an ^^proach developed by Mood and Graybill (1963). We will provide rules to truncate a sequential life testing situation making one of the two possible decisions at the moment of truncation; that is, accept or reject the null hypothesis Ho. To find the expected sample size E(n), some numerical integration procedure (Simpson's 1/3 rule in this work) will have to be used. An example will develop the proposed truncated sequential life testing approach. KEYWORDS Inverse Weibull Model, Sequential Life Testing, Truncation Mechanism, Hypothesis Testing, Expected Sample Size, Underlying Distribution. INTRODUCTION Non-truncated sequential life tests involving the Weibull model have been treated before by De Souza (2000). Many times, however, it is desirable or convenient to truncate the sequential test after some
pre-selected numbers of failures have been observed. The objective is to keep the sample size small, with a resuhing savings in cost. This is the major advantage of a sequential life testing approach in relation to the fixed sample size procedure. In determining an expression for the expected sample size E(n) of a sequential life testing, we will apply to the Inverse Weibull model an approach developed by Mood for the Normal distribution by Mood & Graybill (1963). To find a value for the expected sample size E(n), some numerical integration procedure (Simpson's 1/3 rule in this work) will have to be used. The two-parameter Inverse Weibull distribution has a shape parameter P which specifies the shape of the distribution and a scale parameter 0 which represents the characteristic life of the distribution. Both parameters are positive. The two-parameter Inverse Weibull distribution is given by
'«-f(f'4[?:
t>0
(1)
The hypothesis testing situations will be given by Kapur & Lamberson (1977) and De Souza (2000): 1.
Forthe scale parameter 6: H Q : 0 > e Q ;
Hj:e<eQ
The probability of accepting HQ will be set at (1-a) if 9 = Oo. Now, if 0 = 0i where 0i < 0o, then the probabiUty of accepting HQ will be set at a low level y. 2.
Forthe shape parameter P: Yi^\^>^^\
HJ:P
The probability of accepting H© will be set at (1-a) if P = Po. Now, if P = pi where pi < po, then the probability of accepting HQ will be also set at a low level y. The development of a sequential test uses the likelihood ratio given by the relationship L^.^ l^o\^ presented in Kapur & Lamberson (1977) and De Souza (2000). The sequential probability ratio (SPR) will be given by SPR = Li,i^ / Lo,o^, or yet, for the two-parameter Inverse Weibull model Pi+i
SPR =
exp -
t.
n
V
^
1 >
SPR =
exp -
ePo V0
, t.
P0
' 1
r
0^1 1
1 J
or yet
/-I
r
^1Pii
n{t.)Po-Pi xexp i=l
n
1
M
-I i=l|
The continue region becomes A < SPR < B, where A = y /(1-a) and B = (l-y)/a. We will accept the null hypothesis Ho if SPR > B and we will reject Ho if SPR < A. Now, if A <SPR< B, we will take one more observation. Then, we will have:
810
^'* f[(t.)Po-Pi xexp
(l-«)
i=l
V 0
A-y)
-I
1=1
V i
1
yj
By taking the natural logarithm of each term in the above inequality and rearranging them, we get
^1
nln
..
9^0 V 0
1
-In
Po
fa) <X
p,
< ' + ln ( l - « )
)P0
Po
(2)
V 0
y
I
0
(p.-Po)i4i)
i=l
(3)
i=l V i
i
y
EXPECTED SAMPLE SIZE OF A SEQUENTIAL LIFE TESTING According to (Mood and Graybill 1963), an approximate expression for the expected sample size E(n) of a sequential life testing will be given by E(n):
E(W:) E(w)'
(4)
where the expected value of the variate W'a depends on the random w's and the random variate n. Here, w is given by w = In
ffeOcPo)
(5)
The variate W*n takes on only values in which W*n exceeds hi A or falls short of In B . If one ignores the amounts by which W*n exceeds hi A or falls short of hi B , he or she may say that W*n takes essentially only two values, In A and hi B. When the true distribution is f (t;0,|3), the probability that W*n takes the value In A is P(9,3), while the probability that it takes the value In B is 1- P(0,p). Jhen, according to (Mood and Graybill 1963), the expression for the expected value of the variate W*n will be given by E ( W * ) ^ P(0,p)ln A + [l-P(e,p)]hiB
(6)
Hence, Eqn. (4) becomes g^^^ ^ p(e,p)hiA+[i-p(e,p)]hiB E(W)
(7)
where A = y /(1-a) and B = (l-y)/a. Eqn. (7) enables one to compare sequential tests with fixed sample size tests. The proof of the existence of Eqn. (4) to (7) can be found in Mood & Graybill (1963), pp. 391-392. For a two-parameter Inverse WeibuU sampling distribution, Eqn. (5) will be transformed into
811
w=ln
,lfo-f,) exp
, or yet
fl • ft ° w = ln(c) + (po -Pi)ln(t) - \ - + ^ , (Pi i^o
where
e"' 3 C= - J - x 9P0 Po 0
The expected value of w, E(W), will then be given by
' P + 60
E(w) = ln(c) + (p„ - P , )E[ln(t)] - G j ' d
U"^';
E
(8)
^f^";
Here,
(9)
Itl^'J e^i «P.rLP r V
*^ J
1
(10)
\i
6*^0 r
1
fi-Lo^
fn+V
E[ln(t)]=ln(e)- ^ x f x | E [ ' n ( U i ) e -u. < (1,2 or 4)
(11)
Appendix (1) shows the computation for E[ln(t)]. The solutions for E[l/(t^i)] and E[l/(t^)] are straightforward and due to page number Hmitations will not be presented. When the decisions about these quantities 60, 0i, po. Pi, a, y and P(6, 3) are made, and after the E(n) is calculated, the sequential test is totally defined. Using Eqn. (1) to (11), an example will develop the proposed truncated sequential life testing approach. EXAMPLE This example is related to a low alloy - high strength steel product being life tested. Since this is a well-known product, there is knowledge that it could be represented by an underlying two-parameter Inverse Weibull sampling distribution having a scale parameter G of 2,500,000 cycles and having a shape parameter p of 2.5, which are believed to be the true values for these parameters. It was decided that the values of a was 0.05 and y was 0.10. In the case presented in this example, the following values for the alternative and null parameters were chosen: alternative scale parameter 0i = 2,500,000 cycles and alternative shape parameter pi = 2.0; null scale parameter 60 = 2,000,000 cycles and null shape parameter Po = 2.5. Even after the observation of 20 failure times, it was not possible to make the decision of accepting the null hypothesis Ho or reject the null hypothesis Ho. These values for the corresponding number of cycles (time to failure) of these 20 units were the following: 3,358,581.; 2,244,502.; 2,705,247.; 3,187,634.; 1,790,827.; 3,605,029.; 3,716,727.; 2,928,499.; 3,837,785.; 4,115,048.; 3,071,191.; 5,906,894.; 7,138,968.; 1,907,641.; 2,675,526.; 4,040,664.; 2,117,752.; 1,652,901.; 2,797,409.; 2,425,281.
812
The sequential test results for the two-parameter Inverse Weibull model were the following: TABLE 1 TEST RESULTS FOR THE INVERTED WEIBULL DISTRIBUTION.
Pi =2.0; 91 = 2,500,000; po = 2.5; 60 = 2,000,000 Upper limit
Unit number
Lower limit
_ 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
1694526 10.27942 16.86432 23.44922 30.03412 36.61901 43.20391 49.78810 56.37371 62.95861 69.54350 76.12840 82.71330 89.29819 95.88309 102.4680 109.0529 115.6378 122.2227 128.8076
8.836189 ~6^642992~ 15.42109 13.62447 22.00599 20.51506 28.59088 27.22959 35.17579 34.12661 41.76068 40.66241 48.34558 47.14869 54.93048 53.96407 61.51538 60.39636 68.10027 66.70446 74.68517 73.46568 81.27007 79.01801 87.85497 84.12077 94.43986 91.06505 101.0248 97.96452 107.6097 104.3059 114.1946 111.2875 120.7795 117.6909 127.3643 124.5001 133.9493 131.3394
C
NUMBER OF HEMS TESTED
—1 UJ
{) 0 10 20 30
1 40
SO 60 (0 UJ 70 80 90 IOC ; 11( 1} ' 12( O 13( 14( ISC
Value of X
|
1
2
3
4
5
6
7
8
9 10 11 12 13 14 15 16 17 18 19 20
•
'
•
•
1
1
1
•
1
1
1
1
1
1
1
1
1
1
1
1
REJECT Ho 1
ACCEPT Ho 1
^ ^
Figure 1. Sequential probability ratio results for the Weibull model Now using Eqn. (7) to (11), and with Oi = 2,500,000 cycles; 8 = Oo = 2,000,000 cycles; Pi = 2.0; P = Po = 2.5, a = 0.05; y = 0.10 and electing P(0,P) to be 0.01, we can calculate the expected sample size E(n) of this sequential life testing under analysis. So, we will have
813
C=2.0 (2,000.000)2 5
(2,500,000)^° ^ g g3gg^jo-4 in(c) = InJ8.8388x 10"*) =-7.0312 2.5 > V; T' /
(i5o-Pi)= 2.5-2.0 = 0.5; 8i' = (2,500,000)2"= 6.25x10^2. Q^" = (2,OOO,000)"= 5.657xlO*^ 1 6^1 r
Oi
r . \=
B
-;
E
[41
f ^1 (2,000,000f^rfl-||]
6,25 X 10^2 X -
'
^
4.0x10^^x4.5908
vri;
= 0.3404; E
f . \ Bo ^
"=0.0;
4.0x1012x4 .5908
1 ^
5.657x10^^x0.0 = 0.0
^' ^ (2,ooo,ooo)2-^r[i-—I fn+l
E[ln(t)]=ln(e) - i X I X j;[ln(Ui)e-^i x(l,2or4)
g = 0.116;
i = — = 0 . 4 ; ln(e) = ln(2,000,000) = 14.509; ^ x ^ = 0 . 4 x - ^ ^ =0.0155 p 2.5 P 3 3 ^ft^
Setting n = 20 with U = -
Z'2 000 000^
=
'-
; t =feiluretimesfromthe range (200,000 cycles > t
> 6,000,000 cycles) ; we will have r
N2.5
21 E[ln(t)] = 14.509-0.0155 x X In 2,000,000 ^
i=l
V
1
exp
f ^2.5 ' 2,000,000
((1,2 or 4) =15.3825
J
TheE(w), will be E(W)=-7.0312+ 0.5x15.3825-0.3404+ 0.0 =0.3197 The expected value of the variate W*n, E(W*n), will be given by E(W*)=
P(e,p)lnA + [l-P(e,p)]kiB;
i-y^-iJi-o.io ln(B) = hN^Jl = In 0.05 a
ln(A) = ^^[^^
= I n f ^ ^ ^ l =-2.2513
; E ( W : ) . -0.01x2.2513 + 0.99x2.8904 =2.8390 = 2.8904:
814
Finally,
E(n) . P(e>P)ln A . [ l - P ( e , p ) ] l n B ^ 2J390 ^ ^ ^^^^ ^ ^ Items. ^^ E(W) 0.3197
So, we could make a decision about accepting or rejecting the null hypothesis HQ after the analysis of observation number 9.
A PROCEDURE FOR EARLY TRUNCATION According to Kapur & Lamberson (1977), when the truncation point is reached, a line partitioning the sequential graph can be drawn as shown in Figure 2 below. This line is drawn through the origin of the graph parallel to the accept and reject lines. The decision to accept or reject HQ simply depends on which side of the line the final outcome lies. Obviously this procedure changes the levels of a and y of the original test; however, the change is slight if the truncation point is not too small. As we can see in Figure 2, the null hypothesis HQ should be accepted since the final observation (observation number 5) lies on the side of the line related to the acceptance of ^oI
NUMBER OF ITEMS TESTED
|
Figure 2. A truncation procedure for sequential testing CONCLUSIONS The major advantage of a sequential life testing approach in relation to the fixed size approach is to keep the samples size small, with a resulting savings in cost. It happens that even with the use of a sequential life testing approach, sometimes the number of items necessary to reach a decision about accepting or rejecting a null hypothesis is quite large (De Souza 2000). So, the test must be truncated after a fixed time or number of observations. The truncation mechanism for a sequential life testing approach developed in this work provide rules for working with the null hypothesis HQ in situations where the underlying sampling distribution is the two-parameter Inverse Weibull model. To calculate the expected sample size E(n) of our sequential life testing, some numerical integration procedure (Simpson's 1/3 rule in this work) had to be used. Appendixes 1 and 2 give the development of the Eqn.s necessary to calculate the E(n). In a previous work, using an example similar to the one presented in this paper, and with a non-truncated sequential life test approach for a Weibull sampling model, De Souza (De Souza 2000) was not able to reach a decision about accepting or rejecting a null hypothesis HQ even after obtaining 20 observations. With the truncation mechanism developed in this article, the decision of accepting the null hypothesis was reached with the analysis of only 9 observations or items. This fact shows the advantage of such a truncation mechanism to be used in a sequential life test approach.
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REFERENCES De Souza, Daniel I. (2000). Further Thoughts on a Sequential Life Testing Approach Using a Weibull Model, Foresight and Precaution, ESREL 2000 Congress, Cottam, Harvey, Pape & Tait (eds), Edinburgh; Scotland; 14-17 May 2000; 2: 1641 - 1647, Rotterdam,: Balkema. De Souza, Daniel I. & Lamberson, Leonard R. (1995). Bayesian Weibull Reliability Estimation, HE Transactions, 27:3, 311-320; USA. Erto P. (1982). New Practical Hayes Estimators for the 2-Parameter Weibull Distribution, IEEE Transactions on Reliability, R-31:2,194-197, USA. Kapur, K. and Lamberson, L. R. (1977). Reliability in Engineering Design. John Willey & Sons, USA. Mood, A. M. and Graybill, F. A. (1963). Introduction to the Theory of Statistics., McGraw-Hill, USA. APPENDK 1
COMPUTING THE E[ln(t)]
The E[ln(t)] will be given by
E[Mt)]=|Kt)i(f]^"J-(f] Making
-0^'
wehave c i u = [ - | ] [ ^ ]
dt;
(Al)
dt
i - ^
When t-> 00, U ^ 0 When t ^ 0, U -> oo. Then, Eqn. (Al) becomes: 00
,
E[bi(t)] = Jin - ^ 0
^
00
00
00
e-U du = ln(G) J e ' ^ du - ^ jln(u)e-U du = hi(e) - i Jhi(u)e-U du 0
0
0
The above integral has to be solved by using any numerical integration procedure, for example, Simpson's 1/3 rule. Remembering that Simpson's 1/3 rule is given by b
.
Jf(x)dr=^^j +4f2 +2f3 + - . + 4f^ + f^^j)-error; Making the error = 0; i= l,2,...n+l, weget
00
fln(u)e""du=^xfln(Ui)e""j +41n(U2)e""2 + -+ln(U„+i)e~"»+« 1. The Enn(t)] will then be 0
E[to(t)] = ln(e) - i X ^ x rin(Ui)e~"i +4to(U2)e""2 +... + ln(Un+i)e~"°+' l;Finally:
E[to(t)] = ta(e) - 1 X | x | | ; [ l n ( U i ) e - " i x(l,2or4)
816
(A2)
Condition Monitoring and Diagnostic Engineering Management Andrew G. Starr and Raj B.K.N. Rao (Editors) © 2001 Elsevier Science Ltd. All rights reserved.
MAINTENANCE FUNCTIONAL MODELLING CENTRED ON RELIABILITY
F. J. Didelet Pereira^ and F. M. Vicente Sena^ ^Escola Superior de Tecnologia de Setiibal, Rua do Vale de Chaves, Estefanilha, 2914-508 Setubal, Portugal ^Escola Superior de Tecnologia da Universidade do Algarve, Campus da Penha, 8000 Faro, Portugal
ABSTRACT The maintenance functional modelling centred on reliability must integrate the functional system hierarchy. This knowledge allows one to make a bridge to the system physical structure - components or other level of physical organisation. The system functions undergo a process of continuous degradation until their complete failure. To keep functions "up" is basically to develop the required preventive maintenance tasks to carry out upon the system physical structure.
KEYWORDS Maintenance, Reliability, Functional Modelling, Failure, Function, System and Fault Trees
INTRODUCTION Maintenance and its management have an increasing importance upon the productive environment due basically either to the complexity of the technical system or to the interaction between the environment and the system through the links on the boundaries. The relation of our technical system with the one above it, called wider system, must be regarded as of particular importance. The wider system sets the goals, influences strategy and decisions in the
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technical system, monitoring the system performance and providing the resources to allow system operation. Our model must integrate thefiinctionalsystem hierarchy aUowing one to make a bridge to the system physical structure - con^nents or any other acceptable level of physical organisation. System functions undergo a process of continuous degradation until their complete £ulure. Failure is a term applied when operating capacity is grossly violated. It is partial or complete. The preventive maintenance tasks are applied to the physical structure aimed at keeping system fiinctions "up". The system internal Mure mechanisms must be identified clearly and linked to the potential effects of Mure through system boundaries. The effects and consequences of Mures are important aspects to be taken in consideration when defining preventive maintenance tasks.
THE MODEL CHARACTEMSTICS AND ITS DEVELOPMENT A deep knowledge of conplex techmcal systems allows us to develop a clear understanding of their goals and fiinctions. This knowledge includes all relevant system operational states in accordance with system goals and its operational cycle. Functional modelling is a representation of the knowledge achieved by interpreting a mk of system operational experience and designer intentions. This process builds a system model ki hierarchical terms, relating system goals with the operational requirements of their physical con:q)onents. Functional top-down hierarchy is devetoped until we get clear references to physkal conq)onents. Then the fimctional Mure of any lowest sub fiinction is considered as the top event of a feult tree. Any feuh tree is a systematic and logical process to link the fimctional Mure to its causes components Mures. The application of this method to a complex system feihire process is to devetop many but very small M k trees. However, the Mure propagation up to thefimctionalsystem goals and interfeces must be taken in conskieratioa Specifically, the model is developed sequentially in four steps. The first step is to collect all relevant available informatk>n about the system including operational and Mure data. The hierarchic fimctional analysis allows the identification of certain number of levels in terms of formal and informal rektions. This is a top-down process built in accordance with the method of necessary and sufficient conditions. This "decomposition" process is stopped when there is a clear reference to physical system conponents. To summarise in this step the system goals, principal fiinctions, operational states, operational cycle and subfimctionsare identified and characterised by means of "decon^ation" process. This process must be associated to another in order to identify both the wider system and the environment and their relations to our system. In the second step the Mures of the lowest fimctions identified in step one are considered. Any of those fimctional Mures is the fimctional fault tree top event. This process is shown in a simplified form infigure1.
818
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System Physical Components Figure 1: Simplified diagram linking thefimctionalhierarchy to the physical structure At this point the qualitative and quantitative component faOure information is applied. The quantitative information is to evaluate component failure rate, probability of functional failure and component importance criteria (Vesely - Fussel). In the third step the decision criteria process to identify systematically the effects and consequences of component failures upon the system safety and its boundaries, the environment and system availability is developed. This step requires detailed process decision diagrams to support this kind of systematic process. In the fourth step the decision diagrams fi-om step three are updated and developed allowing one to choose the required preventive maintenance tasks based on consequences of Mure, functions affected, potential events and importance criteria.
MODEL APPLICATION The model is applied to an oil tanker with the following basic characteristics; (a) Displacement: 18732 tonnes, (b) Overall length: 163 m, (c) Medium speed: 14 knots, (d) Main engine: Mitsui B&W 8L 45GFC, 7040 BHP at 170 rpm. The modelling process takes in consideration the requirements of ISM Code and other complementary rules and regulations issued for maritime safety by IMG. The ship's typical operational cycle is shown onfigure2.
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The oil tanker's goal is to carry bulk refined products in safety and quality conditions according to the national and international maritime rules and reguktions at an acceptable cost-benefit to the ship owner. The ship's goal must be kept in mind during all operational states and it is considered the top of the fimctional hierarchy structure (level 0). The principal functions are at level one and for this specific case they are: ship's motion, safety and security, loading/unloading, habitability and production and distribution of electrical energy. Loading in Port
cr>
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Figure 2: Operational cycle of oil tanker The principal fimction "ship's motion" is executed in the foUowing operational states; (a) Manoeuvring to arrive or left port, (b) Navigation in open sea, (c) Navigation in restricted zones; (d) Anchorage waiting for orders. The last operatk)nal state is a transient state in that the main engine is not used continuously but just to keep the ship in position. To execute this fimction the ship has its operational centre in the bridge in co-ordination with the engine room control. Safety in this fimction means mainly to follow the international convention "COLREG" which define the rights of ship passage, its safe speed, and all actions available to avoid collisions taking in consideration the specific weather conditions. The fimctional analysis of "ship's motion" has three sub fimctions: power, propulsion and ship control. The subfimction"power" has the following characteristics; Ca) In general this subfimctionhas not any global redundant capacity, (b) Engine room speed "slow down" or "stoppage", which in extreme cases will afifect the ship's safe operation and loss of charter, causes Mure of this sub fimction. The assessment of potential ship
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conditions related to position, weather, traffic schemes and loading will allow identification of the probability of losses, (c) This function needs large quantities of fiiel oil, lubrication oil and high temperatures so there is a potential for hazardous conditions such asfireand explosion. The subfiinction"power" is analysed into the following subfiinctionsat level three; (a) Fuel oil storage and handling, (b) Fuel oil treatment, (c) Fuel oil feeding, (d) Fuel oil combustion control, (e) Conversion of combustion energy into mechanical energy, (Q Flow of mass and energy of exhaust gasesfi*omcombustion; (g) Refiigeration, (h) Lubrication, (i) Starting and engine reversing, (j) Air delivery. For thefiinctionalfailure "fiiel oil treatment" the following con^3onents failures are identified; (a) Fuel oil heater steam trap, (b) Return steam tubefi*omfuel oil heater, (c) Fuel oil separator, (d) Fuel oil filter, (e) Heating coil of fiiel oil service tank. All functional fault trees are evaluated according to thefi-ameworkof the rules in the Fault Tree Handbook issued by the U.S. Nuclear Regulatory Commission. The feuk tree basic events are evaluated with datafi*omthe maintenance records and in certain cases eitherfiromdatabases or similar systems in operation. The safety and environmental consequences of fimctional oil tankers failures are legally considered in rules and regulation issued by IMO and ratified by most countries. The Classification Societies also have a meaningfiil role in that subject. According to this point some aspects of the formal safety assessment techniques are potentially important elements to introduce in this model clarifying the risk involved in ship operations. The consequences of Mures are identified according to decision diagrams. One of these diagrams is shown infigure3. This diagram points out the important question of evident and hidden failures. Hidden Mlures can be a very hazardous aspect and must be controlled in order to avoid the occurrence of multiple Mures. The layout of the ship's technical installations has a lot of redundant elements subjected to hidden Mures. So when there are hidden Mures we also consider the same kind of consequences as above and the probability of multiple failures. Hidden Mures must be considered specifically in both attended and unattended engine room operation conditions. To analyse the consequences of Mures one must take in consideration that the marine operators have mixed duties in operation and maiatenance and so the relevant aspects of human reliability must be considered. 821
Is the Mure mode evident to operator during normal duties?
Figure 3: Decision diagram for the consequences of evident Mures The maintenance tasks are supported by the above decision diagrams and for the oil tanker the following are defined; (a) Conservation and lubrication tasks, (b) Monitoring tasks, (c) Inspection tasks, (d) On-condition tasks, (e) Replacement based on time tasks, (f) Combination of some the above tasks, (g) Corrective maintenance tasks.
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The scheme of simplified decision concerningfixelinjector Mure is shown in table 1. TABLE 1 FUEL INJECTOR FAILURE Basic Event Importance Criteria Functions Affected by Failure Immediate Potential Events Consequences of failure Maintenance Tasks
Fuel Injector Failure 0,142 Fuel Oil Combustion Control Failure is evident by abnormal change of temperature of exhaust gases and emission of smoke through the ship funnel. Reduction of main engine efficiency - On certain conditions implies main engine stoppage and could produce hazardous conditions for ship safety depending on existing boundary ship conditions. - Task on-condition monitoring using diesel engine monitoring control unit. - Monitoring cylinder exhaust gas ten:^)erature. - According to operational data there is an increase of Mure rate with operating time that means that is justified replacing injectors on a time base.
The subfimction"power" Mure is the most hazardous for safety and availability of oil tanker and the following number of maintenance tasks for that subfimctionwere found; (a) Conservation and lubrication - 9, (b) Monitoring - 54, (c) On-condition - 3 1 , (d) Inspection ~ 6, (e) Replacement based on time - 7, (f) Corrective - 2. The large number of on-condition and monitoring tasks must be pointed out. On the other hand the replacement based on time is only a small part of all tasks. The decision about these tasks is obtained by applying both the WeibuU law to failure and censored times and considering that the shape parameter is increasing in the 95% confidence interval. It is important to point out that all tasks were chosen according to their technical feasibility. However cost considerations would be considered forfixturework. CONCLUSIONS AND FUTURE WORK This model shows a process to provide us a deep knowledge about complex systems pointing out the feedback from real operational conditions. This is a process that needs a lot of information on both system design and operational data.
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To keq) systemfiinctions"up" one needs to develop a systematic preventive maintenance strategy focussed on physical con^nents that affect those functions. The feult trees are developed for lower sub functions allowing to build small feuh trees but in large number. This can be a procedure to reduce conqjlexity. Decision about the maintenance tasks takes in consideration the functional Mure probability, the component importance criteria, functions affected by con^wnent fiiihire, most prolxible answer of operator in case of &ilure, operation of automatic control or not, existence of hidden conditions and consequences offeihires.Because of the number of activities involved the decision process has a high degree of con^lexity. For future work a methodobgy to increase the informal connection in the fimctbnal structure in a horizontal, up down, and top-down ways must be developed. The concept of hidden Mure must be extended for the functional structure. To improve this model the collectk)n of maintenance cost data is justified in order to base the maintenance tasks on technical feasibility and economy. However if one takes in consideration the cost - benefit context the safety and environmental achievements must not be sacrificed. To get a betterfi-ameworkof functk)nal Mures consequences both the formal safety assessment and human reliability techniques must be introduced. REFERENCES Brauer, D. C. and Brauer, G. D.(1987). Reliability-Centered Maintenance. IEEE Transactions on Reliability R-36,17-24. Dobson, B. (1994). Weibull Analysis, ASQC Quality Press, Wisconsin, USA International Chamber of Shipping and Intematk)nal Shipping Federation. Guidelines on the Application of the IMO Intematbnal Safety Management (ISM) Code Kumamoto, H. and Henley, E.J.(1996). Probabilistic Risk Assessment and Management for Engineers and Scientists, IEEE Press, New York, USA Modarres, M.(1993). Functwnal Modelling of Con^lex Systems Using a GTST-MPLD Framework. Proceedings of the First International Workshop on Functional Modelling of Conq>lex Technical Systems, 21-69. Vesely, W.E., Goldberg, F.F., Roberts, N.H. and Haasl, D.F. (1981). Fault Tree Handbook, U.S. Nuclear Regulatory Comission, Washington, USA
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Condition Monitoring and Diagnostic Engineering Management Andrew G. Starr and Raj B.K.N. Rao (Editors) © 2001 Elsevier Science Ltd. Allrightsreserved.
AN IMPLEMENTATION OF A MODEL-BASED APPROACH FOR AN ELECTRO-HYDRAULIC SERVO SYSTEM A. El-Shanti, Z. Shi, D. Luheng, F. Gu and A.D.Ball Maintenance Engineering Research Group, Manchester School of Engineering, The University of Manchester, Manchester, M 13 9 PL, UK Email: ali [email protected] Web: www.maintenanceengineering.com Phone: 0044-161-2754308
ABSTRACT For many years, a model-based condition monitoring (MBCM) approach has proved to be an advanced and powerful diagnostic tool. It is especially true for various kinds of control systems. It can not only detect the faults in the plants and their components, but also the sensor faults and faults of new parts. However, there is no application on the electro-hydraulic control system. This paper explores this technique. The first part of this paper reviews the development of the model-based approach. A model of a selected electro-hydraulic position servo system is built in simulink in the second part of this paper. A few types of faults, such as the amplifier offset, backlash, and fatigue of the servo valve are induced into the system. Finally, some fault detection have been carried out by means of a model-based approach.
KEYWORDS Model-Based Diagnosis, condition monitoring, fault detection, Simulink model, Electro-hydraulic servo system, control systems
INTRODUCTION The complexity and degree of automation of technical processes are continuously growing, due on one hand to the increasing demands for higher performance and quality, and on the other hand to more cost efficiency. Along with this development, the call for more safety and reliability is growing more and more important. Model-based fault diagnosis in automated processes has been receiving considerable attention since the beginning of thel970's, Jones (1973). Several survey papers and some books have been published recently on model-based fault diagnosis, Frank (1993) and Patton (1994). However, as reading these literatures, one may find that, although model-based diagnostic approach has made much progress in theoretical research, its application is still not as popular as expected. 825
However, as reading these literatures, one may find that, although model-based diagnostic approach has made much progress in theoretical research, its application is still not as popular as expected. Why does such an advanced approach receive such rare application? Does it work as people have said? All Elshanti, et al. (2001) using Simulink, has given a full demonstration of model-based diagnostic approach. This paper is a further application of this approach. According to the principle of the model-based approach, a model of a selected electro-hydraulic position servo system is built. It is based on two main components, an amplifier and a servo valve, combined with the cylinder and a position transducer, a mathematical model of a close loop controlled system is carried out in Simulink. Some kinds of faults are induced into the system. One is the offset of the amplifier. Others are fatigue and backlash in the servo valve. Some diagrams in Simulink have been supplied hereby. Simulation results will be given to meet the requirement of the model-based diagnosis for the system. Finally, a model-based approach to the detection and diagnosis of faults in electro-hydraulic servo system is successfully implemented in this paper.
PRINCIPLE OF THE MODEL-BASED DIAGNOSTIC APPROACH The concept of Model-based diagnosis is based on analytical redundancy. A process contains analytical redundancy if an input or output can be calculated by using only other inputs or outputs. The principle of the model - based diagnostic approach is shown in figure 1. The analytical model is built to run parallel to the actual system. The model output of a healthy system should be the same as that of the actual system. The difference between the analytical model and the actual system is called the residual. The residual is used to detect if a fault occurs in the system. Due to the difficulty in building a model exactly the same as the actual system, the practical way to detect faults effectively is to design a threshold. If the residual does not exceed the threshold, the system is referred to as healthy. Once the residual exceeds the threshold, some fault may occur in the system, the diagnostic scheme will send out a fauh alarm. This is called ^aw// detection. The next is to allocate and evaluate the fault that occurred in the system, and is called^w// diagnosis, Isermann and Balle (1997). The model-based approach has significant advantages. The main ones are listed below: • Reusable models. Many components and processes can be built models or have their models when they are designed or manufactured. These models can be re-used not only in control but also in fault diagnosis. • Possibility of diagnosing a "new" device. This is prior to any other detecting technique, because the model does not rely on any kind of experience, but only the model of the new device. • Possibility of diagnosing a sensor fault. This is prior to all passive signal-processing methods. In these meliiods, the sensors are taken granted to be healthy, and once the sensor becomes faulty, the signal collected will be distorted. • Work over a wide operating range. It is natural to deal with dynamic and time-varying problems. However, because of the difficulty in the model building and selecting, especially in non-linear systems, the model-based approach has not taken its part in the practice, as it should have.
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MODELING OF THE ELECTRO-HYDRAULIC SERVO SYSTEM The servo amplifier model The Servo amplifier consists of several main control stages including an input stage, control stages, proportional gain, integral gain and a current driver output stage. It requires command and feedback signals, which have opposite polarities. The gain inverter enables either the feedback or command signal to be inverted if they were of the same polarity. The servo amplifier has a 4-20mA converter, which converts a 4-20mA signal to either 0 to +10V or 0 to -lOV depending on the input polarity. Basically, 4mA results in OV and 20mA results in lOV, currents between 4 and 20mA results in proportional voltage between 0 and lOV. The reason why we modelled the amplifier is that we will be able to induce faults in it. Figure 2 shows the Simulink model of the servo- amplifier in the fault free mode.
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The Servo valve model
The servo valve is the heart of the electro-hydrauHc servo control system. EHS valve is widely used in various control systems to implement precious and heavy load control targets. The servo valve is small in size but has a very high sensitivity. Figure 3 shows the Simulink model of the servo valve.
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The overall system model Figure 4 shows the overall electro-hydraulic servo system model. This consists of the servo valve model, the amplifier model, the cylinder model and the feedback. Figure 5 shows the Simulation result of the whole system in the fault free mode.
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A diagnostic schematic is provided in the form of Simuhnk. Figure 5 shows the diagnostic schematic based on the model of the system. The simulink model without any fault is compared to the data collected. The system data is collected from the faulty conditions. The analytical model is compared to the faulty signal to generate residuals. All the response signals and the residual signals are displayed on the scope. Different thresholds are assigned to different variables. These are used to detect the faults occurring in the system. From the scope, one can find the faults and their location as well as the severity. Here in this paper, the data is from the Simulink simulation results shown in figures 6 and 7. Both fault free and faulty conditions are implemented by inducing some faults in the system. The faulty conditions arise from the following cases: One is the amplifier offset, the second is a backlash in the servo valve and the third fault results from parameters changing due to fatigue in the servo valve. These faults may occur alone or as a combination. The fault simulation is made on the basis of Simulink.
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In figures 6 and 7, N is normal condition and the residual is zero. D is a faulty condition in the servo amplifier and the residual is not zero, but very small. B is a faulty condition in the servo valve and is known as a backlash, where the real output is later than the model and finally the output value is much smaller. K is another faulty condition in the servo valve occurring due to fatigue in ^ e valve, the residual in K is a pulse signal, the final real output is the same as the model output, but the residual still gives a fault alarm. (N)
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CONCLUSIONS In this paper, a model-based condition monitoring approach for an Electro-hydraulic servo system is discussed from the theory to the diagnostic process. Modelling of the EHS system is provided and described in details. This includes models for the amplifier, the servo valve, and the whole system. Furthermore, fault simulation and diagnosis of the Electro-hydraulic servo system are focused on. Finally, simulation results are given in details to meet the requirement of model-based diagnosis for the system.
References A. El-Shanti, Z. Shi, F. Gu and A.D.Ball (2001). Dispelling the rumours about Model-Based Diagnostics, MARCON'2001,USA, C. M. Close, D. K. Frederick (1995). Modelling and analysis of dynamic systems, John Wiley & Sons. Inc. Gertler, J. (1991). Analytical redundancy methods in fault detection and isolation. IFAC symposia, 921. Isermann R and P. Balle. (1997). Trends in the application of model-based fault detection and diagnosis of technical processes, Control Engineering Practice, Vol. 5, 709-719. Isermann R. (1984). Process fault detection based on modeling and estimation methods - A survey, Automatica, 387-404. J Chen, Ron J. Patton (1999). Robust model based fault diagnosis for dynamic systems, Kluwer Academic Publisher, London. J. Gertler and M. Costin (1994). Model-based diagnosis of automotive engines. IFAC Fault Detection, Supervision and safety for Technical processes, Espoo, Finland, 393-402. Jones, H. L. (1973). Failure detection in linear systems, PhD thesis, Dept. of Aeronautics, MIT, Cambridge, Mass. P.M. Frank. (1993). Advances in observer-based fault diagnosis. Proc. TOOLDIAG'93, 817-836, Toulouse, France, CERT. R.J. Patton (1994). Robust model-based fault diagnosis: the state of art IFAC Fault Detection, Supervision and Safety for Technical Processes, 11-24, Espoo, Finland. Wenxin, et al. (1999). Fault diagnosis and tolerant control of control systems. Mechanical Press, China. Willsky, A.S. (1976). A survey of design methods for failure detection systems, Automatic, Vol. 12, 601-611.
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Condition Monitoring and Diagnostic Engineering Management Andrew G. Starr and Raj B.K.N. Rao (Editors) © 2001 Elsevier Science Ltd. Allrightsreserved.
STOCHASTIC PETRI NET MODELING FOR AVAILABILITY AND MAINTAINABILITY ANALYSIS* S. M. O. Fabricius^ and E. Badreddin^ Laboratory for Safety Analysis, Institute of Energy Technology, Swiss Federal Institute of Technology Zurich (ETHZ), Weinbergstrasse 11, 8001 Zurich, Switzerland ^ Chair for Process Automation, Institute of Computer Engineering, University of Mannheim, B6, 23-29, Bauteil C, 68131 Mannheim, Germany
ABSTRACT Stochastic Petri nets are well suited for model-based performance and dependability study of complex systems and have been used in variants since the 1970's. However, most analysis methods use analytical solution techniques based on Markov-theory with the requirement to use memory-less probability distributions. Real world systems often do not exhibit such limited behavior only and this abstraction can severely diminish the value of respective model-based predictions. In addition, it is a well known fact that Markov-modeling of large systems can lead to an explosion of the number of states in the model creating difficulty storing it in a computer's main memory. Discrete-event simulation methodology for the evaluation of Petri nets however can deal with general distributions and allow for further integration of dynamic effects at the same time. In this document, a commercially available Petri net simulator called "PACE" is used. Examples of PACE models for individual repairable components and for system structures are introduced and their suitability for availability analysis is illustrated. It is further shown how maintenance strategies can be modeled and compared by means of simulation. Additional dynamic effects as well as financial performance measures could be incorporated in future work to support optimal maintenance management. KEYWORDS Petri nets, stochastic modeling, availability simulation, maintenance strategy, maintenance management, maintenance optimization, complex system modeling
* This work is financed through a project together with Vantico, Basel and Monthey, Switzerland 833
INTRODUCTION Trends in automation and production (e.g., just-in-time manufacturing) lead on one hand to ever more complex and highly dependable technical and organizational systems. On the other hand, industrial producers are often challenged by fierce competition in their respective markets, particularly in commodity segments. Therefore, plant managers must seek to exploit all efficiency improvement potential. One prospective way to do so, is by trying to increase plant availability through optimization of maintenance and monitoring strategies. Analysis techniques can be helpful in this respect and support the making of founded decisions. This paper focuses on simulation methods and starts by discussing terms of importance as well as Petri nets in general before introducing the PACE Petri net simulator. As examples, two basic well-known system structures are modeled with PACE and used for terminating as well as steady-state simulation experiments. Finally, maintenance strategies are modeled and compared with respect to system availability.
SYSTEM PERFORMANCE EVALUATION In order to analyze a system in a quantitative manner, one or several types of measurable performance criteria must be defined. Depending on the goal of the investigation, such criteria can be reliability or availability of a system or some economic measures as sales revenue or profitability ratios. This text is mainly concerned with system availability in it's two flavors. First, with the transient "instantaneous" availability (the probability that a system is in functional state at a certain point in time, see also DIN 40041, 1990) and second, with the so-called steady-state availability. The later can be more meaningful e.g., in the eyes of a chemical plant manager, who is concerned with the amount of product being produced in a certain production-line during a specified time interval, without being further interested in the intermediate, more abstract and virtual meaning of the instantaneous availability. To analyze a system, a set of assumptions about how it works has to be made, which often takes the form of a mathematical or logical relationship to constitute a model that can be used to improve understanding of the respective system behavior. For simple problems, it may be possible to use mathematical methods to obtain exact information on questions of interest, therefore seeking an analytic solution. If such a procedure to the model is available and is computationally efficient, it is usually desirable to conduct the study in this way. However, in many cases, real-world systems are too complex to allow realistic models to be evaluated analytically and these models must be studied by means of simulation. A computer can be used to evaluate a model numerically and gather data in order to estimate the desired true characteristics of the model. Hence, with simulation, a problem is not "solved", rather, an experiment is run on an executable model to generate data which can be used to estimate performance measures. There is two major impediments to the usefulness of simulation however. First, the models used to study large-scale systems tend to be very complex themselves, costly to develop and writing the computer programs to execute them can be an arduous task indeed. Second, the amount of computer time to simulate complex systems can be large. Other problems involve the correct interpretation of simulation results and the degree of applicability to the real-world situation. The task of creating models has been made much easier in recent years though by emerging user-friendly software tools aiding in graphical model composition and reuse of components by providing libraries and living up to the principles of object orientation (compare Alstrom et al., (1998) and the Modelica simulation language specification efforts). Model execution speed is becoming a less severe restriction since computer processing power keeps increasing while hardware prices continue to fall. The next section discusses one variant of the discreteevent simulation formalism and introduces the PACE Petri net simulator.
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PETRI NETS AND THE PACE PETRI NET SIMULATOR In 1962, C.A. Petri (Petri, 1962) addressed in his dissertation the problem of representing co-operating, concurrent or competing processes by a graphical modeling formalism, subsequently referred to as Petri nets. Formally, a Petri net is a directed bipartite graph. Many books and papers have been written about Petri nets, it's mathematical foundation, the different flavors, application areas as well as application examples. A very small selection is Baumann (1996), Reisig (1985), Lindemann (1998), Marsan et al. (1995), German (2000) and Sahner et al. (1996). A wealth of online information is available as well, e.g., at the University of Aarhus (DAIMI, 2001). PACE is a software tool for the specification, simulation and code generation of discrete-event systems. It uses a special form of extended stochastic Petri nets with time modeling. PACE offers a graphical user interface for editing and simulating which is judged comparatively easy to use compared to other Petri net packages. The extensions to the original Petri net concept are mainly time modeling, incorporation of random behavior with statistic functions, inhibitors, capacity limitations for places, attributation of tokens and connectors, hierarchy by ability to create subnets (called modules) as well as the possibility to inscribe transitions with user-defined Smalltalk code (condition code, delay code and action code). The modeler is thus given great flexibility to define Petri nets to realize desired model behavior. It shall be said, PACE offers no analysis capabilities based on analytical methods, it is purely a discrete-event simulator. Detailed information about PACE can be found in the user manuals (Eichenauer, 2000) as well as on the company website.
PETRI NET MODELS FOR COMPONENTS AND SYSTEMS The Petri net formalism and PACE are now used to create models for availability analysis. Figure 1 depicts a Petri net model of a Boolean component with a functional "up" and a non-functional "down" state. The states are active if the corresponding places are marked by tokens. The transitions in Figure 1 are inscribed with Smalltalk delay code. In particular, the messages "next" are sent to instances of probability distributions - here: "Lifel" and "Repair 1" - resulting in random numbers being returned serving as transition firing delay. Here, the delays represent component life- and repair-times. This Boolean component is a basic building block and is placed in a so-called module (subnet for hierarchical decomposition in PACE), see Figure 2. The state of the module is communicated to higher hierarchies
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Figure 3: Two components combined in series 835
via the two places "upmarker" and "downmarker". Figure 3 shows an arrangement of two components in series, the respective system state logic is represented in Figure 4. As can be seen, the system is only functional if both components are in working order, if one of the components fails, the system leaves it's own functional state "up". Larger and more complex structures can be constructed alike.
TERMINATING SIMULATION In a terminating simulation, the duration of the simulation is fixed as a natural consequence of the model itself. An example is a bank that opens at 9:00 A.M. and closes at 4:00 P.M. serving customers only during opening hours. Here, for simulation, a fixed starting condition exists (opening time) and an event definition (closing hour) marks the end of daily operation. Note, in the general case, the terminating criteria does not have to be tied to simulation time. In this section, the well known 2-out-of-3 system structure is used as a demonstration example to estimate system lifetime hystem for given exponential probability distributions of component life- and repair-times. The simulation duration is not know apriori and is a consequence of how long it takes until the system fails. The system is hereby considered non-renewable, only the time-span to first system failure MTTFF (mean time to first failure) is of interest. Isystem is estimated as an average of lifetimes ln,r resulting from individual simulation runs, see Eqn. (1). 1
*
4§
(1)
A standard simulation experiment setup is used with a number of replications R using independent random number streams each consisting of N separate runs (compare Banks et al., 1996 or Law and Kelton, 2000). The components are all assumed to be in perfect working condition initially.
©T^O
^0
Figure 5: Markov diagram of a 2-out-of-3 system For the special case of exponential distributions, Markov theory can be used to analytically calculate MTTFF. Figure 5 shows the Markov diagram of this system. For failure rate A = 1/9 [1/day] and repair rate // = l, the analytical result is: MTTFF = 21.0. TABLE 1 lists simulation results for different parameters F and R (F corresponds to the number of failures simulated F^^. The numerical values are close to the analytical result. As expected, the confidence interval of the estimate for Isystem decreases for a greater number of system failures simulated. TABLE 1 SIMULATION RESULTS, 2-OUT-OF-3 SYSTEM
Mean Std. deviation Confidence 95%
lOOF/lOR 20.973 2.390 1.709
lOOOF/lOR 20.811 0.629 0.450
1000F/30R 21.020 0.552 0.394
1
STEADY STATE SIMULATION In contrast to the terminating case, in a steady-state simulation, the duration of the simulation is not 836
directly dependent on the evolution of the system itself, rather, it is chosen by the person conducting the simulation experiment. The objective is to understand the long-run behavior, consequently, possible effects of initial conditions must be removed form the simulation results. Experiment parameters are mainly the length of the simulation, the number of replications R and batches B as well as the length of the initial transient phase (of which no data are collected for statistical analysis). Standard simulation practice is used, details are again given in Banks et al. (1996) and Law and Kelton (2000). The steadystate system availability Aoo can be defined as the ratio of system uptime tup to total time the system had at disposal to operate tumi (Eqn. (2)). (2)
A^-
Individual availabilities A^^r - each resulting from a sequential batch run - are averaged (Eqn. (3)) to yield an estimate for system availability Aoo. ^ L1L V y i = ±1 Y -p R / / •f.r N^/ f b,r
A
(3)
•^>o,system
As an example, a renewable system of two identical components combined in series is simulated. The same exponential component probability models as above are used again. The Markov diagram for this system is depicted in Figure 6 and the system availability is calculated analytically as A«,2.?ena/=0.9604.
G- ^0^:^—^0 Figure 6: Markov diagram of the serial system (identical components) The point estimates and 95% confidence intervals for system availability A*, for eight simulation experiments are shown in Figure 7 with parametrization according to the captions on the horizontal axis ("5blOr30000" indicating 10 replications of 5 sequential batch runs each lasting 30'000 days). The point estimates are very close to the analytically computed value but are all slightly higher. The suspicion of upward bias through initial conditions was not proved though. As expected, the more simulation effort, the more accurate is the result (computing times range form 2.5 to 34.0 minutes on a PC with Pentium n
D.9G12 1
1
<'
^
u c c o u
l°™
1
'
1 1
1
11 11
a
1
' I
3
«> E, < 0.9GQO
Simulation Experiment
Figure 7: Simulation results, serial system Figure 8: Ratio processing time to confidence 837
300 Mhz processor). The relation between processing time and the size of the confidence interval ^ = ^procass.njAonfu/ence95% ^^ exponcntial though (scc Figurc 8).
MAINTENANCE STRATEGY MODELING AND SIMULATION Four different maintenance strategies are illustrated and implemented as PACE models in this section. The demonstration model is in all cases a system of six serially arranged identical components. Note, the goal is to demonstrate the feasibility of the Petri net simulation concept rather than trying to fmd an optimal maintenance strategy at this point. The strategies are: 1. Breakdown strategy: Components are operated until they fail. Immediately after failure, repair action is carried out for restoration of the functional state and components are considered to be in perfectly new condition after the repair. 2. Periodic maintenance: All components are overhauled together at the same respective points in time, scheduled according to a predefined andfixedinterval tp. 3. Opportunistic, system-oriented strategy: Each time the system goes down due to component failures, overhaul on all remaining components is scheduled only if the system was operational for a period greater than a certain specified time interval isi (si for "system lifetime"). 4. Opportunistic, component-oriented strategy: Each time the system goes down due to component failures, overhaul is selectively scheduled on the remaining components only if those components were (individually) operational for a time interval greater than tew {cm for "component maintenance") Figure 9 shows the demonstration model together with a maintenance coordination module (detailed in Figure 10) sending out signals in the form of tokens to initiate overhaul. The periodic strategy is displayed in these two figures, the other strategies are implemented likewise. Steady-state simulations with all four strategies were carried out with two sets of component stochastic characteristics (see TABLE 2) differing in the failure model. The first set uses an exponential (Eqn. (4)) and the second a Weibull (Eqn. (5)) failure probability model. F,{t) = \-e-"
(4)
F^(/)=:l-^^''-^
(5)
Figure 9: System of six components arranged in series with periodic maintenance
838
iQ
VtortCofiAjQ
MacJCogwQ
Figure 10: Maintenance logic for periodic strategy (delay = ip) TABLE 2 COMPONENT PROBABILITY MODELS
Set/Process Setl Set 2
Failure Exponential, mean=49days Weibull, Tchar^49days,
Repair Normal, mean=lday, stdev=0.3days di.
Overhaul Normal, mean=0.5days, stdev=0.15days di.
|
form-factor j3=5 Note, the overhaul is assumed to take less time and have a smaller spread compared to the repair variant. This, because preventive overhauls can be better prepared ahead of time, spare parts can be stocked, personnel is ready and the work procedure is better known compared to the repair case. Figure 11 and Figure 12 depict the estimated availabilities with the breakdown availability as a reference. Two effects can be seen very nicely. First, the improvement potential for increased availability by introduction of more sophisticated maintenance strategies is very small if exponential component failures dominate. Second (Figure 11), the periodic strategy is particularly unsuited and can significantly reduce the system availability - compared to the breakdown reference - especially if carried out on a frequent basis (small tp). Note, no cost of the maintenance effort is considered. A cost-benefit comparison of the strategies could demonstrate this effect even clearer. With the Weibull failure model, the breakdown strategy is inferior to all other maintenance strategies and the improvement potential by using a more sophisticated maintenance strategy is significantly larger. Interesting is the fact that the periodic strategy has some obvious optimum periodic interval tp between 20 and 30 days.
Periodic Breakdown 0pp. system-oriented Opp. componentoriented
Figure 11: System availabilities, set 1
Figure 12: System availabilities, set 2
CONCLUSION AND OUTLOOK It has been demonstrated how stochastic PACE Petri net simulations can be used for both transient and steady-state availability analysis. In particular, maintenance strategies can be modeled, simulated and the 839
results can be used to optimize the strategies applied to real plants. The potential availability improvements are largely dependent on the type of failures happening in a system. Yet even when the failure models are not known with precision, theoretical considerations can be useful and can suggest advantages of a certain strategy over another. Two main critiques must be formulated with respect to PACE when being used for modeling as outlined in this text. First, it does not support object-orientation as a modeling paradigm which makes model generation and evolution somewhat tedious. Second, the model execution is considered rather slow since the PACE nets - due to the Smalltalk foundation - are interpreted. It is assumed that native machine code would allow for significantly faster execution. The demonstration models presented here are rather simple. The investigations are of course more interesting for larger models with a variety of failure characteristics and interdependencies between individual components. In future work, costs (investment, depreciation, labor, parts, down-time), resource scarcity as well as additional dynamic effects could be included in extended, possibly hybrid models (combined discrete-event and continuous behavior). Further, the models could be altered to allow for simulations aimed at suggesting dynamic change to maintenance procedures during real plant operation, depending on the plants current states.
REFERENCES Astrom K.J, Elmquist H., Mattsson S.E. (1998). Evolution of continuous-time modeling and simulation. The 12'^ European Simulation Multiconference, ESM'98, Manchester, UK Banks J., Carson J.S., Nelson B.L. (1996). Discrete-Event Simulation, 2""^ ed.. Prentice Hall, New Yersey, U.S.A. Baumgarten B. (1996). Petri-Netze, Grundlagen und Anwendungen, Spektrum Akademischer Verlag, Heidelberg, Germany DIAMI (2001). http://v^^w^.daimi.aau.dk/-petrinet. University of Aarhus, Department of Computer Science, Denmark DIN 40041 (1990). (http://www.din.de)
Zuverldssigkeit, Begriffe, Deutsches Institut fiir Normung, Berlin, Germany,
Eichenauer B. (2000). Pace Benutzer-Handbuch, PACE version 4.0, IBE Simulation Engineering, Glonn, Germany (http ://www. ibepace .com) German R. (2000). Performance Analysis of comunication Systems: Modeling with Non-Markovian Stochastic Petri Nets, John Wiley & Sons, Chichester, England Law A.M. and Kelton W.D. (2000). Simulation Modeling and Analysis, ^^ ed., McGraw-Hill, Boston, U.S.A. Lindemann C. (1998). Performance Modeling with Deterministic and Stochastic Petri nets, John Wiley & Sons, Chichester, England Marsan M.A., Balbo B., Conte G., Conatelli S., Franceschinis G. (1995). Modeling with Generalized Petri nets, John Wiley & Sons, Chichester, England Petri C.A. (1962). Kommunikation mit Automata, PhD thesis. University of Bonn, Germany Reisig W. (1985). Petri Nets: An Introduction, Springer Verlag, Berlin, Germany Sahner R.A., Trivedi K.S., Puliafito A. (1996). Performance and Reliability Analysis of Computer Systems: An Example-Based Approach using the SHARPE Software Package, Kluwer Academic Publishers, Boston, U.S.A.
840
Condition Monitoring and Diagnostic Engineering Management Andrew G. Starr and Raj B.K.N. Rao (Editors) © 2001 Elsevier Science Ltd. All rights reserved.
THE DYNAMIC MODELLING OF MULTIPLE PAIRS OF SPUR GEARS IN MESH INCLUDING FRICTION Ian Howard, Shengxiang Jia, and Jiande Wang Rotating Machinery Research Group, School of Engineering Curtin University of Technology [email protected] GPO BOX U1987, Perth, WA 6845 AUSTRALIA FAX: (61) 08-9266 2681 Tel. (61) 08-9266 7591
ABSTRACT This paper presents a 26 degree of freedom gear dynamic model of two pairs of gears in mesh including the effects of variable teeth stif&iess, friction and a localised tooth crack on one of the gears. The equations of motion are included along with a description of the model. The comparison between models with and v^thout localised teeth damage was obtained by using Matlab and Simulink simulations which were developed from the equations of motion. Coherent synchronous signal averaging techniques were used to obtain the resulting vibration signal average of each shaft of interest and some rudimentary diagnostics are also presented.
KEYWORDS Gear, Dynamic Model, Crack, Diagnosis, Simulation, Mesh Stiffness
NOMENCLATURE Fi2 teeth friction force between gear 1 and gear 2 F34 teeth friction force between gear 3 and gear 4 II mass moment of inertia of output load lin mass moment of inertia of the motor li i == 1-4, mass moments of inertia of the gear 1,2,3 and 4 kb radid stiffness of the bearing and support kc torsional stiffness of the flexible coupling ks shaft transverse stiffriess kst shaft torsional stiffness ki2, bending tooth stiffness between gearl and gear 2 k34 bending tooth stiffness between gear3 and gear 4 mb mass of the bearing and part of the shaft 841
nii Tjj qb qc qi2 q34 ri Tin Tioad Xj Yi Xbi Ybi 9i Gin Gout //
i = 1 -4,masses of gear 1,2,3 and 4 i = 1-4 friction force arms o n gear 1 , 2 , 3 and 4 viscous damping coefficient of bearing pinion, gear and load viscous damping coefficient o f flexible coupling viscous damping coefficient of gear 2 and 3 mesh viscous damping coefficient of gear 2 and 3 mesh base circle radius of gear 1,2,3 and 4 Input motor torque Load torque from load i = 1-4, linear vertical displacement of gear 1,2,3 and 4 i = 1 -4, linear level displacement of gear 1,2,3 and 4 i = 1-6, linear vertical displacement of bearing 1-6 i = 1-6, linear level displacement of bearing 1-6 i = 1 -4, angular displacements of gear 1,2,3 and 4 angular displacements of motor, load angular displacements of motor, load dynamic coefficient of friction
INTRODUCTION To improve the current generation of diagnostic techniques, many researchers are actively developing advanced dynamic models of gear vibration to ascertain the effect of differing types of gear train damage on the resultant gear case vibration. One of the more recent common modelling approaches is to use the coupled torsional and transverse motions of the shafts, along with the changes to the tooth bending stiffiiess as the teeth rotate through the mesh point, [1]. The model described within this paper is based on the one developed by Du, [1] and subsequently modified by Howard et. al., [2, 3]. The previous models developed by Du and Howard et. al., were concerned with a single pair of gears in mesh. The model presented in this paper extends the previous approach to two pairs of teeth in mesh and includes the effect of the friction between the meshing teeth via a 26 degree of freedom model. The comparison between models with and without localised teeth damage was obtained by using Matlab and Simulink to simulate the resultant vibration. One of the difficulties v^th muhiple gear pair models is recovering the vibration information from each shaft of interest. In this paper, coherent time synchronous signal averaging techniques, [4] have been used.
MODEL AND EQUATIONS The model developed here is based on a two stage reduction gearbox, where the teeth numbers of gear 1 is 23, the teeth numbers of gear 2 is 45, the teeth numbers of gear 3 is 25 and the teeth numbers of gear 4 is 47. The overall velocity ratio is given as (45*47)/(23*25) which is equivalent to 3.678. There are a total of 26 degrees of freedom in the model and a schematic diagram of the model is shown in Figure 1. The major assumptions which the dynamic model is based upon are, (i) Resonances of the gearcase are neglected, (ii) Shaft mass and inertia are lumped at the bearings or the gears, (iii) Shaft transverse resonances are neglected (iv) Input shaft and output shaft torsional stiffness is ignored, (flexible coupling torsional stiffiiess is very low), 842
(v) Gear teeth profiles are perfect involute curves, with no geometrical, pitch or runout errors, (vi) Static transmission error effects are very much smaller than the dynamic transmission error effects and so can be neglected.
Bearings
Couplings Motor yw X „ Couplings
Figure 1. Diagram of the 26 degree offi*eedomdynamic model of two pairs of gears in mesh. The vertical direction (xj) is aligned with the pressure line of the gear mesh. The coupling between the torsional and transverse motions of the gears and shafts has been developed as shown in Figure 2, [3].
Figure 2. Coupling between the torsional and transverse motions of the gears and shafts, rp - Base circle radius of input pinion, rg- Base circle radius of output gear, kmb - Linear translation tooth stiffness along line of contact, c-c - Line of contact. The linear translation tooth stiffiiess along the line of contact, kmb, can be calculated by, ^mb
=
-
2 >
843
(1)
where km represents the torsional mesh stiffiiess and Tp denotes the radius of pinion base circle. The resulting equations of motion describing the coupled torsional and transverse model are given by Eqn. 2 - 27,
h^x +^c(^i -4)+'-.^i2M, -rA -^1 +^2)+ -r,e, -x, ^x,)+F,,,r^, + ^c(^i -0,„yr,k,,{r,e, I A
=0,
(3)
=0,
(4)
+ ''2^12 (''2<^2 - ^A - ^2 + ^1 ) +
-^^{^2 -0,yr,k,^{r,G, h^2+r,q,,{n^3 - ^ A -X,+X,)+k^X0,
IA+r,q,,{rA
-r^
-r,e, -x,^x,)-F^,,r^,
-^2)+'•3^34^3 - ^ A -^3 +^4)+^/34^/3 = 0 ,
-x, +x,)+kXO, -^^,)+^3^34M4 -^A -x,-^x,)-Ff,,r^,
=0,
hL, ^qXo^. -o^^Kio^, -o,)=T^,
+ ^12(^1 -^2 -rA
(5)
(6)
(7)
+rA2)+qn[^x "^2 '^A +rA)^^^
m^x^-^kXx2-x,,ykXx2 -x^)-¥k,^{x^ -x,-rA'rr,G,)+q,Xx2
"^i -rA+rfi)=(),
(8) (9)
'"3^3 +^.v(^3 - ^ 2 ) + ^ , ( ^ 3 - ^ M ) + + ^34(^3 - ^ 4 -^3<^3 + ^ 4 ^ 4 ) + ^ 3 4 ( ^ 3 " ^ 4 "'*3^3 +rA,)=0,
w.ic^ +kXx, -x,,)-¥kXx,
(10)
-x,^)-^
+ ^34(^4 -^3 -^AA "^rAVqA^A
-^3 -''4<^4 +''3<^3)=0,
(11)
'«2>^2+^.(y2->'M)+^,(y2-r3)+^/12 =0>
0^)
'"3i>3+^.v(y3->^2)+^,(>'3->'M)+^/34=0,
(14)
'"43>4+^,(y4-3^M)+^.(V4->'66)-^/34 = 0 ,
(15)
'Wft^M + ^/.^M + ^/>^M + K U l - ^I) = 0 ,
(16)
'W/.^M + QhXHl + ^*^A2 + ^, (^M - ^1) = 0 ,
(17)
'"/,^M +^6^M +^A^« +^.v(^63 - ^ 2 ) = 0 '
(18)
^"^^64 +^*^M + ^ 6 4 +^,(^64 " ^3 ) = 0 >
(1^)
844
'Wft^fce +^6^*6 + M w + ^ . ^ 6 - ^ 4 ) = ^ ,
(21)
Kyti+^^^2
(23)
+ ^ft>^62+^,^2 - ^ ^ i ) = 0 '
'W^J^W + ^ 6 ^ 3 + ^fe^^M + K (^63 - >^2 ) = 0 ,
(24)
'wJ M + q ^ h ^ + Kyn + ^, (VM ~ ;^3)=0,
(25)
'«/,A5+^AA5+^/,>'/,5+^,v(yA5-3^4)=0,
(26)
'4 ) = 0 .
(27)
and
CONTACT FORCE, FRICTION FORCE AND FRICTION MOMENT The equation for the normal contact force between gear 1 and gear 2 is given by Eqn. 28, and the friction force between the gears is calculated by Eqn. 29, [3], ^12 = ''2^12 (''2^2 ~ ^A
- ^2 + ^1 ) + ''2^12 V2^2 - ^A
- ^2 + ^1 ) '
Fjn=^^n-
(2^)
(29)
Likewise, the normal contact force between gear 3 and gear 4 is given by Eqn. 30, and the value of friction force between the two gears is calculated by Eqn. 31. ^34 = ^34 (^3 - ^4 - '•3<^3 + r,e,) + q,, [x, -X,Fju=MFn-
r,0, + rfi,),
(30) (31)
The above equations require the torsional mesh stiffness, the ratio of the contact force between the teeth and the dynamic friction coefficient, [5], to be known at every position of the gears throughout the simulation. Figure. 3 shows the parameters as used for the results shown in this paper. In Eqn's 36, r^j, r^2'''/3 ^ ^ ^fA denote the friction moment arms on gears 1-4 respectively as obtained by geometrical analysis [3].
NUMERIC SIMULATION RESULTS The dynamic simulation of the differential equations of motion was achieved using SIMULINK software. After steady state rotational speed of the input shaft had been achieved, 300 revolutions of the input shaft, 200 revolutions of the intermediate shaft and 100 revolutions of the output shaft were simulated for subsequent analysis. In excess of 18 hours was required for the simulation using a 350MHz Pentium III Personal Computer. Coherent synchronous signal averaging was used to recover the vibration information relating to the shafts of interest, [4]. To illustrate the effect of a 845
single tooth crack on the resuhant vibration, one of the 23 teeth on the input shaft had a simulated 5nim fillet crack at the root of a tooth as represented by a change in torsional mesh stiffness as shown in Figure 3a.
?
X 10
3.6
x10
E z
3L
3.5 c
2.5
55
2
Lsl o
V)
—
Undamaged Tooth Crack tooth
s 2.5 15 c o
1
12
5 10 15 20 Shaft Angle (deg)
5 10 15 20 Shaft Angle (deg)
1
B 0.8 (0
0) 0.6 2 i^ 0.4 T3
0.2
^y^
J
^y^"^
^"^^^
^^^^^^
y^
^v
0 5 10 15 20 Shaft Angle (deg)
10 20 Shaft Angle (deg)
^
a> .^ 0.05
0.05
S o c O
•s
•c U-
0
u E 2 -0.05
-0.05
/ 5 10 15 20 Shaft Angle (deg)
10 20 Shaft Angle (deg)
Figure 3. Parameters used throughout the gear simulation, a. Torsional mesh stiffness between gears 1 and 2. b. Torsional mesh stiffness between gear 3 and 4 mesh. c. Load Sharing Ratio between gear 1 and 2. d. Load Sharing Ratio between gear 3 and 4. e. Dynamic friction coefficient between gear 1 and 2. f Dynamic friction coefficient between gear 3 and 4. The resultant signal average (300 averages) of the input pinion velocity 9^ and the corresponding RMS amplitude spectra are shown in Figure 4 with and without the 5mm tooth crack. Figure 5 shows the signal average (300 averages) of the bearing velocity x^j ^ ^ ^^ corresponding RMS amplitude spectra with and without the 5mm tooth crack. Figure 6 shows the output bearing velocity x^^^ averaged over 100 revolutions of the intermediate shaft and output shaft and the
846
corresponding RMS amplitude spectra, when the localised tooth crack was present on the input pinion. 143.2,
P 142.4
<
90 180 270 Shaft Posltion(deg)
50 100 Frequency (Shaft Orders)
360
143.2 8
143
"S 142.8 © 142.6
mmjwmjmmm I
E 142.4 < 142.2 90 180 270 Shaft Posltion(deg)
50 100 150 Frequency (Shaft Orders)
360
Figure 4. The input shaft angular velocity 0^ after 300 averages with and without a crack and the corresponding RMS amplitude spectra. xio
0
90 180 270 gShaft Position (deg)
50 100 150 Frequency (Shaft Orders)
360
xio'
•^
-40
0
UAAAMAAAM;IAAAAAAAAA^
90 180 270 Shaft Position (deg)
360
t
0
50 100 150 Frequency (Shaft Orders)
Figure 5. The input shaft bearing velocity (300 averages) ±^2 ^ ^ ^^^ corresponding RMS amplitude spectra with and without a 5mm tooth crack.
847
x10
20 r-
w
25
0
45
liiiiiiiiiiiiiiiiiiiiiii
90 180 270 ^Shafl Position (deg)
360
0
50 100 150 Frequency (Shaft Orders)
90 180 270 Shaft Position (deg)
360
0
50 100 150 Frequency (Shaft Orders)
x10
Figure 6. Signal average (100 averages) of the output bearing velocity x^^ for the intermediate and output shaft and the corresponding RMS amplitude spectra.
DISCUSSION & CONCLUSION The simulation results shovm in this paper have illustrated the effect of a 5mm tooth crack on the vibration from a multi-shaft gear transmission, where the crack has been shown to cause a local change to the shaft rotational velocity and linear bearing velocity. The signal averaging from 100 or more revolutions of the shaft of interest has been demonstrated to be effective in removing unwanted vibration components from other gear meshing pairs and the vibration from undamaged gears is clearly dominated by the gear mesh frequencies and harmonics. The localised crack appears to cause a significant change to the frequency components present in the signal average across a broad range of frequencies.
REFERENCES 1. S. Du (1997). Dynamic Modelling and Simulation of Gear Transmission Error for Gearbox Vibration Analysis. Ph.D Thesis, University of New South Wales. 2. I. Howard, S. Sirichai, L. Morgan (1998). A Simplified Model of the Effect of a Crack in a Spur Gear on the Resultant Gear Vibration. Proceedings of the 11* International Conference on Condition Monitoring and Diagnostic Engineering Management, Vol 1., pp 397 - 406. Launceston, Tasmania, December 8-11. 3. I. Howard, S. Jia, and J. Wang (2001). The Dynamic Modelling of a Spur Gear in Mesh Including Friction. Accepted for publication. Journal of Mechanical Systems and Signal Processing. Manuscript number MSSP 00/72. In Print. 4. B.D. Forrester, (1996), Ph.D Thesis, Swinburne University of Technology, Melbourne. Advanced Vibration Analysis Techniques For Fault Detection and Diagnosis in Geared Transmission Systems. 5. B. Rebbechi, F. Oswald, D. Townsend, 1996, ASME, DE-Vol. 88, Power Transmission and Gearing Conference, pp 355-363. Measurement of Gear Tooth Dynamic Friction.
848
Condition Monitoring and Diagnostic Engineering Management Andrew G. Starr and Raj B.K.N. Rao (Editors) © 2001 Elsevier Science Ltd. Allrightsreserved.
THE MODELLING OF A DIESEL FUEL INJECTION SYSTEM FOR THE NON-INTRUSIVE MONITORING OF ITS CONDITION Shiyuan Liu, Fengshou Gu, and Andrew Ball School of Engineering, University of Manchester, Manchester, Ml 3 9PL, UK www.maintenance.org.uk Phone+44 (0)161-275 4458
ABSTRACT In this paper a finite difference scheme has been developed and applied to the modelling of a diesel fuel injection system. With the high-pressure pipe divided into many reaches, the partial differential equations governing the pipe flow are discretised into ordinary differential ones, which are in the same form as those representing the pump and the injector so that all the equations are solved simultaneously at each time step. Numerical results for a distributor-type injection system and influences of fuel injection faults have been simulated. It is observed that the pressure at approximately the end of the pipe close to the injector (p^^) is almost the same as the pressure in the nozzle chamber (/?„). Therefore, indirect measurement of p^„ by a clamping sensor around the pipe can provide an alternative non-intrusive means rather than direct measurement of p^ by an intrusive pressure transducer. The monitoring capability of the pipe pressure Pp„ by indirect measurement has been verified by a preliminary experiment on a production diesel engine. This investigation provides a tool for the non-intrusive monitoring of a diesel fuel injection system.
KEYWORDS Condition monitoring, Diesel engine, Fuel injection system, Injection pressure. Injection timing NOMENCLATURE A a c d F k I m N P S s
flow area wave propagation speed damping coefficient pipe diameter spring force spring rate lift; stroke mass number of the divided pipe reaches pressure Surface area perpendicular to the lift axis velocity of the valve; velocity of the needle
/ time u flow velocity V volume axial distance along the pipe X At time increment Ax distance increment y void fraction cam angle eK bulk modulus discharge coefficient h P density r wall shear stress
849
Subscript 0 c d
reference value cylinder combustion chamber delivery chamber; delivery port of the pumping chamber g gaseous/vaporous phase h injector nozzle holes in inlet port ofthe pumping chamber / liquid phase n nozzle needle; nozzle chamber; nozzle needle seat passage
s sp p pm pn pp pv v
sac chamber. spill port of the pumping chamber plunger; pumping chamber middle location of the pipe pipe location at the injector inlet pipe pipe location at the pump output delivery valve; delivery valve chamber; delivery valve seat passage
INTRODUCTION The fuel injection system in a diesel engine plays a dominant role in the combustion process, and consequently has a strong influence on engine performance such as power output, fuel consumption, exhaust emissions and noise emissions. The importance of fuel injection modelling in designing new fuel injection equipment and better understanding of combustion has been recognised for a long time, and several numerical simulations have been developed. The main problem in fuel injection modelling is that it has to be detailed enough to be accurate and modular enough to be flexible. The difference in the approach followed in the various simulation models lie not in the equations used but in the best method of solution ofthe partial differential equations for the fluid flow in the high-pressure pipe. The most popular approach is the method of characteristics (Wylie et al., 1971; Goyal, 1978; Arcoumanis et al., 1996), which shows few problems in handlmg boundary conditions but has problems dealing with possible cavitation in the system. Alternative methods include explicit finite difference schemes (Becchi, 1971; Kumur et al., 1983; Marcic and Kovaacic, 1985) and implicit finite difference schemes (Catania et al, 1994), which have no difficulty in dealing with cavities and now become increasingly attractive as the computer performance is improved significantly. In this paper a fuel injection system model, which considers both the transient pipe-flow dynamics and the hydro-mechanical dynamics of the pump and the injector, has been proposed. The model has the capability of predicting influences of fuel injection faults and damage on the injection process. The finite difference scheme, instead of the method of characteristics, has been developed and successfully applied to solve the numerical model. The purpose ofthe modelling is to provide a theorefical basis for a non-intrusive fuel injection monitoring technique, which is based upon pressure signals measured by a clamping pressure sensor around the high-pressure pipe. MATHEMATICAL MODEL The diesel fuel injection system with a distributor-type pump is shown in Figure 1. For the convenience of modelling the injection phenomena and fault simulation study, the system is divided into three main sections: the pump, the injector, and the high-pressure pipe. These three sections can be modelled with four pressure chambers, two valve actions and one pipe flow. For each of the pressure chambers, a pair of conservation equations can be developed. For each of the valve actions a pair of force equations can be deleloped. The pipe flow can be described with a pair of partial eaquations. Examples of the equations for these three types of models are given in equations (1) to (6) respecttively.
850
1 2
3
4
5
6
7
8 9
Plunger Spill port Delivery port Inlet port Pumping chamber Delivery pipe Delivery chamber 8 Delivery valve 9 Delivery valve chamber 10 High pressure pipe 11 Nozzle chamber 12 Nozzle needle 13 Sac chamber
10 11 12 13
Figure 1: Diesel fuel injection system with a distributor-type pump Conservation of fuel in the pumping chamber: dV„
dl
P_ _ __o
dt 1 . P.p-Pp dt
\Pin-PA
\P
(1)
P_
' dt . 2 .
, Pd-Pp
\Psp-Pp\
Pd-Pp\
. 2. ~ dt
(2)
Equilibrium of forces on the delivery valve: dL
(3)
-di = '^ ds
1
dt
m„
(4)
The pipe flow is assumed to be one-dimensional transient, thus the partial differential equations of motion and continuity are employed: du du 1 dp — + w — + —— dt dx p dx
pd
(5)
dp dp (6) ~^ + u-^ + pa dx dt dx ^ Vapour cavitation occurs in regions close to vapour pressure at approximately zero. This results in the formation of cavities, which are filled with vaporised fluid and reduction locally of the speed of sound and bulk modulus, although the density decreases only slightly. Therefore, when the pressure of the fluid falls to the same level as the vapour pressure p^, the fluid will become a homogeneous, bubbly, two-phase fluid mixture instead of pure-liquid. The above equations concerning the change of pressure will be no longer suitable, and should be replaced by the following ones:
851
(7)
dYn ^ dt
(P -p. P V„{p-p,) dy dt
dy dx
?1\
\
p du Pg- p, dx
^
.
dV^
(8)
(9)
Here y is a new dependent variable, termed the void fraction. It is defined as the ratio of the volume of gaseous/vapour fliel in a region to the total volume of the region. The density, bulk modulus and wave propagation speed of the mixture are adjusted respectively as: (10)
p-- -YPi . + ( 1 - r)Pi K^K, K -
(1-
a
(11)
rK« ^yKi
=if
(12)
NUMERICAL ALGORITHM An implicit finite difference method is used to discretise the partial differential equations (5) and (6) or (9). The pipe is divided into A^ reaches with iV+1 sections, and the length of each reach is Ax. The partial differential equations for section / are respectively converted into ordinary differential ones: 1 Ap,
Aw, dt ' •' -u' A x
dt
dt
A ^
Aw. Ax
Ap, Ax
'Ax
4r, —^, Pid
/ = 2,3,...,iV
(13)
/ = 1,2,...,A^ + 1
(14)
/ = 1,2,...,A^ + 1
p^- Pi ISx '
(15)
where Aw, is the difference of velocity in section / and defined as (Ap, and A;', have similar forms): W2 - w,
/= 1
Aw, = 0.5(w,,,-w,_,)
/ = 2,3,...,^
(16)
Now the two variables w^, and w^„ respectively introduced in nozzle chamber equations and valve motion equations can be treated as known at any time: 852
\P.-Px\\P
\PN.l-Pn\\P Combining equations (13) and (14) with equations (1) to (6), there are IN + \A ordinary differential equations at each time step. If cavitation occurs in a chamber or in a pipe section, equations (7), (8) and (15) will replace equations (6) and (14) respectively. Several implicit numerical methods such as the Newton-Raphson iterative method can be easily used to solve these ordinary differential equations. Since there are only 2A^ + 14 variables in these equations, a unique solution is guaranteed at each time step. However, not all of the 2A^ +14 ordinary differential equations are always necessary to be solved simultaneously during an entire engine cycle. Simplification of the calculation can be achieved according to different injection stages. For example, when the pump plunger and the delivery valve are both moving but the pressure in the nozzle chamber is not high enough to lift the nozzle needle, there are 2N + 9 changing variables in the system. These variables include F^, p^, F^, p,, F,, p , , /,, s^, u,(i = 2,3,...,N-I), p^ (i = l2,...,N+ 1) and p„, while the 2N + 9 equations are (1) to (8), (13), (14) and (10). Vapour cavitation need not be taken into consideration. 4
NUMERICAL AND EXPERIMENTAL STUDY
The calculation is carried out for a fuel injection system with a distributor-type pump. Figure 2 shows a typical the numerical results obtained for the delivery valve lift (/^), the nozzle needle lift (/„), the pressure in the pumping chamber ( Pp), the pressure in the delivery chamber (p^), the pressure in the delivery valve chamber (p^), the pressure at approximately the start of the pipe close to the pump (Pp^X the pressure at the middle of the pipe {Pp^\ the pressure at approximately the end of the pipe close to the injector (Pp„), the pressure in the nozzle chamber (p„), and the pressure in the sac chamber (/?,), at pump speeds of 1000 r/min. It is observed that the pressure in the delivery valve chamber (/? J is quite different from the pressure in the nozzle chamber (p„), This is obviously because of the complex wave propagation in the high-pressure injection pipe. However, the pressure at approximately the end of the pipe close to the injector (Pp„) is almost the same as the pressure in the nozzle chamber (p„), so indirect measurement of Pp„ by a clamping sensor around the pipe can provide an alternative non-intrusive means rather than direct measurement of p„ by a piezoresistive transducer. This time history of pressure Pp„ can be used to evaluated many important injection parameters including the time of needle opening and closing, the opening injection pressure and the maximum injection pressure. Combined with our previous investigation (Gu and Ball, 1996; Gu et al., 1996), the needle lift can be divided into four phases, including the retracting, the opening bouncing, the advancing and the closing bouncing. The needle impact behaviour via measurement of the transmitted impact vibration can be used to indirectly estimate many key fuel injection parameter values such as the time of needle opening and closing. 853
, 0.3 F \ 02 10.1
:i'°
:H
j)j
20 60
fc40
.I\A
20 60 0^ 40 ^20
20 0-0r
0-Or
Figure 2: Numerical results at a pump speed of 1000 r/min The influences of fuel injection damages and abnormal injections have also been simulated, including abnormal retraction volume of the delivery valve and secondary injection, abnormal opening pressure of injection, coking of nozzle holes, limitation of needle lifl and too large needle lift, delivery valve leakage, plunger leakage, etc. Here an abnormal retraction volume of the delivery valve is given as an example. As shown in Figure 3, it is the job of the delivery valve to interrupt the high-pressure circuit between the high-pressure fuel-injection pipe and the pump plunger, as well as to relieve the high-pressure pipe and the nozzle space by reducing pressure to a given static level. This reduction in pressure causes the nozzle to close rapidly and precisely, as well as preventing undesirable fuel drizzle (secondary injection). To achieve such a function, for example, in a constant-volume delivery valve, part of the valve-element stem is shaped like a piston (retraction piston) and is precisely lapped into the valvestem guide. When the plunger's helix terminates the fuel delivery, and the spring closes the delivery valve, the piston enters the valve stem guide and closes off the high-pressure pipe from the pump plunger chamber. This means that the volume available to the fuel in the high-pressure pipe is increased by the retraction piston's stroke volume. This volume is called the retraction volume, which can be expressed as S^h, where S^ is the surface area of the retraction piston perpendicular to the lifl axis and h is the retraction distance that the retraction piston moves after the piston enters the valve stem guide. Figure 4 shows the influence of the retraction distance h on the injection process. It is observed that secondary injection becomes more likely to happen when the retraction distance decreases. This fault can be expected to be detected by monitoring the pressure at approximately the end of the pipe close to the nozzle chamber (p^^) and the impact vibration of the needle opening.
854
Valve seat Retraction piston Ring-shaped groove Valve stem Vertical Slot Figure 3: Constant-volume delivery valve
0.3 h=2mm 0.2 0.1 20 30 0,3 h=\mm 0.2 0.1 0 ^RO.3
<e 0.2 J 0.1
30
J
r~'\ {
r\^ 40
50
40
50
60
^ 60
/j=0.5mm
/
1'
0 30 •^ 0.3 /F=0.2mm 0,2 0,1 30 20 0.3 h=Omm 0.2 0.1 0 30
1 40
A 50
HA 40
J
50
60
60
O A
[J L..
40
30
50
40
50
Cam Angle 0 -& Q,O
Cam Angle 9 -6 Q,O
Figure 4: Numerical results with different retraction distances Figure 5 shows the measured pipe pressures under a normal condition and a faulty condition with an engine speed of 1000 r/min and load of 40 N-m. The fault was artificially set by adding a 4mm thick loop between the delivery valve and the fastening screw. This resulted in a decreased initial spring force acting upon the delivery valve and an increased volume of the delivery valve chamber. Therefore, the delivery valve is expected to open earlier under A the fault condition than under the ' normal condition. This phenomenon has been verified 1 0 \jWV\A/V\A'-'''-V^--'^w---^ from the measured pipe pressures •^ ^ffi\( in Figure 5, thus tiie pipe pressure by indirect measurement can provide a non-intrusive means for the fuel injection monitoring. However, due to the complex transfer property from the pipe pressure to the clamp sensor, the measured pressure is quite different from the actual Figure 5: Measured pressures under normal and fault conditions pressure in the pipe. For the purpose of on-line monitoring, further investigation should be undertaken. (a) Normal condition
f ' ^
i\i y^\f
Cam Angle 0 , {2
SUMMARY AND CONCLUSION In this paper a finite difference scheme has been developed and applied to solve the numerical model of a diesel fuel injection system. The pipe is divided into many reaches and the discretisied ordinary differential equations are in the same form as those representing the pump and the injector so that all the equations are solved simultaneously at each time step. A cavitation chamber or pipe section is no longer treated as a boundary condition of other sections, which is adopted thus very difficult to deal 855
with in the method of characteristics. The time step is not strictedly limited to the Courant condition for stability of the solution procedure. Numerical results for a fuel injection system with a distributor show that the pressure at approximately the end of the pipe close to the injector ( p ^ ) is almost the same as the pressure in the nozzle chamber (p^). Therefore, indirect measurement of p ^ by a clamping sensor around the pipe can provide an altemative non-intrusive means rather than direct measurement of p„ by a piezoresistive transducer. Along with the needle impact behaviour via measurement of the transmitted impact vibration, the pipe pressure p^„ can be expected to estimate many key fuel injection parameter values.The monitoring capability of the pipe pressure /7^ by indirect measurement has been verified by a preliminary experiment carried out on a production diesel engine. Further investigation is being undertaken and it is anticipated to provide a tool for the non-intrusive monitoring of diesel fuel injection systems. REFERENCES Arcoumanis C , Fairbrother, R. C , and French, B. (1996). Development and Validation of a Computer Simulation Model for Diesel Fuel Injection Systems. Proceedings of IMechE, Part D, Journal of Automobile Engineering 210:2^ 149-160. Becchi, G. A. (1971). Analytical Simulation of Fuel Injection in Diesel Engines. SAE Technical Paper No. 710568. Catania, A. E., Dongiovanni, C , Mittica, A., Badami, M., and Lovisolo, F. (1994). Nimierical Analysis Versus Experimental Investigation of a Distributor-Type Diesel Fuel-Injection System. ASME Journal of Engineering for Gas Turbines and Power 116:4, 814-830. Goyal, M. (1978). Modular Approach to Fuel Injection Simulation. SAE Technical Paper No. 780162. Gu, F. and Ball, A. D. (1996). Diesel Injector Dynamic Modelling and Estimation of Injection Parameters from Impact Response, Part 1: Modelling and Analysis of Injector Impacts. Proceedings of IMechE, Part D, Journal of Automobile Engineering 210:4,293-302. Gu, F., Ball, A. D., and Rao, K. K. (1996). Diesel Injector Dynamic Modelling and Estimation of Injection Parameters from Impact Response, Part 2: Prediction of Injection Parameters from Monitored Vibration, Proceedings of IMechE, Part D, Journal of Automobile Engineering 210:4, 303-312. Kumur, K., Gaur, R. R., Garg, R. D., and Gajendra-Babu, M. K. (1983). A Finite Difference Scheme for the Simulation of a Fuel Injection System. SAE Technical Paper No. 831337. Marcic, M. and Kovacic, Z. (1985). Computer Simulation of the Diesel Fuel Injection System. SAE Technical Paper No. 851583. Wylie, E., B., Bolt, J., T., and El-Erian, M. F. (1971). Diesel Fuel Injection System Simulation and Experimental Correlation. SAE Technical Paper }^o. 710569.
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Condition Monitoring and Diagnostic Engineering Management Andrew G. Starr and Raj B.K.N. Rao (Editors) © 2001 Published by Elsevier Science Ltd.
USE OF FACTORIAL SIMULATION EXPERIMENT IN GEARBOX VIBROACOUSTIC DIAGNOSTICS J. M^czak and S. Radkowski Institute of Machine Design Fundamentals, Warsaw University of Technology, Warszawa, Poland
ABSTRACT The work concerns identification of the origins and development of early stages of failures based on numerical factorial experiments and information found in the vibroacoustical signal that is generated during a simulation process. The possibility of generation of additional component is presented on the basis of an exemplary analysis of the contact conditions disturbances. Application of the discussed identification method can lead to the generation of huge amount of data. The conversion and selection of diagnostic information, analysis of that information and retention of the results is the goal of the presented method as well as problems of selection of signal analysis methods that enable realization of assumed aims.
KEYWORDS Gears, cluster analysis, factorial simulation, vibroacoustic diagnostics TASK FORMULATION The interest in the procedures of determining and forecasting failure states and in the growth of reliability of the diagnosis of supervised machines and devices result from the fact that the system that has been designed, which accounts for the possibilities of providing the conditions monitoring (CM), allows for avoiding the losses related to stoppages resulting from failures, thus decreasing the cost of excessive storage of spare parts as well as the cost related to the occurrence of failures. This calls for adopting the Condition Based Maintenance - CBM as the operations and maintenance strategy, or in other words for introducing a system of evaluation of the technical condition relying on the collected data about a machine's operational parameters and about the parameters of residual processes. In many cases, despite
857
careful selection and implementation of a diagnostic system and despite the relevant expertise in terms of trend analysis, the occurrence and development of defects among the diagnostic team members, the forecasts of time to failure turn out to be very imprecise or they even lead to the adoption of incorrect operational decisions. This often results from the fact that we are unable to describe, with relevant precision, the mechanism of evolution of a product's load and load capacity parameters. Hence there is enormous interest in the procedures of determining and forecasting the failure states, growth of credibility of reliability assessment of monitored machines and devices, and in the introduction of reliability based condition maintenance (RCM). By assumption, the RCM system should enable the achievement of acceptable probability of failure occurrence in a given element. The basis of the discussed method is the assumption that the project was developed according to "safe life" or "fail safe" principle which permits the occurrence of defects. For example, while constructing means of transport it is assumed that the resistance of critical elements to fatigue should in normal conditions exceed the assumed operational period. This means that no fatigue signs should be seen during normal operations, particularly no cracks or fatigue-related surface wear. In reality this goal is rarely attained. Even those constructors who follow the "safe life" principle provide for the possibility of carrying out diagnostic inspections whose purpose is to track the development of fatigue-related defects.
IMPACT OF THE PARAMETERS OF TOOTHED GEAR MODEL ON THE STRUCTURE OF THE GENERATED VIBROACOUSTIC SIGNAL A particularly important element of the process of condition diagnosis involves the determination of the impact that individual parameters of a system have on the values and the frequency structure of a dynamic response. For a defined level of model's complexity, while particularly accounting for the impact of the spatial layout of the diagnosed model, this calls for determination of a procedure for conducting a numerical experiment that will enable the determination of the impact that subsequent parameters will have on the process of vibroacoustic signal generation. When using factorial analysis we can initially determine the area in which the global extreme is located while only relying on the linear approximation of the function we are searching for. The example of application of the factorial method in the task of simulation experiment planning was presented in M^czak&Radkowski (2001). In general it should be noted that the use of such research allows one to assume the postulate of describing the extreme of function with multiple variables in the following form:
id
Then, upon including a linear-only (local) mathematical relationship:
858
y = ao +i]a;«/
(2)
we can, based on the results obtained from a relatively small number of experiments, define the values of a, coefficients. Assuming that relationship (2) should designated the real function with a minimum average squared error: Sj,={y-yY{y-y)
= {y-Uaf{y-Ua)
= y^y-2Ju^y-Ju''Ua
(3)
and that we are looking for an extreme: ^^2U^y-2aU^U da
=0
(4)
we will finally obtain:
a = {u^u) ^U^y
(5)
where: U - matrix of the experiment's plan, y - simulated value of the function during a numerical experiment.
RESULTS OF THE EXPERIMENT Bearing in mind the simplicity of the method and the clarity of obtained results, we subjected to factorial analysis the spatial model of a gear (as defined in detail in the work by Filonik, M^czak & Radkowski (1998) - Figure 1), with parameters defined in Table 1. Assuming a two-level plan of the experiment, first we examined the impact that changes of the system's parameters have on the fi-equency structure of the obtained
IH.M,
pinion
Figure 1: Diagnostic model used in the experiment
859
signal. On the basis of the model's initial analysis and the results of earlier research, (M^czak&Radkowski (2001)), we considered the following parameters of the model as the important ones: torsional stiffness of clutches at the input and output shafts, moment of inertia of the pinion shaft and wheel shaft, moment of inertia of the propelled machine (electric dynamometer), slope of the motor's characteristics and bearing spacing. The change of the bearing spacing corresponds to the change of the bending stiffness of shafts. The variable values of the meshing force, as obtained during the simulation for various values of these parameters, were subjected to a Fourier transform while using for analysis their first four harmonics of meshing frequency. In order to account for the distribution and the size of individual harmonics, we proposed that two measures were introduced:
•JP
(6)
(7)
LH,--^
HHf The first of them (6), upon having been standardized, points to a total level of the power of first four harmonics while the second one (7) points to the participation of the first harmonic in this signal (tangent fiinction). The measures defined in such a way were subjected to cluster analysis (Radkowski (1995)). TABLE 1 Parameters of a gear model Module Number of teeth / pinion Number of teeth / gear Helix angle Addendum modification coeff. / pinion Addendum modification coeff / gear
1.25 1.25 1.25 42 43 43 68 68 69 0 0 0 0.504 0.504 -0.032 0.504 -0.048 -0.032
Upon taking into account Euclidean measure of distance, the probability of results of subsequent 64 experiments is presented in Figure 2. What is noteworthy is the big variety of the system's responses to changes of the bending stiffness of the shafts (a twofold growth of stifftiess). Such an effect is on the one hand connected with the change of the frequency of the shafts' proper vibration and on the other with the amount of shafts' deflection for the same radial force. In the case of small stiffness the shaft deflection is the dominant error and thus it defines the frequency structure of the meshing force. The other parameters whose changes of value may have impact on the quality of the response's frequency structure are the inertia moments of the input and output shaft (Table 2).
860
cluster analysis: L H1/L, 242_68_++, meshing force c^ - torsional stiffness of clutches J
- inertia moment the pinion shaft
J ^ - inertia moment the wheel shaft J ^ - inertia moment of the dynamometer Eg - slope of the motor's characteristics Ljj - bearing spacing
irdlll S74553613846546233574941345850423551435936524460 1 92S17 226101S 5211329 62214
4 2011271228 39554056 4664 4763
parameters set
Figure 2: Cluster analysis of the experiment results for measures (6) and (7). TABLE 2 Relationship between the spectrum structure and the value of inertia moments for pinion and wheel shafts Ip 4-
ig
L
LHi
+
0.21
0.67
+
-
0.25
0.3
-
+
0.45
0.11
-
-
0.75-1
Cluster (3) (10)
Description Even level of T' and 2"^ harmonic Dominance of 2"^^ harmonic High growth of the level of 2"^ harmonic
(9) 0.07 (1)&(2) Very high growth of the level of 2""* harmonic
By using the thus obtained results, in order to examine the impact of the distribution of the addendum modification coefficients, we selected such a set of model's parameters so as to obtain the maximum relationship between the structure of spectra and the analyzed factors, which could be manufacturing errors or changed design parameters. Let us examine the impact of the correction. The experiment was conducted for three various distributions of addendum modification coefficients: both positive ones, positive shift of pinion profile and negative shift for the wheel, as well as the both negative ones (Figure 3). Let us note that the change of distribution of addendum modification coefficients is accompanied not only by a different stiffness values but also by a different location of the contact section with respect to the meshing pole as well as the change of the length of the contact section.
861
HH100 42 68
I
E 3 . 20
E E
1
-/\
J
i_ -F-
- 1
-I
^—.JF—
1
^ t
- ^
- V - -|
1
" W
1
4-_ \ ^ ^ — I —
1
T
0
1
i
'
\ — ^ ^
(A 0) •43 16 0)
1 — -Jr - 1 — -y- --r
1- \ — Vr - \
pitch line [mm]
"^
= 0.50 = 1.25.2^ =42 = 1.25,2. =43 Z2=68,Xi = 0.50 = 1.25.z. =43 Z2=69pc^ =^.03
= 0.50 = -0.05 = -0.03
Figure 3: Meshing stiffiiess for a pair of toothed wheels along the contact section for various distributions of addendum modification coefficients cluster analysis: H1 H2 H3 H4, z42_68_++, meshing force
c^ - torsional stiffness of dutches 1500
Jp - inertia moment the pinion shaft J ^ - inertia moment the wheel shaft J^ - inertia monnent of the dynamometer Eg - slope of the motor's characteristics L^ - bearing spacing
.^1000
8 241632 7Z31531 3 1« 4 20122811 2733574S41 MSeS04237S34561 3e4eS4«2K5143M3eS244e0 1 9 2517 22S1018 521 1329 622 14»39554056486447e3
parameters set
Figure 4: Cluster analysis of the experiment results for measure (6). Addendum modification coefficients (0.504, 0.504)
862
For thus modified main parameters of meshing we conducted the analysis of the relationship between the value of the meshing force and the output torque and various values of inertia of a gear shaft. The results of the concentration analysis conducted for positively corrected teeth of a wheel and pinion and for positively corrected wheel are presented in Figures 4 and 5. Let us note that the similarity function is to a great extent dependent on the remaining parameters of the model. For example, the impact of the change of the stiffness of teeth as a result of varied correction became essential at the moment of assuming a toothed wheel which is characterized by small inertia moment (Figure 6a). cluster analysis: H1 H2 H3 H4, z43_68_+0, meshing force
c^ - torsional stiffness of clutches J - inertia moment the pinion shaft J ^ - inertia moment the wheel shaft J^ - inertia moment of the dynannometer Eg - slope of the motor's characteristics L|j - bearing spacing
500
24 1632 7 2 3 1 5 3 1 3 19 4 20122811 2733574941 3442505837534561 3 8 « 5 4 6 2 3 5 S 1 43S93652 4460 5 211329 6 221430 125 9 17 226 101839S54864405647
parameters set
Figure 5: Cluster analysis of the experiment results for measure (6). Addendum modification coefficients (0.504, -0.048)
CONCLUSIONS The results of the simulation research related to the impact that individual parameters of the model diagnosed system have on the level and frequency structure of the generated signal confirm the need for such analyses. On the one hand, we get more information on the sensitivity of a model to changes of parameters, and on the other through simulation we are able to gain more knowledge on the physical relations between the changes of the technical condition and the related disturbance of the process of diagnostic signal generation. In addition, in model research we have the possibility of verifying the defect-oriented procedures of separation of diagnostic parameters.
863
Meshing force changes
a)
Figure 6: Meshing force in the function of load and meshing stiffness (related to the change of the distribution of addendum modification coefficients) for (a) small gear shaft inertia moment - parameter set #1 (b) big gear shaft inertia moment - parameter set #3.
REFERENCES Osinski Z. and Wrobel J. (1982), Teoria Konstrukcji Maszyn (Theory of Machine Construction), PWN, Warszawa. Radkowski S. (1995). Low energy components of vibroacoustic signal as the basis for diagnosis of defect formation, Machine Dynamics Problems, 12, Filonik R.,M^czak J. and Radkowski S. (1998). Apparent Interface Method as a Way of Modeling the Meshins Process Disturbances, Machine Dynamics Problems, 19. M^czak J., Radkowski S. (2001), Factorial analysis of the parameters of the diagnostic model of gear, Proceedings of the XXVIII Sympozjum ''Machine Diagnostics", W^gierska Gorka 2001, Poland
864
Condition Monitoring and Diagnostic Engineering Management Andrew G. Starr and Raj B.K.N. Rao (Editors) © 2001 Elsevier Science Ltd. All rights reserved.
ONLINE FAULT DETECTION AND DIAGNOSIS OF COMPLEX SYSTEMS BASED ON HYBRID COMPONENT MODELS S. Manz Institute of Industrial Automation and Software Engineering, University of Stuttgart, 70550 Stuttgart, Germany http://www.ias.uni-stuttgart.de
ABSTRACT Up to now model-based online fault detection and diagnosis is rarely applied in process automation and chemical industries. The main reason is the big effort, which is necessary to develop a comprehensive model for a technical system under various circumstances. However the growing complexity of plants and facilities requires increasingly the use of formal methods to analyse and monitor the system behaviour. In this paper a fault detection and diagnosis method based on qualitative models and combined with dynamic models is proposed. The method is component-oriented. A basic feature of the concept is its ability to build automatically clusters of qualitative and dynamic components, which can be reused as single components. An application example of a three-tank-system shows that such khid of models, the so called hybrid models, are capable of solving fault detections and diagnosis problems. KEYWORDS model-based analysis, qualitative reasoning, dynamic systems, online fault detection, diagnosis, hybrid components, complex systems, three-tank-system
INTRODUCTION For the industrial automation of a plant the development of monitoring functions for online fault detection and diagnosis is as important as the realisation of control functions. The aim of fault detection and diagnosis is to protect human beings and environment from danger and to avoid damages as far as possible. Very often engineers use model-based solutions for fault detection and diagnosis. These models usually contain a detailed mathematical description of the plant. In this case the temporal changes of dynamic systems are described exactly in the model. But the building of complete mathematical models
865
for monitoring is very costly and complicated, in particular for complex dynamic systems. Therefore it is useful to build qualitative models beside the mathematical descriptions. The advantage of qualitative models is that the internal physical relations have not to be represented exactly, so the qualitative models contain only situations in which something "happens" [1][2]. Within the scope of the research area "Qualitative modelling of complex dynamic systems" the use of already existing mathematical models in combination with qualitative models is examined for online fault detection and diagnosis of complex systems. In this context the hybrid modelling method SQMD (Situation based Qualitative Monitoring and Diagnosis) has been developed [3][4]. ONLINE FAULT DETECTION AND DIAGNOSIS Figure 1 shows the base concept of the model-based online fault detection and diagnosis. The starting point is a process model of the normal and the faulty operation modes. The process model runs online, that means in parallel to the activities in the plant. The task of the evaluation is to compare the currently measured process states with the model states. If the model of the normal operation mode contains the measured process states, then the technical process is in a fault-free operation mode. If the measured states are not in the normal but in the faulty model then it has to be assumed that the technical process is in a faulty mode. In case of a detected faulty mode, the next step is locating the fault, which is a diagnosis task. According to the type of fault it is either sufficient to warn the operator or it is necessary to stop the system.
plant
process model of the normal and the faulty operation modes
I
evaluation
T
fault detection (monitoring) fault localization (diagnosis)
Figure 1: Online monitoring and diagnosis
866
Depending on the type of the technical process various models are used for process monitoring. For dynamic systems quantitative dynamic models such as systems of differential equations are usually applied. In these models a dynamic process is described exactly, so that a deterministic behaviour is guaranteed. The disadvantage however lies in the complexity of these models. The more components the system contains, the more complex is the model. For complex models the calculations are too runtime consuming to be used in an online monitoring systems, hi opposite to that, qualitative models are less runtime consuming. For that reason qualitative models can be use for online monitoring of complex systems. In general these models can be built quite simple and fast, because of the simplified description of the technical process. But the qualitative models describe only the static behaviour of a system and can therefore not be used for analysing the dynamic behaviour of systems. Another disadvantage is the nondeterministic behaviour of the models because of the fuzzy description of the process. Based on the quantitative and qualitative modelling methods the SQMD (Situation based Qualitative Monitoring and Diagnosis) concept has been developed. SQMD uses hybrid models for online monitoring and diagnosis. Hybrid models contain qualitative and dynamic components and combine the advantages of both methods. So it is possible to realise online monitoring and diagnosis for the detection and localisation of faults and failures in complex dynamic systems. SQMD CONCEPT The SQMD concept is splitted into an offline and an online part, as shown in Figure 2. The offline part contains the model building of the hybrid components and the specification of the system structure. For this part the layout or specification of the plant in form of piping- and instrument-diagrams (pi-diagram) and dynamic models (e.g. system of differential equations) yields the starting point for modelling. The main process of the online part is the state space reduction based on the hybrid components, the system structure and the online information (sensor- and actuator data) of the plant. The reduced state space can be used for online fault detection and diagnosis. Layouts, Specifications: e.g. pi-diagram, dyrtamical model
Component j - - - .L.ibrary
System Structure
Hybrid Components
offline: model building online: state space reduction
Reality (sensor/actuator data of the plant)
- H
Reduced State Space
Figure 2: SQMD concept
867
Fault Detection and Diagnosis
In the following the SQMD concept will be described in more detail on the application example of a threetank-system. Figure 3 shows the simple system of three coupled tanks. The system consists of seven components: three tanks, two pipes (with valves to stop the outflow of thefirstand second tank) between them and two pumps. The two pumpsfillthefirstand last tank with a liquid. The pipes between the tanks have a limitedflowrate. The last tank has a hole where the liquid canflowout. Pump2
Pump1
Pipe23
Pipe12
Figure 3: Three-Tank-System Model building (offline) One major benefit of the method is the easy component-oriented modelling. The offline part consists of the "model building" of the hybrid, i.e. qualitative and dynamic components. Dynamic models include the mathematical description of the components. Qualitative modelling is carried out on the base of interval arithmetic that means an engineer assigns to every physical quantity of a component different intervals, which describe qualitatively the normal and faulty behaviour of this component. All physically possible interval combinations of all quantities concerned are described by situations and stored in the so called situation table. Figure 4 represents the model building for the three-tank-system. The hybrid model of the component tankl is shown on the left side. This model contains the qualitative part as a situation table with all possible situations (e.g. full or empty) of the tank. These situations should be described using intervals by the flow rate Q (inflow + outflow) and thefillinglevel h of the tank using intervals. Taking the filling level, it has a discrete value in the situations "empty" [Ocm,Ocm] and "full" [60cm, 60cm], whereas in the situations "filled" it can have a value within the interval (0cm,60cm). Furthermore the alteration tendencies of every situation must be described. So thefillinglevel can be increasing, decreasing or stay unchanged. For example in the situation "overflow" the tank is full and thefillmglevel is increasing. This situation does not belong to a normal operating mode and should be marked v^th a special attribute (e.g. if thefluidin the tank is an acid, so the situation must be marked as hazardous). The component pump is simply modelled and contains only the situations "on" or "off. The component pipe (including the valve) should be modelled according to the same procedure as the component tank. TheflowQ is subdivided into the intervals Q > 0 (flow is positive), Q = 0 (noflow)and Q < 0 (flow is negative). We also consider the faulty situation "blocked" which can be simulated by a closed valve. All components of a technical system are modelled using this method independently of their use in the system. Beside the component models, individual system equations are needed. Those are derivedfi^omthe system structure using the laws of energy conversation (first and second KirchhofPs laws). As an example consider the interface between the first tank and the pipe 12 in Figure 4. Using Kirchoffs Laws, two
868
equations can be built: pressure_out_tankl =pressure_in_pipel2 outflow_tankl = inflow_pipel2 Based on all system equations all hybrid components are connected online to each other and analysed and evaluated in predefined time intervals. This is a part of the state space reduction and it is described in the following section.
Pump2
Pump1
Hybrid Components
i : ^ ^ - . ,
Pipe12
.•.i..-^-
Pipe23
System Structure
Three-Tank-System
' T^'^l^ O Tetnlr O
Sy$tem Equations
Tanki Qualitative (statical)
^mi \ii^^^' Empty/Increasing Filled/Increasing
Quantitative (dynamical) dt
A
pressure_out_tank1 = pressurejn_pipe12 outflowjanki = ]nflowkj)ipe12
A
h : filling level Qin: inflow
Full
A : cross - section tank
Overflow
a: cross - section outflow
Situation Tabie
Differential Equati0n
"all possible situations"
"dynamieofthetank''
Figure 4: Model building for the example of a three-tank-system State Space Reduction (online) The principle of the state space reduction is shown in Figure 5. The appropriate analysis is based on the hybrid components, the system structure, and the data of sensors and actuators of the technical process. With this input information the observer calculates periodically all possible trajectories for a defined time slot and then the state space is reduced. The reduced qualitative state space contains all states and transitions of the system for the predefined time slot and can be screened for possible process deviations and process faults. This is the task of the fault detection and diagnosis.
869
The results of the state space reduction for the three-tank-system will be discussed. The following initial and boundary conditions are presumed for the simulation: > At the beginning (t = 0 sec) the first tank is half full (level h = 30 cm), the second tank is filled a little (level h = 10 cm) and the third tank is empty. > The inflow in the first tank is high (pumpl on) > No inflow in the last tank (pump2 off) > All pipes are opened up to t = 20 sec, later on the first pipe (pipe 12) will be blocked. This faulty behaviour can be simulated by a closed valve. Reality
Hybrid Model
(Plant)
Time Informatiofi
Component Specifications and System Structure
Observer
Trajectory Calculation for time slot [ta.tb]
i
; online
Reduced QuallUitlve StE^e Space
Figure 5: State space reduction Figure 6 presents the first two resulting state spaces for the given initial and boundary conditions, beginning ai t = 0 sec. In this figure the reduced state space includes all possible states of the first time slot t = [0,20] sec and the following time slot t = [20,40] sec. The online calculation starts at t = 0 sec with the above described initial conditions. Then the observer calculates all possible trajectories for the first predefined time slot of / = [0,20] sec as a prediction for all possible states. The calculation of the trajectories is performed within the given dynamic model. The result shows, that the level h of the first tank ranges between h = 40 cm and h = 49 cm, of the second tank between h = 10 cm and h = 14 cm and of the third tank between h = 1 cm and h = 2 cm. With this information all impossible situations are excluded fi-om the situation table and the result is a reduced qualitative state space for the given time slot. It is now very easy to read the table. For example in the first state of the reduced table the pumpl is "On", the first and second tanks are in the situation "Filled/Increasing", the two pipes are in the state "Positive" and the pump2 is "Off'. That means that there is a flow from the first tank into the second tank and then in the third tank. The reduced table contains also possible faulty situations such as a blocked pipe.
870
The second reduced state space in Figure 6 is calculated for the following time slot t = [20,40] sec. Considering the boundary conditions the first pipe is totally blocked, that means no outflowfromtankl in tank2. In this case the tankl will overflow within the time slot. This dangerous state is included in the reduced state space and so the overflow of the tank can be recognized in time. Online-Information
Hybrid Model
t « 0 sec: 40 cm "Tani(2~ 10 cm f»Tank3- 1 cm
^»Tank1 "
Pumpi
Pump2
^^m
^Pumpel ^PunweZ
= 011 sOff
System Equations pressure_out_tankl = pfessurejn_pipe12 Pipe12
Pipe23
Trajectory Calculation for time slot [0,20] sec
hTank1=49cm lTank3=
2 Cm
Qpumpi = on Qpump2=Off
Reduced State Space for [0,20] sec FHIeci/increasing Positive
On
Fiiled/lncfeasing
On On
Filled/Increasing
On
Filled^ncreasing Positive
Filled/Increasing
Off
iOn
Filled/Increasing
Fiiied/lncreasing
Off
On
Filled/Increasing Positive
Blocked
Filled/Decreasing
Off
On
Filled/hcfeasing [Blocked Filled/Unchanged
Blocked
Filled^ecreasing
Off
f t - 40 sec: ^ ^Tanki ~ O v e r f l o w ^ '^Tank2 " "^ ^ ' 'Tank3
S^^l^MMSBS
On
Positive
Empty/hcreasing
Off
Blocked Filled/Decreasing Positive
Empty/Increasing
Off
Filled/lncfeaslng Positive
Filled/Increasing
Empty/Unchanged Off
Filled/Increasing
Blocked Filled/Unchanged
Blocked
Positive Filled/Increasing Blocked Fined/Decreasing Positive Fiiied/lncreasing
Sudden appearance of a fault in the system: Pipe12 totally blocked!
Empty/Unchanged Off
Trajectory Calculation for time slot [20,40] sec
Reduced State Space for [20,40] sec
^'^
3 cm
^Pumpl "
|lft)pWri^t19ed
T
Trajectory Calculation for time slot [40,60] sec
Figure 6: State space reduction for the tiiree-tank-system
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The calculation shown in Figure 6 is perfomied periodically for every given time slot and yields different parts of the reduced qualitative state space. The next step consists of the analysis and evaluation of the reduced qualitative state space in order tofindfaulty or dangerous states. If there is a faulty or dangerous state in the reduced state space, then it is the task of the fault detection to notice and react to this faulty state (e.g. an overflow of the tank) to prevent from danger. Besides the online fault detection the diagnosis of faults and failures andfeultlocalization is also possible. The diagnosis is performed by the backward tracing of the states on the basis of their given transitions. CONCLUSION The major advantage of the hybrid modelling method SQMD is the simple modelling of complex dynamic systems. There are two important aspects. On the one hand already existing mathematical models are combined with qualitative models so that complex systems can be modelled and simulated. On the other hand only the interesting part of the state space is analyzed, that means these states can be evaluated online with low computation power. For the future the model building, analysis and the evaluation should be done in a special SQMD toolbox for Matlab/Shnulink. So the engineer can be supported in a comfortable way and the main part of the tasks can be performed automatically. REFERENCES [1]
de Kleer J. and Brown 5.S.: A Qualitative Physics based on Confluences, in Bobrow D.G. (Editor), Qualitative Reasoning About Physical Systems, North Holland, Amsterdam., pp 7 - 83, 1994 [2] Kuipers B. J.: Qualitative Reasoning: Modeling and Reasoning with Incomplete Knowledge, MIT Press, 1994 [3] Manz, S.: Qualitative Modeling of a Three-Tank-System, Interkama-ISA Tech, DiisseldorC^Germany, Proceedings on CD-ROM, 1999 [4] Manz, S.: Online Monitoring and Diagnosis based on hybrid component models, ICSSEA2000, Paris, 2000 [5] Manz, S.: Fuzzy Based Qualitative Models in Combination with Dynamical Models for Online Monitoring of Technical Systems, CIMCA2001, Las Vegas 2001.
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Condition Monitoring and Diagnostic Engineering Management Andrew G. Starr and Raj B.K.N. Rao (Editors) © 2001 Elsevier Science Ltd. All rights reserved.
MEASURES OF ACCURACY OF MODEL BASED DIAGNOSIS OF FAULTS IN ROTORMACHINERY P. Pennacchi and A. Vania Dipartimento di Meccanica, Politecnico di Milano, Milano, Via La Masa 34,1-20158, Italy
ABSTRACT Model based diagnostic techniques can be used successfully in the health analysis of rotormachinery. Unfortunately, a poor accuracy of the model of the fully assembled machine, as well as errors in the evaluation of the experimental vibrations caused only by the impending fault, can affect the accuracy of the fault identification. This can make difficuh to identify the type of the actual fault as well as to evaluate its severity and its position. This paper shows some methods that have been developed to measure the accuracy of the results obtained with model based techniques aimed to identify faults in rotating machines. The results of some first investigations on the capabilities of these methods, carried out using the machine response simulated with mathematical models, are shown in the paper.
KEYWORDS Diagnostics, Fault Identification, Rotor dynamics. Vibrations, Model validations.
INTRODUCTION The early detection of faults and malfunctions in rotating machines can be provided by condition monitoring systems. However, in order to obtain useful information to carry out suitable maintenance actions, a fault diagnosis is required. A first preliminary screening of the most probable types of faults that can have generated an alarm can be obtained with a fault symptom analysis. However, more significant information can be provided by model based diagnostic techniques, Bachschmid et al. (1999, 2000-1, 2000-2). Moreover, these methods allow the fault to be localised and its severity to be evaluated. Usually, the model of the fully assembled machine allows the dynamic behaviour of the rotors, the bearings and the foundation to be simulated. The rotor train can be modelled with beam Finite Elements (FE), while the dynamic effects of the fauhs can be simulated with suitable sets of forces and moments that are applied to the nodes of the FE model. Therefore, the identification of a fault can be obtained by evaluating the system of excitations that minimises the error between the machine experimental response and the numerical response evaluated with the model. Usually, this error is called residue. In order to allow the results of 873
different analyses to be compared the residues are normalised. Weighted least square methods can be used to identify the equivalent forces and moments that simulate the faults. Although the vibrations of the shafts along the spans between two adjacent supports can give very useful information to identify the machine faults, usually, in real machines, only shaft and support vibrations measured at the journal bearings are available. This requires more careful fault identification analyses to be carried out. Unfortunately, also in absence of faults the rotating machines are subjected to vibrations that, usually, are caused by a residual unbalance of the rotors as well as by misalignments of couplings and supports. These excitations cannot be easily modelled as they are unknown. Therefore, fault identification techniques require to evaluate the transient vibrations of the machine obtained by subtracting the vibrations measured in normal state fi-om those measured after the fault occurrence. If the non-linearity of the system is negligible, these transient additional vibrations represent the machine response caused only by the fault. However, if the two speed transients occur in different thermal conditions of the machine or if other factors, in addition to the malftmction, affect the system vibrations, the accuracy of the results of the fault identification can be poor. Similar effects can be caused by the presence of noise in the vibration signals. Moreover, the accuracy of the results of model based techniques can be significantly influenced by the care with which the model of the fully assembled machine has been developed. Usually, a preliminary careful tuning of the model is required before applying it for detailed investigations. Owing to a poor accuracy of the experimental response, as well as of the model, the normalised residues associated to different identified faults can be fairly similar. This can make it difficult to diagnose the type of the actual fault as well as to evaluate its severity and its position. This paper shows some methods that have been developed to measure the accuracy of the results obtained with model based techniques aimed to identify faults in rotating machines. The information provided by these methods can be very useful to evaluate which of the identified faults has the highest probability to be the actual one. The results of some first investigations on the capabilities of these methods, carried out using the machine responses simulated with mathematical models, are shown in the paper. Although only simulated responses have been used, this investigation has allowed a useful sensitivity analysis of the developed methods to be carried out. Successful results already obtained by the analysis of experimental responses will be shown in a future publication.
MEASURES OF ACCURACY OF THE RESIDUES As said above, the error between the theoretical vibrations obtained with the model and the experimental vibrations is called residue. A normalised value of the residue, Su can be obtained using the following expression:
If
^ex
'^ex
where * indicates complex conjugation, Xex is the vector of the experimental vibrations, while X/^ is the corresponding vector of the theoretical vibrations obtained with the model. The machine fault is identified by minimising the residue; therefore, the most probable fault should be that one with which the lowest residue is associated. As the identification procedure can be time consuming only the experimental data collected for a limited number of suitable angular speeds of the machine are analysed. Sometimes, this strategy is necessary also to avoid to use an overabundance of repeated observations in the identification procedure as this could cause numerical problems or identification errors. Obviously, different selections of the angular speeds, that is different subsets of experimental data, can give quite different results of the fault identification. For instance, if the model does not simulate the response of the system very well near the critical speeds the selection of vibration data measured in speed ranges near the resonances should be 874
avoided. Anyhow, the excitations obtained with the identification procedure can be used to evaluate the system response at any angular speed associated with the experimental data. This allows a further residue estimation, 82, to be evaluated. Sometimes, depending on the choice of the data used for the fault identification, although a very low value of the residue £\ is obtained, the residue estimation £2 is significantly higher. This is a symptom of a poor accuracy of the fault identification. This can be the consequence of a wrong selection of the type of fault as well as of a poor adequacy of the model. Further indexes allow the above mentioned causes of very different values of the residues £\ and £2 to be discriminated. As the object of a fault diagnosis is to identify the actual machine malfunction as well as to simulate the system response, it is important to obtain a low value of both the residues £\ and £2. Usually, both the residues £\ and £2 are computed considering the errors evaluated at different degrees of freedom and at different machine angular speeds. Therefore, they can be called global residues. On the contrary, further residue estimates can be obtained by selecting the vibration data evaluated at each single angular speed or at each single degree of freedom (d.o.f). These residue estimates can be denoted with ^o) and £"dof respectively. They can be very useful to show the dependence of the error on the angular speed and the measurement point. In the case of an ideal identification of the fault the residues should be scarcely affected by the speed value and the d.o.f. Owing to this, the analysis of the curves of the residue fo vs. the angular speed, as well as the curve of £dof vs. the degrees of freedoms, can give very important information on the accuracy of the identified fault and the adequacy of the model. In general, the ratios between the standard deviations of the residues £^^ and ^^dof and their respective mean values represent good measures of the identification accuracy. The residues are a normalised evaluation of the error between theoretical and experimental vibrations. With reference to this, it is important to consider that the amplitude of the machine vibrations can be quite different depending on the angular speed and the measurement point. Therefore, the analysis of the curve of the absolute error vs. the angular speed can emphasises some information that are masked by the curves of the normalised residues. In the end, interesting results can be obtained by a correlation analysis between theoretical and experimental data. The real and imaginary parts of all the theoretical and the experimental vibrations can be located into the two real vectors X//, andXe^c, respectively. Then, the Modal Scale Factor (MSF) and the index called Modal Assurance Criterion (MAC), Ewins (1984), can be evaluated as follows: MSF = xlx,Jxlx,, \YT
Y
MAC = - ^
(2) 2
L
(3)
In general, the two indexes MSF and MAC are used in modal analysis and model updating; however, they can be applied also to many other problems. If Xth coincides with JC;c the value of the indexes MSF and MAC is 1. Anyhow, as the identification procedure is based on a least square method, in this application the MSF value is always very near to 1. On the contrary, the MAC index gives a measure of the dispersion of the data from the straight line whose angular coefficient is the MSF value. In addition, a linear regression analysis of the data contained in the vectors Xth and Xex can be carried out. The difference between the angular coefficient of the regression line and the ideal unity value is a further measure of accuracy of the fault identification. In the end, an additional information is represented by the value of the standard deviation of the errors obtained with the linear regression analysis.
875
ANALYSIS OF SIMULATED RESPONSES DUE TO FAULTS The validation of the method developed to measure the accuracy of the results of the identification techniques has been carried out by simulating the experimental response of a rotor train composed of a high-intermediate pressure turbine (HP-IP) and a low pressure turbine (LP). The system response has been evaluated with a model of the fully assembled machine. Each rotor was supported on two oil-film journal bearings while a rigid foundation of the machine has been considered. Figure 1 shows the Finite Element (FE) model of the rotors. The supports have been numbered from #1 to #4. The first balance resonance of the HP-IP turbine was nearly 1500 rpm while the second critical speed was nearly 2900 rpm. In order to simulate an ideal experimental response of the system due to a local bow of a rotor, two equal but opposite bending moments have been applied to the ending nodes of a finite element of the mesh located near bearing #2 (Figure 1). The machine response has been evaluated in vertical and horizontal direction in the range from 400 rpm to 3000 rpm. Only the radial vibrafions evaluated at the four journal bearings have considered in the faults identification procedure. These degrees of freedom have been numbered from one to eight, starting from bearing #1 to bearing #4. 1X Horizontal vibrations
I
I
s
i Brg.#2 HP-IP tublne
H \ ••••• t
^^**».<'''*
Brg#4
Fait section
Brg.#1
Brg.#1 Brg.#2
Brg#3
•^•J
Brg.#4 LPturtine
Figure 1: Finite Element Model of the rotor train. Figure 2: IX vibrations due to only the unbalance masses applied to the HP-IP and the LP turbines. Sometimes, although the actual type of fault has been considered in the identification of a rotating machine malfunctioning, the values of the residues evaluated with model based diagnostic methods are not very low and also the accuracy of the fault identification is rather poor. Often, this is the consequence of a poor adequacy of the machine model. Similar effects can be caused also by the fact that the experimental transient response of the system obtained by subtracting the reference vibrations vectors, measured with the machine in normal state, from those measured after the fault occurrence, can be due not only to the fault but also by the effects of other factors like, for instance, differences in the machine thermal state during the two speed transients that have been considered. The presence of errors in the evaluation of the machine vibrations due to only the fault has been simulated by adding the 1 x rev. (IX) vibrations caused by the two opposite bending moments to the response obtained with the model by applying an unbalance mass to the rotor both of the HP-IP turbine and the LP turbine. In this way, the simulated vibrations induced only by the shaft bow have been lightly corrupted. Table 1 shows the excitations that have been applied to the FE model of the rotor train. Figure 2 shows the Bode plot of the IX vibrations, evaluated in horizontal direction at each bearing, which are due to only the two unbalance masses. These vibrations represent the contribution of the error in the evaluation of the experimental response actually due to the fault, that is the shaft bow. Moreover, in order to simulate a not unusual lack of accuracy of the models, the stiffness and damping coefficients of bearing #2 have been multiplied by a scale factor of 1.25. This mis tuned model of the system has been used in the fault identification procedure while the original model has been used to generate the fictitious experimental vibrations.
876
TABLE 1 EXCITATIONS APPLIED TO THE FINITE ELEMENT MODEL OF THE ROTOR TRAIN TO SIMULATE CORRUPTED EXPERIMENTAL VIBRATION DATA ASSOCIATED WITH A MACHINE RESPONSE DUE TO A LOCAL BOW
Unbalance masses Bending moments Phase FE Amplitude FE Amplitude Phase Turbine Turbine [Degrees] [Kgm] Node Node [Nm] [Degrees] 30° 0.240 HP-IP 31 18 4.000E5 HP-IP 270° 120° 0.180 HP-IP 32 60 4.000E5 LP 90°
Identification of the correct type of fault A first analysis has been carried out by supposing that the machine vibrations were caused just by a local bow of a shaft (Case 2). Therefore, a system of equivalent excitations represented by two opposite bending moments applied at the nodes of a single finite element has been considered in the identification procedure. This analysis has been carried out using the vibrations associated with eight angular speeds equally spaced in the range from 750 rpm to 2500 rpm. Some results of this investigation are summarised in Table 2. The phases of the identified moments are in good accordance with the exact values while the amplitude is overestimated. Partly, this is the consequence of the analysis of a corrupted response of the system. In fact, even using the mis tuned model for identifying the fault, if the vibrations induced only by the bow are considered (Case 1) the error in the estimate of the amplitude of the bending moments decreases from 40% to 28% (see Table 2). Figure 5 shows the dependence of the residues on the angular speed. In the range below 1000 rpm the residues 6^^, are higher than the their mean value /4{o. The effects of discarding the vibrations evaluated at bearing #3 are significant only in the range below 1400 rpm. Figure 6 shows the dependence of the absolute error on the angular speed. The pronounced peak in the error curves near 1500 rpm are due to a shift of the first critical speed of the mis tuned model of the HP-IP turbine caused by the errors introduced in the coefficient of bearing #2.
uase 4: untaiance
S
Brg.#1
Brg.#2 HP-iPtu(t)ine
Brg.#3
Brg.#4 Do.f.
LP turbine
1: Brg.#1 Vert.
3: Brg.#2 Vert.
5: Brg.#3 Vert.
7: Brg.#4 Vert.
2: Br9.#1 Honz.
4: Brg.#2 Honz.
6: Brg.#3 Horiz.
8: Brg.#5 Honz.
Degrees of freedom
Figure 3: Dependence of the residue £\ on the fault position. Case 2: shaft bow. Case 4: unbalance.
Figure 4: Global residues si vs. the degrees of freedom.
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TABLE 2 MEASURES OF ACCURACY OF FAULT IDENTIFICATIONS
Case 4 Case 3 Case 2 Case 1 0.8181 Global residue 0.3334 0.5027 ^1 0.4616 0.7666 Global residue 0.4942 0.3222 €l 0.7199 0.4071 0.5271 0.3568 Mean value of residue SQJ / 4 a) 0.2567 0.1029 0.1121 0.1225 Standard deviation of f^ Okto 0.7658 0.5117 0.6573 0.3820 Mean value of residue £aof /4dof 0.1738 0.1576 0.3087 0.1624 Standard deviation of £dof CTedof 1.1658 Angular coefficient 0.9579 m 1.0303 0.9738 0.8470 0.6080 Modal Assurance Criterion 0.8870 0.8180 MAC 19.14 Regression error: stand, dev. CTr [^m] 11.58 7.56 12.63 Equivalent excitations of Amplitude 5.130E5Nm 5.621E5Nm 5.621E5Nm 0.9942 Kgm 57° the identified fault 264° / 84° 263° / 83° 263° / 83° Phase 28 Fault position (FE node) 32 and 33 32 and 33 Node n. 32 and 33 Case 1: Identification of a rotor bow. Analysis of the response due to only the bending moments. Case 2: Identification of a rotor bow. Analysis of the response, due to the bending moments and the unbalances, evaluated at all the bearings (#l-^#4). Case 3: Identification of a rotor bow. Analysis of the response, due to the bending moments and the unbalances, evaluated at the bearings #1, #2 and #4. Case 4: Identification of a rotor unbalance. Analysis of the response, due to the bending moments and the unbalances, evaluated at all the bearings (#H#4).
•
Case 2
.
•
—>—
Case2 Case 4
Case 4
500
1000
1500
2000
2500
3000
Figure 6: Absolute error vs. angular speed.
Figure 5: Global residues si vs. angular speed.
Figures 7,8 and 9 show the Bode plots of the IX horizontal vibrations evaluated at bearings #1, #2 and #3, respectively, using the identified bending moments. These vibrations are compared with the fictitious experimental response. Although the amplitude of the identified bending moments is overestimated the accordance between the theoretical and the experimental curves is rather good. The measures of accuracy of the local bow identification obtained with this investigation emphasise a partial lack of accuracy of the machine model and of the experimental response ascribed to only the fault.
878
Brg.#1 - 1X - Honzontal
8rg.#2 - I X - Horizontal
I" 8 Case 2 Case 4
^^'^ri^^^
,
Exp. Case 2 Case 4
i. -90
Figure 7: Bode plot of the 1X vibrations evaluated at brg. #1. Comparison between original data (Exp.) and regenerated data (Bow, Unbalance).
V
Figure 8: Bode plot of the 1X vibrations evaluated at brg. #2. Comparison betv^een original data (Exp.) and regenerated data (Bow, Unbalance).
Brg.#3-IX-Horizontal
Correlation Analysis
i ^\ rrrr77..-
1
Exp.
• _ — .
Case 2 Case 4 1500 Rpm
-
^ —
-
Case 2 Case 4
1500 Rpm
Figure 9: Bode plot of the IX vibrations evaluated at brg. #3. Comparison between original data (Exp.) and regenerated data (Bow, Unbalance).
-240
-180
-120
-60 0 60 120 Expenmental data - [MPDJ
180
240
Figure 10: Correlation analysis between theoretical and experimental vibrations. Regression analysis.
Identification of a wrong type of fault A further analysis has been carried out by supposing that the machine vibrations (IX) were caused by a rotor unbalance. Also in this analysis the corrupted response of the system and the mis tuned model have been used. Some results of this investigations are shown in Table 2 and in Figures 3-^9. The curve of the residue S], evaluated for a single unbalance mass located at different sections of the HPIP turbine (Fig. 3) does not show a quite evident minimum value. Moreover, the global residues £i and si are rather high. These high values do not depend on the vibrations evaluated at a specific degree of freedom, as proved by the curve shown in Fig. 4. Figure 5 shows that the residues £2 exceed the value of 0.8 in the speed ranges below 1000 rpm and above 2000 rpm. Moreover, when the speed approaches 3000 rpm both the residue £2 and the absolute error increase significantly. The Bode plots of the IX horizontal vibrations evaluated at bearings #1, #2 and #3, respectively, using the identified unbalance, are shown in Figures 7, 8 and 9. These vibrations are compared with the fictitious experimental response. In accordance with the results above described, the comparison between the theoretical and the experimental vibrations emphasises significant errors in the speed range below 1200 rpm as well as in the range above 2500 rpm. These are the consequences of the identification of a wrong fauh. Figure 10 shows the resuhs of a regression analysis between the theoretical vibrations evaluated with the identified unbalance and the
879
fictitious experimental data. As said above, in the ideal case of an exact identification the real and imaginary parts of both theoretical and experimental vibrations should be aligned along a straight line whose angular coefficient is 1. In this case, owing to the identification of a wrong fault, the angular coefficient, m, of the linear regression function is 1.1658 and also the MAC value differs from unity significantly (Table 2). The two bold lines shown in Fig. 10 define a band whose half amplitude is three times the standard deviation, of, of the errors evaluated with the regression analysis. Also the high value of (Jr (cTr = 19.14 ^m) is a further index of a poor identification of the fault.
CONCLUSIONS The results of a symptom analysis can give some basic information for a correct diagnosis of faults in rotating machines. However, a more reliable identification of the actual type of fault, as well as an evaluation of the fault severity and its position, can be obtained with model based diagnostic techniques. Unfortunately, some different faults can cause very similar symptoms; therefore, if the model of the fully assembled machine has not been carefully tuned or if the experimental data are affected by errors, the fault identification methods can provide inaccurate results. In order to measure the accuracy of the fault identifications some methods have been developed. The results obtained by the analysis of simulated responses have allowed the capabilities of these techniques to be emphasised. The usefulness of the developed methods has been confirmed also by the results obtained with experimental case studies that will be shovm in future publications.
REFERENCES Bachschmid, N., Vania, A., Tanzi, E. and Pennacchi, P. (1999). Identification and Simulation of Faults in Rotor Systems: Experimental Results. Dynamic Problems in Mechanics and Mechatronics, Proc. of EURO DINAME 99, Wissenschaftszentrum SchloB Reisenburg der Univeritat Ulm, Giinzburg, Germany, 3-11. Bachschmid, N. and Pennacchi, P. (2000). Model Based Malfunction Identification from Bearing Measurements. 7th Int. Conf On Vibrations in Rotating Machinery, Proc. of IMechE, University of Nottingham, UK, 571-580. Bachschmid, N., Pennacchi, P., Tanzi, E. and Vania, A. (2000). Accuracy of Modelling and Identification of Malfunctions in Rotor Systems: Experimental Results. Journal of the Brazilian Society of Mechanical Sciences, Vol. XXII, No 3, ISSN 0100-7386, 423-442. Ewins, D. J. (1984). Modal Testing: Theory and Practice, Research Studies Press, Letchworth, UK.
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Condition Monitoring and Diagnostic Engineering Management Andrew G. Starr and Raj B.K.N. Rao (Editors) © 2001 Elsevier Science Ltd. Allrightsreserved.
FAILURE ANALYSIS AND FAULT SIMULATION OF AN ELECTROHYDRAULIC SERVO VALVE Zhanqun Shi^'^, Fengshou Gu\ Andrew Ball^ & Hong Yue^ ^Maintenance Engineering Research Group, The University of Manchester, Manchester, M13 9PL, UK ^ The School of Mechanical Engineering, Hebei University of Technology, Tianjin, 300130, China
ABSTRACT This paper presents the failure analysis and fault simulation of the electro-hydraulic servo (EHS) valve. The aim is to provide solutions to some difficulties that exist on the fauh diagnosis of the EHS valve such as failure modes and fault symptoms. The failure analysis will give the fault modes of the valve by means of failure investigation. The fault symptoms are obtained by means of fault simulation. According to the principle of the valve, a mathematical model has been derived in this paper. After combining the math-model with the failure modes, the fault model is developed for use with the EHS valve. A new approach, digital fault simulation (DPS), is then developed to implement the simulation. In this approach, some faults can be investigated, including faults in operating principle failure and faults that can not be implemented in the test rig. As results, many useful symptoms can be obtained from the simulation. This will provide signature basis for the maintenance and fauh diagnosis of EHS valve. KEYWORDS Failure mode, fault diagnosis, fault simulation,fluidpower, electro-hydraulic servo valve, symptom
INTRODUCTION Although Electro-hydraulic servo valves (EHS valve) are widely used in various control systems to implement precious and heavy load control target for many years, their fault diagnoses have not been solved till now. The reason may comefromtheir integrity. An EHS valve integrates mechanical parts, electrical units and hydraulic units into a highly compacted unit. Most of these parts or units are small in size and sensitive to the environment. As a kind of control system, it doesn't output vibration signals like rotating machines. In practice, it is even difficult to mount a sensor in it. Further more, thefluidin it generates a lot of noise, which may submerge the useful information. This makes it very difficult to detect and diagnose the fault or failure occurred in the EHS valve. Several attempts can be found in
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this subject, see Shi (1999), Bull (1996) and Sihua (1991). Others investigate EHS system faults mainly on actuators, see Watton (1995) and TT, Le (1998). Unfortunately, research on the fault and failure of the EHS valve is far from satisfactory. The EHS valve is the most important unit and determines the performance of the electro-hydraulic system. If any fault or failure occurs in the valve, the function of the control system will deteriorate considerably. Serious faults may cause whole system break down and even human injuries. Till now, there are some shortcomings in the diagnosis of the EHS valve, they are mainly as follows: • Difficulties exist in measurement due to the high integration. • Case study is insufficient. • Fault symptom is difficult to extract. • Research expense is too high for most researchers. In this paper, an economic way is introduced to analyse the fault and failure of the electro-hydraulic servo valve. It is referred to as a digital fault simulation (DPS) method. Based on the failure mode analysis of the valve, a fault model is developed to represent the behaviour of the valve when it is in fault conditions. Like system simulation, fault characteristics can be givenfromthe digital calculation. This method is widely used in digital circuit design for optimisation but not found as an application in control systems. This paper uses the DPS method in the electro-hydraulic control system simulation. Based on the understanding of the common failure modes, a typical fault model of the EHS valve is developed to include correlated failure modes. Using the fault model, fault simulation is implemented and fault symptoms are obtained for different failure modes. Discriminations of the symptoms are also studied. In addition, a novel idea is proposed for effective fault diagnosis. The first author would like to give thanks to the Tianjin NSFC and Hebei NSPC of China for their support in this research project. Magnet
Armature
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'M r I^^>^N \
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Fig.l Construction of electro-hydraulic servo valve
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2 FAULT MODE ANALYSIS Figure 1 shows the structure of the electro-hydraulic servo valve. It consists of a torque motor, a flapper nozzle and a spool valve. Its operating principle is that the output signal is proportional to the input signal. Details can be found in Watton (1989). Lu (1996) has discussed the maintenance of the valve. Here in this paper, some common failure modes are summarised as follows. 2.1 Failure modes in torque motor The torque motor may lead to failure or fault in the following ways: (1) Break down or short circuit in the coil, (2) Faulty wires to the amplifier, (3) Lose connection in the coils, and (4) Unbalance exist in the four air gaps 2.2 Failure modes in flapper nozzle The failure modes in the flapper nozzle might be: (1) One or both of the nozzle holes blocked, (2) Some dirty particles stuck to the flapper, (3) Flapper erosion of some sort, (4) Flapper not in null position when no signal input, and (5) Flapper broken. 2.3 Failure modes in spool valve The spool valve consists of some sensitive parts. Each of them may cause failure in the following forms: (1) Filter block due to the oil contamination. This failure may result in the crushing of the filter, and consequently, the throttles, the nozzles and other circuits will be blocked. (2) Unbalance exists in the two throttles. This will make the spool bias in one end. (3) Spool lock in the housing due to some contamination, (4) Leakage in the valve. This may cause the null bias of the valve and the decay of the performance. (5) The clearance between the spool and the housing is enlarged due to wear. This may decrease the pressure gain of the spool valve. The failure modes listed above can be classified into two classes. The first class is fault from parameter alteration. This class may include coil failures, unbalances, flapper erosion or stuck particles and spool wear, etc. The second class is fauh from essential change in the flow behaviour, such as nozzle blocks, throttle blocks and faulty wires, etc. Some fauhs can be modelled by modifying the parameters from the mathematical model, which is referred to as parameter variation model. However, some faults, like blocks, have to be redeveloped their fault model independently due to the changes in the principle. This type of fault model is called working principle changing model. These two kinds of fault model will be discussed below. 3 FAULT MODEL OF THE EHS VALVE 3.1 Mathematical model of the EHS valve According to Watton (1995) and Shi (1998), the static model of the electro-hydraulic servo valve in a fault free situation can be represented by equations (1 ~ 4). 883
^'
A,-
(1)
'.'.JK
Q = C,KD,x^\-ip,-p,)
(2)
jc, is the displacement of the spool, A/ is the differential input current in the coils, r and b are the construction sizes of the flapper, K^ is the rigidity of the flapper, Q is the output flow rate of the valve, Q is the flow coefficient of the spool valve, D, is the diameter of the spool, and p, and p^ are the supply pressure and load pressure respectively. The other two coefficients are: K,=2^N,Q>^
(3)
4 = ^(1 + ^')
(4)
where a and /^ are the construction sizes of the torque motor, A^^. is the number of turns of the coil, Og is the polar magnetic current when the armature is in the null position, and /I is the ratio of flow rate, p is the density of the oil. 3.2 Fault model due to parameter variation According to the operating principle, if one of the coils fails, only the other coil will be working and the number of turns of the whole coil will be half of the usual. This will cause parameter changes in equation (1) and (2). In equation (3), if one of the coil fails, the number of turns of coil A^^ will be half of the fault free condition. This makes the coefficient K'l = 5Q%K^, and the input current v^U change: If coil 1 fails, then Az -
I^-i
If coil 2 fails, then A/ = /Q + / If both of the coils fail, then A/ = 0 If the coils lose connection, the input current will be broken. The erosion of the flapper will change the coefficient Kj. The supply oil pressure will influence the coefficients a^^, a^^, and C33. If unbalance exists in the air gaps of the torque motor, the gain K„ will change. All changes in the mathematical model will lead to some fault symptom in the output signal. Some changes can cause serious problems, some not. The influences of them will be discussed in the fault simulation. 3.3 Fault model on the operating principle Sometimes, the failure in the servo valve may change its mathematical model in operating principle. Basically, the sensitivity of the valve comes from some balances in nozzles and throttles. These nozzles and throttles as well as some gaps in the valve are as small as 0.3mm. Therefore, if these parts
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fail, block for example, certain balance will be destroyed. The mathematical model will change essentially. In the unbalanced situation, the static is redeveloped as equation (5) and (6): K^i = (K, -KJO + r{p^ -p,)A, = (K^ -K^ -r\Z7iC]p,x,,)^Kf{r
~r\SnC',p,x,.,)0 +K^ir + b)((r + bW + x,) + hf)0-rp,A,
^K,(r^b)x,
(Ps - Pt )A = Kj i{x, + (r + b)0) + 2C,;rD, cos0, (p, - p, )x^ = Kf{r^
(6)
b)0 + {Kf + 2C,KD^ cos 0, (p, - p, ))x.
K^ is the mechanical rigidity of the torque motor, K^ is the mechanical rigidity of the boring spring in the torque motor, 0 is the rotating angle of the flapper, A^^ is the area of the nozzle hole, 0^ is the orifice angle of the flow in the spool valve, Xj- is the distance between the flapper to the nozzle. XJQ , is used when the flapper is in the null position and x^-^ when theflapperis in total contact with the nozzle, p^ and Pf, are the pressures in the left and right nozzles respectively. In the fault free condition, they will be balanced when theflapperis in the null position. However, once any block occurs in either of the nozzles, they have to be calculated separately as in equation (7) and (8):
PH
=
~
^
(8)
1-f 0.91(1+ x,./x,J^ Where the block coefficient of the nozzle is bounded by: 0 < A: < 1. It takes the value of 1 when completely blocked, and 0 when no block at all.
4 FAULT SIMULATION OF THE EHS VALVE The following are simulation results of the electro-hydraulic servo valve. Figure 2 is the fault free condition. The input curve (top drawing) is in the form of a sinusoidal wave, and the bottom drawing is the outputflowrate of the valve. It can be found that the output completely follows the input signal. Considering that coil failure is taken as the typical fault in the parameter variation fault simulation, figure 3 shows the failure in one of the coils. Figure 3(a) shows coil 1 failure and figure 3(b) shows coil 2 failure. It can be found that if one of the coils fails, a zero-frequency component exists in the output. In both cases, the output amplitudes are the quarter of the normal case. Either coil 1 or coil 2 failure can be classified by checking the phase of the output. If the output is in the same phase as the input but smaller in amplitude, it means that coil 2 may have a fault. If the phase of the output is opposite to the input, the fault may occur in coil 1. Another typical failure in the EHS valve is blockage in nozzles and orifices. Figure 4 shows the response of the valve with different blocks in the nozzles and orifices. Figure 4 (a) shows the block in one of the nozzles, whereas figure 4 (b) shows the block in one of the orifices. The simulation also 885
finds that partical block will cause the same fault in theflapper.Comparing figure 4(a) and (b) leads to the following. (a) Any block in the nozzles or the orifices will push the spool to the end of the housing. (b) Theflapperwill also move to the left or right in the block situation (c) Different block situations have different symptom in theflapperand the spool 0.04
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Fig. 4 Fault simulation in blocks
(b) Orifice block
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Table 1 shows the classification of different types of blocks. Where R and L denote that the block occurs in either the right or the left side. '+' and '-' denote the direction of the spool valve and the flapper. From the signs of the symptoms, the position of the fault can be clearly classified. It is essential to measure the spool displacement and the rotating angle of the flapper. As these symptoms are extracted from the internal response, two sensors are necessary to be embedded into the valve. Unfortunately, all EHS valve doesn't have this consideration. Therefore, a novel idea is proposed in this paper to develop a sensor embedded EHS valve. TABLE 1 POSITION ON VARIOUS BLOCKAGE Parts Nozzle block Orifice block
LorR left right left right
X of spool + +
0 of flapper + +
5 CONCLUSION In this paper, the failure modes of the electro-hydraulic servo valve are analysed in regards to its three components: torque motor,flappernozzle and spool valve. Consequenfly, some of these failure modes are modeled on the base of mathematical models of the servo valve, both in static and dynamic. The fault models show that the failures in the valve may sometimes lead to parameter bias and sometimes cause changes in operating principle. The fault simulations of these fault models give the symptoms in output signals. These symptoms provided us with a possible way of detecting faults in the servo valve. In addition, the simulation results in the block fault give the designer a new idea. Measuring the spool position and the flapper position is essential in monitoring the fault of the electro-hydraulic servo valve. REFERENCES (1) D. R. Bull, et al, (1996), A computational tool for failure modes and effects analysis of hydraulic systems, FPST-\o\. 3, ASME, 113-1118 (2) Lu, W., (1996), Failure Treatment in Hydraulic Systems, Hunan Science & Technology Press, China. (3) Shi, Z. et al, (1999), Fault diagnosis of hydraulic control systems based on wavelets and NN, Proceedings of the DYMAC'99, Manchester, UK, 517-520. (4) Shi, Z. et al, (1998), Fault diagnosis of electro-hydraulic servo systems based on neural networks, ICAIE'98, China, pp. (5) Sihua Ge, (1991), Fault diagnosis in hydraulic systems, Xian Jiaotong University Press, China (6) TT. Le, J. Watton, (1998), An artificial neural network based approach to fault diagnosis and classification offluidpower systems, Proc. oflmechE, Vol. 211, Part I, 307-317 (7) Watton, J, (1995), An online approach to fault diagnosis of fluid power cylinder drive systems, Proc. oflmechE, Vol. 208, Part I, 249-262 (8) Watton, L, (1989), Fluid Power System, Prentice Hall, UK.
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Condition Monitoring and Diagnostic Engineering Management Andrew G. Starr and Raj B.K.N. Rao (Editors) © 2001 Elsevier Science Ltd. Allrightsreserved.
A MULTIPLE CONDITION INFORMATION SOURCES BASED MAINTENANCE MODEL AND ASSOCIATED PROTOTYPE SOFTWARE DEVELOPMENT Wenbin Wang and Yunxian Jia Centre for OR and Applied Statistics, School of Accounting, Economics and Management Science University of Salford, Salford, M5 4WT UK Email: [email protected]
ABSTRACT In condition monitoring practice, the primary concern of maintenance managers is how long the item monitored can survive given condition information obtained to date. This relates to the concept of the condition residual time where the survival time is dependent upon the age of the item monitored, but also upon the condition information obtained. Once such a probability density function of the condition residual time is available, a consequential decision model can be readily estabhshed to recommend a 'best' maintenance policy based upon all information available to date. This paper reports on a development of a multiple condition information sources based maintenance model and associated prototype software package. The residual life prediction model is developed on the basis of filtering theory, and the decision model is established in terms of a criterion function of interest. The available information is processed first using principal component analysis, and a few principal components were selected as the input to the residual life model. Model parameter estimation, optimal decision making and the framework of the prototype software have also been briefly discussed. KEYWORDS Maintenance, model, condition based maintenance, principal component analysis, maintenance decision, prototype software INTRODUCTION The use of condition monitoring techniques within industry to direct maintenance actions has increased rapidly over recent years as plant has increased in cost, complexity and automation. Technical advances in condition monitoring techniques have provided a means to achieve high availability and to reduce unscheduled production shutdowns. There have been numerous papers contributing to the technology aspect of condition monitoring, as evidenced by the proceedings of COMADEM over recent years, Rao et al (1988-2000). The research highlighted in these papers is
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characterised by engineering skill, knowledge and experience. Compared with the extensive literature on condition monitoring techniques and their applications, relatively little attention has been paid to the important problem of modelling decision making in condition based maintenance decision support modelling. Only a few have appeared. Christer and Wang (1992,1995) addressed maintenance decision problems of directly monitored systems in which the actual condition of the system can be observed by condition monitoring. Chister et al (1997) presented a case study of furnace erosion prediction and replacement using the state space model and Kalman filtering. In Christer and Wang, (1995), Wang et al (1996 and 1997) various models were explored and discussed without actual applications. Wang and Christer (2000) presented a general condition based maintenance model for a stochastic dynamic system using filtering theory. There have been attempts in modelling condition based maintenance decision making using Proportional Hazard Model (PHM), Kumar and Westberg (1997), Makis and Jardine (1991). However, there is a fundamental problem in the PHM models as only the current condition information is used to predict the future development of the item monitored, rather than the whole monitoring history. This paper reports on a recent development of a condition based maintenance (CBM) model and its prototype software package, which aims to provide maintenance engineers with a user friendly tool to help them in making appropriate maintenance decisions. The model and the software developed are based upon the model reported in Wang & Christer [2000], Wang [2001], but certain extensions have been made to take account of the recent developments. THE MULTIPLE INFORMATION BASED CBM MODEL AND ITS PARAMETER ESTIMATION It is defined in Wang, Christer and Sharp (1995) that the time lapse from any point that the operating system is checked by condition monitoring to the time that it may fail is called the conditional residual time (CRT). This means that the residual time is not only dependent upon the age or time of the item has survived, but also upon the condition information obtained to date. If at time / we are interested in how long the item monitored can survived given the age and condition information obtained, the problem reduces to determining the residual time conditional upon t and available condition information. It has been observed that there may be many different types of information collected for the purpose of equipment health monitoring and maintenance, and not all of them are useful. If the number of the information sources is limited of a few, all of them can be used without imposing serious problems for subsequent residual life prediction. It is noted however, that in practice, a large number of excess or correlated information is also collected, which will make the model unnecessarily complicated and some of them should not be used. In this case, the first problem we may need to resolve is to reduce the dimension of condition information collected, and to find a way to identify the most useful information for the residual time prediction model. Previous studies have revealed that only a few key information sources that could have correlation with the residual time we are interested, and most information themselves may be correlated with each other. Here, we suggest to use the Principal Component Analysis (PCA) to carry out a pre-analysis of the information collected. This will reduce the dimension of information to a few key independent principal components representing over 95% information contained in the original data. 1, The pre-analysis of the multiple condition information Suppose that we have obtained p types of correlated condition information at each checking point, which are denoted by random variables zi, Z2,..., Zp. The object of the PC A analysis is to take/? random variables zj, Z2,..., Zp and find combinations of these to produce indices y^^^ ,y^^^ ,...,y^^ that are uncorrelated. The lack of correlation is a useful property because it means that the indices are measuring different 'dimensions' in the data. However, the indices are also ordered so that y^^^ displays the largest amount of variation , y^^^ displays the second largest amount of variation, and so on. In
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general, it is not necessary for us to use all of the indices in CBM modelling because the variances of most of the indices will be so low as to be negligible. In order to obtain the principal components y^^^ ,}f^^ ,...,)f^^ , we need to find the eigenvalues of the sample correlation matrix of the condition information. The correlation matrix is symmetric and has the form 1
^12
^IP
^21
1
^2p
1 SI
^2
(1)
1
Where C is the correlation matrix and Cy=9, is the correlation between z, and Zj. The variances of the principal components are the eigenvalues of the matrix C. Assuming that the eigenvalues are ordered as A; X2 ... /Ip 0, then A/ corresponds to the ith principal component y^'^ = a,^z^ + a^^z^ +... + a^^z^
(2)
where, var(y^^^ )= A, and the constants an, ai2,..., atp are the elements of the corresponding eigenvector. The steps in the principal component analysis can be stated as follows: (1) Start by coding the random variables z/, Z2,..^,Zp to have zero means and unit variances. (2) Calculate the correlation matrix C. (3) Find the eignvalues A/, A2,..., Xp and the corresponding eigennectors a/, a2,..., ap. The coefficients of the ith principal component are then given by a, while A/ is its variance. (4) Discard any components that only account for a small production of the variation in the data. For example, starting with 8 variables it might be foimd that the first two components account for 95% of the total variance. On this basis the other 6 components may reasonably be ignored. 2. CBM Modelling assumptions and notation We propose the following modeling assumptions. a. Items are monitored regularly at discrete time points, and condition information is measured at these points. b. The life time of the item is classified as a two-stage process with the firs stage from new to a point where a fault has been first identified, and the second stage from this point to a failure. This second stage is referred to the well known delay time concept, Christer and Wang (1995), Christer and Wang e^ a/(1995). c. The relationship between the observed condition information and the residual life holds only over the failure delay time. d. Both stages are assumed to be independent and follow some distribution fiinctions. e. The condition information obtained at time t\, say the current time over the delay time period, is a random vector the elements of which are described by distribution functions which arefiinctionsof the current residual time. The notation used in this paper is as follows: • t\ denotes the ith and the current monitoring time since the item was identified to be faulty but still operating. • x^ denotes the random variable of the residual time at time t[.
891
• yi^{yi^^> yF^-'-yi"^^ } denote principal components of the condition information obtained at ti, where m is the number of the first few key principal components, and yi^\ yPK-.-yl'^^ are statistically independent each other. • Yi=^{yuyi.iyyi.2y>»,yyi} denotes the history of cumulative key principal components up to U, the current monitoring decision point. • p(xi\ Y'l, ti) denotes thepdf. ofxi conditional upon Yi. • P(yi^^\^i) denotes the pdf. ofyp^ conditional upon xi. The relationship between xt and x,./ is as follows: ^
\ ^,., - (/, - /,_,), if x,_, > t. - r,_,, [not defined, otherwise
^^^
The relationship between y,^-^ and Xi is described \>yp(yF\Xi), which is yet to be determined. We wish to establish the expression of p(xi\ Yi, U ) so that a consequential decision model can be constructed on the basis of such a conditional probability. It can be shown, Wang and Christer (2000), ihdXp(xi\Yi, ti) is given by AyilYi.|,/,j where using the chain rule, the joint distributions, pfjc,, y, |Fi U) and/?5',|Fw, ti.) are given respectively by Wang and Christer (2000) as
pU.y, I Yi.„0=p(yi U„Yi.„/,)p(Ar, I Y„,o=P(yi \xMx^^..^,h) P(y< I YM,<,) = ]p(yi I x,Mx, I ¥,_„/,>&,. where
(5) (6)
piy, I ^,)=P{yf\y?' -yT' I x,)=p{yf' \ xMy?' I Jf,)-/'(x'"' I ^,)
f,
P(J^,-,|Yi.,)^,-,
(7)
J , p{x,,,\Y.,_,)dx,.,
It can be seenfi-omequations (5)-(8) that \fp(xo\Yo) 2indp(y!'\xi) (j=^l may be determined recursively.
m) are known equation (4)
3, Establish the distributions ofp(xo\Yo) andp(yP^\Xi) To make equations (5)-(8) computable, we need to specify the distribution forms for p(xo\ Yo) and p(yF\xi). Since Yo is not available in most cases so that we can set p(xo\Yo) =p(xo). Candidates distributions for p(xo) could be Normal, Weibull, Lognormal or Gamma. For most mechanical items such as bearings and gears, an appropriate choice for the delay time distribution is the Weibull distribution. The distribution of >'r ^=7 m) conditional upon jc, can be chosen as a normal or other distribution. Here we also select Weibull distribution as the distribution of yi^^ conditional upon JT,, that is
892
The conditional relationship betwQQnyP and ;c, is established by the following functions:
- 4 ^ = A(j) + BU)e-'^'''''
j =l
m
(10)
where y4(/;;, 509 and CQ) are parameters to be estimated from the data. This is, in fact, established a negative correlation between >^/^'^ and JC, as expected in condition monitoring practice. 4, Estimation of the parameters We wish to estimate the parameters of the delay time distribution of item andAQ), B(j), C(j) and Tj(j) based upon the observed life data and the available condition monitoring readings using the maximum likelihood method. The likelihood function is written as L
)
(11)
Wherej!?(^.|o9 denote probability density of observing • given that o has occurred, L is the number of items tested, xu is the observed information at tt and «/ is the last monitoring check for items /. When the initial delay time distribution of the items is WeibuU, after some manipulation, the likelihood function of equation (11) becomes L
g
m
L
til
j=\
l=\
1=1
-t' -
r(y)/Mf/u«r/v-^(^^<"/~''*)^^^<^) ,(/,l!'/{A{j)^Bij)e-'^''"^ -Oy
(^(7) + BU)e-'''^^''-'''r'^')e
))
(12)
where a and y^are the two parameters of Weibull distribution for the item's delay time distribution,;;/^ is the jth reading of the Ith item in the ith monitoring check and tif is the failure point of Ith item. Maximising equation (12) we may easily obtain the estimates of the unknown parameters of a, fi A(j), B0),C(j)and7j(j)(i=J=l rn). 5. The conditional residual time distribution and the decision model Following the assumptions earlier, we obtain the conditional residual time distribution of the item considered using equations (3) to (10). Because the recursive process is rather complex, here we give the resuh directly. See equation (13). m
M^, I ¥;,/,) =
i
-ti^^yi''
'-=^-^ 0
y=i
—
k=i
893
/(^(/)+5(y>-^<>><''*''-'* >))'^»)-(«(:r,+/,.)/
(13)
The fundamental decision to make at various monitoring points is whether we should replace the item or not given all information available. If the answer is yes then what is the best time for such a replacement. Suppose that the average cost of a failure is C/, mean cost of per monitoring is Cmy and Cp denotes the mean preventive replacement cost, m is the number of past monitoring, ti is the current monitoring check point and / is the possible replacement time. We have the total expected cost per unit time is given by equation (14). C{t) = -
(c^ -c^)P(x,
,.(.-o(i-p(:c.<.-,i.^,o)-^]z^^^^^:^-'^-^-^
(14) dz
If the minimum of equation (14) is outside of a specified planning period, say (U, U+j), there is no need to perform the preventive replacement, otherwise a planned replacement should be recommended within the planning period. THE FRAMEWORK OF THE CBM MODELLING PROTOTYPE SOFTWARE In this section we briefly discuss the development of the CBM modelling prototype software which is based upon a special case of the model introduced above. Given the availability of the model, the main task in the development is to transform the model into an applicable package with user friendly graphical interfaces. The package is developed using Visual Basic, a powerful window based visual programming environment, and Fortran NAG routines are used to carry out numerical analysis such as integration and optimisation. It should be pointed out that the prototype software introduced here is only based upon one condition random variable. The prototype software based upon multiple condition random variables is being developed. The main framework of the software package is described as follows (see figure 1). CBM Software Package
I Project Management
Parameter Estimation
Input
Decision Optimasition
Figure 1: The framework of the CBM software package In the Project Management subsystem, the main functions are to set up a new project (such as plant item), deleting old projects, selecting an existed project and managing the related Database. A Project Management database named "cbmproject" is built to perform the above functions. In the Data Input subsystem, the main functions are to input and modify data used by CBM modelling. There are two types of data which need to be input or revised in Data Input subsystem. One is the general data such as Cost per condition monitoring. Cost per preventive replacement, Cost per failure and downtime data etc. The other type of data is conditional monitoring data such as component installation date, condition monitoring date, measured condition information etc. Figure 2 shows the conditional monitoring data input interface. In the parameter estimation subsystem, a robust parameter estimation module has been developed. The users only need to select the related components which will be used to estimate the parameters. The
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system will automatically estimate all parameters. Here, the related components must have both conditional monitoring data and life data (such as replacement record). It should be pointed out that,
}JP!^SipfflM
'^^^mmMsmmkui:u^-'f:^^:^
^m^^^^mrr
^^rr^'T^r^^^'H^'^^TTTrr^
-t^KP^^'Wr:
Figure 2: The CBM conditional monitoring data input interface because the estimation process is rather complicated, using VB6.0 itself to carry out the optimisation process is not feasible. The estimating process in this case is performed using VB6.0 to call NAG routines (DLL). Figure 3 is the parameter estimation interface.
Figure 3: The parameter estimation interface
895
In Decision Optimisation subsystem, the software package has provided six selection items for users to view the "Residual time distribution" and the optimal maintenance decision support. If the user wants to view the "Residual time distribution" of a component, he or she should first need to select "Residual time distribution" item from the "View Result" menu, and then select a component and a time point, the "Residual time distribution" will appear on the display window. Figure 4 shows the interface of the "Residual time distribution". a^
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Figure 4: The interface of the "Residual time distribution" If the user wants to view a maintenance decision support in terms of cost, downtime or reliability he should select the menu item "optimal decision by cost", "optimal decision by downtime" and "optimal decision by reliability" respectively, and the friendly graphical results and maintenance advice will be printed on the screen. Figure 5 shows the interface of "optimal decision by cost" function. The CBM software package has been tested on six bearings with vibration data to predict their residual lives and make maintenance recommendation. Since the life data of the bearings are available, this enables us to compare it with the prediction. The predicted results are satisfactory.
CONCLUSION This paper reports on a CBM model and its prototype software development. The fundamental concept behind the model is the conditional residual time which differs with the conventional concept of the residual time in that it not only depends upon the current age of the item but also the condition information available to date. Principal component analysis is used to reduce the information to a tractable size with an independent property in the case of multiple information sources. The CBM prototype software based upon the CBM model provides a friendly graphical interface and robust parameter estimation ability. It should be pointed out that this CBM prototype software is only based upon one condition random variable. The prototype software based upon multiple condition random variables is being developed.
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Figure 5: The interface of "optimal decision by cost" REFERENCES [1] Christer A.H., Wang W. (1995). A delay time based maintenance model of multi-component system IMA Journal ofMathematics Applied in Business & Industry, 6, 205-222. [2] Christer A.H., Wang W. (1992). A model of condition monitoring inspection of production plant /. J. Prod Res: 30, 2199-2211. [3] Christer A.H., Wang W., and Sharp J.M. (1995), A model of condition monitoring by stochastic filtering proceedings of COMADEM95, July, 1995, Kingston, Canada, (Rao B.K.N, Moore T.N., and Jeswiet J., Eds.), 329-336, 1995. [4] Christer A.H., Wang W., J.M. Sharp (1997). A state space condition monitoring model for furnace erosion prediction and replacement. Euro. J. Opl. Res., 101, 1-14. [5] Jardine A.K.S., Banjevic D. and Makis V. (1997). Optimal replacement policy and the structure of software for condition-based maintenance Journal of Quality in maintenance engineering 3:2, 109-119. [6] Kumar D., and Westberg U. (1997). Maintenance scheduling under age replacement policy using proportional hazard modelling and total-time-on-test plotting Euro. J. Opl. Res., 99, 507-515. [7] Makis v. and Jardine A.K.S. (1991) Computation of optimal policies in replacement models IMA J. Maths. Appl Business & Industry, 3, 169-176. [8] Rao B.K.N. et al eds (1988-1999). Proceedings of COMADEM international congress and exhibitions. [9] Wang W., Christer A.H. (1995). A simple condition monitoring model for a direct monitoring process Euro. J. Opl. Res., 82, 258-269. [10] Wang W., Christer A.H. (2000). Towards a general condition based maintenance model for a stochastic dynamic system Journal of the Operational Research Society, 51:145-155.
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[11] Wang W., Christer A.H., and Sharp J.M.(1996). Stochastic decision modelling of condition based maintenance, proceedings of COMADEM96, 16-8 July, 1996, Sheffield, (Rao B.K.N, Smith R.A,, and Wearing J.L., Eds.), Sheffield Academic Press, Sheffield, 1175-1184, 1996. [12] Wang W., Scarf P., and Sharp J.M.(1997). Modelling condition based maintenance of production plant, proceedings of COMADEM97, 9-11 June, 1997, Espoo, Finland, (Erkki Jantunen Ed,), Julkaisia-Utgivare-Publisher, Espoo, 75-84, 1997. [13] Wang W., (2001), A model to predict the residual life of rolling element bearings given monitored condition information to date, submitted to IMA J. of Management Mathematics.
Condition Monitoring and Diagnostic Engineering Management Andrew G. Starr and Raj B.K.N. Rao (Editors) © 2001 Elsevier Science Ltd. All rights reserved.
PLANT RESIDUAL TIME DISTRIBUTION PREDICTION USING EXPERT JUDGEMENTS BASED CONDITION MONITORING INFORMATION W, Wang and W. Zhang Centre for OR & Applied Statistics School of Accounting, Economic & Management Science University of Salford, M5 4WT, UK
ABSTRACT The paper reports the use of expert judgement as leading information to predict the reported component's residual time. The expert judgement is made on the bases of measured condition information and is treated as a random variable, which may follow a probability distribution. Since most expert judgement is in the form of a set of integer numbers. We can either directly use a discrete distribution or use a continuous distribution with data transformation. The key concept is the use of condition residual time where the residual time at the point of checking is conditional on, among others, the past expert judgements made on the same plant to date. Stochastic filtering theory is used to predict the residual time given available expert judgements. KEYWORDS Expert judgement, condition monitoring, condition based maintenance, conditional residual time. INTRODUCTION Condition monitoring plays a veryqmportant role in industrial plant maintenance as one of the maintenance policies because of its efficiency in detecting plant potential failure, and hence reducing the number of failures and its consequence. One of the key issues in condition based maintenance is the prediction of the residual time distribution given measured condition monitoring information to date, and then a subsequent decision model can be established. However, the information collected by condition monitoring is usually first processed by
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experienced engineers, and then a judgement on the plant condition is made and recommended to the maintenance managers in support of their maintenance decision making. If the measured condition information shows an obvious trend, then the information can be directly used by the residual life prediction model reported in Wang and Christer (2000). It is noted however, that the information collected may contain too much noise, and therefore, careful examination and interpretation of the information is required, which forms the bases of expert judgement. Since in most cases, the expert judgement information is available, which contains engineer's experience and expertise. This makes the expert judgement information a valuable information to be used in maintenance decision making. That is also the reason why we use expert judgement here as part of leading information to model the plant's residual time. As we mentioned before, expert judgement adds value to the decision process, but it is also subject to certain problems as follows. First, the judgement is very subjective and subject heavily to the experience and skills of the person looking at the information. Secondly, the recommendation is usually made at the point of checking without looking back of the historical information collected before, that is the judgement is made independent of previous performance. Thirdly, and most importantly the judgement is made based upon engineering experience, which is essential, but other factors such as cost, quality and downtime are not considered, and therefore, if of limited value to subsequent maintenance decision support. The first problem and part of the second problem can be resolved by an appropriately designed expert system in fault diagnosis, but the third problem remains. In order to provide a better decision support to industry using condition monitoring information, we aim to transform the expert judgement into a more meaningful format which can be directly used for decision making. In this paper, a model to predict the residual time distribution using expert judgement is reported.
MODEL FORMULATION Normally expert judgement is made of a set of integer numbers, and each number indicates a possible condition of the component. Expert judgement could be on a continuous scale. For example it could be a continuous number between [0,1], number 1 means the component will fail immediately, and number 0 means the component is in a normal state, the greater the number is, the serious the component's condition is. There is a general relationship between the expert judgement and the underlying residual time of the component concerned. For example, if the expert judgement says that the component is in a normal working order based upon his interpretation of the measured condition information, we would expect a longer residual time compared with that of recommendation that the component is in need of attention. It is noted however that this relationship holds only after when the component is identified to be defective, that is, over the period that the expert has suspected that something may be wrong. This period is referred to as the failure delay time, Christer and Waller (1984). For the purpose of model formulation, we propose the following assumptions. •
Plant item is monitored regularly at discrete time points.
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•
•
Expert judgement is made at each monitoring point to assess the item's condition, which is a set of integer numbers ranged from7 to n, where 1 implied that the item is OK, while n indicated that the item may need immediate attention. When the judgement is above 1, we assume that a negative correlation between the judgement and the residual time exists. This negative correlation governs the relationship between the expert judgement and the residual time, though in a probabilistic manner.
The notation used in this study is as follows; •
•
t^ denotes the / th and the current monitoring time since the item was identified to defective but still operating. x^ denotes the residual time at time /,. y, denotes the expert judgement made at time t^. It could be any integer between 1 and n, for example, if n=6, then we may have (1) The component is operating normally. (2) The component is operating but shows a minor problem, can be left till later. (3) The component is operating and shows signs of deterioration. It is advisable to take some preventive action at the next planned maintenance. (4) The component is operating, but requires attention. (5) The component need to be replaced immediately. (6) The component has failed. ^ = {y, 5 yi-\^y,-2 v-^^^i} denotes the history of cumulative expert judgements obtained up to t^, the current monitoring decision point. p(x^ lY^) denotes the pdf. of x^ conditional upon Y^.
•
P{yi I ^i) denotes the pdf. of y, conditional upon x,.
• •
•
Since the residual time at r, is the residual time at /,_, minus the interval between /, and r,_j provided the item has survived to /, and no maintenance action has been taken, it follows that
(1) not defined,
otherwise.
The relationship between y^ and x, is yet to be identified, but it may be described by a distribution, say, p{y, / x,). We will discuss this later. 0
We wish to establish the expression of p{x, lY^) so that a consequential decision model can be constructed on the basis of such a conditional probability. It can be shown that, Wang and Christer (2000), p(x, 77,) is given by
901
where using the chain rule, the joint distribution, p(:c,, j^, /}^_,), and p(y^/Y^_^) are given respectively by = p(y,/x,)p(x,/Y,_,)
p(x,,y,/Y,_,)
(3)
and 00
piy, / J',-,) = \p(y, IX. )p{x, I y,., )dx,
(4)
0
where Pi^Jy.-^)-
P{x..^-it,-t,_,)IY,_,)
(5)
It can be seen from equations 3 and 4 that if p(x^ lY^) and p{y^ /x,) are known, equation 2 may be determined recursively. Equations 1-4 constitute a stochastic filtering process, which is a special case of the model in Wang and Christer (2000). To make equation 2 computable, we need specify the distribution forms for p{x^ / KQ ) and p{y^ / x , ) . Since there may be no condition monitoring reading available when the defect of the component was first initiated, so that p(x^lY^^ = P(XQ) which is just the conventional pdf. of the component's delay time. For the delay time distribution, an appropriate choice is the Weibull distribution which has been widely used modelling component delay life time distribution. The pdf. of the Weibull distribution is given as p(x,)^afi(coc,r-U'-^''
(6)
Since the expert judgement is discrete, the distribution of y, conditional upon x^ may be chosen as the classical discrete Poisson and Binomial distributions respectively. The pdf of the Poisson distribution is given by
k=5
where the conditional relationship between y, and x^ may be established by letting the mean
902
A = A + Be ^''', where A, B and C are parameters to be estimated from the data. By setting up this, we established a negative correlation between y^ and x, as expected. For the Binomial distribution, p{y. /^,) =
,/'
^y''(1 -P)"-''
(8)
In this function, the mean is np, by letting np = A + Be'^""', we also can have the same negative correlation between y^ and x, as before. We may also use a continuous distribution, but can set the relationship between y^ and x^ as follows, Piy, = l / x j = f / ( Z > / Z , . . . , P(y, =n/x,)=["^f(Z)dZ-
(9)
Where ^(Z) oc ^ + 5e"'"'.
PARAMETER ESTIMATION We wish to estimate the parameters based on the available expert judgements. Here we will use the maximum likelihood method. The likelihood function can be written as
where P(« / o) denotes condition probability of observing • given that o has occurred, m is the number of components tested, s^ is the observed information at t^ and rij is the last monitoring check for bearing j . This is called the multiplication law of likelihood where L is the fft
likelihood of ^ « ^ events. ./=i
At each monitoring check r,, we have two pieces of information, the first is the expert judgement y^, and the second is the item has survived over r,. For example, the first one, using Bayes theorem, we have the joint probability density function Piyi.xo >h) = p(y,/x,
>t,)P(x, >t,).
(11)
Since XQ = x, + r, from equation 1, we have p(y^ /XQ >t^) = p(y^ Ix^ > 0) = p{y^). Noted here
903
that throughout the text a lower case p is used to denote pdf. and a upper case P is used for probability. The likelihood function is given by, Wang and Christer (2000) ^ = (flPiy, 'y.-^)^(^.-. > ^ -1..^ IY,.,))p(x„ =t,-tJYJ
(12)
The likelihood for m items is given by m
nj
where rij is the number of condition monitoring checks for the y th item, yj^ is expert judgement of the j th monitoring check, x^, is the residual time of the j th component at its ith monitoring check point r^,, and 7^,_, is the monitoring history of the j th component to the / - 1 th check. NUMERICAL EXAMPLES In this section we fit the models introduced earlier to the condition monitoring expert judgement type data, one is the simulated data, the other is the three pumps data from a large soft-drinks company. Simulated data Among the distribution of p{y^ / x,) we considered, we first tried the Poisson and Binomial distributions since they are the ready discrete distributions can be employed. Assuming that P{XQ ) is given by equation 6, we wish to estimate a, J3, A,B and Cfromour artificial data. The likelihood ftmction of equation 13 converged and produced some estimated parameters. It noted that however, after re-using the fitted values in a simulation test, the result is far away from our expectation, which produced a completely different set of data. There could be two reasons for this misfit. The first could be that our assumption is wrong, that is, there is not correlation between y^ and x,. This is, however, very unlikely in condition monitoring unless the expert judgements are completely wrong. The other reason related to the nature of the distributions we used. As we know that both the variances of Poisson and Binomial distributions increase as x^ decreases if we set A or np = A + Be'^""'. This is not appropriate in out case as we would expect the variance of the expert judgement should be smaller as more abnormal condition monitoring information have been observed. We may need to find a distribution which has a non-increasing variance property. Among the discrete and continuous distributions, the normal distribution has a constant variance if we set E{Z) = ^ = A + 5e"^*'. The
904
relationship between y^ and x, is given by P(y,^2/x,)=f'^l^~~^dZ
(14)
?(>;,= 3/x,)= r - p = - €
(15)
2
and 1
P{y^^nlx;)^
(^-'^)'
Z r— c ^-' dZ
(16)
•^'' v2;rcr where w = ^ + 5e''^'''. After some manipulation, it can be shown that p(x, / 7,) and p{y^ I }^_,) are given by
(x.+O"-'«-'"<""'»'nfe"
'•'' dZ
and r - ^ ( x , +/,)^-^e-^«^^'"'>^'n f e"
2a^
dZdx,
where Z^ depends on the value of y,. The likelihood function based on equation 17 and 18 is as follows, iZ-{A+Be ^"JJ 'J")y
(19) where m is the number of components. Maximising equation 19 yields another problem as Z, is positively correlated with A, and therefore we have to use a profile likelihood with a range of fixed Zj values. The result is given in Table 1.
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TABLE 1 ARTIFICAL DATA'S PARAMETER ESTIMATION RESULT A
B
C
0.594 3.057 0.029 L669 2.560 0.029 L828 8.838 0.029
^1
1 2 3
^2
^3
a
L782 2.657 OMll 2.610 3.343 omii 5.104 7.637 (^mii
P
(7
1.2 0.141 1.2 0.118 1.2 0.409
F -31.373 -31.373 -31.373
F is the log likelihood function value. Since the number of parameters to be estimated is the same, the value of F is a good indicator of the goodness of fit. From table 1, it can be seen that there is no difference, so we simply choose the first set result, that is when Z, = 1. To test the model formulation, we re-used this set of parameters in a simulation, and generated a sample of simulated expert judgements, and then re-estimated the model parameters. Comparing the parameters from this simulated sample with the above result, we found that the first group of parameters estimated when Zj = 1 is reasonable and this also partly confirmed our model formulation. For estimated parameter values using simulated data, see Table 2. TABLE 2 SIMULATION DATA'S PARAMETER ESTIMATION RESULT A
B
C
0.771 2.898 0.037 1.849 1.91 0.037
^1
1 2
^2
^3
1.482 2.455 2.317 2.957
a
P
G
F
omii ()mii
1.2 1.2
0.193 0.127
-16.047 -16.047
Real data This data set comes fi'om a maintenance data set of pumps of a large soft-drinks company in England, Wang et al (2001). The expert judgement is made of 4 integer numbers ranged fi-om 1 to 4 as shown below. (1) The pump is operating normally. (2) The pump is operating and shows signs of deterioration. It is advisable to take some preventive action at the next planned maintenance. (3) The pump is operating, but requires immediate attention. (4) The pump has failed. In this case, there were maintenance interventions between condition monitoring for which the modelling is beyond the scope of this paper. For simplicity, we treat the data as maintenance free data, that is when there is a maintenance, we treat it as a renew point. Because in this example, no expert judgement 4 is available, and expert judgement 1 is not our concern, so we are left with only expert judgements 2 and 3 to be used in our model, and we only need Z, to specify the interval between >^j = 2 and 3. We fit the model into these data, after some manipulation, we obtained the estimated parameter values shown in table 3.
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TABLES REAL DATA'S PARAMETER ESTIMATION RESULT A 0.795 2.0 2.029 4.33
B 0.381 14.18 1.93 11.78
C 0.005 0.132 0.006 0.946
^1
1 2 3 4
a 0.007 0.007 0.007 0.007
P 3.162 3.162 3.162 3A62
a 0.087 0.022 0.46 0.869
F 45.7 45.7 45.7 43.226
As before F is the log likelihood function value. We cannot really choose a set of the results from F, since they are so close. It seems that the third set of the results with Zj =3 is better, so we apply this set of results to our model. We seek to plot the predicted residual times of the pumps at various monitoring points based upon estimated model parameters. The functional form of p(x, /Y,)is given in equation 9. The predicted residual time distributions at some monitoring points given the history information based upon the pumps data are plotted in Fig.l.
300
time of checking
residual time
Figure 1: Predicted conditional residual time of three pumps From Fig.l, we can see that the predicted residual times are reasonable, which give us confidence of the model established. The important point here is the ability of the model to pick up individual remaining lives given both the expert judgement history Y and the current age. At the beginning of the monitoring all pumps are assumed to follow an identical Weibull delay
907
time distribution, namely P{XQ). AS the time goes and more expert judgements become available, we can observe completely different patterns of the pump lives which are indicated by our model.
CONCLUSION This paper reports the development of a model using expert judgement as leading information to predict the residual time distribution. We used the normal distribution as the distribution describing the relationship between the expert judgement and the residual time. The discrete type of expert judgement is transformed into a continuous distribution by dividing the range into various interval each corresponding to a particular judgement category. The model was fitted to simulated and real world data, and the results are satisfactory.
ACKNOWLEDGEMENTS The research reported here is partly supported by EPSRC under grant number GR/M96582.
REFERENCE 1. Christer AH and Waller WM (1984). Reducing production downtime using delay-time analysis, y. ORS, 35,499-512 2. Wang W and Christer AH (1995). A simple condition monitoring model for a direct monitoring process. Euro. J. Opl Res., 82, 258-269. 3. Wang W, Christer AH and Sharp JM (1996). Stochastic decision modelling of condition based maintenance, proceedings of COMADEM96, 16-8 July, 1996, Sheffield, (Rao B.K.N, Smith R.A., and Wearing J.L., Eds.), Sheffield Academic Press, Sheffield, 1175-1184,1996 4. Wang W and Christer AH (2000). Towards a general condition based maintenance model for a stochastic dynamic system, J. Opl. Res. Soc, 51:145-155 5. Wang W (2001). A model to predict the residual life of bearings given monitored condition information to date. Submitted to IMA J. of Management Mathematics. 6. Wang W, Scarf PA and Smith MA (2001). On the application of a model of condition based maintenance. J ORS, 51, 1218-1227
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Condition Monitoring and Diagnostic Engineering Management Andrew G. Starr and Raj B.K.N. Rao (Editors) © 2001 Published by Elsevier Science Ltd.
OPTIMISING COMPLEX CBM DECISIONS USING HYBRID FUSION METHODS
R Willetts*, A G Starr*, D. Banjevic**, A.K.S. Jardine**, A.Doyle* •Manchester School of Engineering Maintenance Engineering Research Group (MERG), Oxford Road ManchesterM13 9PL,UK [email protected], [email protected] http://www.maintenance.org.uk **University of Toronto Department of Mechanical and Industrial Engineering, CBM Laboratory 5 King's College Road, Toronto, M5S 3G8, Canada baniev(a),mie.utoronto.ca. iardine(a).mie.utoronto.ca http://www.mie.utoronto.ca/cbm ***WM Engineering Limited Manchester Science Park, Pencroft Way Manchester, Ml5 6SE Tonv(g)wmeng.co.uk http://www.wmeng.co.uk
ABSTRACT Maintenance actions must be predicted, planned and integrated into a company's overall production and maintenance schedule. This can be best obtained through the successful identification of failure modes and the subsequent development of a cost-effective maintenance strategy. Computerised maintenance management systems (CMMS) have traditionally based maintenance actions upon changes within the trend of predefined parameters obtained from condition monitoring. However, the identification, monitoring and subsequent fusion of data from key parameters can provide greater confidence in maintenance decisions. This paper presents the results of a case study jointly undertaken between the Maintenance Engineering Research Group (MERG) of the University of Manchester and the CBM Laboratory of the University of Toronto. The aim of the study was to use the EXAKT™ and MIMIC 2001 software packages to establish key vibration monitoring parameters for equipment within a paper mill and aid the maintenance optimisation process.
KEYWORDS Condition Based Maintenance, Integrated Systems, Key Performance Indicators, Optimising Maintenance Decisions.
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INTRODUCTION The decision to use a particular maintenance policy often depends on the experience and preferences of the engineer defining the programme. However, these poHcies may not be the most effective in terms of minimisation of cost and production disruption for the defined failure modes. An efficient system needs to be capable of defining the most effective combination of monitoring parameters by the ftision of all available information. This paper will describe a case study where data from an existing CMMS is converted and combined with failure history and age concerns to produce an optimal replacement interval. A description of how the Data Fusion Framework can be used to aid this integration process is also included. CONDITION MONITORING - THE NEED FOR INTEGRATED PARAMETERS. Condition based maintenance (CBM) is a strategy that has received a great deal of attention in both the research and industrial communities. Its underlying concept is to provide the prediction capabilities of failures prior to their occurrence. To achieve this, CBM makes use of condition monitoring which establishes the present condition of the plant under investigation by the regular monitoring of defined parameters. By trending the data produced, the normal safe working condition of the plant item over a period of time can be established and thus any deviation from the norm can be identified. Kennedy (1998) states however, that the output of condition monitoring is data. It is the interpretation and combination of this data with engineering knowledge that allows maintenance decisions to be made. These decisions are seldom based upon the output of a single parameter. More often they are based upon the fusion of the engineers knowledge and experience with the analysis of the relationships between groups of parameters. However, determining these relationships can be a complex process as there are numerous monitoring techniques available, each with their own data format and relative complexities. Data Fusion One recently developed model that could aid the integration of measurement parameters is the Data Fusion Framework developed by The Joint Directors of Laboratories (JDL) Data Fusion Working Group. This fi-amework was originally developed for use within the US defence network, for the identification and classification of enemy targets. Hall and Linas (1998), described the four levels of fusion in this framework, these are: 1. Object refinement 2. Situation refinement 3. Threat refinement 4. Process refinement Hall and Garga (1999) described level 1 data fusion as a process that combines information to form a representation of an individual object. This process is undertaken by completion of four key functions, these are: 1. Alignment of the data into consistent sets of units and co-ordinates. 2. Estimation of the object attribute and descriptions. 3. Extension of the object's attributes and estimation of its type. 4. Refinement of the estimated description of the object. Level 2 data fusion develops a description of the objects in relationship to their environment. Level 3 predicts future events and alternative hypotheses for the defined objects. Level 4 fusion is often associated with sensor management and forms decisions based upon the estimates provided by the previous levels, whilst monitoring the entire data fusion process to increase the performance of the system. The data fiision framework is not a definitive structure but a series of guidelines that the development team can base their ideas upon. Hall and Garga (1999) state that one of the main 910
problems facing the engineer attempting a data fusion project is the accurate mapping of the 'problem domain' to the 'solution domain'. One of the main reasons for this could be that, depending upon the system requirements, fusion can either be undertaken at all levels or at a single stage. Intelligent Systems A number of systems have been developed in both the research and industrial communities that have attempted to not only determine the relationships between the different parameters but also perform diagnostics. Artificial Intelligence (AI), particularly expert systems and neural networks, has proved to be a popular environment for this research, however more traditional environments such as muhi-variate statistics have also been used. For example, Harris and Kirkham (1997) described a system that used a combination of neural networks and expert systems to perform diagnostics on generic forms of bearings. Smith (1996) described a system in which a group of neural networks were used to combine data from three sensors within a CNC machine to establish five different types of tool wear. Taylor and Maclntyre (1998) described a generic diagnostic system based upon the fusion of different sensor outputs. The industrial community has also developed several systems, for example Trave-Massuyes and Milne (1997) described possibly one of the most successful of these systems called the Tiger system. This is an on-line model-based system designed to monitor the operating conditions of gas turbines. The MAINTelligence system^ works at the generic level and allows the data from different monitoring systems to be combined within a diagnostic system. However this system requires extensive knowledge of the process under investigation, as the knowledge base needs to be up-dated for each new fault. Andersson and Witfelt (2000) described the ADVISOR system, as being based upon the combination of neural networks and expert systems to provide a diagnostic aid for the analysis of failures within rotating machinery. Multivariate statistical systems The EXAKT^'^ system developed by the CBM Consortium at the University of Toronto, unlike the systems is not a diagnostic tool. It attempts to optimise the maintenance interval of groups of machines by modelling the relationships between different parameters and the probability of change. This modelling is denoted as the Proportional Hazards Model (PHM) and allows the system to produce a statistical model of the plant item based upon the combination of the parameter history and the risk of failure. To enable this combination a Markov chain is used to describe the behaviour of the condition over time. This is then used with the PHM, the working age of the units, and costs incurred by failure and replacement to produce a chart that shows the remaining optimum life of the defined machine. This type of system could be the next step on from a traditional integrated CBM system. Computerised maintenance management systems CMM systems have been used throughout industry for a number of years. There are numerous systems available on the market, with varying levels of functionality. MIMIC 2001 developed by WM Engineering is unusual in that it combines a work control system with an integrated CBM system. MIMIC 2001 is a modular system comprising of: • CBM and Asset Manager - allows the user to define and manage the assets held within the plant; • Work Control Manager - allows jobs to be scheduled based upon the reports produced by the Asset Manager; • On-line Monitoring - allows on-line monitoring of defined parameters within the plant; • Report Manager - allows reports to be produced on different groups of parameters. These reports can range from parameters on alarms to job histories for each parameter.
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The CBM and Asset manager makes use of a function called the Asset Hierarchy, which is used to uniquely identify measurement parameters. Measurements can be trended to form a time-based history of the parameter, which when analysed by maintenance personnel can result in actions, such as work requests. Although MIMIC is very comprehensive in its functionality there is a perceived need for improved integration within the different internal modules and to other management systems.
CASE STUDY This case study was undertaken on condition monitoring data collected over a 10 year period from a variable speed paper mill, from one of several leading monitoring systems presently used within the plant, ranging from on-line production control to manually collected condition monitoring data. The database used in this case study was structured using the Asset Hierarchy of the MIMIC system to reflect the different processes and areas of the plant and uniquely identify each functional unit. Readings were taken at predefmed intervals to monitor changes in fault conditions, for example looseness, bearing damage, harmonics and overall vibration levels. Extracted Data The experimentation was based upon the data collected on 18 identical units, each with 8 measurement parameters relating to different failure modes. These parameters are used to monitor vibration signals in both the vertical and axial directions. Table 1 shows these parameters together with a measurement description. There was an average of 140 data points for each parameter with an average monitoring interval of 21 days. TABLE 1 Description of Measurement Parameters Parameter
Direction
Failure Mode
Units
Overall Mesh Harmonics Acceleration
Vertical Vertical Vertical Vertical Vertical Axial Axial Axial
Overall Vibration Gear Mesh Harmonics Acceleration High frequency bearing damage Overall Vibration Gear Mesh Harmonics
Velocity (mm/s) Velocity (mm/s) Velocity (mm/s) Acceleration (g) ESP Envelope (g) Velocity (mm/s) Velocity (mm/s) Velocity (mm/s)
ESP Overall Mesh Harmonics
A total of 64 defined failure events were obtained from the job history stored within the MIMIC work control system. These events were classified as either failures (denoted EF) or suspensions (denoted ES). Failures were defined as events directly caused by an unexpected failure within that machine. Suspensions were defined as events that are not directly caused by a failure, for example lubrication, preventative maintenance etc. This resulted in 7 events being classed as failures and the remainder being classed as suspensions. The majority of these suspensions occurred due to preventative maintenance actions being undertaken either on a fixed time basis or as the result of another failure. Building models within the EXAKT^'^ system A number of steps are involved in the building of an EXAKT^'^ model: 1. Data preparation, which converts the original measurement and failure history into the EXAKTTM format. 2. Establishing the optimum combination of parameters (called co-variants) which are the most relevant for the defined groups of machines. 3. Defining a series of equally spaced bands for each co-variant from zero to an upper level, which encompasses approximately 98% of the measurements. These bands are used to form 912
the basis of a probability matrix, which defines the probabihty of the measurement changing state in the next monitoring interval. From these stages a decision model is built, which combines the above information with the costs of preventative maintenance and failures. The model calculates a number of features that are used to predict the optimal replacement period. The primary feature is the cost function, which is based upon the total costs that would be incurred at different risk values. This is then used to determine the optimal replacement interval at the least cost and an analysis of the remaining life for each unit can be performed. Data Preparation A major problem, not only in the condition monitoring world, but in system integration in general, is one of communication between different systems. To enable this case study to be completed, the original data from the MIMIC was manually converted to the EXAKT^"^ format. However, the software needs to have a complete data set to allow any analysis to be undertaken. This means that any missing data has to be predicted and included. During this case study two different but common situations were identified: 1. Only one or two data points were missing - these values were interpolated from the surrounding points to give an estimated value. 2. Large number of missing points - an average value based upon the readings observed in the other identical units was calculated. Definition of optimum number of parameters It was uncertain if all of the 8 parameters originally being monitored for each machine were statistically significant for the identified failure modes within the machine. To determine their significance a Weibull analysis was undertaken on all possible combinations. A comparison of these analyses showed that the best model for the defined failure modes would be based upon the combination of vertically monitored acceleration and axially measured gear mesh. Definition of state bands The MIMIC system, like the majority of CMM systems, makes use of manually set fixed alarm and warning levels to signal the change in a parameter's state. These levels are often based either upon the experience of the user or the baseline condition of the machine. The EXAKT'^'^ system is somewhat different in the definition of these levels because instead of fixed warning and alarm levels the system defines a series of state bands which define the different conditions the plant item can be in. These bands are used within the Markov chain to determine the probability of the plant item changing state in the next monitoring period. These bands can be either automatically determined or manually altered by the analysis of the spread of the measurements against working age to improve the defined model. Table 2 shows a comparison of these bands with the calculated bands and the MIMIC alarm and warning levels. TABLE 2 Comparison of MIMIC alarm and warning levels and the EXAKT'^'^ state bands EXAKT Band 1^' •^nd -jrd
4'h
Calculated Acceleration Gear Mesh 0.804 6.2 1.606 2.408
MIMIC Manually Set Acceleration Gear Mesh 0.5 1 2 1.0 1.5 3 5 2.4
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EXAKT Decision Model The decision model is based upon the combination of the above stages with the cost of either replacement at failure or for preventative maintenance. It is estimated that an unexpected failure of these units would resuh in a 24 hour shutdown of the entire plant, incurring a cost of £120k; a fixed time replacement (preventive maintenance) would result in a 4 hour shutdown with an incurred cost of £20k. From this information a cost function was determined that identified the incurred cost per day at an optimal risk factor for that group of machines. Figure 1 shows the cost function calculated during this study, whilst table 3 shows a comparison between employing the optimal replacement policy and a replace only at failure (ROOF) policy. This comparison shows that the expected time between replacement would decrease from 3081.43 days to 1765.23 days but there would be an expected saving in incurred costs of 37.2%. TABLE 3 Summary of cost analysis for the defined model Cost (£/Day) Optimal Policy Replace at failure Saving
24.45 38.94 14.49 (37.2%)
Preventive Replacement Cost (£/Day) 8.71 (35.6%) 0 (0.0%) -8.70487
Failure Preventive Replace on Failure Replacement Replacement (%) (£/Day) (%) 23.2 15.7505 76.8 (64.4%) 100.0 38.943 0.0 (100%) 76.8 23.1924 -76.8
Expected Time Between Replacements (Days) 1765.23 3081.43 -1316.2
j|_jjjgj_xp| 80
Cost Function Preventive replacement cost Replacement at failure cost
70
f 40d em Risk Ip/cf] (iisizarci^K)
Figure 1: Cost Function showing Costs that would be incurred for the optimum policy and the replace only on failure Justification of optimal replacement policy using cost analysis To ensure that the defined model is predicting the correct information a manual analysis of the results obtained was undertaken. This comprised of four stages, these were: 1. Obtain information from model; 2. Analyse decision reports for each unit; 3. Build report code matrix; 4. Cost analysis. Step 1 - Information from model The cost analysis requires a number of different variables to be obtained from the defined EXAKT model. Table 4 summarises these variables and defines the values obtained from the model. 914
TABLE 4 Variable for use in cost analysis Variable Name Optimum replacement policy cost Expected time between replacement Replace only on failure cost (R.O.O.F.) Expected savings from optimal
Units £ Days £ £
Value 24.45 1765.23 38.94 14.487
Step 2 - Analyse decision reports for each unit Figure 7 shows a typical decision graph for one of the units in this case, unit number 18. Analysis of the decision made at the end of each of these histories can be used to validate the developed model. The present decision of this graph is 'don't replace', however the chart shows a number of points in the 'replace immediately' region, which is an indication of an event occurring during this history.
wmm.
aaHi W^0ismmmmnk I^^el^lon
S0O
lOdO
1600
"^mmrm A0B «* loss tdi
:^M
2000
4^ 0$§4B7^OCAMesh
Figure 2: Decision graph for unit 19AD showing a decision of 'don't replace' Step 3 - Build report code matrix There are 12 different reporting codes used within the system, which are defined based upon the interaction of the replacement decisions with the defined trend state at the end of the history, table 5 shows these codes. TABLE 5 Decision Matrix - showing available decisions and number of decisions observed within the model
Replace at current record Replace at previous record Replace if inspection point were interpolated Don't replace
Failed 0 3 0 4
915
Suspended 0 0 0 40
In Operation 0 0 0 17
Step 4 - Manual cost analysis The actual calculations used to justify the EXAK'H^ model are dependent upon the codes reported in the decision matrix (table 5). In this case study, the cost analysis was based upon a comparison of two theoretical daily costs per day and the actual cost per day. These theoretical costs are based upon two situations the first is failure and suspended history events only and the second contains failure, suspended histories and still in operation (suspensions). The actual cost per day is based upon the total costs incurred if both the failures and preventive maintenance actions were allowed to occur. For this case study the actual cost per day TAC = £36.95. To enable the theoretical costs to be calculated for each of the two given situations the total working age for all of the decision codes needs to be calculated. Table 6 defines these ages in relation to the decision codes. TABLE 6 Total working age for all histories at the defined decision codes Failed 0 1434 0 3645
Replace at current record Replace at previous record Replace if inspection point were interpolated Don't replace
Suspended 0 0 0 29641
In Operation 0 0 0 18857
There are two different theoretical daily cost calculations possible for the cost analysis. These costs are defined in table 7, which shows both situations. TABLE 7 Calculations of theoretical cost per day for the two given situations
Decision Failure Suspension In operation Totals Daily Cost/day
4 43
Casel Total Cost (£) 480000 860000
47
1340000
Num
Working Age (Days) 1434 33286 34720 38^^59
Num 4 43 17 64
Case 2 Total Cost (£) 480000 860000 340000 1680000
Working Age (Days) 1434 33286 18857 53577 31.36
Comparison of savings both actual and theoretical Table 7 shows the calculated costs that would be incurred if the EXAKT"^"^ maintenance interval was used. To determine the relevance of these daily costs per day, the percentage change of the second theoretical case against the actual total cost (TAC) was calculated and then compared to the percentage change of the optimal policy cost (obtained from the cost function, table 3). Table 8 shows that if the optimal policy was used the company could possibly see a 33.84% saving in maintenance cost. However, when the decisions were analysed an actual saving of 15.15% was evident. TABLE 8 Comparison of optimal policy and actual EXAKT decision Decision Actual decisions Optimal policy
TAC (£) 36.96 36.96
Theoretical Costs (Case 1) (£) 31.36 24.45
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Saving, (%) 15.15 33.84
DISCUSSION OF CASE STUDY Maintenance actions are seldom based upon the change in a single parameter. Often the decision to undertake maintenance is based upon a change in the relationship of key parameters. However, determining which of the defined parameters are key for the overall unit can be a difficult process. The statistical model developed within this case study attempted to identify these parameters and subsequently optimise the CBM process. The results of this case study identified a number of features that need further consideration. The first of these was that the number of parameters presently being monitored were found to be not well related to the machines under investigation; only 2 of the original 8 parameters were used for the modelling process. The most probable reason for this reduction is that the parameters are defined based upon identified failure modes, for example, misalignment and bearing damage. A requirement of the system is that the third party database contains information regarding the type of event that resulted in the end of that particular history. Therefore if that type of failure mode has not been evident in the event history of the machine prior to the modelling process, its significance on the model would be greatly reduced, but this could alter if any of these failure modes subsequently occur. At present the system cannot cope with this type of novelty detection and therefore requires a model to be constructed for every new failure mode. The EXAKT''''^ system works by defining 2 different failure modes, namely failures and suspensions. The classificafion of these failure modes can be subjective depending upon the failure history recorded within the traditional CMM system. It was found that a large proportion of the maintenance actions were opportunistic, either when a failure was identified in one of the other units or when the plant was on a shutdown period. Analysis of the decision models showed that 4 identified 'failures' were actually suspensions. It was further found that a number of the identified 'suspensions', which were defined as being 'in service' at the present interval, showed a failure at some point prior to the end of the history. The most likely reasons for this feature are inaccuracies in the reporting of work undertaken, the time at which the actions were first reported and the time when the machine was returned to service. The cost analysis was undertaken upon the developed model to show the difference between the different theoretical daily costs and the present actual costs using the optimal replacement policy. The difference in possible savings could be due to a number of reasons. The most probable reason is the way in which events are classed most evidently when the original work history is either incomplete or inaccurately reported. The comparison of expected to actual saving showed that the model developed is relatively accurate in its construction. However, the amount of savings are not only dependent on the accurate building of the model, but also on correct calculation of the incurred costs within the company. CONCLUSIONS AND FURTHER WORK Maintenance decisions are seldom based upon the change in a single parameter, but by analysing the relafionships between different parameters. Traditional CMM systems often rely on user experience during the analysis of these trends but this can be a difficult task due to the numerous techniques and systems available. EXAKT is one system that has been developed to aid the process of analysis. This case study showed that the combination of key parameters within a statistical model could aid the reduction of maintenance costs within a typical engineering company. The model enabled a possible saving of approximately 15% in the maintenance cost for that particular machine and 917
event type. The choice of which parameters to use is dependent not only upon the successful identification of failure modes, but also the failures observed during the plant's working life. Detection of novel situations within the working life of plant items needs further work. There are numerous tools available for this process, for example Principal Component Analysis, which clusters together groups of measurements relating to defined situations so that any novel situations will result in a new cluster. These can subsequently be included in the model and further decrease disruption of production capability. The EXAKT"^^ system makes extensive use of statistical methods in its modelling and subsequent decision process within the level 1 data fusion framework. However, a number of areas were highlighted where intelligent systems could be better used to aid the development of this type of system. These areas include problems with missing values in the initial data, novelty detection within parameter association and the diagnostics of the actual problem. REFERENCES 1. Andersson C & Witfelt C. 2000, ''Advisor:- A prolog implementation of an automated neural network for diagnosis of rotating machinery ", http://www.visualprolog.com/vip/articles/CarstenAndersonyadvisor.html 2. CBM Consortium, 2000, "EXAKT^^ - The CBMOptimiser", http://www.mie.utoronto.ca/labs/cbm 3. Design Maintenance Systems (DMSI) 2000, "MAINTelligence monitor ", http://wvsrw.desmaint.com 4. Hall D.L. and Garga A. K., 1999, "Pitfalls in data fusion (and how to avoid them) ", Key note speech, EuroFusion99, ISBN 0 9537132 0 2 5. Hall D.L., and Llinas J, 1998, "An introduction to multi-sensor data fusion", Proceedings of the IEEE international Symposium on circuits and systems. Vol 6, pp537-540 6. Harris T.J and Kirkham C , 1997, ''A Hybrid Neural Network System for Generic Bearing Fault Detection" , Proceedings of COMADEM 97, Vol 2, pp 85-93, ISBN 951-38-4563-X 7. Kennedy I., 1998, "Holistic Condition Monitoring", Wolfson Maintenance, Proceedings 1'^ Seminar on Advances in Maintenance Engineering, University of Manchester 1998 8. Oliver Group, 2000, http://oliver-group.com/ 9. Smith G.T, 1996, "Condition Monitoring of Machine Tools ", Southampton Institute, Handbook of Condition Monitoring, Rao (Ed.) 1'' edition, pp 171-207, ISBN 185617 234 1 10. Starr A, Esteban J, Hannah P, Willetts R, Bryanston-Cross P, 2000, "Strategies in Data Fusion for Condition Monitoring", Proc 3rd Int. Conf Quality, Reliability, Maintenance, Oxford (invited) 11. Taylor 0. and Maclntyre J., (1998), "Modified Kohonen Network for Data Fusion and Novelty Detection Within Condition Monitoring", Proceedings EuroFusion98, pp 145-155 12. Trave-Massuyes L., Milne R., 1997 ''Gas-turbine condition monitoring using qualitative model-based diagnosis", IEEE Expert, May/June 1997, pp22-31
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Condition Monitoring and Diagnostic Engineering Management Andrew G. Starr and Raj B.K.N. Rao (Editors) © 2001 Elsevier Science Ltd. All rights reserved.
DIAGNOSTICS OF HONEYCOMB CORE SANDWICH PANELS THROUGH MODAL ANALYSIS R. Basso', C. Cattaruzzo^ N. Maggi^ and M. Pinaffo' 'Dipartimento di Ingegneria Meccanica, Universita di Padova, Italy ^Officine Aeronavali, Tessera-Venice, Italy
ABSTRACT The possibility of using a new diagnostic technique to identify the presence of localised debonding in metaUic sandwich panels with honeycomb cores is analysed in this paper. The technique is based on the natural frequencies measurement of panels excited with an impulsive load. A preliminary simulation demonstrated that when there is a debonding it is possible to identify peaks relative to the vibrations of the debonded skin seen as a plate clamped at boundary. The tests on panels with artificial debonding, using an accelerometer and a high sensitivity displacement laser sensor, validated the simulation. The no-contact displacement sensor is suitable for its capability to pick up the vibrations of the skin when placed above the debonded area, but its frequency field is limited as it can only identify defects larger than about 60-70 mm. On the contrary the accelerometer reveals the debonded areas if it is placed outside them. KEYWORDS Sandwich structures, Modal Analysis, Debonding detection, Honeycomb structures. Vibration measurement.
INTRODUCTION Many parts of aircrafls (flaps, wings, ailerons, elevators and rudder) are made up of sandwich panels with an aluminium alloy honeycomb core. Even though in the past few years non-metallic composite materials have been used more and more, these structures are still used because of their great flexibility, high resistance, low density and isolation properties to noise. These panels sometimes have areas where the skin and core have become debonded, due to the vibrations caused upon impact as well as due to moisture infiltration or defects in the construction made during the assembly. During revision, these areas are identified using ultrasonic probes. The damaged area is subsequently repaired using a procedure in which the localised skin is removed, the core substituted, if it is damaged, and a new skin bonded back on. Ultrasonic technique to diagnose damaged areas takes a significant amount of time because the surface to be examined is so large and because the surface has to first be carefully cleaned using solvents and then covered with a gel which has to be removed at the end of the revision.
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Debonded areas having a diameter less than 30 mm are particularly difficult to identify. Furthermore, in the sandwich structure the opposite side of the ultrasonic probe is difficult to examine because of the significant transmission loss through the honeycomb structure. The aim of this paper is to evaluate the possibility of using a complementary diagnostic technique based on the dynamic response of the panels when they undergo an impulsive excitation. In fact, it is well known that structural damage leads to variations in the modal parameters and by measuring these it is possible, in many cases, to identify and localise the damaged area. In this work, also, we would to compare two vibration transducers, an accelerometer and a no-contact motion sensor. The technique proposed in this paper is fairly simple since it is based on the comparison between the frequencies of an undamaged panel and those of the damaged one. It is important to point out, however, that modal analysis should be considered complementary to the ultrasonic technique and used to speed up the process of identifying debonded areas. The analysis of frequencies could be considered to be a prehminary indicator of the presence of possible debonding. Then the final analysis would nonetheless be carried out using the ultrasonic technique since it provides greater precision regarding the position and extent of the debonding. To evaluate the feasibility of this technique, a preliminary numerical analysis using finite elements was carried out followed by experimental modal analysis tests on four sandwich panels, one of which was undamaged while the other three had areas which had been artificially damaged.
MODAL ANALYSIS WITH FEM In order to evaluate the possibility of using a diagnostic technique based on the modal analysis of sandwich structures with a honeycomb core, a numerical study using finite elements was carried out on four panels built for the purpose of both the numerical study and for the following experimental analysis. One of the panels was undamaged while the other three had circular "debonding" with a radius of 75 mm created artificially in different positions by not applying the 0.2 mm-thick adhesive. It is important to note that simulated defects may be slightly different than real ones. In fact, in the simulated defect used here between the skin and the core there was no adhesive, while in the case of a real debonding, the adhesive remains attached either to the skin or the core. Therefore, in the simulated debonding, the skin was free to oscillate and behaves like a fixed circular plate, while in the case of real debonding, the skin may be not completely free to oscillate. The total dimensions of the panels studied were 500x500x25.6 mm. The skins were made up of two 0.8 mm thick sheets in a aluminium alloy Al-Zn, 7075, hardened and aged to the T6 state. These layers can be considered isotropic and, because of their characteristics, three elastic parameters were sufficient. The core was made up of NIDRATAN® honeycomb in alloy Al-Mg, 5056. The basic cell is a regular hexagon with 6 mm between any two sides. The cell walls are 0.1 mm thick and 24 mm high. The honeycomb core can be considered orthotropic, i.e. having three symmetrical planes, and therefore 9 independent elastic parameters were needed to describe its behaviour. These parameters were calculated based on the constants of the constitutive alloy. The x-y plane constants were calculated as suggested in Masters & Evans (1996). The elastic parameters (Ez, Gxz, Gzy, Vxz and Vzy) of the x-z and y-z planes were calculated using ANSYS 5.5.1 to carry out FEM simulations since data suitable for calculating them cannot be found in the literature. To make these calculations, traction and shear tests were carried out on the basic cell of the honeycomb and consequently the parameters were defined. In Table 1 the mechanical characteristics of the two alloys of the base and of the honeycomb can be seen. The model of the undamaged panel was made using 400 SHELL91 elements of the ANSYS code. The SHELL91 elements were conceived specifically for modelling sandwich structures (each element has 8 nodes and 6 degrees of freedom for each node). The model of the elements is further defined by the thickness of the various layers and the direction of the fibres if composite materials and support materials with an orthotropic behaviour are used. The model of the elements uses the "sandwich logic" which was specifically designed for modelling sandwich structures which have two thin layers and a
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fairly thick core. It can be assumed that the core supports the entire shear load and that the skins support the bending load. TABLE 1 BASIC ALLOYS AND HONEYCOMB MECHANICAL CHARACTERISTICS BASIC ALLOYS
Density (kg/m"^)
Al-Zn 7075 2798
Young's modulus (MPa)
71000
Shear modulus (MPa)
26692
Poisson ratio Breaking stress (MPa) Yield stress (MPa)
0.33 480 420
HONEYCOMB
Al-Mg 5056 118.2 2660 Ex = Ev = 0.173 74000 Ez= 10083 G,v = 0,043 27820 G^ = 892.9 Gvz = 343 0.33 Vxy = Vxz = Vxz = 280 130
0.33
The model of the panel with the debonding was based on the one presented in Liew et al. (1997), where a typology of a defect analogous to the one being studied is presented. The debonding between the skin and the core was set up as an empty space having the same thickness as the missing adhesive. The simulated constraints were free body constraints obtained by coimecting the panels to four fixed points using CoMBlNl4 elements which were assigned a stiffness of 20 N/m. A description of the model used is presented in more detail in Basso et al. (2001). Figure 1 shows same patterns of the finite element model.
Fig. 1: Model of panels. A Undamaged panel. B A quarter of damaged panel. C Detail of the empty volume at the centre of the damaged panel. The Block Lanczos model, which is based on the Lanczos algorithm, was used to solve the eigenvalue problem since it is faster than other methods. The naturalfrequencieswere held in consideration more than the other results obtained from the numerical simulation to evaluate the feasibihty of the method. Nonetheless, the modal shapes, and the relative nodal lines, were useful in estabUshing the measurement points for the experimental tests. The results of the numerical simulation in the panel with the debonding, compared to the undamaged one, show new frequencies, and therefore the defect created new vibrating modes. Furthermore, it is worth noting that the natural frequencies of the panel did not undergo significant variations. The new ways of vibrating refer only to the circular area of the skin above the unbonded area. In fact, this area has a different stiffiiess than the one in the undamaged panel as the skin and core are independent of one another (there is no adhesive to connect them) and therefore the debonded skin behaves like a plate clamped at boundary. 921
EXPERIMENT The experiment to verify the feasibihty of the proposed diagnostic technique was carried out on 4 sandwich panels: one was integral and three each had an artificial circular debonding, 150 mm in diameter. In the 3 panels with a defect, the debonding was carried out respectively: in the centre, in the middle near one side, in a comer. When the ultrasonic technique was used to verify the panels, only the central artificial debonding presented a total absence of glue between the skin and the honeycomb, while the other two presented traces of glue between the skin and the core even though the skin was completely detached fi-om the honeycomb. Though the traces of glue were not used intentionally, their presence actually made the latter two debondings more similar to real debonding. The panels were excited using an impulse force hammer and the natural fi-equencies were read using both a piezoelectric accelerometer (PCB type M352C65) and a highly sensitive laser displacement sensor (MEL type M5/2). This latter instrument was used in such a way that the natural frequencies of its support did not interfere with the ones of the panel being tested. A dynamic signal analyser SIGLAB was used for signal processing and fi'equency spectra visuahsation.
Fig. 2: Test rig: accelerometric (left) and displacement (right) measurements. Only the natural fi-equencies were studied so as to avoid a complete modal analysis by observing the modal shapes as well. This was done for two reasons: first of all because carrying out a complete modal analysis would require a lot of fime and, just for this, secondly because it would not be worth using it as a diagnostic technique complementary to the ultrasonic technique. The fi-equency ranges was limited to 5 kHz for accelerometric measurement and to 2 kHz for displacement one. This last is less than the first because the motion signals produced beyond that limit are too weak to be picked up by the used laser sensor. The two sensors were positioned in different places, both above the debonded area and away fi-om it. The hammer hit the panel in various points, but in particular it hit the panels along the borders. All of the frequency spectra obtained are the result of an average of several frequency spectra obtained by keeping fixed both the point of measurement and the point of impact of the hammer.
RESULTS In Figures 3 and 4, some examples of the results of the numerous experimental tests can be seen. The spectra of the panels with localised debonding, in addition to presenting peaks for the same frequencies as the undamaged panel, show new peaks due to the extra degrees of freedom in the structure corresponding to the debonded area. These new peaks correspond to the frequencies of the parts of the debonded circular areas. This was verified calculating the natural frequencies of a circular plate clamped at boundary.
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Fig. 3: Examples of acceleration spectra. The accelerometer was placed outside the debonding. A Undamaged panel. B Central debonding. C Debonding near the middle of one side. D Debonding in a comer. A
L»>*^i»^VMwW ' ^ N N A ' N ' ^ . A ^ \ A J K ^ A » V N ^ ^
B
*-,
fy'^^AyVyj WfvW1ft/yA>u^^^
»»\v«^^-vu
500
1000 Hertz
1000 Hertz
1500
|W>^^vMVvV W ^ j t M ^ X i A ^ - ^ ' ^ ^ ^ 1 U A > 4 U ^ JX M ^M A /V MW^^TVWU,,,^^
1000 Hertz
Fig. 4: Examples of displacement spectra. The laser sensor was placed above the debonding. A Undamaged panel. B Central debonding. C Debonding near the middle of one side. D Debonding in a comer. The spectra depend on the position between the sensor and the debonding, while they seem to be indifferent to the position between the sensor and the impact point. The accelerometer is better able to 923
identify the new peaks if they are positioned outside of the debonded area, while the displacement sensor is able to identify the new peaks caused by the debonding only if the beam is directly above an debonded area. In the latter case, the limit of the frequencies which can be read is about 2 kHz, and, considering the results of the simulation with fixed circular plates, it can be deduced that with the laser sensor it is possible to see debondings with a diameter greater than ca. 60-70 mm. Even though the accelerometer is able to read free vibrations of the debonded skin even when the it is far from the defect, it is difficult to interpret the signals it provides with respect to those provided by the laser displacement sensor at the edges of the debonded area, hi order to overcome this limitation, a technique was studied to make it possible to identify the debonded area. With this technique it is possible to read the spectra of the signal of the accelerometer in three different points of the panel, having continuously applied the impulsive force in the same point. By comparing the spectra obtained with this method to those relative to the undamaged panel, it is possible to find the debonded area.
CONCLUSIONS This paper analysed the possibility of using a new diagnostic technique to identify the presence of localised debonding in metallic sandwich panels with honeycomb cores. This technique is based on reading the natural frequencies of panels excited with an impulsive load. A preliminary FEM simulation revealed that when there is a debonding, in addition to the having the same peaks as the undamaged panel, it is possible to identify peaks relative to the vibrations of the debonded skin seen as plate clamped at boundary. The same peaks which were seen in the simulation were also identified as vibrations in the experiment using an accelerometer or a high sensitivity displacement laser sensor. The no-contact displacement sensor is, however, the preferable of the two since it is able to pick up the vibrations of the skin when placed above the debonded area. On the other hand, the frequency field of the displacement sensor is limited as it can only identify defects, which are larger than ca. 60-70 mm. The limitation of the accelerometer is that it requires more comphcated measurement procedures, but if in the future an automatic technique is set up, it should be possible to perform an entire component inspection by a single impulsive excitation and, therefore, in short time. The diagnostic technique studied could help reduce the time needed to carry out the ultrasonic technique used today. In fact, the laser technique if applied with many sensors placed over the more critical areas of the component may perform in short time a rough inspection of the component. The accelerometer technique may be applied to locate many defects, placed everywhere inside the component, or at least to exclude the presence of defects, by a single impulsive load application. Moreover, for both techniques it is important to highlight the possibility to perform the inspection without removing the component from the aircraft and without stripping it from paint or sealant.
References Basso R., Cattamzzo C , Maggi N., Pinaffo M. (2001). Modal analysis of sandwich panels with localized debonding. (in Italian), 16* AIDAA Conference, 24-24 Sept. 2001, Palermo, Italy Liew K.M., Lim C.S. (1997). Analysis of dynamic responses of delaminated honeycomb panels. Composite Structures 39:1 -2, 111 -121. Masters I.G., Evans K. E. (1996). Models for the elastic deformation of honeycombs. Composite Structures 35:4 403-422. Paolozzi A., Peroni I. (1998). Identification of models of a composite plate for the purpose of damage detecfion. Aeronautica, Missili e Spazio 67:3-4, 119-129 Rao J.S. (1999), Dynamics of plates. Marcel Dekker, Inc. Scott Burton W., Noor A.K. (1997). Structural analysis of the adhesive bond in a honeycomb core sandwich panel. Finite Elements in Analysis and Design 26, 213-227. Soedel W. (1993). Vibration of Shells and Plates, Marcel Dekker, Inc.
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Condition Monitoring and Diagnostic Engineering Management Andrew G. Starr and Raj B.K.N. Rao (Editors) © 2001 Published by Elsevier Science Ltd.
ASSESSMENT OF STRUCTURAL INTEGRITY MONITORING SYSTEMS B. de Leeuw ^ and F.P. Brennan ^ 'UCLNDE Centre Department of Mechanical Engineering, University College London Torrington Place London WC1E7JE England
ABSTRACT Recent advances in electronics and remote communication technology have led to new activity in the area of structural integrity monitoring. A new discipline of designing and specifying the requirements of structural monitoring systems is emerging. In order to be effective, the design of these new systems must be led by an understanding of the requirements of maintenance and structural integrity calculations. In addition, monitoring functions need to be characterised and objectively evaluated. Measurement of performance characteristics needs to be established so that different equipment can be objectively compared. Crack detection and sizing systems for example, can be evaluated in terms of POD (Probability of Detection) and POS (Probability of Sizing). Similarly stress/strain-monitoring devices need to be evaluated in terms of accuracy of measurement with respect to material, environment, static and dynamic loading parameters, repeatability and drift. In addition, stress/strain-monitoring systems are likely to respond differently to different loading regimes. For example, some systems will respond well to low R-ratio cyclic stresses but may not perform as well under high mean stress or vice-versa. Similarly the frequency of applied cyclic stresses will also effect performance of monitoring systems. This paper presents details of a recent laboratory study of two Stress Monitoring systems. The two systems, ACSM (Alternating Current Stress Measurement) and the Stress Memory Unit were subjected to performance reliability trials and results presented in a manner which allow comparison with other systems. The aim of the study was to investigate the basis for performance reliability trials of stress/strain-monitoring systems and ways in which data can be presented. In the future, this type of data will be integrated into structural reliability calculations and may be used to assist conventional inspection scheduling.
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KEYWORDS Structural Integrity Monitoring, ACSM, stress monitoring, assessment
INTRODUCTION The safety and integrity of engineering structures has always been a primary concern for operators and regulatory authorities. Un-noted flaws in structures could result in catastrophic failure, which would not only be very costly in monetary terms but could also lead to loss of life. To avoid such an event it is important to be able to determine the location and extent of any structural damage so that appropriate action can be taken to ensure structures and components are fit-for-purpose. Currently the general procedure to ensure the integrity of large structures such as offshore platforms consists of applying a number of established non-destructive testing and evaluation techniques on selected members. The inspections are carried out by a number of specialised resources such a deep sea-divers and remotely operated vehicles (ROV). This usually follows a planned schedule that could span over a period of months and tends to be a costly operation as well as potentially dangerous for the people directly involved. Although this form of inspection has proved to be useful in reducing the number of catastrophic failures in many types of engineering structures it still has a number of shortcomings. The most important is the fact that inspections are only carried out at predefined scheduled times. Rare or extreme events may cause flaws in an engineering structure which are unlikely to be detected until the next scheduled inspection, which could be too late. Such a situation arose during the Northridge earthquake (California, 1994), several structures were weakened (but undetected) following the main shock, however they only collapsed when a major aftershock occurred [1]. Recent developments in electronic sensors and data acquisition and transmission systems and their reducing cost has led to the emergence of a new branch of non-destructive evaluation known as 'Structural Integrity Monitoring' (SIM). SIM can be defined as continuous monitoring of engineering structures, where the aim is to record data relating to the integrity of the structure with the view to comparing it to a previously set trend based on the historical behaviour of the structure. Any major differences observed between the data should be investigated further as it could indicate the presence of a flaw in the structure. New state of the art in Structural Integrity Monitoring is very different to some of the existent monitoring techniques such as vibration monitoring. Massive improvements in sensing technologies, data acquisition and storage systems and GSM communication systems have greatly expanded the possibilities available for SIM. It is possible for example to distribute a number of wireless sensors over a structure and have the data transmitted to a central receiving point where the data can be processed. Data can be analysed over a monthly period for example or for some applications it may be necessary to analyse it in real time. One of the main aims of SIM is to store as much historical data about the structure as possible to enable future comparisons to be made, and to give indications on the behaviour of the structure during rare or extreme behaviour such as storms or even collisions. The new state of the art SIM techniques under development result in some important direct benefits for end users, maintenance crews and manufacturers of engineering structures. It allows for safe inspections to be carried out in areas of difficult access or that are potentially dangerous. The storage of historical data potentially closes the design loop [2], providing manufacturers with invaluable historical data indicating the performance of their structure in service. SIM minimises the level of human involvement required, reducing downtime and human errors [1]. Automation in general will improve safety and reliability. Continuous monitoring may also potentially affect the working life of a structure in a less obvious way. Currently the design life of a structure is determined in advance by considering its design and typical experiences. This may not be very accurate as it does not take into account 926
individual structures throughout its working Ufe with respect to the degree and conditions of usage. This is Ukely to vary from one structure to another and thus similar structures could have very different operational lives. SIM would change this, as by monitoring continuously the relevant maintenance can be carried out when necessary and decommissioning would only take place once this is absolutely necessary. Structural Integrity Monitoring not only promises to eliminate the uncertainty associated with inspection scheduling but will, in addition, allow for new advanced structural design philosophies that were not previously possible. An example of this is the "Controlled Failure Design" concept being researched at the UCL NDE Centre [3]. This predetermines the least inconvenient failure mechanism at the design stage. Structural Monitoring would allow engineers to monitor and verify the progressive failure process without affecting safety. Structural Integrity Monitoring is an emerging technology, much research still has to be carried out in order to assess the applications available and to determine if they are capable of performing their tasks with an acceptable level of reliability, sensitivity, accuracy and cost effectiveness [4]. This paper presents preliminary work investigating the operating characteristics of two innovative remote stress-monitoring devices (the Alternating Current Stress Measurement (ACSM) technique and the Material Stress Memory unit).
ASSESSMENT OF SIM SYSTEMS Along with the recent advances in electronics and remote communication systems comes an increased number of innovative SIM techniques. These vary in terms of application and parameters recorded to determine the integrity of a particular structure. The level of performance of each monitoring technique will vary depending on the type of structure being monitored. It is therefore important to be able to evaluate the accuracy of the measurements provided by the different techniques in order to assess their suitability for monitoring the integrity of any particular structure. Factors affecting the monitoring performance of a particular application include construction material, the operating environment, the type of loading experienced by the structure (eg static or dynamic responses), repeatability and drift. One point that should be noted its that no one SIM technique will provide an ideal solution for the monitoring of a structure's integrity. A combination of these should be applied to ensure maximum monitoring efficiency, therefore assessment of combined techniques is also necessary. For effective comparisons between systems it is necessary to design a procedure that will enable a direct and easy assessment of each system under certain predetermined criteria or parameters. These criteria will vary depending on the type of structure the application is to be used for. The SIM techniques can initially be tested within a laboratory environment in order to pre-determine system characteristics. Ideally one would want to end up with single indexed figure indicating the relative performances of SIM technologies for different applications. Currently Crack detection and sizing systems have such performance measures. Here the performance of the applied techniques are established using POD (Probability of Detection) and POS (Probability of Sizing) respectively. However with SIM a greater number of influencing parameters and large volumes of data makes comparison of system characteristics more difficult. When assessing the accuracy of a SIM device there a some factors of particular interest that need to be considered. Theses include Repeatability - The capability of the device to reproduce similar results every time a test is repeated under identical conditions.
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Sensitivity - This the sensitivity of the sensor in terms of its response to various stress levels as well as the sensitivity of the device in recording interferences such as background noise and how these affect the overall data recorded. Drift - This is the extent to which there are whole shifts in the mean of the cyclic data recorded without there being a significant change in the structures condition. These should be considered under different loading conditions and operating environments. Research into the analysis of the data and its presentation in a format that makes direct comparisons between SIM systems possible is still in its preliminary stages. Many of the earlier work is based on statistical inferences however it has become obvious that these are not always adequate when looking at the recorded data distributions. Some of the methods considered for representing SIM data are presented below: Data Distributions - These consist of graphic displays of the frequency distributions of the recorded data. The best performing systems would have a very steep data distribution with very little data dispersion from the mean. It is necessary to devise a procedure that enables the calculation of the maximum permissible deviation from the mean under the various loading conditions. This may be done by applying S-N curves. Mean over time - A useful way of determining the presence of drift in recorded data is to plot the variation of the cumulative mean of Mean Stress and Stress Range of a designated period of time. Figure 1 below illustrates such representation for mean stress, the solid line represents the actual mean stress applied, the dotted lines illustrate the drift in the data recorded using two different SIM techniques.
CO M
55
86 84i
74f 72r
7oir Figure 1: Variation of Mean Stress over 15 min intervals Mean vs Median - When considering the data h may be useful to compare the variation of the mean with the variation of the median of the data with time. Major differences between the two values should indicate a change in the general form and position of the data distribution. This shows that both these parameters should be considered as erroneous conclusions could be made if the statistical mean is only considered as a basis for analysis. Variation of standard deviations - This has the potential of providing useful information regarding the overall spread of the data. However as will be seen later it must be considered along with other assessment parameters as it may provide misleading results under some conditions. The standard deviation can also be used as an indication of the settling period for a particular monitoring device. This is the time it takes between initial operation and its optimal operation. Hence variations in the data after this period could be assumed to have not been caused by the sensing device. This can be seen as 928
the point at which the standard deviation ceases to change over time, hence tending to a horizontal line. The general idea behind this is illustrated in figure 2.
Settling Time
Standard Deviation
Time Figure 2: Variation of Mean Stress over 15 min intervals
PRELIMINARY EXPERIMENTS SIM Techniques utilised Two Stress Monitoring techniques were evaluated during this study. The background to each of these is given below. Alternating Current Stress Measurement (ACSM) ACSM is a non-contact stress measurement technique developed at University College London and is based on the more established 'Alternating Current Field Measurement' (ACFM) method. The basis of both these techniques is that the uniform electric field is induced into the section of a component to be inspected, resulting in a near surface magnetic field due to the 'skin effect ''. The presence of surface breaking flaws will lead to changes in the magnetic flux density, which can be measured and analysed to determine the size of the flaw. This is the principle upon which ACFM operates. ACSM broadens this application to enable the non-destructive and non-contact measurement of stress [5]. Material Stress Memory Unit (MSM) This new Structural Monitoring Technique measures and logs the occurrence of stress/strain cycles. The system used is based on an 120 ohm resistance strain gauge. The advantages of the Stress Memory Unit over conventional stress measurement systems is that it is a miniature, passive and self contained (wireless etc) device designed for use in the field. Outline of Procedure All the experimental investigations were set-up and carried out in the NDE Laboratory at University College London. For operation of the ACSM Probe a U12 Crack Microgauge was required Both of these are manufactured by TSC Inspection Systems Ltd [6]. The probe was simply attached to one of the faces of the test specimen used. The other face was thoroughly polished to allow the attachment of the Strain Gauge, which was used with the Material Stress Memory unit. The test specimen itself was a mild steel plate, 130mm x 50mm xlOmm. The specimen was dynamically loaded using a servohydraulic INSTRON test machine operated under load control.
' 'Skin Effect' - when a high frequency alternating current is applied to a conductor, the current will flow in a thin layer of the conductor 929
Although both these monitoring devices operate with their own separate software packages a virtual data logging application was used to record the experimental data. Voltage outputs from the ACSM Probe, the Material Stress Memory Unit and the load cell were input into the data logger. This enabled ail the data to be recorded simultaneously. Due to the large volume of data involved in these tests the ASCII file format was used to store the data. The test specimen was subjected to a combination of Mean Stress and Stress Ranges ranging between 60 - 110 MPa and 40 - 120 MPa respectively. The cyclic frequency was set at 0.5 Hz and all the tests were run over twenty four hour periods. A full description of the experimental set-up and the date manipulation involved in this investigation can be found in reference [7]. Results The data obtained from the two monitoring devices were normalised with respect to each other in order to enable direct comparisons to be made. Figure 3 below illustrates the two parameters of the stress cycles that were considered during the analysis. AMP
Time
Figure 3: Illustration of measured parameters Hence changes in H and/or AMP indicate relative changes in the average level of mean stress and/or stress range experienced by the test specimen. One of the performance characteristics investigated was the medium term 'Drift' of the data. In order to do, this averages of H and AMP on a fifteen-minute basis were plotted against time for the whole duration of the test periods. The data obtained for both applications was compared directly to the output from the load cell. Sample plots from one of the tests are illustrated in figures 4a and 4b below. a. Variation of Mean AMP over Time
b. Variation of Mean H over Time
Hours
Hours
s
Figure 4: Average Mean Stress recorded over Time It can be seen that for the Stress Amplitude the results for both devices coincide well with the load cell output for the whole duration of the test. When looking at the results for the mean stress it was
930
observed that there was some drift in the data recorded from the Material Stress Memory Unit. However in actual terms the equivalent stress variation equated to +1-2 MPa which is negligible. Another basis for comparison was the actual data distributions for the mean stress and stress amplitude ranges for the two devices. Figures 5a and 5b show samples of the data distributions for one of the tests where applied Mean Stress and Stress Ranges were 60 MPa and 120 MPa respectively. a. Distribution of Data for Mean Stress Recorded by ACSM
O
b. Distribution of Data for Mean Stress Recorded by MSM
6000
6000
4000
4000
2000
2000
0
0
3
0 20 40 60 80 100
0 20 40 60 80 100 P4
Mean Stress (MPa)
Mean Stress (MPa)
Figure 5: Sample Data Distributions for Recorded Mean Stress For the measurement of Mean Stress can be seen that the greatest proportion of the data correlates well the load cell output of 60 MPa. Results obtained for measurements of the stress range were on the whole also found to be accurate. However a small number of outlying points were observed in the data recorded by the Material Stress Memory Unit. In order to enable more accurate comparisons to be made between data sets of the different experimental tests, changes in the Standard Deviation as a percentage of the Mean Stress (H) and Cycle Amplitudes (AMP) were observed. The results obtained are summarised in figures 6a and 6b below.
Variation of % Standard Deviation from Mean for Cycle Stress 30 Stress Range
25
m 40 MPa
20 15 10
Load Cell
ACSM
0 H^J?='sC?"^^
60 80 110
MSM
Jlltin—unJl a .
60 80 110 60 80 110 Mean Stress (MPa)
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B 60 MPa D 120 MPa
Variation of % Standard Deviation from Mean for Cycle Stress Range Stress Range D 40 MPa • 60 MPa o 120 MPa
60
80
110
60 80 110
60 80 110
Mean Stress (MPa)
Figure 6: Variation of Standard Deviations from Mean values of cycle stress range and cycle mean stress. Overall both monitoring devices were found to accurately measure the cyclic stress experienced by the test specimen. A general observation for both these applications was that their performance did seem to be dependent on the applied cyclic stress range, improving at larger stress ranges. The only sets of results that could be considered unsatisfactory was the data collected using ACSM at the lowest monitored mean stress and cyclic stress range, both at 40 MPa. Closer inspection of the data distributions for these particular tests provides an explanation for this. From these it was observed that the cause of the high estimates for the standard deviations were caused by two small clusters of data located at either side of an otherwise very concentrated distribution. The likely cause of the problem is a low sensitivity of the ACSM sensor making it difficult to accurately take measurements at lower stress ranges [8]. This would increase the effect that any background noise may have on part of the data, which may be represented by the two clusters of data on either side of the mean. These observations did highlight the point that the standard deviation did not always provide an accurate measure of the overall accuracy of the applications and should therefore cannot be used as a stand alone measure of the accuracy of a structural integrity monitoring device. The results obtained using the Material Stress Memory Unit were found to be comparable, if not more accurate, than those obtained using the ACSM probe. At this early stage of development of both these structural monitoring techniques the results are very promising, especially bearing in mind the ACSM system was not designed for dynamic stress monitoring but for static stress measurements only. The simplicity of application for both these applications and their accuracy compared to other structural monitoring techniques in existence potentially make these very attractive tools for this type of inspection.
CONCLUSION New state of the art Structural Integrity Monitoring applications yield a number of important benefits to the various parties involved in structural engineering in terms of reduction of costs, increased safety and in the possibility of closing the design loops. However as more SIM systems become available it is becoming increasingly important to develop a procedure for assessing these against each other in terms of suitability for particular applications. Much of this work will entail devising ways of analysing and representing the vast amounts of data recorded by SIM systems. It was shown that care must be taken 932
when using simple statistical parameters such as standard deviations and means as measures of performance, and that these should always be used in conjunction with other measures. From the experimental investigations it can be concluded that the two monitoring systems investigated showed great potential as stress monitoring devices. This is bearing in mind that both these systems are in their design stages and one of them, ACSM, was not originally design for dynamic stress measurement. Also the simplicity of their practical application compared to other structural monitoring techniques make them attractive tools for this type of inspection. REFERENCES [1] [2] [3]
[4] [5] [6] [7] [8]
Chang F. (1997). Structural Health Monitoring: A Summary Report on the First Stanford Workshop on Structural Health Monitoring, Stanford University. Moss R.M. and Matthews S.L. (1995). The Structural Engineer. In-service structural monitoring: A state-of-the-art review 73:13,214-217. Dover W.D., Brennan F.P. and Etube L.S. (2000) Proceedings of the Fifth International Conference on Engineering Structural Integrity Assessment. Structural Integrity Monitoring using Alternating Current Field Measurements. 307-316 Health & Safety Executive. (1998). Progress in Structural Monitoring. Offshore Technology Report-OTO 98 046 Chen K., Brennan F.P. and Dover W.D. (2000). NDT&E International. Thin-skin AC field in anisotropic rectangular bar andACPD stress measurement 33, 317 - 323 TSC Inspection Systems Ltd, 6 Mill Square, Featherstone Road, Wolverton Mill, Milton Keynes MK12 5RB De Leeuw B. (2001). MSc Thesis. Assessment of a Structural Integrity Monitoring Device. Dept. of Mechanical Engineering, University College London Dover W.D., Brennan F.P. and De Leeuw B. (2001). Proceedings of OMAE 2001: 20th International Conference on Offshore Mechanics and Arctic Engineering. ACSMStressprobe: a new non-contacting stress measurement technique for the offshore industry
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Condition Monitoring and Diagnostic Engineering Management Andrew G. Starr and Raj B.K.N. Rao (Editors) © 2001 Published by Elsevier Science Ltd.
EXPERIMENTAL VALIDATION OF THE CONSTANT LEVEL METHOD FOR IDENTIFICATION OF NONLINEAR MULTI DEGREE OF FREEDOM SYSTEMS G. Dimitriadis School of Engineering, The University of Manchester Oxford Road, Manchester Ml3 9PL
Abstract System identification methods for nonlinear dynamical systems could find uses in many applications such as condition monitoring, finite element model validation and stability determination. The effectiveness of existing nonlinear system identification techniques is limited by various factors such as the complexity of the system under investigation and the type of nonlinearities present. In this work, the Constant Level Identification approach, which can identify multi degree-of-freedom systems featuring any type of nonlinear function, including discontinuous functions, is validated experimentally. The method is shown to yield accurate identifications of an experimental dynamical system featuring two types of stiffness nonlinearity. The full equations of motion are accurately extracted, even in the presence of a discontinuous nonlinearity. Keywords Nonlinear systems, system identification, multi degree-of-freedom systems, bilinear stiffness, cubic stiffness. Condition Monitoring. Introduction There already exist methods like the NARMAX model (Billings & Tsang 1989), higher order spectra (Simon & Tomlinson 1984) and the restoring force method (Crawley & Aubert 1986) which can identify aeroelastic systems given the inputs and outputs. However, these methods have still not reached the level of maturity necessary to allow their application to general aeroelastic systems. Both NARMAX and the higher order spectra method are incapable of identifying systems with discontinuous nonlinearities, such as bilinear stiffness or freeplay, which are common in aeroelastic systems. The restoring force method does not share this limitation, but its application to multi degree of freedom systems is still problematic.
935
A further consideration that must be made is whether the identification process is parametric. The analysis of an identified system is much simpler when the terms in the model resulting fi*om the identification process are parametric, i.e. model explicitly the non-linearities present in the system. However, both NARMAX and the restoring force method yield better results when using non-parametric as well as parametric terms. Hence the resulting model contains terms without any physical meaning. In Dimitriadis & Cooper (1998) the Constant Level Identification (CLI) approach for the identification of nonlinear dynamical systems was presented. The method is a development of the Restoring Force technique, adapted to identify any nonlinear function(s) present in one of the system states without the need for curve fitting. The method is flexible enough to be able to deal with a large class of nonlinear systems and functions. For example, in Cooper & Dimitriadis (1997), a demonstration of the use of the CLI approach in conjunction with gust load prediction methods for aeroelastic systems can be foimd. Until now, the method had only been demonstrated on simulated systems. In this paper, CLI is employed to identify two experimental systems with two different nonlinear functions, namely cubic and freeplay stiffness. First, the mathematical basis of the method will be briefly explained and, subsequently, the experimental results will be presented. Mathematical basis of CLI Dynamical systems are usually described by the general equation Mq + Cq + Kq = F(0
(1)
where M, C and K are the mass, damping and stiffhess matrices respectively, q is the displacement vector and F is the excitation force vector. With the CLI method, use is made of the fact that, if the nonlinear function depends on only one of the state variables then, at an arbitrary response level, the restoring force due to the non-linearity is constant. The approach estimates the exact equation of motion of the system by curve-fitting the response at this chosen response level. The first crucial aspect of the CLI method is to multiply the equations of motion throughout by the inverse of the mass matrix, so that the mass matrix is no longer a quantity that needs to be identified. The Restoring Force equation becomes q + M-'f(x) = M-'F
(2)
where x is the state vector, given by x = [q q]^ and now M'^F must also be treated as an unknown. The crucial assumption behind the CLI method is that the restoring force function f(x) has linear components and that the nonlinear components depend on only one of the state variables, say the ith component, xt. Then, f(x) can be re-written as f(x) = L[x,
•• x,_,
x,^,
936
•••
x^J+g(x,)
where m is the number of modes (or degrees of freedom) of the system, L is a constant matrix coefficient of size 2mx(2m-l) and g(x/) is a 2mx\ vector of linear and/or nonlinear functions which depend only on Xi. Then, equation 2 becomes ^2j'+M-^g(x,) = M-^F
q + M *L[XI
(3)
Figure 1: Picture of experimental setup keeping in mind that this equation, and hence the CLI method, only applies to systems where the nonlinearity really is a function of only one state variable. Finally, M"^F(0 is written as M"* Aw(0 where A is a 2mxl vector of constant amplitudes, w(0 is the 2/wxl vector of the measured inputs to the system and F(0=Aw(/). Hence, the governing equation of the CLI approach is obtained as q + M-'L[x,
••• x._, X,,, ••• X2„,f+M-'g(x,) = M-'Aw(0
(4)
Given measurements of q, q, q and w at times tj where x, is a constant, equation 4 can be expanded in order to solve for the unknown constants L = M"*L, N=M'^g(A:/) and A = M"'A. Expanding for all times tj, equation 4 becomes
^x,a,) • x,{t^)
•
^M(A)
^/+I('I)
•
• ^2»(',)
Mti)
• •
*,-l('2)
^/.iCl)
•
• *2»('2) Mh)
i"!
[ 4,, '
1 1 ^k,lm-\
.^,(^,) •
•
X,-l('«l)
^/+l(f|)
•
• *2»,(^») M'n)
937
ij
4
.=.
•^*(',)1
M-\
(5)
for A=l,... ,/w and where n is the total number of time instances /,. Equation 5 can be solved to obtain every line of L , N and A separately. Hence, each of the m equations of motion is also identified separately. Equation 5 also demonstrates an additional advantage of multiplying throughout by the inverse of the mass matrix, namely that the number of unknowns is reduced, speeding up the computation and also improving the accuracy of the fit. At the time instances tj where Xt is a constant, N is also a constant. However, at a general time instance, /, N=N(r). The variation of N with time can be obtained once L , N and A have been evaluated by rewriting equation 4 as N(0 = - q - L h
••• x._, X,,,
••. X2„f+Aw(/)
(6)
The end result of the CLI procedure is the complete identification of the equations of motion of a given system in the form q + L[x,
... x,_, X,,,
... X 2 , J + N ( 0 = Aw(0
(7)
Define C = M"'C and K = M'^K. Then L = [C K] but where either C or K is missing a column. The missing linear information is imbedded in N(r) together with the nonlinear information. Plotting out N(/) against Xi{t) will yield the shape of the nonlinearity added to the linear term missing fi-om L . An important consideration regarding the proposed method concerns the effect of performing the identification procedure at various levels and not just the one, i.e. applying equation 5 at times tj where ^/=ci, C2,..., where ci, C2 etc are various constant values. In the case were a significant amount of noise is present in the response data, it is suggested that using a large number of levels would have a beneficial effect, since it would average out the noise contribution. Experimental Validation The CLI method is here demonstrated on a l-Degree-of-fi-eedom (DOF) mass-spring dynamical system. A photograph of the experimental set-up is shown in figure 1. The rig consisted of two masses independently supported by a couple of cantilever steel plates. The two masses were attached to each other by a coupling spring. Each mass was independently excited by means of a shaker driven by a signal generator, the excitation signal being measured by means of a force gauge. One accelerometer on each mass measured the acceleration. The excitation signals used for the experiment were random with a flat spectrum between lOHz and 30Hz. Two different configurations were tested: cubic spring attached to mass 2 and freeplay spring attached to mass 2. The latter case lead to a bilinear stif&iess since the mass remained attached to the linear cantilever plates The cubic stiffiiess was implemented by means of a steel ruler and the fi-eeplay by means of a steel ring moving between two pegs Identification of system with cubic stiffness A cubic spring was attached to mass 2 in the form of a steel ruler under transverse loading. The CLI method was applied to randomly forced response data fi"om the experimental rig. Figure 2 shows the identified Niiyi) term variation plotted against the displacement of mass 2. Ni(y2) is still linear but figure 2 shows a slightly cubic variation for A^2(y2)- The identified matrices were:
938
c=
5.5302
-0.4777
-0.4023
4.1891
60
1
K = 10'
2.2825
0
1.5603
-0.3093
0
2.7629
1
1
••}"•
'
1
40 y
•
20 h
-20 h
X
'•
\
-40 h
-60 -2
i
-1.5
-1
i
-0.5
i
i
0
0.5
1
1.5
Figure 2: ^liyi) term for system with cubic stiffness
2 xlO^
30 O
1.6
1.8
2
O
[dentificatiqh Experiment
2.2
2.4
time (s)
Figure 3: Comparison of experimental and identified responses for system with cubic stiffness As in the cubic case, the equations of motion were completed by curve-fitting the variation of N v^th yi. Since, 'Nxiyi) is linear it was curve-fitted by a 1st degree polynomial. The resulting polynomial was M(V2)=-0.3081xl0V2 -0.0184. Note that the first order coefficient, -0.3081xlO^ is approximately equal
939
to the K(2,l) term, thus satisfying reciprocity. Niiyi) was estimated by curve-fitting figure 2 by a cubic polynomial: A^2(y2)=1.3039xlO^>^^ + 5.7876xl0V2^ + 2.2553xl0V0-1067
(8)
The terms in equation 8 are of order: y2^: 0(1), y2^: 0(10'*), y2^: 0(10), y2^: 0(10"^). Hence, the 0th and 2nd order terms can be neglected and, since the 1st order term is due to the linear cantilever springs, the cubic stiffness caused by the steel ruler is given by Arcubic=l-3xl0V2^- The equations of motion can be written as (after substituting for term A^, 2 the slope of equation 11 and for ^2.2 ^^^ 1st order coefficient of equation 12).
"1 0]J>^'1 i
5.5302
r + -0.4023
[0 ijUJ
-0.4777 4.1891
I;;}
+ 10'
2.2825
-0.3081
-0.3093
2.2555
H-
0
1.5603H',(/)
30x10^2
2.7529^2(0 (9) '
This equation w^as verified by calculating its response to the same excitation signals that were used on the experimental system, see figure 3 which compares the identified and actual accelerations. Identification of system with bilinear stiffness A freeplay spring was attached to mass 2 in the form of a steel ring moving between two restraining pegs at a distance S apart. Mass 2 was still supported on the cantilever plates hence the resulting spring was bilinear. The CLI method was applied to randomly forced response data from the experimental rig. The distance between the two pegs was set to ^ 1.05mm. Figure 4 shows the identified nonlinear term, N2(y2), variation, plotted against the displacement of mass 2. The identified matrices were
0
0.5
1
1.5
h xlO"' Figure 4: A^2(y2) term for system with freeplay stiffiiess, ^ 1.05mm
940
2.7471
-0.6596"
-0.6093
3.8485
K = 10'
" 1.1006
0"
-0.2795
0
A=
0.3486 0.3303
The bilinear stiffness only affects N(y2)- The plot of ^(^2) against jV2 was linear, as in the cubic stiffness example. Its slope was K^^ =-0.3121x10^^. The region between the two dashed lines in figure 4 will be called the inner region, the other two regions being called the left and right outer regions. The width of the inner region is 1.05mm, i.e. equal to <5but the region is not centred around zero. The inner stiffness, i.e. the stiffness between the two dotted lines, K\^, is essentially the linear stiffness, since it is due to the linear cantilever plates supporting mass 2. The outer stiffiiesses are between 26% and 41% higher than the inner stiffness. The three stiffness regions were obtained as follows: I
• • •
i^out,ieft=l.9994x10^ for3;2<-5.0xlO-^ A:in=1.5285xlO^ for -5,0xl0"^5.5xlO-^
Hence the nonlinear function is given by 1.9994x10^2+2.4574 if ;;2 <-5-0x10"'* ^2(>'2) =
(10)
1.5285x10^2 if -5.0x10"' 5 . 5 x l 0 - '
With equation 10, a complete set of identified equations of motion is obtained, after substituting the slope of M 0^2) for the value of K^^. ~\ 0" \yA
2.7471
{ K -0.6093 0 1 \h\
-0.6596 3.8485
+ 10'
1.1006
-0.3121
-0.2795
0
0
0.3486w,(0 0.3303M;2(0
(11)' In equation 11 the value Xj 2 is quoted as zero. It is also possible to substitute K\n for ^^2 2, in which case Niiyi) would have to be changed accordingly. The system described by equation 11 is very close to the actual experimental system, as can be seen in figure 5 where the acceleration responses of the real and identified systems to the same forcing signal are plotted against time. The agreement between the two responses is very good. Conclusions In this paper, the Constant Level method for the identification of nonlinear systems was evaluated on an experimental dynamical systems with two types of nonlinearity, cubic and bilinear stiffness. The CLI method is a simple identification method that can identify both the equations of motion and the nonlinear functions of systems with nonlinear terms that depend on only one of the state variables. The approach was found to identify successfully the experimental system given forced response acceleration data. The cases that were identified included a case with cubic stifftiess and a case with bilinear stifj&iess. In both cases the identified equations of motion were numerically integrated and the resulting responses were compared to the measured responses, yielding very good agreement. It was concluded that the CLI
941
technique is a very effective method for identifying both continuous and discontinuous nonlinearities. Further v^ork should concern the experimental validation of the method on more complex systems with many degrees of freedom.
-O
0.8
0.9
1
1.1
1,2 A.3 lime (s)
1.4
1.5
Identificatiqh ExpenmenT
1.6
1.7
Figure 5: Comparison of experimental and identified response for bilinear system, ^ 1.05mm References Billings, S.A. and Tsang, K.M. (1989). Spectral analysis for nonlinear systems, part I: Parametric nonlinear spectral analysis. Mechanical Systems and Signal Processing 3:4, 319-339. Cooper, J.E. and Dimitriadis, G. (1997). Prediction of maximum loads due to turbulent gusts using nonlinear system identification. In Proceedings of the CEAS International Forum on Aeroelasticity and Structural Dynamics, Volume II, pp. 71-78, Rome, Italy. Crawley, E.F. and Aubert, A.C. (1986). Identification of nonlinear structural elements by force-state mapping. AIAA Journal 24:1, 155-162. Dimitriadis, G. and Cooper, J.E. (1998). A method for identification of non-linear multi-degree-offreedom systems. Proceedings Journal of the Institute of Mechanical Engineers, Part G 212, 287-298. Simon, M. and Tomlinson, G.R. (1984). Use of the Hilbert transform in modal analysis of linear and non-linear structures. Journal of Sound and Vibration 96:4, 421-436.
942
Condition Monitoring and Diagnostic Engineering Management Andrew G. Starr and Raj B.K.N. Rao (Editors) © 2001 Elsevier Science Ltd. All rights reserved.
A COMPARATIVE FIELD STUDY OF FIBRE BRAGG GRATING STRAIN SENSORS AND RESISTIVE FOIL GAUGES FOR STRUCTURAL INTEGRITY MONITORING Y M Gebremichael, B T Meggitt^, W J 0 Boyle, W Li, K T V Grattan, B McKinley*, L Boswell*, K A Aames** S E Aasen""^ & L Kvenild^^ P Y Fonjallaz^^' City University, Northampton Square, London EC IV OHB, UK Dept. of Electrical &*Dept of Civil Engineering ^EM Technology, London UK **Norwegian Public Roads Authority, Bridge Department, Pb. 8142 DEP, 0033, Oslo "'^Oceanographic Company of Norway, Pir-SenteretN-7005 Trondheim, Norway "^""^ACREO AB, Stockholm, Sweden
ABSTRACT A spatially multiplexed WDM based fibre Bragg grating sensor system has been developed as part of a condition maintenance and structural integrity monitoring programme of large civil engineering structures. The system has been field deployed for real-time strain monitoring on a new 346m long steel road bridge in Norway. Two sections of the bridge were instrumented with both fibre Bragg grating sensors and conventional resistive strain gauges. In this paper laboratory as well as field resuhs are reported and comparisons are made between the performance of fibre and conventional gauges.
KEYWORDS WDM, Multiplexing, Fibre Bragg grating, strain gauge, strain, structural integrity
INTRODUCTION Ageing and environmental degradation of large civil engineering structures such as bridges is a major concern facing structural engineers in terms of safety and maintenance costs. Current structural health monitoring methods are largely based on visual inspection and use of discrete strain sensor elements. There is a major requirement in civil engineering applications for multiplexed sensor systems with an on-line, real-time structural condition monitoring
943
capability, enabling lifetime-assessment and maintenance scheduling to be determined with minimal disruption to the use of the structure. Fibre Bragg grating (FBG) sensors [1][2] are leading candidates for such applications in civil engineering, marine and aerospace industries [3] [4] due to their stability for long-term measurement, quasi-distributed sensor configuration and high multiplexing capabilities for strain field mapping across large structures. In contrast to electrical resistive strain gauges, the use of silica dielectric optical fibre sensors means that in addition to their immunity to EM interference, they show both good corrosion and mechanical-fatigue resistant properties leading to improved sensor operational lifetimes in both embedded (concrete) and surface attached (steel) applications. Further, use of wavelength-encoded sensors makes the system independent of fibre intensity losses, and hence the remote sensing over extended distances (km's), and permits a high multiplexing capability to be effected with a greatly reduced cabling requirement, cutting installation time and cost. In order that FBG strain monitoring systems can be accepted as a measurement standard in general field applications, it is important to demonstrate that their performance in both laboratory and field tests are comparable, or better, than the conventional resistive foil gauge systems. In this regard, we have recently carried out such a comparative study, under both static and dynamic loading conditions, between fibre grating and resistive foil gauges when surface attached on both laboratory engineering structures and a new steel box road bridge structure (Mjosundbrua), in Norway. In this paper we present the results from field trials on the road bridge with x32 fibre sensor network employing both single-axis and 3-axis strain rosettes and also resistive foil gauges.
BRIDGE INSTRUMENTATION AND SENSOR LAYOUT A dual system of optical sensor and resistive strain gauge sensor was used to monitor two sections of the field trials road bridge, one near the support, S-section and another at the mid span, M-section as shown in figure 1. The sensor placement was such that for each resistive gauge in place, there is a corresponding Bragg sensor, thus measurements from both systems were compared directly. Thirty FBG sensors and eight resistive gauges were used to monitor the bridge. The opfimum location of the sensors on the bridge was selected based on results from finite element analysis of the structure. The sensors were attached to the steel structure S-Section
M-Section
S)
UJ
4
- ^ SOm
• ^ )
M
SOm
Figure 1: Schematic diagram of the field trial road bridge
944
i^)
directly as single sensors and rosette configurations. Thermocouples were also placed at four points near the sensors for temperature measurement and in addition some of the gratings were arranged so that they respond to thermal effects isolated from mechanical loading strain for long-term thermally compensated strain measurement.
FIBRE BRAGG GRATING SENSOR SYSTEM One of the main advantages of fibre Bragg grating sensor system in applications such as monitoring large structures is the ability to use the fibre both as a sensor and signal transmission medium. This simplifies the instrumentation, minimising the required cabling drastically for multi-point sensing applications such as in large structures and alleviating the need for shielded cables, as the fibre is intrinsically immune to EM interference. FBG sensors are integral in the fibre structure and sensing applications arise due to the selective wavelength filtering effect of the section of the fibre containing the sensor grating. When broadband radiation is launched into a fibre connected to Bragg grating, a narrow band of the source spectrum is back-reflected. The centre wavelength of the reflected light, Xs, is given by 2neA, where A, is the grating pitch length and Ug the effective refractive index of the core. The strain and temperature sensitivity, and thus the sensor applications, arise from the wavelength shift (change in both the pitch length of the grating and the change in the refractive index of the core) induced by the change in the measurand/s. The wavelength shift, AXB, is influenced simultaneously by changes in temperature, T, and strain, 8, modifying the Bragg parameters as: AAs=As{a + <^)AT + A,(\-p,)As
[1]
where A,B is the nominal wavelength of the grating, ^ is thermo-optic coefficient of the fibre, a is thermal expansion of the fibre and pe is effective photo-elastic constant. From the above relation it can be seen that the measured wavelength shift is a combined effect of both strain and temperature effect. Any meaningful strain measurement must thus be corrected for fibre thermal effects. A number of temperature corrected strain measurement techniques have been reported [5]. In this work, a strain-isolated sensor is used for thermal compensation [6] [7]. The FBG system utilises a WDM interrogation technique [8] [9] to de-multiplex the return signals from the array of gratings. The instrumentation is capable of interrogating four sensors per channel for high strain measurements (>±2,000jis), although up to eight sensors can be configured along each fibre reducing the maximum strain measured to about >±l,000|.is. The latter strain value is usually greater than a large structure such as a bridge is subjected to over its normal service life. The strain resolution of the system at full bandwidth (>10ms) is about ±5)18.
CONVENTIONAL FOIL GAUGE SYSTEM The conventional foil gauge measuring system was designed to accommodate a maximum of 12 resistance strain and temperature sensors for the bridge trials configured as 5 single-axis measurement points and 1 rosette. Thermocouples were also included for temperature measurement at four different locations on the two sections of the bridge. All the strain
945
gauges were connected in a full bridge arrangement. The system was specified to measure strain in the range of Ijie to 1000^8 to about Ijie resolution.
LABORATORY TESTS Prior to field deployment, a series of static and dynamic test was carried out in the lab throughout the design and development stage of the instrument to evaluate the performance of the system. Time-varying strain measurements are of interest in large structures such as the field-trial bridge, which was estimated to have a natural frequency of ~5Hz. To this end the dynamic performance of the system was analysed. In the dynamic train testing, a fibre with a single Brigg sensor was bonded to a Im long steel cantilever beam. A resistive strain gauge was also attached to the steel very close to the FBG sensor. The beam was fastened to a mechanical clamp at one end while the other end was free for loading or vibration. Figure 2a and figure 2b below show results obtained with both FBG and resistive strain gauges when the cantilever beam was forced into a lOHz damped vibration. During these tests the optical system was set to a bandwidth of 140Hz. Although the strain gauge readings were not calibrated strain values at this time, it is clearly seen that the two sets of readings follow closely. Static loading tests were also carried out on a similar cantilever beam, in this test
\i
0.5
-
A A
J\
\
""
0.0
hI
S" o -05 o>
1
Iy Vy y y Time (s)
Time (s)
Figure 2b: Dynamic strain readings, foil gauge
Figure 2a: Dynamic strain readings, FBG sensor
- '
-lOO.Op
at
I
-ISO*
I 50
100
150
Time (arbitrary units)
Time(s)
Figure 3a: Stepped loading and unloading strain. FBG sensor
946
Figure 3b: Stepped loading and unloading strain. Resistive gauge
however, the resistive gauges v^ere calibrated to read strain and the results form a Bragg sensor and a corresponding resistive gauge when loading the beam incrementally are shown in the figure 3a and figure 3b below. Results such as these were used in sensor calibration and evaluation of sensor attachment technique for strain transfer efficiency. Various bonding substances and techniques were tested and a successful technique of bonding was developed in the laboratory with a constant and near 100% strain transfer.
BRIDGE TESTING Two sets of field trials were planned in the course of the project. Initially a small scaleloading tist was performed at the time of the installation of the two sensor systems with a further major field trials planned for a later period. In this paper results from the preliminary loading tests only are presented. A SOT truck was used during these preliminary tests. Three static loading positions were identified and each loading condition was repeated three times for data comparison and measurement repeatability verification. Dynamic tests were also carried out with the truck driven at 30Km/hr over the length of the bridge. Although data was collected for all the test conditions and various loading positions, a sample of data set is presented here, as a representative of all the tests carried out. During all the tests, the optical fiber sensor system and the conventional resistive gages were run simultaneously to compare results.
STATIC LOADING The axle of the truck was positioned on the sensor points at the M-section (50m away from each support). The bridge was unloaded between each test to obtain a zero reference and load driven to the test position then stopped there for about ten seconds while measurements were taken as shown in figures 4a and 4b.
-20.0
0.0
20.0
40.0
60.0
80.0
100.0
120.0
140.0
160.0
180.0
Time (s)
Time (s)
Figure 4a: Static loading strain, FBG sensor
947
Figure 4b: Static loading strain, foil gauge
DYNAMIC LOADING For dynamic tests, continuous measurements were taken while the truck was being driven from off-the-bridge on to-the-bridge and off again at 30Km/hr. Figure 5a and Figure 5b show results from such a test. The strain was seen to peak when the truck reached the sensing points (in this case at the M section sensors). The compressive and tensile strains recorded depict the behaviour of the bridge as it was being loaded over various points on and between the six supports.
«
40.0
2 CO
20.0
1 *°|c
0.0 |-
-20.0
-20.0
0.0
20.0
40.0
60.0
80.0
100.0
120.0
20
Time (s)
40
60
100 r
f
-
80 [
• O
40 \
T
• 0
9
•
FBG1 Strain FBG2 Strain Resistive Strain
•
f
-
100
Figure 5b: Dynamic strain, Resistive gauge
Figure 5a: Dynamic strain, FBG sensor
60
80
Time (s)
* • 1
• 1
4
6
Sensor position
Figure 6: Strain measured at various points across the bridge for one loading condition - a comparison offibreand foil gauge results
948
The results shown in figure 6 represent measurement data taken from 11 of the 32 FBG sensors at various points on the bridge and the corresponding readings taken with the 8 resistive strain gauges during one of the static loading conditions when the SOT truck is parked at the mid-span section (sensor positions 6,7 & 8 on the graph). While higher strain readings were recorded at these points, near the support section (sensor positions I to 5) a small strain was recorded as expected, some of these points were under compressive strain for this particular loading position. Figures 7-9 represent measurement data taken with three axis rosettes from both the FBG system and the conventional resistive strain gauge system at the S section of the bridge for one of the various loading positions tested. The angular orientation of the sensors making up a rosette is'such that 0° represents a sensor aligned along the length of the bridge (maximum strain axis). The data is presented here for comparison purposes only. Note that the data from the FBG system is shown at full bandwidth while the data from resistive gauge system is time averaged.
DISCUSSION For each loading test, both fibre and resistive gauge measurements were taken. The results show a good agreement between the resistive gauges and Bragg grating sensors for both static and dynamic tests. On average measurement results from the Bragg grating sensor system showed a standard deviation of less than 1 p-s, while the resistive gauges showed a maximum standard deviation of 1.8 |LI£. Although provision was made for temperature compensation with a Bragg sensor attached to a strain isolated separate steel as well as conventional thermocouples for measuring local temperatures, no attempt is made here to use such data for compensation as the tests carried out over a short time period (120 seconds maximum), which is too short time for any thermal fluctuations to occur on such a large structure. For long term structural health monitoring however, temperature compensation becomes a concern and as far as this work is concerned, such technique is in place and is due to be implemented in future longer term testing. This work has demonstrated the advances and practicality of using fibre Bragg grating sensors in the instrumentation and monitoring of large civil engineering field structures. It has also shown that when operating in field conditions, FBG sensors can be surface bonded to structures with as much ease as conventional strain gauges elements and thus the superior performance of FBG sensors over foil gauges and in particular multi point sensing for site management can be realised making the FBG system cost effective. ACKNOWLEDGEMENTS This work is part of the EU sponsored BRITE/EURam 'Millennium' Project on the 'Monitoring of Large Civil Engineering Structures for Improved Maintenance'.
949
RESISTIVE GAUGE DATA
BRAGG GRATING DATA
^f0^Wi^ "^ 100
1
w^ -40.0 -20.0
0.0
20.0
40.0
60.0
80.0 100.0 120.0 140.0 160.0 180.0 200.0
Figure 7a: Bragg sensor, oriented at 0°
0.0
20.0
40.0
60.0
80.0
100.0
120.0
140.0
160.0
180.0
-S.0 .10.0
L_ 20
25
10
15
20
25
Time (s)
Figure 8a: Bragg sensor, oriented at 45°
2
I
Figure. 7b: Strain gauge oriented at 0°
Time (s)
f
_l 15
10
Time (s)
Time (s)
-20.0
-5
Figure 8b: Strain gauge oriented at 45°
*IKV\|fliW
-15.0 Y
JAMflMl
-20.0 k -25 0 l -30.0 -20.0
0.0
200
400
60.0
80.0
100.0
120.0
140.0
160.0
180.0
Time (s)
20
Time(s)
Figure 9a: Bragg sensor, oriented at 90°
Figure 9b: Strain gauge oriented at 90°
950
25
REFERENCES [1] Kersey A D, Davis M I, Patrick H J, LeBlanc M, Koo K P, Askins C G, Putman M A and Friebele E J. (1997). Fiber Grating Sensors. J. Lightwave Technol. 15 No. 8 1442 -1463. [2] Grattan K T V & Meggitt B T (Eds). (2000), Optical Fiber Sensor Technology, Advanced Applications, Kluwer Academin Publishers. Dordrecht, The Netherlands 79 - 187. [3] Ferdinand P, Ferragu O, Lechien J L, Lescop B, Marty V, Rougeault V S, Pierre G, Renouf C, Jarret B, Kotrotsios G, Neuman V, Depeursings Y, Michel J B, Uffelen M V, Verbandt Y, Voet M R H and Toscano D. (1994). Mine operating accurate stability control with optical fibre sensing and Bragg grating technology. BRITE-EURam STABILOS project Proc.SPIE 2360 162-6. [4] Uttanlchandani D. (1994). Fibre optic sensors and smart structures: Developments and prospects. Electronics and communications journal, 237-246. [5] Liu, T Fernando G F, Zhang L, Bennion I, Rao Y J and Jackson D A. (1997). Simultaneous strain and temperature measurement using a combined fibre grating/extrinsic Fabry-Perot sensor. 12th International conference on optical fibre sensors (OFS). 40-43. [6] Gebremichael Y M, Meggitt B T, Boyle, W J 0 Li W, Grattan K T V, McKinley B, Boswell L. (2001). Practical temperature compensated Bragg grating strain sensor system in smart bridge application: long term structural integrity monitoring. To be published. [7] Haran F M Rew J K & Foote P D. (1998). A strain isolated-Fibre Bragg Grating sensor for temperature compensation of fibre Bragg grating strain sensors. Meas. Sci. Technol. 9 1163-1166. [8] Ning Y N, Meldrum A, Shi W J, Meggitt B T, Palmer A W, Grattan K T V & Li L. (1998). Bragg grating sensing instrument using a tuneable Fabry-Perot filter to detect wavelength variations. Meas. Sci. Technol. 9 599 - 606. [9] Kersey A D, Berkoff T A and Morey W W. (1993). Multiplexed fiber Bragg grating strain-sensor system with fiber Fabry-Perot wavelength filter.Optics Letters 18, No. 16 13701372
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Condition Monitoring and Diagnostic Engineering Management Andrew G. Starr and Raj B.K.N. Rao (Editors) © 2001 Published by Elsevier Science Ltd.
THE APPLICATION OF OIL DEBRIS MONITORING AND VIBRATION ANALYSIS TO MONITOR WEAR IN SPUR GEARS J.A. Barnes, A.G. StanMaintenance Engineering Research Group. Manchester School of Engineering, Oxford Road, Manchester, Ml3 9PL, UK
ABSTRACT The implementation of a reliable data fusion or intelligent system for the analysis of gearbox vibration and oil debris data has the potential to significantly reduce maintenance costs and to improve fault detection capabilities. An example of this is in the case of faults such as the early stages of gear scuffing where the fault can seem less severe in terms of vibration than it really is. Combining the two technologies would mean an increased perspective of the condition of a gearbox and so enhance the information available for fault diagnosis. It is important to consider that inaccurate sensors will decrease the accuracy of any fusion system. The purpose of this stage of the work is to set up a test rig and monitor the failure of a set of overloaded unhardened spur gears. Once the testing procedures and measured parameters have been refined, this information will be used to investigate the application of a data fusion system to a gearbox. Vibration data, analysis from a portable oil diagnostic system, particle counts from an on-line induction particle counting unit, and microscope analysis data are monitored during the test. These parameters are to be used to chart the wear of the gears through to failure. This paper provides a review of the oil debris monitoring systems and techniques used in the work to date. KEYWORDS Wear Debris Analysis, Tribology, Gears. INTRODUCTION The integration of oil debris analysis and vibration analysis for the detection of gear faults is not a new idea, and has the potential to significantly reduce maintenance costs. A national survey conducted in 1997 put the cost of wear alone on UK industry at around £650 milUon per year (Neale and Gee 2000). If the cost of unnecessary failures were to be added to this then the total would be significantly higher. Work into the data fusion of several data sources would mean earlier fault detection as different technologies can pick up different faults at different stages. This is an advantage of fusing data sources in a fusion architecture or intelligent system as damage detection capabilities can be improved compared with those of a single sensor. Another advantage of this is that quantitative and qualitative 953
information can be combined (Starr et al 2000) so that observational information can be incorporated into the system. In order to put an effective data fusion system into practice for the reliable detection of gearbox faults and minimisation of downtime, this work centres on developing an understanding of how the parameters to be fused relate to each other during the wear process. A gearbox vibrationtesting rig has been modified to accommodate the inclusion of oil debris monitoring. Oil debris monitoring of gearboxes is generally done offline, as samples are taken and analysed in a separate laboratory. Some of the parameters and methods used for testing the condition of oil are listed below: Solid Particle Counting. Viscosity. Moisture. Acid Number. I Colour. Spectroscopy. Ferrography.
TEST MG AND APPARATUS The test rig is designed in a back-to-back arrangement and can accommodate the testing of industrial helical and spur gears. In this case, a pair of unhardened spur gears of facewidth 30mm are being tested. The shaft mountings for the gears are purposefully axially misaligned by 1.5mm and the gears are overloaded by means of a torque plate to 58.86Nm. The pinion and wheel have 30 and 45 teeth respectively and the pinion is driven at 1200rpm. In this work, it is only ferrous material that is of concern, so techniques such as spectroscopy, acid number, and moisture content are of little use for the purposes of the experimentation. The rig has been set up to with equipment to measure several parameters. These are: Two magnetic plug chip collectors positioned in the direct flow of the oil. Samples from these are taken each hour the rig operates. A 6 channel Vickers Industrial QDM® Oil Debris Monitoring System Series 3P3194 which counts particles by induction, retaining them for further analysis. The system records debris particles over 500 microns by induction. An ART Instruments Portable Oil Diagnostic System (PODS). The unit is compliant to ISO 11171 and has been set up for the purposes of the experiment to measure to MIL-STD-1246. Samples are taken every two hours. Samples taken from the magnetic plugs every hour along with a direct sample from the oil sump. The debris collected by the QDM system is also taken and these are further analysed using a Meiji microscope with DT frame grabber software. Two sets of time data from accelerometers placed on the shaft housing close to the test gears, along with a trigger signal are recorded using a DIFA measuring systems 16 channel, 16 bit data acquisition and analysis unit. 6 samples of 32768 points with a frequency band of 12.8kHz are taken for each hour of rig operation.
OIL SAMPLING To use particle analysis as an effective condition monitoring tool, it is essential that the samples taken from the system contain a representative selection of debris particles (Predict DLI). The debris
954
collection on the back-to-back gear testing rig was set up with this in mind and so the magnetic chip collectors were positioned in the direct oil flow in the tank. Debris should be collected from these by firstly dipping the plugs into a solvent to remove the oil from them and then by using adhesive tape to transfer the debris to glass slides for analysis under the microscope.
Figure 1: The Back-to-Back Gear Testing Rig.
CHI in Magnetic
Oil out
Chq> I j Collectors
P«rip«xTaiik Torque Plate Shaft Bearinp /
W^
DC Motor TestOcars
Drive Gears
A615 P h ^ Housing ^^« Figure 2: Schematic of the Rig's Arrangement. The Vickers QDM system has similar requirements in that it works best when in the full flow of the system. This was achieved by adding a pumping circuit to the rig with a fiher on the return feed so that
955
oil is constantly flowing through the A615 magnetic chip detector housing during the rig's operation (Figure 2). The PODS can be operated online or sample bottles can be used. For the purposes of this test, the sample bottle method was used and figures 3-6 show the trends over 24 hours of testing for the different particle sizes counted. The decrease in the number of smaller particles and increase in the number of particles over 25 microns is clearly visible over this period.
Particle Count -1 Micron 0) •§ ^ £ "5 ^ •O I 2
9.20E*07 8.70e-K)7 8.20e*07 7.706*07 7.206+07 6.70e*07 6.20E+07 5.70E+07 5.206*07
I:
Particle Count - 2 Microns
p^^''•M^^^g
8.00
kJ^
10.00
12.00
Reading
Reading
Fig 3: Particle Counts for 1 micron and 2 microns. Particle Count - 5 Microns
0.00
2.00
4.00
6.00
8.00
Particle Count -10 Microns
10.00
12.00
4.00
6.00
8.00
Reading
Reading
Fig 4: Particle Coimts for 5 microns and 10 microns. Particle Count - 25 Microns
Particle Count -15 Microns
S! u
1CO OL
f> E
z3 Reading
1.606+05 1.406+05 1.206+05 1.006+05 8.006+04 6.006+04 4.006+04 2.006+O4 0.006+00
Reading
Fig 5: Particle Counts for 15 microns and 25 microns.
956
Particle Count - 1 0 0 Microns
Particle Count - 50 Microns
9m
2JOO
9m 1B.00 izm
Reading Number
4XSi
9m
DuSS
16.00
12.00
Reading Number
Fig 6: Particle Counts for 50 microns and 100 microns.
Fig 7: ART Instruments Portable Oil Diagnostics System (PODS). After this initial period of testing there is little major damage to the gears, although oxidation has appeared on the teeth and minor pitting and scuffing damage is clearly visible to the naked eye. Two views of the test wheel are shown below after 20 hours of operation. Whilst the damage in the LH picture is by no means extensive, it appears to be from spalling or surface work hardening. There are, however, visible pits on the tooth (RH picture) suggesting that spalling is more likely.
Figure 8: The Wheel at 20 Hours.
957
CONCLUSION AND FURTHER WORK To date, a review of oil debris analysis techniques has been conducted, and a rig for the purpose of vibration testing and oil debris analysis has been put into operation. Preliminary results have been collected and further analysis and testing are necessary to continue the work in the direction specified. The work detailed in this paper shows some initial oil debris results fi-om the tests conducted on the back-to-back gear testing rig. Future work will gather data fi"om the wear-out of several sets of gears and will be used to detail the wear processes so that wear mechanisms can be detailed. Some goals for future work are listed below: Short Term: ^ • To plot the vibration data using vibration algorithms such as NA4 and FM4 (Dempsey 2000). • To classify the wear debris and chart this in a database according to shape, size and other features. • To use the data collected to investigate the fusion of multiple data sources. • To improve the image capturing techniques for capturing debris images. Long Term: • To develop a data fusion systemfi*ombased on results from vibration data and oil debris data generated in the tests. • To develop a prototype system which fuses data from multiple sources for the diagnostics of gearbox faults.
REFERENCES 1. Carl S. Byington, Terri A. Merdes and James D. Kozlowski (1999). Fusion Techniques for Vibration and Oil Debris/Quality in Gearbox Failure Testing. Condition Monitoring '99 Proceedings of the Litemational Conference on Condition Monitoring held at University of Wales, Swansea, UIC, pgl 13-128. Coxmoor Publishing Company 2. A Starr, J Esteban, R Willets and P Hannah (2000). Data Fusion for Advanced Condition Based Maintenance. Condition Monitoring 2001 - Proceedings of the International Conference Monitoring held at St. Catherine's College, Oxford, UK. Coxmoor PubHshing Company. 3. Paula J. Dempsey (2000). A Comparison of Vibration and Oil Debris Gear Damage Detection Methods Applied to Pitting Damage. Comadem 2000 (p767). 4. Dana Martin and Joe Van Dyke, P.E (1998). Integrating Vibration, Motor Current, and Wear Particle Analysis with Machine Operating State for On-line Machinery Prognostics/ Diagnostics Systems (MPROS). PREDICT/DLL 5. Bradley Payne, Andrew Ball, Fengshou Gu and Weidong Li (2000) A Head-to-Head Assessment of the Relative Fault Detection and Diagnosis Capabilities of Conventional Vibration and Airborne Acoustic Monitoring. Proceedings of the 13* International Congress on Condition Monitoring and Diagnostic Engineering Management (COMADEM 2000), Texas USA, pg233-242. December 2000. 6. M J. Neale and M Gee (2000). Guide to Wear Problems and Testing for Industry. Tribology in Practice Series. Professional Engineering Publishing. ISBN 1 86058 287 7. 7. J A. Williams (1994). Engineering Tribology. Oxford Science Publications. ISBN 0 19 856503 8. 8. Sxirapol Raadnui. The Analysis of Debris In Used Grease Samples. Condition monitoring 2000. pg 971-979.
958
Condition Monitoring and Diagnostic Engineering Management Andrew G. Starr and Raj B.K.N. Rao (Editors) Crown copyright © 2001. Published by Elsevier Science Ltd. All rights reserved.
IDENTIFICATION OF NON-METALLIC PARTICULATE IN LUBMCANT FILTER DEBRIS G. C. Fisher and J. A. Hiltz Defence Research Establishment Atlantic Dartmouth, Nova Scotia, Canada B2Y 1Z7
ABSTRACT The examination of particulate debris dispersed in lubricant or trapped on a lubricant filter has long been used as a means of evaluating and monitoring equipment health. Traditionally this examination has focused on identifying and quantifying metallic wear particles as the means by which diagnostic information is retrieved. While the diagnostic value of metallic wear debris is of paramount importance to equipment condition monitoring, there are occasions when identification of non-metallic species is important. Such species can indicate lubricant degradation, thermal stressing of system components and the ingress of harmful contaminants. All too often the diagnostic value of non-metallic particulate is ignored or, at best, used to indicate lubricant change intervals. Three case studies are employed to demonstrate the use of two techniques, powder x-ray diffraction and polarized light microscopy, to identify non-metallic constituents of lubricant debris. KEYWORDS Condition monitoring, case studies, particulate, non-metallic, identification, x-ray diffraction, XRD, polarized light microscopy, PLM INTRODUCTION The aim of maintenance management is to ensure reliable, efficient operation of mechanical equipment. This is, in part, accomplished through the use of condition monitoring techniques to detect and manage component wear processes and to monitor lubricant degradation and
959
cleanliness. The capabilities of the typical equipment health monitoring laboratory are therefore focused on those condition monitoring techniques that accomplish these tasks. Such techniques can include physical property testing and Fourier-Transform infrared spectroscopy of lubricants, vibration analysis and the use of data from on-line sensors. However, some of the most traditional and useful techniques are those that monitor wear metal content in the lubricant or filter debris. These include ferrography, atomic emission, inductively coupled plasma and x-ray fluorescence spectroscopy. Most of these techniques are designed to detect metallic particles. While this is understandable in light of the purpose of maintenance management, it does leave most equipment health monitoring laboratories somewhat lacking in terms of their ability to analyze non-metallic particulate. Situations occasionally arise where identification of non-metallic particulate in lubricant or filter debris is crucial to resolution of an observed problem. This report presents three such case studies. EQUIPMENT All x-ray diffractions were collected using a Philips 3040/00 diffractometer that utilized a Cu Ka (40 KV, 55 mA) incident beam with a 10mm mask and 1** divergence slit Diffracted xrays were detected using a PW1964/96 scintillation detector with a 1** anti-scatter slit and a 0.1 mm programmable receiving slit. Scans were run from 20's of lO*' to 100**. Reference diffraction patterns are from the International Center for Diffraction Data (ICDD), Swarthmore, Pa. Transmission polarized light microscopy was done using a Nikon Labophot-2POL microscope using a lOX 0.25NA strain free objective with a central dispersion staining stop. For Becke line testing, samples were mounted for in high dispersion Cargille""^ fluids of appropriate refractive index. RESULTS AND DISCUSSION Case Study Ul During the return voyage to home port after an extended refit, a Canadian auxiliary military vessel experienced problems with its steering gear. Frequent replacement of the lubricant filters was necessary due to excessive differential jwessures. As well, the hydraulic fluid (3GP-36 Grade 32) in the system was observed to be dark brown as opposed to its typical amber coloration. Laboratory examination of the lube oil filters indicated the presence of significant quantities of a fine white particulate. A similar particulate could be removed by filtration of the hydraulic fluid. It was inmiediately expected that the observed particulate was either a precipitated lubricant additive or an accumulation of wax deposits. Determination of the correct cause (source) would require identification of the particulate.
960
One technique that can be used to identify powdered or particulate materials is x-ray diffraction. This technique takes advantage of the fact that crystalline solids consist of regular ordered atomic lattices that can, under the right conditions, interact with incident radiation leading to diffraction, Cullity (1978). Diffraction occurs when the Bragg equation: X - 2dsinO
(1)
is satisfied. In this equation X is the wavelength (in nm) of the incident radiation, d, or dspacing, is the distance (in nm) between the atomic planes in the lattice causing the diffraction, and 0 is the angle formed by the incident radiation and the atomic plane. In the typical powder x-ray diffraction experiment a specific x-ray wavelength (usually Cu Ka) is focused onto a sample and 9 is varied by rotating the sample. When the Bragg equation is satisfied diffracted x-rays are detected by an x-ray detector that moves in concert with the sample. By plotting detector response versus angle a diffraction pattern is obtained.
Figure 1: X-ray diffraction pattern of steering gear particulate. Figure 1 shows the diffraction pattern obtained from the white particulate removed from the steering gear filters. Similar results were obtained for the particulate filtered from the system's hydraulic fluid. Each peak in Figure 1 corresponds to an atomic plane whose orientation relative to the incident x-ray beam has satisfied the Bragg equation. Knowledge of A. and 0 permits calculation of the d-spacing for each plane. Some of the d-spacings calculated for the particulate are shown in Table 1. The calculated d-spacings in a diffraction pattern can be compared to literature values for known species to identify phases present in the sample. As demonstrated in Table 1, this pattern represents calcium palmitate.
961
TABLE I COMPARISON OF SAMPLE D-SPACINGS AND REFERENCE VALUES FOR CALCIUM PALMITATE (ICDD PATTERN 5-12) Reference 15.4 9.17 4.41 4.06 3.943 3.434 2.36
Sample 15.2 9.22 4.42 4.09 3.95 3.40 2.37
Palmitate salts can be used as thickening and corrosion protection agents for mineral oils. However, these materials are not used in 3-GP-36 hydraulic fluids. Discussions with system maintainers indicated that prior to refit a sufficient quantity of commercial corrosion inhibitor oil had been added to comprise 10% of the capacity of the steering gear lubricant system. This had been done to protect the components from corrosion during the refit, which did not include work on the steering gear itself It had been intended to monthly circulate the hydraulic fluid/inhibitor oil throughout the system. However, this had not occurred as planned. Further, the duration of the refit extended 18 months beyond the original completion date. It seems likely that the palmitate additive precipitated during the extended overhaul period and that this was not noticed until it was churned during the return voyage. Having only instrumentation available in a typical condition monitoring lab would have made identification of the particulate difficult. Precipitation of an additive would have been suspected as a possible cause of the incident and the presence of an inhibitor oil in the hydraulic fluid could have been indicated by viscosity and other physical property tests. However, identification of the particulate as calcium palmitate would have only been possible with Fourier transform infrared spectroscopy, and then only if the instrument had the capability of analyzing a solid material, as opposed to the more typical systems in condition monitoring labs which are designed for liquid analysis. Case Study i^2 A significant quantity of a hard black deposit was found in the centrifuge of an auxiliary diesel engine (MWM Deutz) on a patrol ftigate. X-ray diffraction of the deposit did not yield a particularly useful pattern, likely due to the presence of lubricant traces and amorphous carbon. A portion of the deposit was therefore heated for several hours at 800**C to remove these materials. Table 2 shows the d-spacings of the major peaks from this pattern and indicates that they correspond to a mixture of anhydrite (CaS04) and lime (CaO). To confirm whether these materials were actually present in the original deposit or were produced during the heat treatment, a portion was examined using transmittance polarized
962
light microscopy (PLM). Transmittance PLM is the study of optically transparent or translucent materials immersed in a fluid of known properties. TABLE 2 COMPARISON OF SAMPLE D-SPACINGS AND REFERENCE VALUES FOR ANHYDRITE (ICDD PATTERN 37-1496) & LIME (ICDD PATTERN 37-1497) Sample 3.49 2.85 2.78 2.47 2.40 2.33 2.21 1.701
Anhydrite 3.499 2.849
Lime
2.777 2.473 2.406 2.328 2.209 1.701
An important factor in this analysis is the refractive index, n. The refractive index of any material is the ratio of the speed of light in a vacuum (Cv) to the speed of the light in the material (Cm). Hence m
(2)
PLM has long been used to characterize particulates as comparison of morphology and refractive indices to literature values can identify the particle. The Becke line test can be used to measure refractive index. This test takes advantage of refraction phenomena to compare the refractive indices of two materials. Typically, a crystalline powder is placed in a liquid in which it is insoluble and this solid/liquid mixture is examined by transmittance PLM. Once the crystal is in focus, the focal plane is moved higher creating a halo either around or inside the particle. The halo is caused by refraction of light by the crystal. Movement of the focal plane up through the crystal causes the halo to move toward the medium of higher refractive index. Therefore placement of an unknown crystal into a liquid of known refractive index (liquids by definition have only one refractive index) will identify the crystal refractive indices as being higher or lower than that of the liquid. Through the use of several liquids of differing refractive index, an estimate of the crystal's refractive indices can be obtained. Mason (1983). The unheated powder from the diesel was placed in a fluid having a refractive index of 1.550. This examination indicated that the deposit primarily consisted of black non-crystalline particles (amorphous carbon). However, two different species of translucent materials were detected. One species had a single refractive index that was significantly greater than 1.550. Subsequent immersion in higher refractive index fluids indicated that the refractive index of this material was in fact greater than 1.800. Lime has a single refractive index of approximately 1.84. The other species had three refractive indices that were all slightly
963
greater than 1.550. Anhydrite is monoclinic and has three refractive indices: 1.57,1.575 and 1.61. These results support the identification of anhydrite and lime in the deposit by x-ray diffraction. The lime is likely a precipitated oil additive as marine diesel lubricating oils often contain basic additives, such as CaO, to neutralize acidic species resulting from oil degradation or contamination. The presence of the anhydrite was somewhat more difficult to explain. There are two potential sulfur sources that can result in the formation of anhydrite. The first is from the oxidation of the sulfur in the distillate fuel. The second is the zinc dialkyl dithiophosphate-based additive present in the oil. Metal dialkyl dithiophosphates, including zinc diilkyl dithiophosphates, are commonly used as thermo-oxidative antioxidants. These chemicals act to decompose peroxides and inhibit the propagation step of the oxidation process shown in Figure 2, Al-Malaika (1983). 1. 2.
RH ^^^^"^ >R*+HOO* (a)R^W2->R00* {b)ROO*-\-RH -^ROOH-^R* 3. (a) ROO * +HOO* -> ROOH + Oi (b)ROO*^R*->ROOR {c)R*'¥R*-^RR 1. Initiation -formation ofhydroperoxide andalkyl radicals in presence of oxygen and metal ion 2. Propagation -formation ofa) alkylperoxide radical and b) regeneration ofalkyl radical 3. Termination Figure 2. Reactions involved in the oxidative degradation of hydrocarbon molecules in lubricating oils. The reaction between zinc dialkyl dithiophosphate and hydroperoxides, formed by the thermo-oxidative degradation of the hydrocarbons in diesel lubricating oils, are shown in Figure 3. One of the products arising from the reaction between the antioxidant and the peroxides is sulfur trioxide. In the presence of moisture, sulfur trioxide forms sulfuric acid and lime forms calcium hydroxide. Calcium sulfate is a product of the reaction of these two chemicals. The techniques employed by most condition monitoring labs are directed at identifying metallic species and characterizing lubricant degradation. As such, characterization of a deposit consisting of inorganic species would not be possible.
964
A^ S
HO.
Oft
-S
Figure 3: Reaction between zinc dialkyl dithiophosphate and hydroperoxides.
Case Study f^3 For a number of months in 2000, coolant systems for the CIWS guns of several CF ships were experiencing problems shortly after being returned from overhaul. Upon return and installation, each system would be filled with deionized water/glycol mixture and, upon running, the systems filter would quickly clog. Subsequent cleaning of the filters indicated that a black-green particulate was the culprit. An x-ray diffraction of the deposit was collected and Table 3 indicates that the minerals enstatite (MgSiOa) and forsterite (Mg2Si04) account for the observed d-spacings. To further verify the presence of these minerals the deposits were examined using transmittance PLM. The Becke line test indicated the presence of two different rhombohedral species having refractive indices of approximately 1.65. Enstatite and forsterite are rhombohedral magnesium silicates having refractive indices ranging between 1.64 to 1.68. Enstatite and forsterite mixtures are available commercially as silica-free blasting media. These materials were being used in the contractor's facility and were apparently entering the cooling system during maintenance work. A subsequently introduced flushing procedure prior to installation has alleviated the problem.
965
The identification of the particulate deposit in the gun coohng systems and subsequent determination of their origin would not have been possible using traditional condition monitoring methodologies. TABLE 3 COMPARISON OF SAMPLE D-SPACINGS AND REFERENCE VALUES FOR FORSTERITE (ICDD PATTERN 34-189) & ENSTATITE (ICDD PATTERN 35-610) Sample 3.90 3.18 2.88 2.52 2.46 2.35 1.752
Forsterite 3.881
2.510 2.457 2.346 1.748
Enstatite 3.170 2.874 2.523 2.455 1.759
CONCLUSION The case studies discussed here presented the capabilities of x-ray diffraction and transmittance polarized light microscopy in identifying non-metallic constituents of lubricant filter debris. These methods were able to characterize a precipitated organic oil additive, an inorganic thermal degradation by-product of diesel lubricating oil and a mineral contaminant. The analytical methodologies typically available in equipment health monitoring labs focus on identification of metallic species (to assess component wear) and the physical condition of the lubricant. While such techniques are capable of addressing the majority of condition monitoring problems, situations will arise where they cannot identify the occurring problem. It is not our intention to suggest that condition monitoring labs (rush out and, delete) purchase x-ray diffractomcters and PLM microscopes. (In fact,delete) Acquisition of such instruments and maintenance of operator expertise (for these techniques, delete) is costly, particularly for x-ray diffraction. However, it would serve such laboratories well to have access to these techniques through universities or commercial support agencies for the times they are needed.
REFERENCES Al-Malaika, S. (1983) Comprehensive Polymer Science- The Synthesis, Characterizations, Reactions and Applications of Polymers, Chapter 19, Volume 6 - Polymer Reactions, Pergamon, Toronto. Cullity, B.D. (1978). Elements of X-ray Diffraction, Addison-Wesley, Don Mills, Ontario. Mason, C.W. (1983). Handbook of Chemical Microscopy, Wiley & Sons, New York.
966
Condition Monitoring and Diagnostic Engineering Management Andrew G. Starr and Raj B.K.N. Rao (Editors) © 2001 Elsevier Science Ltd. All rights reserved.
INFLUENCE OF TURBINE LOAD ON VIBRATION PATTERN AND SYMPTOM LIMIT VALUE DETERMINATION PROCEDURES T.Galka Institute of Power Engineering 5 Augustowka St, Warsaw 02-981, Poland e-mail: [email protected]
ABSTRACT Technical condition diagnosis is based on relation between vectors of condition parameters and measurable symptoms. There are also other parameters that influence the latter. In machines that transform large amounts of energy, e.g. steam turbines, substantial influence of load should be expected. Little attention has been paid to this problem, apart from the statement that load should not be neglected in diagnostic symptom assessment. Experimental data show that vibration-related diagnostic symptoms exhibit marked influence of turbine active load and that there are considerable differences between various symptoms. Some general principles can, however, be found. Quantitative determination of relations between load and individual symptoms is a complex task, but experimental relations for individual machines (or groups of machines) can be estimated. These relations will obviously depend on machine layout and design features. Measured symptom levels may thus be 'normalized' with respect to turbine load, which allows for a more meaningful diagnosis. Load influence also turns out to be important for symptom limit value determination from experimental data. Modification of relevant procedures in accordance with previously developed theoretical model results in improved estimation accuracy. This is illustrated by several examples for utility steam turbines.
KEYWORDS technical condition, steam turbine, technical diagnostics, diagnostic symptom, limit value
INTRODUCTION Quantitative technical diagnosis is expected, in general, to determine the extent of damage of an element or object under consideration and to estimate its residual life. This is achieved on the basis of available diagnostic symptoms. In order to estimate how far has the lifetime consumption gone, and therefore to make well-founded decisions concerning future operation and necessary repairs, we obviously need some sort of scale. In many diagnostic systems a three-stage scale is used, involving (for a given symptom) three characteristic values: basic, limit and admissible one.
967
Basic values can in principle be determined from reference measurements (see e.g. Orlowski and Galka, 1993), and admissible values are usually provided by machine manufacturer, on the basis of safety considerations. Limit values can be determined from the energy processor model (for details, see Natke and Cempel, 1997), which describes how technical condition evolution is represented in time histories of measurable diagnostic symptoms. This model employs certain simplifying assumptions and therefore allows for a neat mathematical description, comparatively easy for subsequent treatment. When dealing with raw experimental data, however, we seldom obtain a smooth, monotonic, increasing curve predicted by the model. Instead we have an irregular broken line, sometimes even with sections of negative slope. This is due to the fact that, apart from technical condition evolution, there are many other factors influencing measured symptom values. Which factors should be taken into account, and to what extent, will depend on a particular machine under consideration. For objects that transfdrm large amounts of energy (like, for example, steam turbines), we may intuitively predict that intensity of this process, given by output power or load, will influence residual processes power and therefore symptom values (Galka, 1999). This sounds quite obvious, but this issue has not drawn much attention, apart from quantitative and rather trite statement that this influence is not negligible and should be taken into account. Of course, accounting for machine load is important not only for symptom limit value determination. In practical diagnostics we often observe sudden and considerable increase or decrease of symptom value, too abrupt to have been caused by normal deterioration of technical condition. This is usually explained by some 'disturbance' or by random nature of phenomena under consideration. It should be worthwhile, however, to analyze if any of such 'disturbances' might be attributed to load variations, and to what extent. This certainly would allow for a more precise technical condition diagnosis.
BACKGROUND Theoretical background of symptom limit value determination procedures, based on energy processor model and symptom reliability concept, together with relevant mathematical treatment, have been described elsewhere (Cempel, 1993; Natke and Cempel, 1997). Here we recall only some results. With certain simplifying assumptions, power V of residual processes that accompany the principal process of energy transformation can be expressed as
where 0 denotes time, Ob is time to breakdown (which, within the framework of this model, implies ultimate 'death' of the object) and VQ = V(0 = 0). Power of residual processes is, in general, not measurable, so our inference on object technical condition is based on observable symptoms S: 5(0) =(^¥{6)],
(2)
where 0 denotes some symptom operator. With such simple equations, further treatment (for given types of symptom operator - see Orlowski and Galka, 1997) is quite straightforward and leads to convenient analytical relations. Such description, however, is not fully adequate for complex objects, especially if they are repaired during their operational life. As it has already been mentioned, there are other factors that influence S{0) and some of them must be taken into account. Adaptation of the basic model for steam turbines (Galka, 1999) should, in particular, include at least the following factors: 968
- logistic vector L (describing differences between individual machines and consecutive life cycles), - irreversible degradation factor h (results of which cannot be removed during overhaul or repair), - active load Pu. Even with reasonable simplifying assumptions, such development of the basic model brings about inevitable complication of mathematical description. In particular, time dependence of V (see Eq.l) becomes complex (Orlowski and Galka, 1999):
vxe.@) = v,,xx\-x^-^)'
(3)
where subscript / denotes i-Xh life cycle, 6^ is time that starts from zero at the beginning of object life {0 starts from zero at the begirming of each life cycle, i.e. after each overhaul or repair) and Xi=AL.KPu)
.
(4)
Logistic vector L is, for a given life cycle, assumed to be constant. We may also note that influence of irreversible degradation should be small if our data cover a period not very long by comparison with turbine service life (which is, say, thirty to forty years) and we haven't yet come close to its end. We may thus expect that, in certain circumstances, influence of load should prevail. This suggest practicability of experimental evaluation of this influence. It must be kept in mind that relation between load and residual processes power (and hence symptom vector) is not of the 'direct' type; it might be more appropriate to say that they both depend on the same vector of parameters that include steam pressure and temperature, control system settings, condenser vacuum, ambient temperature and many more.
EXPERIMENTAL DATABASE The above considerations refer to all types of diagnostic symptoms. In our work we have relied mainly on vibration-based ones, which are known to provide much information on machine condition while being relatively simple to acquire and process. All data discussed below refer to 200 MW, 3000 rpm steam turbines, of which over sixty are now operated by Polish power industry. These machines were produced in Russia and Poland and installed in Polish power stations from early 60s to early 80s; some of them were later modernized (mainly lowpressure turbine and control system). We chose a group of eleven turbines in three power stations which, apart from minor details, may be considered identical. Their operational life was about 160,000 to 180,000 hours, so we may expect that influence of irreversible degradation has not become predominant and its advance during the period covered by our study (about two years) has been small. Each turbine-generator unh has nine vibration measurement points (seven bearings plus two points on the low-pressure casing). We measured absolute vibration velocity in the range up to 10 kHz and used 23% CPB power density spectra. Frequency bands are characterized by their mid-values. For each point we analyzed vibration levels in bands determined from the turbine vibrodiagnostic model (see e.g. Orlowski and Galka, 1993), each level being considered an individual symptom. Given three measurement directions (vertical, horizontal and axial), we obtain a set of 240 symptoms for each machine. All measurements were taken during steady-state operation, in most cases with automatic frequency and load control. This means that during a single measurement cycle (about twenty minutes) load may change within narrow limits, by a few megawatts. Because of this, full load range, i.e. from 140 to 205
969
MW, was divided into 5 MW intervals and results were averaged within each interval. In order to provide comparison between different symptoms, results of this averaging were related to the mean value from all measurements.
RESULTS OF ANALYSIS Due to differences in vibration generation mechanisms, frequency range covered by this analysis can be divided in two sub-ranges, conventionally referred to as 'harmonic' and 'blade' ones. The former contains vibration components resulting directly from rotary motion; in our case, first four harmonic and the 0.5yo sub-harmonic components are taken into account. The latter includes vibration components generated as a result of interaction between steam flow and turbine fluid-flow system, their individual frequencies being thus determined by number of blades in individual stages. In the case of 20b MW turbines considered there this range spans from about 1 kHz to about 8.2 kHz. Altogether we have 135 symptoms from the harmonic range and 105 symptoms from the blade one. It might be expected that relations between turbine load and vibration velocity level in these two frequency sub-ranges should exhibit considerable differences and data analysis confirms this prediction. From the quantitative point of view, load dependence of vibration symptoms is stronger in the blade range: if mean value of all readings of a given symptom is taken as 100%, we have average minimum of 40.4% and average maximum of 170,4% for 105 symptoms from this range. Corresponding values for 135 'harmonic' symptoms are 57.7% and 155.3%, respectively - the difference is thus evident. It is interesting to note that this is consistent with everyday diagnostic experience, which shows that vibration levels in the blade range are more sensitive to even small changes of turbine operational parameters. Most experimental symptom vs. load curves in the harmonic range exhibit certain regularity. For low loads, approximately below 160 MW, these curves are quite steep, then become comparatively flat and, at the very end of the entire load range, slope increases again. Typical examples are shown in Fig.l. For highest load levels slope is usually positive, while for the lowest ones it can be either positive (Fig. I a) or negative (Fig. lb). Extrema of the load-symptom relations are thus typically close to either lower or upper limit of the load range, which is clearly seen from data shown in Table 1. This leads to a conclusion that, in order to minimize load influence in procedures involving experimental data processing (e.g. symptom limit or basic value estimation), we may just, as a simple measure, reject data obtained for the lowest and highest load levels. In the blade frequency range, corresponding curves are far less regular and in most cases their slopes are markedly higher. Some conclusions, however, are similar. Distribution of extrema among individual load intervals is more 'uniform' than for harmonic components, but still most of them are found in extreme ones (see Table 1). We may thus conclude that also in this case rejection of data obtained at very low or very high load should improve accuracy of relevant procedures. In the case of 200 MW turbines considered here, this 'useful' load range may be estimated as 165 -^ 200 MW. It should be noted that for turbines operated in the base-load mode, which seldom operate at extreme loads, this is not a particularly great loss of data - in this very case, only about 13% of all measurement results is rejected. Although curves in the blade frequency range are less regular, they have some common characteristic features that may be related both to design and to operational characteristics. Symptoms related to vibration components generated by the low-pressure turbine fluid-flow system in most cases exhibit substantial decrease for the highest load levels, while the remaining part is often more or less monotonic; two examples are shown in Fig.2. This is explained by the fact that highest load can be attained and maintained only with high steam flow rate and at low condenser pressure (i.e. high 970
vacuum). In such conditions, steam flow is regular and uniform, with no backwards flow (which occurs at lower load, especially with poor condenser vacuum), and vibration generation is less intensive. 200 MW turbines bearing 3 axial, 100 IHz band
170
180
Load, IVIW
(a) HP-IP bearing, vertical, 100 Hz 200 M\N turbines bearing 2 vertical, 100 Hz band
170
180
Load, MW
(b) rear IP bearing, axial, 100 Hz Fig. 1. Examples of experimental symptom vs. load curves in the harmonic frequency range. Crosses denote values derived from experimental data and continuous curves are least-square fits. Right-hand slope is in most cases positive, while left-hand one can be either negative (a) or positive (b). For symptoms related to the high-pressure turbine fluid-flow system, mainly to the rotor stages, there is often a considerable increase at low load levels. This is most probably caused by steam flow asymmetry, resulting from the group-type control system, employed in these turbines. They are fitted with four distinct groups of high-pressure inlet vanes, each fed via its individual control valve. Last of these should, in principle, open only above the rated load. This means that at low load steam flow through the high-pressure turbine is asymmetrical and non-uniform, and hence vibration components generated by its fluid-flow system are higher.
971
200 MW turbine LP casing front horizontal, 5000 Hz band
170
180
Load. MW
(a) LP casing front, horizontal, 5000 Hz 200 MW turbine Bearing 4 axial, 4000 Hz
170
180
Load, MW
(b) Front LP bearing, horizontal, 4000 Hz Fig.2. Examples of experimental symptom vs. load curves in the blade frequency range for the LP turbine. Crosses denote values derived from experimental data and continuous curves are least-square fits. Most symptoms related to the LP turbine exhibit marked decrease for highest load levels. In practice there are often problems with maintaining rated steam pressure and temperature, caused mainly by boiler-related troubles, so necessary load can be maintained only at higher flow rate. Fourth control valve thus opens at rated load or even below. Steam flow rate becomes higher, but at the same time the flow is more uniform. This explains why, in most cases, symptom vs. load curves for the high-pressure turbine - especially at the side of steam inlet, i.e. HP-IP bearing - show considerable negative right-hand slope. On the other hand, higher steam flow can cause slight increase of 'harmonic' symptoms and, as a result, curves for two types of symptoms for the same measurement point almost resemble mirror images. Example is shown in Fig.3.
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Table 1 Extrema of experimental symptom vs. load curves in individual load intervals Load range from - to 141-145 146-150 151-155 156-160 161-165 166-170 171-175 176-180 181-185 186-190 191-195 196-200 201-205
Number of extrema blade frequency range harmonic frequency range max. min. min. max 2 21 12 10 12 13 7 23 7 2 12 7 8 18 7 28 18 16 17 25 6 1 2 0 5 5 4 8 1 0 0 12 7 0 0 3 5 0 3 13 13 2 7 3 0 0 8 9 34 25 26 13
APPLICATION Experimental vibration database is often used to estimate basic and limit vibration levels. This is particularly important when a diagnostic system, even a simple one, is introduced for machines already in operation for a considerable time. Input data for relevant algorithms should be consistent, which means that they should be obtained in comparable conditions. This refers also to machine operational parameters, including load. In principle, we can introduce load influence in formulae that describe relation between power of residual processes and extent of technical condition deterioration (see previous sections); we only have to provide mathematical description of curves such as shown in Figs.l, 2 and 3, which is no special problem. Then it is possible to derive relevant equations for S(9) relations and employ procedures similar to those used for the basic energy processor model (Cempel, 1993). This, however, leads to complex and unwieldy relations. It is more convenient to 'normalize' measured symptom values with respect to some reference conditions and then use such data for straightforward calculations. Such 'normalization' is a slightly time-consuming, but basically simple numerical procedure. Obviously, if special procedures and reference data are implemented in a diagnostic symptom, it can be performed concurrently with data acquisition. It is worth noticing that in many cases 'normalized' data yield better fitting of experimental vibration level histograms, which is an indirect proof of method's suitability. Example is shown in Fig.4. Judging from current experience, introduction of such 'normalization' with respect to load in symptom limit level estimation procedures results in a change of a calculated value by, in most cases, a few percent. Example is shown in Table 2. It is noteworthy - and in fact quite surprising - that, despite considerable influence of load on ^'ibration levels in the blade frequency range, estimated limit values are (with just one exception) almost the same for all three data selection procedures. In most cases limit values shown in Table 2, estimated from 'normalized' data, are slightly higher than those estimated from 'raw' data, but this refers to just this example and certainly is not a general rule.
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200 MW turbine Bearing 2 horizontal. 160 Hz 200 180 160 0
140
1 120 o
I 100 M 0)
>
80
0^
60 40 .0
20
170
180
Load, MW
(a) 50 Hz 200 MW turbine Bearing 2 horizontal. 3150 Hz band 160 140 120 100 80 60 40 20
140
150
160
170
180
190
200
210
Load, MW
(b) 3150 Hz Fig.3. Examples of experimental symptom vs. load curves for the HP-IP bearing, vertical direction. Crosses denote values derived from experimental data and continuous curves are least-square fits. Most symptoms relating to the HP turbine from the harmonic frequency range exhibit positive slope (a), while in the blade frequency range this is reversed (b). Accounting for load influence obviously improves limit value determination accuracy and, for this one reason, should be introduced, if only relevant relations are known. In most cases resulting change is not large, but sometimes can even reach about 10%. It seems that first two harmonic components are most sensitive, but again this is only a preliminary conjecture, based on available data. As work is still in progress, more detailed conclusions shall probably follow.
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Table 2 Comparison of vibration limit levels (200 MW turbine, rear IP bearing, vertical direction) Frequency range
harmonic
blade
Frequency band [Hz] 25 50 100 160 200 1600 2000 2500 3150 5000 6300
Limit level [mm/s] estimated from 'normalized' data 165 to 200 MW all data 0.225 0.226 0.225 3.332 3.362 3.209 3.889 3.633 3.635 0.926 0.909 0.905 0.918 0.914 0.924 0.127 0.129 0.129 0.096 0.097 0.096 0.160 0.160 0.157 0.189 0.196 0.195 0.385 0.386 0.380 0.120 0.107 0.108 200 MW turbine Bearing 4 vertical, 50 Hz
<=0
(,5;1] (0;,5]
(1,5;2] (1;1,5]
{2,5;3] (2;2,5]
(3,5;4] (3;3,5]
(4,5;5) (4;4,5]
(5,5;6] (5;5,5]
> 6,5 (6;6,5]
Vibration velocity, mm/s
200 MW turbine Beraing 4 vertical, 50 Hz
<=0
(,5;1] (0;,5]
(1,5;2] (2.5;3] (3,5;4] (4,5;5] (5,5;6] (6,5;7] {1;1,5] (2;2,5] (3;3,5] (4;4,5] (5;5,5] {6;6,5] >7 Vibration velocity, mm/s
Fig.4. Experimental vibration velocity histograms: top, 'raw' data; bottom, 'normalized' data (front LP bearing, vertical direction, 50 Hz band). Continuous lines show Weibull distribution fitting (for details, see Orlowski and Galka, 1997). 975
ACKNOWLEDGMENT Results used and quoted in this paper have been obtained with the support of the State Committee for Scientific Research, within the framework of the 7 T07B 04116 Research Project.
REFERENCES Cempel C. (1993). Theory of energy transforming systems and their application in diagnostics of operating systems. Applied Mathematics and Computer Science 3:3, 533-548. Galka T. (1999). Application of energy processor model for diagnostic symptom limit value determination in steam turbines. Mechanical Systems and Signal Processing 13:5, 757-764. Natke H.G. and Cempel C. (1997). Model-Aided Diagnosis of Mechanical Systems. Springer-Verlag, Berlin-Heidelberg-New York. Orlowski Z. and Galka T. (1993). Influence of turbine modernization on its basic vibration spectra. Proceedings of the COMADEM'93 International Congress, University of the West of England, Bristol, 442-448. Orlowski Z. and Galka T. (1997). Symptom reliability - a new tool for quantitative assessment of turbine technical condition. Proceedings of the lASTED Conference 'High Technology in the Power Industry', Acta Press, Anaheim, 65-70. Orlowski Z. and Galka T. (1999): Effect of material degradation processes on vibration limit level determination procedures. Proceedings of the COMADEM'99 International Congress, Coxmoor Publishing Co., Oxford, 371-379.
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Condition Monitoring and Diagnostic Engineering Management Andrew G. Starr and Raj B.K.N. Rao (Editors) © 2001 Elsevier Science Ltd. Allrightsreserved.
STUDY ON THE MOVEMENT REGULATION OF GRINDING MEDIA OF VIBRATION MILL BY NOISE TESTING
Jiang Xiaohong, Pu Yapeng and Zhang Yongzhong College of Mechanical and Electronic Engineering, China University of Mining and Technology, Xuzhou City, Jiangsu Province, China
ABSTRACT The physic meaning of testing and analyzing the noise intensity of vibrating mill is discussed in this paper. Through a series testing of noise intensity on the sample machine, the influence of vibrating amplitude and frequency of vibrating mill to the variation trends of noise intensity are studied. The range of peak point of noise intensity emerged is investigated under the condition of large amplitude. Through analyzing equal noise intensity, the operating parameters to meet different pulverizing requests are determined, that is, favorable operation parameters for large granularity material or for ultra-fme grinding are given out respectively. KEYWORDS Vibrating mill, grinding media, equal noise intensity, testing
INTRODUCTION For today Nanometer technique developing vigorously, the role of ultra-fme pulverizer become more and more important. Vibrating mills are ideal equipments for ultra-fme grinding. How to adjust the kinetic parameters and dynamical parameters of vibrating mill to make the grinding media reaching the best movement state, are the key problems for further increasing productivity and improving products quality. It is the subject that the scholars in the field have been investigating. Due to the randomicity and non-linearity of grinding media movement, it makes the problem become quite
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complex. In this paper, in order to investigate the movement regulation of grinding media collective, a series testing have been made. The investigating experiments include two parts: vibrating signal testing and noise testing. The method and results analysis about vibrating signal testing have referred in another paper [1], this paper only discuss about noise testing. PHYSICS MEANING OF NOISE TESTING The mutual effects among media or between media and the cylinder will give out sound. This effect can be divided into two forces: vertical force and transverse force. Vertical force results in change of the velocity direction of grinding media, while transverse force make the media revolving. Obvious, the sound intensity produced by vertical force is greater than that produced by transverse force. Given energy of sound is Ea, and the dissipative heat energy by media friction is En, and the total wasting energy of mill E can be decided: E = E a + En
(1)
When materiel is not filled, grinding media surface and the wall of cylinder are smooth, friction coefficient is very small, then E a » En, so: E«Ea
(2)
That is, the total wasting energy of mill equal nearly to sound energy. According to the conclusion, it can be educed several points of view as follow: (1) If the measured sound intensity level of a mill is equal, it can be considered, then, at the different measuring time, the total wasting energy of the mill is equal. (2) If the absorb power of a mill have the same quantity at different time, then the sound intensity level is nearly the same. (3) If the operating parameters are in high energy level, that is, the amplitude, frequency and filling rata are all in their large value, but the sound intensity is low, then it can be considered that transverse force is prominent while the vertical force is small. Whereas, if the amplitude, frequency and filling rate are all in low value, but the sound intensity is in high level, then it can be considered that vertical force is prominent while the transverse force relatively small. It needs to say, to understand relatively prominence degree in movement of the grinding media collective has great practical meaning for powdering operation. It has been known, for higl^ efficiency break up the grain of large dimension, the vertical force among grinding media or media with the wall of cylinder expects great. While if the materiel is needed to mill as fine as possible, the transverse force in the movements of grinding collective expects great, so that granularity can reach a higher class. The output of powder is always a contradiction with the granularity. But if the operation parameters could be well adjusted, it can meet different pulverizing requests. NOISE TESTING AND RESULTS ANALYZING
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Design of Testing Device The adjustable operation parameters of vibration mill are mainly vibrating amplitude A, the rotate speed of the vibration mill n and the filling rate of the m i l l / The vibration mill for testing has several qualifications as follow: The amplitude of vibrating mill is adjustable (0'^20mm) ; The rotate speed of the mill motor can be changed continuously (300-1338rpm); filling rate is in the range of (30-85%). The diameter of vibration mill for testing is 200mm. Grinding media is round bar; its diameter is 12mm. Noise intensity and vibrating testing system as Figure 1. Vibrating Mill
' 1 1
3 562A Dynamic
Acceleration
Impulse
Transducer
Shock Meter
Recorder
Signal Analyzer
—J
Sound meter Oscillograph
Figure 1. Noise Intensity and Vibrating Testing System Experiments Methods of Noise Testing In order to investigate the movement regulation of grinding media collective, a series testing have been made. The purpose of these testing is mainly to determine the movement regulation of grinding media collective at different amplitude, rotate speed and filling rate. At the same time, try to elicit some regularity conclusions firom testing results analyzing and provide experiment groundwork for theory modal. The noise test consists of two parts: (1) The vibrating amplitudes of the mill: 3mm, 6mm, 9mm; the vibrating frequency: 300rpm, 500rpm, 700rpm, 900rpm; the filling rate: 30%, 50%, 70%, 85%. Combining the three parameters, there are 44 experiments. (2) Noise intensity testing and signal analysis under equal vibration intensity of mill. Vibration intensity are 3g,4g, 5g,6g, g=9.8m/sec^; The vibrating amplitudes of the mill: 3mm, 5mm, 6mm, 7mm, 9mm, 13mm, 15mm; The filling rate of mill: 70%). Combining the three parameters, there are 28 experiments. The Results Of Noise Intensity Testing And Analysis Noise Intensity Testing of Different Vibrating Amplitude (A), Frequency (n) and Fill Rate (f) Fig.2-Fig.4 express the variation trend of noise intensity at the same amplitude but different rotation speed, taking the filling rate as horizontal coordinate. It can be seen, as the rotate speed increasing, the noise intensity increases obviously, but the variation trend of noise intensity is not the same. As the filling rate increasing, the noise intensity appears the increasing trend. Only one exception, on the situation ^=3mm,/=70% that disobey this rule. The noise intensity of y4=3mm,^70%) is less than that of^=3mm, /=50%. 979
Fig.5~Fig.7 given out the variation trend of noise intensity at the same amplitude but different filling rate, taking the rotate speed as horizontal coordmate. It can be seen, as the filling rate increasing, the noise intensity appear basic the increasing trend, but not obvious; noise intensity increases monotonously; the curves of noise intensity have nearly the same shape. The noise intensity of
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Figure 11: When /=85%, the influence of n and A to the noise intensity
Fig.8~Fig.ll show the variation trend of noise intensity at the same filling rate but different vibrating amplitude, still taking the rotate speed as horizontal coordinate. It can be seen, as the amplitude increasing, the noise intensity increases; variation trend of noise intensity appears increasing monotonously, and the curves have almost the same shape. }^oise Intensity Testing Of Equal Intensity Vibration Taking the amplitude as horizontal coordinate. Fig. 12 given out the variation trend of noise intensity at the condition of 70% filling rate but different vibrating intensity. Though vibrating intensity is the same, the noise intensity is not the same. It can be seen fi-om the figure, the noise intensity have similar variation trend under the four vibrating intensity. The change of the amplitude has something to do with curve variation trend. In the range of ^=3~5mm, the amplitude has great influence to the noise value, while in the range of 5mm<^<15mm, the influence of amplitude to the noise value is not obvious. In the process of large amplitude and low rotate speed changed to small amplitude and high rotate speed, the noise intensity is not monotonously increased. There is ubiquity a low vale area. In 981
great amplitude area (^>8 mm) , there is a peak point of noise intensity as the amplitude changes. In the experiment, peak point appears when amplitude A is between 10 mm and 14mm. Fig. 13, taking the vibrating intensity as horizontal coordinate, express the variation trend of noise intensity in the condition of 70% filling rate but different vibrating amplitude. Obviously, the noise intensities have the similar variation trends under the 7 amplitudes and all appear monotonously increasing trend; as the vibrating intensity increasing, the noise intensity increases obviously. That indicate the rotate speed have great influence to the value of noise intensity. It is worth while to mention, in the equal intensity condition, when the amplitude A=3, the noise intensity is the greatest, while ^=15, the noise intensity is the smallest.
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l>loise Signal Analysis From the graph of signals of time domain and frequency domain, it can be seen that the frequency contained in noise signal are very complex. The frequency band is quite wide, the signal between 0 to 2kHz is very abundant, and there is no prominent frequency, as approximately broadband noise. This indicates that the movement of grinding media collective is clear nonexistence rule. CONCLUSION (1) Analyzing the general trend of noise intensity testing, we learn that, in the condition of great amplitude and low speed, the noise intensity is low. In small amplitude but high rotate speed situation, noise intensity is high. That is, the under the condition of great amplitude and low speed, the transverse force of grinding media collective is prominent while vertical force is weak. This is not advantageous for materiel smash, but is favor to fine milling. In small amplitude but high rotate speed situation, the vertical force of grinding media collective is prominent while transverse force is weak; it is beneficial to materiel smash. This is corresponding to the results of vibrating testing in paper [1]. (2) Under the condition of equal vibrating intensity, the valley area of graph in noise intensity trend
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indicates that even in high rotate speed, there still exist the situation that transverse force of grinding media is dominant. This area is very important. Working in this area, the vibrating mill can enhance fine milling effect, at the same time; it can reduce the disadvantageous influence to the equipments caused by great amplitude. (3) Under the condition of equal vibrating intensity, the peak point of noise intensity in great amplitude area ( ^ ^ 8 mm) is caused by the influences of rotate speed and amplitude to the noise intensity. When vibrating intensity is high, choose the operation parameters correspond to the peak point, it can increase the smash rate of materiel, at the same time; reduce the disadvantage influence to the equipments due to high rotate speed. References 1 Jiang Xiaohong, Pu Yapeng and Zhang Yongzhong. (2000). Study on the Movement Regulation of Grinding Media of Vibration Mill by Vibration Testing. Proceedings of the First International Conference on Mechanical Engineering, China Machine Press, Beijing, China, 405-406 2 Pu Yapeng. (1997) .The Research on Efficiency Improving of Ball Mill and Movement Regulation of Media Collective of Vibration Mill. Bachelor Thesis, Jiangsu, Xuzhou, China, 61-74
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Condition Monitoring and Diagnostic Engineering Management Andrew G. Starr and Raj B.K.N. Rao (Editors) © 2001 Published by Elsevier Science Ltd.
GAS TURBINE BLADE AND DISK CRACK DETECTION USING TORSIONAL VIBRATION MONITORING: A FEASIBILITY STUDY Ken Maynard', Martin Trethewey^, Ramandeep Gill ^, ' Applied Research Laboratory, The Pennsylvania State University ^ Dept. of Mechanical Engineering, The Pennsylvania State University State College, PA 16801, USA
ABSTRACT The primary goal of the this paper is to summarize field demonstrations of the feasibility of detecting changes in blade natural frequencies (such as those associated w^ith a blade or disk crack) on operating turbines using non-contact, non-intrusive measurement methods. This paper primarily addresses the results of application of this non- intrusive torsional vibration sensing to a large wind tunnel fan and a jet engine high-pressure disk. During the operation of rotating equipment, blade natural frequencies are excited by turbulence, friction, and other random forces. These frequencies couple with shaft torsional natural frequencies, which may then be measured. Laboratory testing was conducted to affirm the potential of this method for diagnostics and prognostics of blade and shafting systems. Field installation at the NASA Ames National Full-Scale Aerodynamic Facility (NFAC) reaffirmed the ability to detect both shaft and blade modes. Installation on a high-pressure (HP) disk in a jet engine test cell the manufacturer's facilities demonstrated that thefiindamentalmode of the turbine blades was clearly visible during operation. The results of these field tests have resulted in high confidence that this technique is practical for diagnosing and tracking blade and disk cracks. KEY WORDS Disk cracking; blade cracking; condition-based maintenance; failure prediction; torsional vibration.
BACKGROUND The detection of blade and shaft natural frequencies in the torsional domain requires that the signal resulting from excitation of the rotating elements by turbulence and other random processes is measurable. If measurable, these mtural frequencies may be tracked to determine any shifting due to cracking or other phenomena affecting torsional natural frequencies. Difficulties associated with harvesting the potentially very small signals associated with blade shaft vibration in the torsional domain could render detection infeasible. Thus, transduction and data acquisition must be optimized for dynamic range and signal to noise ratio (Vance, 1988; Maynard and Trethewey, 1999; Maynard et
985
al. 2000). An overview of the laboratory results may be found in Groover, 2000, and Maynard and Trethewey, 2000. The advantage of using shaft torsional natural frequency tracking over shaft lateral natural frequency tracking for detecting cracks in direct-drive machine shafts is twofold •
A shift in natural frequency for a lateral mode may be caused by anythmg which changes the boundary conditions between the rotating and stationary elements: seal rubs, changes in bearing film stifihess due to small temperature changes, thermal growth, misalignment, etc. So, if a shaft experiences a shift in lateral natural fi'equency, it would be difficult to pinpoint the cause as a cracked shaft. However, none of these boundary conditions influence the torsional natural frequencies. So, one may say that a shift in natural fi'equency in a torsional mode of the shaft must involve changes in the rotating element itself, such a crack, or perhaps a coupling degradation.
•
Similarly, finite element modeling of the rotor is simplified when analyzing for torsional natural frequencies: these boundary conditions, which are so difficult to characterize in rotor translational modes, are near non-existent in the torsional domain for many rotor systems. This means that characterization of the torsional rotordyanamics is more straightforward, and therefore likely to better facilitate diagnostics.
Detection of the small torsional vibration signals associated with blade and shaft natural frequencies is complicated by transducer imperfections and by machine speed changes. The use of resampling methods has been shown to facilitate the detection of the shaft natural frequencies by: (1) correcting for torsional transduction difficulties (Maynard and Trethewey, 1999) resulting from harmonic tape imperfections (printing error and overlap error); and (2) correcting errors as the machine undergoes gradual speed fluctuation (Maynard et al. 2000; Groover, 2000). In addition, correction for more dramatic speed changes \^as addressed by Groover (2000). These corrections made laboratory testing quite feasible. Transducer setup and methodology The transducer used to detect the torsional vibration of the shaft included a shaft encoded with black and white stripes, an infrared fiber optic probe, an analog incremental demodulator and an A/D converter. Figure I shows a schematic of the transducer system. Fiber optic cable A-D converter
Fiber optic probe
1
m
Shaft encoded with equally spaced black and white stripes
Figure I: Schematic of transducer setup for torsional vibration measurement
986
The implementation of the technique under laboratory conditions was previously presented in Maynard and Trethewey, 1999, and Maynard et al 2000. Figure 2a shows the tabletop rotor with eight "blades", and the resulting spectrum J^igure 2b) shows the blade group, and the individual detuned blade ("rogue blade").
Figure 2: Torsional Spectrum of Laboratory Rotor with One Detuned Blade
APPLICATION TO AGING TURBINES In vehicular turbines, the loss of turbine blades has resulted in accidents and fatalities. With the aging of commercial and military fleets, fatigue cracking and failure of discs and blades becomes a more pressing issue. For example, some engines in one specific family of engine s (currently used in about four thousand commercial aircraft) are approaching twenty years of age. Although these engines experienced little blade and disk cracking early in their lives, cracking has been detected during regularly scheduled inspections on aging engines. There is some concern that this cracking may accelerate as the engines continue to age, and may require increased inspections. The possibility of a crack precipitating and resulting in blade failure between scheduled inspections increases with age, and an in situ system could provide the user/maintainer with blade health information. Similarly, military aircraft engines are exhibiting symptoms of aging, which include blade and disk failure. From 1982 to 1998, 55% of USAF engine caused mishaps were related to high-cycle fatigue (HCF) of engine parts (Davenport, 1998), predominately blades and disks at the blade attachment (Davenport, 2001). In additbn, about 87% of the risk management inspections were related to this HCF. Thus, a large portion of the maintenance budget is associated with HCF of blades and disks at the blade attachment. NASA AMES NATIONAL FULI^SCALE AERODYNAMIC COMPLEX (NFAC) FANS The NFAC facility is the largest wind tunnel in the world, able to test a full-scale 737 in the largest section (see Figure 3). The fans used to drive the wind tunnel, shown in Figure 4, have experienced some blade cracking in the past, and the repairs made continued inspection unpractical. NASA decided to use modal impact testing of the blades to track the natural frequencies of the blades to detect shifts that might be associated with cracking. However, it was determined that the frequency might shift as much as ten percent simply by rotating the rotor 360° and retesting the same blade. Since this was on the order of the expected shift due to cracks, other methods were considered. A feasibility study was conducted using torsional vibration on an operating fan.
987
•O'WTEsrsecTroN
Figure 3: National Full-Scale Aerodynamic Complex (NFAC) at NASA Ames
Figure 4: NFAC Fans The shaft was encoded with zebra tape, and testing was performed during operation of the fans. The results are shown in Figure 5. The first two peaks correspond to overall shaft torsional frequencies, based on simple dynamic models developed by the vendor in the 70s. The third peak, near 13.5 Hz, corresponds to the blade group. Of particular interest is the tight packing of the fifteen blade modes, especially knowing that the impact testing on a single blade could vary by 1 Hz or more during a single test session. We believe that the variable pitch mechanism (VPM) docs not provide a repeatable boundary support for the blades when it is not operating, but provides uniform and repeatable results during operation, when the VPM is preloaded. Subsequent to this testing, impact testing was performed on a blade with the oil pump operating to attempt to allow torsional coupling. The resulting naturalft^equencywas found to be about 14.6 Hz This testing demonstrated the ability of this measurement system to detect blade natural frequencies during operation of the NFAC fans.
Figure 5: Averaged Torsional Spectrum of NFAC Fan
INSTRUMENTATION OF AN AIRCRAFT JET TURBINE The HP turbine of a commercial jet engine was instrumented to detemiine the feasibihty of detecting the blade natural frequencies during operation using torsional vibration. The testing was perfomied in a warm air test facility, under load at about 9400 RPM. Figure 6 shows the fully assembled commercial engine and the HP disk as installed in the warm air test facility.
Figure 6: a) Fully Assembled Jet Engine; b) HP Disk in Warm Air Test Facility Several individual blades were tested by the engine manufacturer to determine the natural frequencies. These blades were fixtured to simulate the boundary conditions during operation. The modal testing results are summarized for the three specimen blades in Table 1. The instrumentation included a 200-stripe zebra tape and fiber optic probes. In addition, the output from a 60-tooth speed encoder was used as backup. The zebra tape did not survive the test, and the speed signal from the 60-tooth gear was used. The use of the 60-tooth wheel limited the frequency range of the data to onehalf the number of teeth times the
iBIade F12 Blade C14 Blade B7 1 Average 1 Std. Dev.
Mode 1 2450 2413 2434 2432 19
Mode 2 3943 3853 3875 3890 47
Mode 3 6038 6091 5994 6041 49
Mode 4 1 8409 8469 8364 8414 53
Table 1: Results of Modal Testing of Sample Blades
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speed of the shaft, or about 4500 Hz. Figure 8 shows the torsional spectrum of the HP turbine rotor. Note that torsional natural frequencies of the shafting system should change very little with speed, and coupled blade and shaft torsional modes increase very slightly with speed due to stiffening by axial force. So, we look for fequencies that are not shifting with speed. Note that the first peak at about 2400 Hz is close to the first mode from the blade modal testing. Earlier experimental and analytical work, however, indicates that blade natural frequencies can be significantly altered by Figure 7: 60-tooth speed encoder coupling with shaft torsional modes (Maynard and Trethewey, 1999; Maynard et al. 2000). It was shown that, for a small desktop rotor with much larger blade to rotor mass ratio, the difference might be more than 30%, and that the frequency may be higher or lower than rig impact testing results. Although intuition might imply that for relatively small blades coupling would be less of a factor, the effects of torsional coupling should be clarified using a dynamic torsional model. -lU
9000 rpm. 60 tooth -16 •
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3000 3200 3400 Frequency. Hz
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4000
4200
Figure 8: Torsional Spectrum of HP Turbine
SUMMARY AND CONCLUSIONS The techniques developed for detecting torsional natural frequencies in the laboratory were implemented on a large wind tunnel fan that has experienced blade cracking, and a jet engine that has experience cracking in the disc at the blade root. The goals of the implementation project were to demonstrate the feasibility of field application in fans and turbines, and to establish a baseline for each class of machine. The data acquired clearly demonstrated the feasibility of field implementation, and established baseline natural frequencies for blades and shafts. However, interference from tape related spectral content was experienced. This interference was not experienced in the laboratory due to shaft size, access, and environmental differences. It is believed that this spectral content is associated not with tape printing error or overlap, but was introduced by the installation. This may be overcome by using a more precisely fabricated encoding device (such as the
990
60-tooth speed encoder at the jet engine test facility), or by developing correction algorithms for the installation error. Future work Research to establish the size of detectable cracks in each class of machine is needed. For instance, turbine engines have experienced cracking in the HP disk at the blade root. It is important that we estimate the size of the detectable crack and the remaining useful life of the disk at the frequency shift detection threshold to establish the torsional vibration method as practical in field machines. Correction of the installation errors miist be accomplished to remove ambiguity and make the technology widely accessible. This work is currently underway. I
Acknowledgement This work described herein was supported by the Southern Company through the Cooperative Research Agreement Torsional Vibration and Shaft Twist Measurement in Rotating Machinery (SCS Contract Number C-98-001172); NASA Ames Research Center Grant Condition-Based Monitoring of Large-Scale Facilities, (Grant NAG 21182); by Multidisciplinary University Research Initiative for Integrated Predictive Diagnostics (Grant Number NOOO14-95-1-0461) sponsored by the Office of Naval Research; and by NASA Glenn/GE Aircraft Engines Turbine Disk Crack Detection Using Torsional Vibration: A Feasibility Study (GEAE Purchase Order 200-1X-14H45006). The content of the information does not necessarily reflect the position or policy of the United States Government, and no official endorsement should be inferred.
REFERENCES: Davenport, Otha B. (1988). Symposium,
Operational Readiness and High Cycle Fatigue.
Turbine Engine
Davenport, Otha B. (2001), Personal conversation, 29 June 2001, Groover, Charles Leonard (2000). Signal Component Removal Applied to the Order Content in Rotating Machinery, Master of Science in Mechanical Engineering Thesis, Penn State University. Hernandez, W., Paul, D., and Vosburgh, F (1996). On-Line Measurement and Tracking of Turbine Torsional Vibration Resonances using a New Encoder Based Rotational Vibration Method (RVM). SAE Technical Paper 961306, Presented at the Aerospace Atlantic Conference Maynard, K. P., and Trethewey, M. W. (2000), Blade and Shaft Crack detection Using Torsional Vibration Measurements Part 1: Feasibility Studies. Noise and Vibration Worldwide 31:11, 9-15. Maynard, K. P., and Trethewey, M, W. (2001) Blade and Shaft Crack detection Using Torsional Vibration Measurements Part 2: Resampling to Improve Effective Dynamic Range, Noise and Vibration Worldwide, 32:2, 23-26. Maynard, K. P., and Trethewey, M, (1999). On The Feasibility of Blade Crack Detection Through Torsional Vibration Measurements. Proceedings of the 53''^ Meeting of the Society for Machinery Failure Prevention Technology, 451-459. Maynard, K. P.; Lebold, M.; Groover, C ; Trethewey, M. (2000). Application of Double Resampling to Shaft Torsional Vibration Measurement for the Detection of Blade Natural Frequencies. Proceedings of the 54th Meeting of the Society for Machinery Failure Prevention Technology^, 87-94.
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McDonald, D, and Gribler, M. (1999). Digital Resampling: A Viable Alternative for Order Domain Measurements of Rotating Machinery. Proceedings of the 0^ Annual International Modal Analysis Conference. Parti, 1270-1275. Potter, R. (1990). A New Order Tracking Method for Rotating Machinery," Sound and Vibration, BOSS. Sawyer, John W., Ed. (1980). Sawyer's Turbomachinery Maintenance Handbook, 1st Ed., Vol. II, Turbomachinery International Publications, 7-34ff. Vance, John M. (1988). Rotordynamics of Turbomachinery, John Wiley & Sons, 377ff
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Condition Monitoring and Diagnostic Engineering Management Andrew G. Starr and Raj B.K.N. Rao (Editors) © 2001 Elsevier Science Ltd. All rights reserved.
THE FLOW-INDUCED VIBRATION OF CYLINDERS IN HEAT EXCHANGER W.Takano\ ICTozawa^ M.Yokoi^ M.Nakai ^and LSakamoto^ ^ Department of Precision Engineering, Kyoto University, Yoshida-honmach, Sakyo-ku, Kyoto 606-8501, ,JAPAN ^Junior College, Osaka Sangyo University, Daito, Osaka 574-8530, JAPAN ^Department of Mechanical Engineering for Transportation, Osaka Sangyo University, Daito, Osaka 574-8530, JAPAN
ABSTRACT Flow induced vibrations occur in general heat exchangers and nuclear reactors. They cause the tube failures through fretting wear or fatigue. Fluid elastic instability may lead to vibration amplitudes laige enough to cause tube-to-tube clashing and in such cases will lead to relatively rapid failures. Therefore mechanisms of elastic vibration induced by cross flow are significant problems for large vibration amplitudes. The aims of this study are to analyze the vibration of cylinders at various flow velocities and to get the fundamental data of design for the prevention of accident in heat exchanger. hi ftas study, the experimental apparatus was built so that cylinder tip motions could be measured at various flow velocities. The experiments were conducted in different cylinder arrays. The cylinder vibration was induced in normal direction to flow by an alternate vortex. Moreover, once the flow velocity was increased to a certain value, the cylinder oscillation amplitude increased rapidly with flow and had a maximum. When five cylinders were arranged in a row normal to flow, the wandering of energy was generated between the two cylinders. For three cylinders arranged in tandem, two cylinders in upstream and downstream positions mutually vibrated out of phase.
KEYWORDS Nonlinear Vibration, Nuclear Reactor, Altemate Vortex, Flow Liduced Vibration, Wandering of Energy
993
INTRODUCTION Recently general heat exchangers and nuclear reactors are designed with each tube very close. At the same time, higher mass flow rate is needed to achieve higher eflQciencies of heat exchangers. But it is reported that vibration of tube bundles leads to tube failures asflowvelocity increases. Various tube failures in the nuclear industries have been given in the survey p^er by Paidoussis[I]. Therefore mechanisms of flow induced vibrations are significant problons. Moreover they have occurred in agricultural crop-wind interactions[2] and ciliated insideftroatsin biomechanics. In this study, the experimental ^paratus was built so that cylinder tip motions could be measured at various flow velocities. Thefimdamentaldata for cylinder motions at variousflowvelocities were presented and the mechanisms offlowinduced vibration were analyzed for prevention of accidents in heat exchangers.
EXPERIMENTAL FACILITY AND PROCEDURE The water channel facility, shown schematically in Fig.l, was designed and built for this study. It is about 250cm long, 100cm wide and 250cm high. Water in the lower reservoir © is dehvered to the iqjper reservoir ® using the line pump. Water in the reservoir © circulates throu^ the chamber, the working section and the reservoirs. The uniform flow velocity is provided by adjusting height of water in the reservoirs ® and opening of a valve located at the entrance of the chamber. Figure 2 shows the woiking section, which is 5mm wide by 71mm high andfebricatedfix)mPerspex (trade name, a kind of acrylic resin ). It is fixed to the chamber witii four bolts and connected to reservoir © by the pipe of diameter 20mm- Two kinds of thin cylinders of length 70mm were used instead of tubes: 0.5mm and 0.6mm in diameter. The ends of the cylinders arefixedto the base plate and the cylinders are regarded as cantilevers. The flow velocity around cylinders is indirectly calculatedfix)mthe flow sectional area of the woricing section by measuring the time required to deliver a liter water to the reservoir ©. A pair of strain gauges was fixed close to the root of flie cantilever cylinders so that the cylinder motions in orthogonal direction (streamwise and transverse direction) could be monitored. The stain gauges were calibrated and output
Reservoir (2) FFT analyzer Low pass filte|r Reservoir (2)
Water flow
^Strain gauges Fig. I Schematic diagram of water channel
Fig.2 Working section
994
voltage from the strain gauge amplifier was found to vary linearly with the cylinder tip deflectioa Thus cylinder tip deflection could be determined from the output voltage. Since its amplitude was very small, unnecessary high frequency components were removedfix)moutput signals through the low pass filter. Frequency analyses for these obtained data were performed by the FFT analyzer. Moreover when the data for several cylinders were required at the same time, cylinder deflections were recorded by the data recorder. In this way reUable cylinder response data could be obtained. As Fig.3 illustrates, visualization experiments of cylinder motion and the interstitialflowaround cylinders were conducted The flow was illuminated along perpendicular path from the side via the halogen lamp, which produced the white tight. Thetighttraveled in straight lines through the slit so that it could not be scattered by Perspex of working section and water. Theflowwas visualized via blue ink, wltich was injected into theflowfromthe streamlined upstream. The photographic records of theflowwere taken from above the cylinder using a camera with the shutter speed as small as 1/1000s and high sensitivefilmsASA 1600.
EXPERIMENTAL RESULTS (1) Single cylinder: Vibrational mechanisms of cylinders in crossflowfall into various categories, such as turbulent buffeting, vorticity excitation andfluid-elasticvibration. At first the more detailed experiments for single oscillating cylinder in uniform waterflowwere carried out and theflowwas visualized so that the vibration mechanism could be made clear. In this study, non-dimensional reducedflowvelocity u, = Upl fd, reduced amplitude ^ = A/ d and reduced dumping coefficient c„ = mS/ (pd^) were employed asflowvelocity, amplitude and dumping coefficient, where u^: flow velocity, / : natural frequency of a cylinder in quiescent water , d : cylinder diameter, A : amplitude, m : mass per unit length of a cylinder, 8: logarithmic decrement of damping, p : density of water. Figure 4 shows the motions of a cylinder at variousflowvelocities. X and Y axes represent the transverse and streamwise cylinder deflections respectively. From this figure, it can be seen that the transverse deflection exceeds the streamwise one. The cylinder deflections | are plotted against the flow velocity U, for damping coefficients c„ =0.98,1.05 and 1.98 in Fig.5. Contrary to expectation, the oscillation was not observed at small flow velocity. As the flow velocity is incremented, the amplitudes abmptiy start to grow at a certain velocity. When theflowvelocity increasesfiirther,the amplitude decreases. Theflowwas visualized in order to examine the mechanism of such phenomena tliat the transverse amplitude was larger and the cylinder started to be stationary at a certainflowvelocity. Figures 6 (a) and (b) illustrate the flow
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Fig.4 Traces of oscillating cylinder
995
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Fig. 5 Amplitude versus flow velocity
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around oscillating and stationary cylinders, respectively. For a oscillation cylinder, an alternate vortex could be observed at the side face of cylinder. However it could not be observed for a stationary cylinder. When an alternate vortex occurs, the flow exfoliationfiiomthe surface of cylinder leads to the decrease of pressure. Then the cylinder transversely vibrates. Additionallyfrequencyof altemate vortex shedding is in resonance with a naturalfrequencyof a cylinder in water at theflowvelocity where the oscillating cylinder amplitude is the largest (2) Five cylinders arranged in a row normal to flow: Five cylinders are arranged in a row normal to flow with pitch-to-diameter ratio of 2.0 as shown in Fig.7(a). The strain gauges were fixed to the roots of the center cylinder C and its adjacent cylinder S. Figure 8 shows the amplitude responses of the cylinders C and S againstflowvelocity u,. Cylinders do not oscillate at a smallflowvelocity. The cylinder oscillating amplitudes increase rapidly at theflowvelocity of about 3.9. The amplitudes have the maximum at the flow velocity of 5.4, and decrease with the fiirther increase offlowvelocity. For the cylinders C and S, Fig.9 represents time histories andfrequencyspectra of cylinder deflection atflowvelocities of 4.0,6.2 and 7.9. Thisfigureshows that when the amplitude of the cylinder C is large, the amplitude of the adjacent cylinder S is small and vice versa. That is, the wandering of energy is generated between the two cylinders. This suggests that adjacent cylinders interact each otiier and
996
fluid-elastic vibration occurs. (3) Three cylinders arranged in tandem: Figure 7 (b) shows three cylinders arranged in tandem. The strain gauges werefixedto the roots of two cylinders in upstream and downstream positions. Figure 10 shows the amplitudes e, of two cylinders versus theflowvelocity u,. Asflowvelocity increases, the cylinders start to vibrate at theflowvelocity of about 3.5. The amplitudes become larger as flow velocity is incremented. Both cylinders reached the maximum deflection at the flow velocity of about 6.5. For the cylinders C and D, Fig. 11 represents time histories andfi^quencyspectra of cylinder deflections atflowvelocities of 3.7, 5.2 and 6.5 as shown with arrows in Fig. 10. Though the wandering of energy is observed in the above mentioned experiment (Fig. 9), (a) u, = 4.o
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Fig. 11 Time histories andfrequencyspectra of cylinder deflections
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997
such phenomenon does not occur in this cylinder array. However, the cylinders S and D are found to oscillate out of phase at eachflowvelocity. (4) Three columns and five rows array: Figure 7(c) illustrates the array of 15 cylinders which were arranged in 3 columns and 5 rows. The strain gauges werefixedto the cylinder 1 in the second colunm and the cylinders 2 and 3 in the third column. The transverse deflections of these cylinders were measured. In Fig. 12, the amplitudes are plotted against the flow velocity u, for the cylinders 1, 2 and 3. Cylinders start to vibrate rapidly at the flow velocity of 3.8. As flow velocity is increased, the amplitude also becomes larger. Figure 13 shows time histories of the cylinder deflections atflowvelocities (a) u, =6.9, (b) c/, =5.1 and (c) c/,=4.2. Figure 13(a) reveals that cylinders 1, 2 and 3 vibrate in phase at the fastflowvelocity of 6.9. However in Fig.l3(b), the c}dinder 1 in the upstre^ cohimn and the cylinder 2 and 3 in the downstream column mutually vibrate out of phase in the slowerflow.Moreover in Fig.l3(c), the cylinders 1 and 2 oscillate out of phase, andftiecylinders 2 and 3 are also qjpeared to vibrate out of phase. These vibrational modes are schematically illustrated v^th arrows in Fig. 14. It is found that the cylinder vibration modes depend ontiieflowvelocity. (a)
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CONCLUSIONS The experimental apparatus was built so that cylinder tip motions could be measured at various flow velocities and the fundamaital data could be obtained. From the experiments offlowinduced vibration for
998
different cylinder arrays, the following results were obtained (1) PhotogK^hic records offlowfor single cylinder show that an alternate vortex can be observed at the side face of cylinder. The cylinder vibration is induced in normal direction to flow by this alternate vortex. Moreover once theflowvelocity is increased to a certain value, the cylinder oscillation amplitude increases rapidly withflowand has a maximum. (2) The cylinders arranged in a row normal toflowinteract each other. As a result, the wandering of energy is generated between two adjacent cylinders. (3) For the array where 3 cylinders are arranged in tandem, cylinders in upstream and downstream positions mutually vibrate out of phase. (4) In the case of the cylinder array of 15 cylinders (3 columns and 5 rows), the cylinder vibration modes depend on theflowvelocity. References [1] Paidoussis (1997). Flow Induced Vibrations in Nuclear Reactors and Heat Exchangers. Proceedings of the lUTAMIAHR Symposium on Practical Experiences with Flow Induced Vibrations, 1-80. [2] Finnigan, J. J. (1979). Turbulence in Waving Wheat. Boundary Layer Meteorology 16,181-211.
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AUTHOR INDEX Aaraes,K.A. 943 Aasen, S.E. 943 Adgar,A. 733 Al-Araimi, S.A. 449 al-Attas, S.M. 251 Al-Balushi, K.R. 449 Al-Najjar,B. 217 Alsyouf,!. 217,227 Andrade,F.A. 399,521 Antoni,]. 89 Bach,Fr.-W. 169 Bachschmid, N. 663 Badreddin,E. 201,833 Badri,A. 97,531 Ball, A. 311,353,367,411, 849, 881 Ball, A.D. 263, 329, 383, 465,531,545,561,741, 749, 825,97 BanjevicD. 909 Barnes, J.A. 953 Bartelmus,W. 283 Basso, R. 919 Baydar,N. 411 BenSasi,A. 311 Berry, E. 531,741 Bladen, K. 75 Boswell,L, 943 Boyle, WJ.O. 943 Brandon, J.A. 135 Brennan,F.P. 925 Bryanston-Cross, P. 275 Buchal,R.O. 185 Byerley,T,V. 291 Cattaruzzo, C. 919 Charchalis, A. 489,495 Chen, P. 419,427, 657, 725 Choi,E.-S. 801 Chrpova,E. 503 Cooper, J.E. 615 Coy, J. 67,545 Cruz,S.M.A. 319 Cui,D. 609 Culverson, KJ. 625 deLeeuw,B. 925 De Souza, Jr., D.I. 809 Dempsey, P.J. 433
Didelet Pereira, F.J. 817 Dimitriadis, G. 935 Doyle, A. 909 Elhaj,M. 749 El-Shanti,A. 97,825 El-Shtewi,H. 367,383 Esat,I.I. 399,521 Fabricius, S.M.O. 201, 833 Falade,F. 235 Feng,R 419,427 Fisher, G.C. 959 Fog,T.L. 641 Fonjallaz, P.Y. 943 Fontana, M. 103 Frankowiak, M.R. 481 Friis-Hansen, A. 641 Fujiwara, H. 143 Galka,T. 967 Gao,J. 757 Gebremichael, Y.M. 943 Gill,R. 985 Gindy,N, 757 Girbig,P. 673 Gotzias,N.D. 649 Grattan, K.T.V. 943 Gribok,A. 765 Grosvenor, R.I. 375,391, 481 Grz^dziela, A. 495 Gu,F. 263,311,353,367, 383,531,545,561,649, 741, 749, 825, 849, 881 Hall,L.D. 21,697 Hanasaki, K. 153 Hannah, P. 275 Hess,A.J. 75 Heyns,P.S. 457,537,681 Hills, P.W. 297 Hiltz,J.A. 959 Hines,J.W. 765 Hoffman, A.J. 537 Holroyd,T.J. 29 Hope,A.D. 593 Hotaf,V. 503 Howard,!. 841 Husband, M. 263, 353 Hwang, C.-H. 801
1001
Ito,M. 513 Iwnicki, S. 329 Jack,L.B. Ill Jamaludin, N. 37 Jantunen, E. 781 Jardine, A.K.S. 909 Jennings, A.D. 375,391, 481 Jennings, ID. 361 Jia, S. 841 Jia,Y. 889 Joe Au, Y.H. 49 Jones, B.E. 49 Juri6ic,D. 193 Kacprzynski, G.J. 75 Kaewkongka, T. 49 Kanarachos, A.E. 649 Kanemitsu, Y. 143 Kawai,T. 513 Kennedy, V.R. 375 Kim, C.H. 689 Kim, S.J. 689 Kljajin,M. 441 Komura,H. 657,725,793 Korendo,Z. 717 Kroger, W. 201 Kukla,P. 741 Kvenild, L. 943 Lahdelma, S. 119 Lee,S.K. 127 Lee,T.W. 689 Lee,Y.B. 689 Li,L.Q. 153 Li,W. 67,545,943 Liang, B. 329 Liu,L. 709 Liu,S. 849 Lucifredi, A. 103 Luheng,D. 825 MacIntyre,J. 553 M^czak,J. 857 Maggi,N. 919 Manz,S. 865 Marques Cardoso, A.J. 319 Matushita, O. 143 Maxwell, J. H. 243 Maynard,K. 985
Mba,D. 21,37,697 McGarry,K. 553 McKinley,B. 943 Mechefske, C.K. 57,185, 585,709 Meggitt,B.T. 943 Moore, T.D. 599 Nagahashi,K. 345 Nagai.M. 345 Nakai,M. 993 Nandi,A.K. Ill Nezu,K^ 571 Niho,N. 725 Niho,T. 657
Radkowski, S. 857 Rahman, A.G.A. 251 Rakar,A. 193 Rakowski, R.T. 49 Ramli,R. 251 Randall, R.B. 1,89 Rao, Raj B.K.N. 633 Rasmussen, B. 765 Reimche,W. 169 Resor,B. 985 Rhim,Y.C. 689 Roberts, S. 135 Roemer, MJ. 75 Rosario,D.A. 243
Pantelelis, N.G. 649 Papandreou, N. 649 Park,J.-B. 801 Park,J.-H. 801 Parkin, R.M. 67, 545 Payne, B.S. 263,311,353, 561 Pennacchi, P. 663,873 Perry, D. 209,473 Pietruszkiewicz, R. 367, 383 Pietsch,0. 169 Pinaffo,M. 919 Pooley m, J.C. 13 Pfevratil, L. 503 Prickett,P.W. 375,391, 481 Pyssysalo,T. 119
Saarinen, K. 717 Sadek,H. 593 Sakamoto,!. 993 Salvan, S.M.E. 67 Samanta,B. 449 SatoT. 345 Sawa,Y. 513 Scheer,C. 169 Scheffer,C. 537 Schoombie,W. 457 Shao,Y. 571 Shi,Z. 161,609,749,825, 881 Shibata,K. 793 Shimomura,K. 793 Shin,D. 577 Silvestri,P. 103 Sinmiers,B. 263,353 Smit,W.G. 681 Song,S.O. 577 Stander, C.J. 457, 537 Starr, A.G. 209,275,473, 599, 909, 953 Sudmersen,U. 169 Sun,G. 57
Qu,L. 177
Taguchi, M. 345 Takano,K. 143
Okamoto, J. 345 Okubo,H. 143 Orkisz,M. 717 Ota,H. 345
1002
Takano,W. 993 Takano,Y. 513 Thompson,!. 297 Timusk,M.A. 585 Tokito,T. 571 Toukonen,J. 717 Toyota,!. 419,427,657, 725 Tozawa,K. 993 Treasure, R.J. 615 Trethewey,M. 985 Tsukada,K. 153 Turner, J.R. 375,391,481 van der Merwe, N.T. 537 Vania,A. 663,873 Vicente Sena, F.M. 817 Wang, J. 161,609,841 Wang,L. 593 Wang,W. 889,899 Wang,Z. 609 Webb, P. 757 Webber, CJ.S. 561 Wen,G. 177 Willetts,R. 909 Wnek,M. 717 Wright,!. 741,749 Xiaohong, Jiang 977 Xu,B. 177 Yapeng,Pu 977 Yesilyurt, I. 465 Yokoi,M. 993 Yongzhong, Zhang 977 Yoon,E.S. 577 Yue,H. 161,609,881 Zhang, H. 177 Zhang, W. 899 Zhang, Y. 161 Zhao,H. 161
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