A review on machinery diagnostics and prognostics implementing condition-based maintenance

Mechanical Systems and Signal Processing - Tập 20 - Trang 1483-1510 - 2006
Andrew K.S. Jardine1, Daming Lin1, Dragan Banjevic1
1CBM Lab, Department of Mechanical and Industrial Engineering, University of Toronto, 5 King's College Road, Toronto, Ont., Canada M5S 3G8

Tài liệu tham khảo

Martin, 1994, A review by discussion of condition monitoring and fault-diagnosis in machine-tools, International Journal of Machine Tools and Manufacture, 34, 527, 10.1016/0890-6955(94)90083-3 J. Lee, R. Abujamra, A.K.S. Jardine, D. Lin, D. Banjevic, An integrated platform for diagnostics, prognostics and maintenance optimization, in: The IMS ’2004 International Conference on Advances in Maintenance and in Modeling, Simulation and Intelligent Monitoring of Degradations, Arles, France, 2004. S. Nandi, H.A. Toliyat, Condition monitoring and fault diagnostic of electrical machines—A review, in: Thirty-Fourth IAS Annual Meeting, vol. 1, Phoenix, AZ, USA, 1999, pp. 197–204. Pusey, 1999, Assessment of turbomachinery condition monitoring and failure prognosis technology, The Shock and Vibration Digest, 31, 365, 10.1177/058310249903100502 Wang, 2001, Assessment of gear damage monitoring techniques using vibration measurements, Mechanical Systems and Signal Processing, 15, 905, 10.1006/mssp.2001.1392 Wang, 2002, Review of condition assessment of power transformers in service, IEEE Electrical Insulation Magazine, 18, 12, 10.1109/MEI.2002.1161455 A. El-Shafei, N. Rieger, Automated diagnostics of rotating machinery, in: 2003 ASME Turbo Expo, vol. 4, Atlanta, GA, USA, 2003, pp. 491–498. Saha, 2003, Review of modern diagnostic techniques for assessing insulation condition in aged transformers, IEEE Transactions on Dielectrics and Electrical Insulation, 10, 903, 10.1109/TDEI.2003.1237337 R.M. Tallam, S.B. Lee, G. Stone, G.B. Kliman, J. Yoo, T.G. Habetler, R.G. Harley, A survey of methods for detection of stator related faults in induction machines, in: Proceedings of the IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives, New York, 2003, pp. 35–46. Sabnavis, 2004, Cracked shaft detection and diagnostics: A literature review, The Shock and Vibration Digest, 36, 287, 10.1177/0583102404045439 Austerlitz, 2003 Kirianaki, 2002 C. Davies, R.M. Greenough, The use of information systems in fault diagnosis, in: Proceedings of the 16th National Conference on Manufacturing Research, University of East London, UK, 2000. Xu, 2003, Robust isolation of sensor failures, Asian Journal of Control, 5, 12, 10.1111/j.1934-6093.2003.tb00093.x Russ, 2002 Nixon, 2002 Wang, 1993, Early detection of gear failure by vibration analysis II. Interpretation of the time–frequency distribution using image processing techniques, Mechanical Systems and Signal Processing, 7, 205, 10.1006/mssp.1993.1009 Utsumi, 2001, Use of wavelet transform and fuzzy system theory to distinguish wear particles in lubricating oil for bearing diagnosis, Electrical Engineering in Japan, 134, 36, 10.1002/1520-6416(20010115)134:1<36::AID-EEJ5>3.0.CO;2-L Heger, 2004, Optical wear assessment system for grinding tools, Journal of Electronic Imaging, 13, 450, 10.1117/1.1760757 Ellwein, 2002, Identifying regions of interest in spectra for classification purposes, Mechanical Systems and Signal Processing, 16, 211, 10.1006/mssp.2001.1456 Dalpiaz, 2000, Effectiveness and sensitivity of vibration processing techniques for local fault detection in gears, Mechanical Systems and Signal Processing, 14, 387, 10.1006/mssp.1999.1294 A.J. Miller, A new wavelet basis for the decomposition of gear motion error signals and its application to gearbox diagnostics, M.Sc. Thesis, Graduate Program in Acoustics, The Pennsylvania State University, State College, PA, 1999. S. Poyhonen, P. Jover, H. Hyotyniemi, Signal processing of vibrations for condition monitoring of an induction motor, in: ISCCSP: 2004 First International Symposium on Control, Communications and Signal Processing, New York, 2004, pp. 499–502. Baillie, 1996, A comparison of autoregressive modeling techniques for fault diagnosis of rolling element bearings, Mechanical Systems and Signal Processing, 10, 1, 10.1006/mssp.1996.0001 A.K. Garga, B.T. Elverson, D.C. Lang, AR modeling with dimension reduction for machinery fault classification, in: Critical Link: Diagnosis to Prognosis, Haymarket, 1997, pp. 299–308. Zhan, 2003, Adaptive model for vibration monitoring of rotating machinery subject to random deterioration, Journal of Quality in Maintenance Engineering, 9, 351, 10.1108/13552510310503222 Wang, 2001, The application of some non-linear methods in rotating machinery fault diagnosis, Mechanical Systems and Signal Processing, 15, 697, 10.1006/mssp.2000.1316 Wang, 2003, The application of pseudo-phase portrait in machine condition monitoring, Journal of Sound and Vibration, 259, 1, 10.1006/jsvi.2002.5076 Koizumi, 2000, Diagnosis with the correlation integral in time domain, Mechanical Systems and Signal Processing, 14, 1003, 10.1006/mssp.1999.1258 Wang, 2001, Fault identification in rotating machinery using the correlation dimension and bispectra, Nonlinear Dynamics, 25, 383, 10.1023/A:1012985802317 Zhuge, 1991, Signature analysis for reciprocating machinery with adaptive signal-processing, Proceedings of the Institution of Mechanical Engineers Part C—Journal of Mechanical Engineering Science, 205, 305, 10.1243/PIME_PROC_1991_205_125_02 Baydar, 2001, Detection of incipient tooth defect in helical gears using multivariate statistics, Mechanical Systems and Signal Processing, 15, 303, 10.1006/mssp.2000.1315 Schoen, 1995, Effects of time-varying loads on rotor fault detection in induction machines, IEEE Transactions on Industry Applications, 31, 900, 10.1109/28.395302 De Almeida, 2002, New technique for evaluation of global vibration levels in rolling bearings, Shock and Vibration, 9, 225, 10.1155/2002/647652 Liu, 2004, Online rotor mixed fault diagnosis way based on spectrum analysis of instantaneous power in squirrel cage induction motors, IEEE Transactions on Energy Conversion, 19, 485, 10.1109/TEC.2004.832052 Ho, 2000, Optimisation of bearing diagnostic techniques using simulated and actual bearing fault signals, Mechanical Systems and Signal Processing, 14, 763, 10.1006/mssp.2000.1304 Randall, 2001, The relationship between spectral correlation and envelope analysis in the diagnostics of bearing faults and other cyclostationary machine signals, Mechanical Systems and Signal Processing, 15, 945, 10.1006/mssp.2001.1415 Stack, 2004, An amplitude modulation detector for fault diagnosis in rolling element bearings, IEEE Transactions on Industrial Electronics, 51, 1097, 10.1109/TIE.2004.834971 Blankenship, 1995, Analytical solution for modulation sidebands associated with a class of mechanical oscillators, Journal of Sound and Vibration, 179, 13, 10.1006/jsvi.1995.0002 M.A. Minnicino, H.J. Sommer, Detecting and quantifying friction nonlinearity using the Hilbert transform, in: Health Monitoring and Smart Nondestructive Evaluation of Structural and Biological System III, vol. 5394, Bellingham, 2004, pp. 419–427. Goldman, 1999 Harris, 2002 N.T. van der Merwe, A.J. Hoffman, A modified cepstrum analysis applied to vibrational signals, in: Proceedings of 14th International Conference on Digital Signal Processing (DSP2002), vol. 2, Santorini, Greece, 2002, pp. 873–876. Wang, 2002, Rotating machine fault detection based on HOS and artificial neural networks, Journal of Intelligent Manufacturing, 13, 283, 10.1023/A:1016024428793 Xiong, 2002, A novel application of wavelet-based bispectrum analysis to diagnose faults in gears, International Journal of COMADEM, 5, 31 Yang, 2002, Third-order spectral techniques for the diagnosis of motor bearing condition using artificial neural networks, Mechanical Systems and Signal Processing, 16, 391, 10.1006/mssp.2001.1469 Parker, 2000, Fault diagnostics using statistical change detection in the bispectral domain, Mechanical Systems and Signal Processing, 14, 561, 10.1006/mssp.2000.1299 Chow, 1995, Three phase induction machines asymmetrical faults identification using bispectrum, IEEE Transactions on Energy Conversion, 10, 688, 10.1109/60.475840 N. Arthur, J. Penman, Inverter fed induction machine condition monitoring using the bispectrum, in: Proceedings of the IEEE Signal Processing Workshop on Higher-Order Statistics, Banff, Alta., Canada, 1997, pp. 67–71. Li, 2003, Gear crack early diagnosis using bispectrum diagonal slice, Chinese Journal of Mechanical Engineering, 16, 193, 10.3901/CJME.2003.02.193 McCormick, 1999, Bispectral and trispectral features for machine condition diagnosis, IEE Proceedings—Vision, Image and Signal Processing, 146, 229, 10.1049/ip-vis:19990673 Qu, 1989, The holospectrum: A new method for rotor surveillance and diagnosis, Mechanical Systems and Signal Processing, 3, 255, 10.1016/0888-3270(89)90052-6 C.B. Yu, H.B. He, Y. Xu, F.L. Chen, Identification method of acoustic information flow of bearing state, in: Condition Monitoring ’97, 1997, pp. 311–315. Chen, 1998, Diagnosing spindle defects using 4-D holospectrum, Journal of Vibration and Control, 4, 717, 10.1177/107754639800400604 Qu, 1998, Holospectrum during the past decade: Review & prospect, Zhendong Ceshi Yu Zhenduan/Journal of Vibration, Measurement & Diagnosis, 18, 235 Hayes, 1996 Mechefske, 1992, Fault detection and diagnosis in low speed rolling element bearing. Part I: The use of parametric spectra, Mechanical Systems and Signal Processing, 6, 297, 10.1016/0888-3270(92)90032-E Dron, 1998, Fault detection and monitoring of a ball bearing benchtest and a production machine via autoregressive spectrum analysis, Journal of Sound and Vibration, 218, 501, 10.1006/jsvi.1998.1837 Stack, 2004, Bearing fault detection via autoregressive stator current modeling, IEEE Transactions on Industry Applications, 40, 740, 10.1109/TIA.2004.827797 M.J.E. Salami, A. Gani, T. Pervez, Machine condition monitoring and fault diagnosis using spectral analysis techniques, in: Proceedings of the First International Conference on Mechatronics (ICOM ’01), vol. 2, Kuala Lumpur, Malaysia, 2001, pp. 690–700. Wang, 1993, Early detection of gear failure by vibration analysis I. Calculation of the time–frequency distribution, Mechanical Systems and Signal Processing, 7, 193, 10.1006/mssp.1993.1008 F.A. Andrade, I. Esat, M.N.M. Badi, Gearbox fault detection using statistical methods, time–frequency methods (STFT and Wigner–Ville distribution) and harmonic wavelet—A comparative study, in: Proceedings of the COMADEM ’99, Chipping Norton, 1999, pp. 77–85. Meng, 1991, Rotating machinery fault diagnosis using Wigner distribution, Mechanical Systems and Signal Processing, 5, 155, 10.1016/0888-3270(91)90040-C Pan, 1998, Fault diagnosis of joint backlash, Journal of Vibration and Acoustics, Transactions of the ASME, 120, 13, 10.1115/1.2893797 Koo, 2000, The development of reactor coolant pump vibration monitoring and a diagnostic system in the nuclear power plant, ISA Transactions, 39, 309, 10.1016/S0019-0578(00)00019-7 Baydar, 2001, A comparative study of acoustic and vibration signals in detection of gear failures using Wigner–Ville distribution, Mechanical Systems and Signal Processing, 15, 1091, 10.1006/mssp.2000.1338 Cohen, 1989, Time–frequency distribution—A review, Proceedings of the IEEE, 77, 941, 10.1109/5.30749 Bonato, 1997, Bilinear time–frequency transformations in the analysis of damaged structures, Mechanical Systems and Signal Processing, 11, 509, 10.1006/mssp.1997.0094 Gu, 2002, Non-stationary signal analysis and transient machining process condition monitoring, International Journal of Machine Tools and Manufacture, 42, 41, 10.1016/S0890-6955(01)00097-9 Loughlin, 2000, Conditional moments analysis of transients with application to helicopter fault data, Mechanical Systems and Signal Processing, 14, 511, 10.1006/mssp.1999.1287 Young, 1993 Staszewski, 1994, Application of the wavelet transform to fault detection in a spur gear, Mechanical Systems and Signal Processing, 8, 289, 10.1006/mssp.1994.1022 Wang, 1996, Application of wavelets to gearbox vibration signals for fault detection, Journal of Sound and Vibration, 192, 927, 10.1006/jsvi.1996.0226 Rubini, 2001, Application of the envelope and wavelet transform analyses for the diagnosis of incipient faults in ball bearings, Mechanical Systems and Signal Processing, 15, 287, 10.1006/mssp.2000.1330 Luo, 2003, On-line vibration analysis with fast continuous wavelet algorithm for condition monitoring of bearing, Journal of Vibration and Control, 9, 931, 10.1177/10775463030098002 Aretakis, 1997, Wavelet analysis for gas turbine fault diagnostics, Journal of Engineering for Gas Turbines and Power, 119, 870, 10.1115/1.2817067 G.O. Chandroth, W.J. Staszewski, Fault detection in internal combustion engines using wavelet analysis, in: Proceedings of the COMADEM ’99, Chipping Norton, 1999, pp. 7–15. Dalpiaz, 1997, Condition monitoring and diagnostics in automatic machines: Comparison of vibration analysis techniques, Mechanical Systems and Signal Processing, 11, 53, 10.1006/mssp.1996.0067 Baydar, 2003, Detection of gear failures via vibration and acoustic signals using wavelet transform, Mechanical Systems and Signal Processing, 17, 787, 10.1006/mssp.2001.1435 Addison, 2002, Low-oscillation complex wavelets, Journal of Sound and Vibration, 254, 733, 10.1006/jsvi.2001.4119 Xu, 1991, Research on Haar spectrum in fault diagnosis of rotating machinery, Applied Mathematics and Mechanics, 12, 61, 10.1007/BF02018068 Tonshoff, 2003, Application of fast Haar transform and concurrent learning to tool-breakage detection in milling, IEEE/ASME Transactions on Mechatronics, 8, 414, 10.1109/TMECH.2003.816830 A.J. Miller, K.M. Reichard, A new wavelet basis for automated fault diagnostics of gear teeth, in: Inter-Noise 99: Proceedings of the 1999 International Congress on Noise Control Engineering, vols. 1–3, Poughkeepsie, 1999, pp. 1597–1602. Boulahbal, 1999, Amplitude and phase wavelet maps for the detection of cracks in geared systems, Mechanical System and Signal Processing, 13, 423, 10.1006/mssp.1998.1206 Meltzer, 2004, Fault diagnosis in gears operating under non-stationary rotational speed using polar wavelet amplitude maps, Mechanical Systems and Signal Processing, 18, 985, 10.1016/j.ymssp.2004.01.009 Wang, 2003, Wavelet transform with spectral post-processing for enhanced feature extraction, IEEE Transactions on Instrumentation and Measurement, 52, 1296, 10.1109/TIM.2003.816807 Yen, 2000, Wavelet packet feature extraction for vibration monitoring, IEEE Transactions on Industrial Electronics, 47, 650, 10.1109/41.847906 Zhang, 2005, Best basis-based intelligent machine fault diagnosis, Mechanical Systems and Signal Processing, 19, 357, 10.1016/j.ymssp.2004.06.001 Toliyat, 2003, Rail defect diagnosis using wavelet packet decomposition, IEEE Transactions on Industry Applications, 39, 1454, 10.1109/TIA.2003.816474 Yang, 2005, Fault diagnosis of rolling element bearings using basis pursuit, Mechanical Systems and Signal Processing, 19, 341, 10.1016/j.ymssp.2004.03.008 Lin, 2004, Mechanical fault detection based on the wavelet de-noising technique, Journal of Vibration and Acoustics, 126, 9, 10.1115/1.1596552 Peng, 2004, Application of the wavelet transform in machine condition monitoring and fault diagnostics: A review with bibliography, Mechanical Systems and Signal Processing, 18, 199, 10.1016/S0888-3270(03)00075-X Stellman, 1999, Monitoring the degradation of a synthetic lubricant oil using infrared absorption, fluorescence emission and multivariate analysis: A feasibility study, Lubrication Engineering, 55, 42 G.O. Allgood, B.R. Upadhyaya, A model-based high-frequency matched filter arcing diagnostic system based on principal component analysis (PCA) clustering, in: Applications and Science of Computational Intelligence III, vol. 4055, Bellingham, 2000, pp. 430–440. I.K. Fodor, A Survey of Dimension Reduction Techniques, Lawrence Livermore National Laboratory (LLNL) Technical Report, UCRL-ID-148494, University of California, Livermore, CA, 2002. Grimmelius, 1995, On-line failure diagnosis for compression refrigeration plants, International Journal of Refrigeration, 18, 31, 10.1016/0140-7007(94)P3709-A L. Yang, M.Z. Yang, Z. Yan, B.Z. Shi, Extraction of symptom for on-line diagnosis of power equipment based on method of time series analysis, in: Proceedings of the Sixth International Conference on Properties and Applications of Dielectric Materials, vol. 1, Xi’an, China, 2000, pp. 314–317. Sinha, 2002, Trend prediction from steam turbine responses of vibration and eccentricity, Proceedings of the Institution of Mechanical Engineers Part A—Journal of Power and Energy, 216, 97, 10.1243/095765002760024872 Jardine, 1987, Application of the Weibull proportional hazard model to aircraft and marine engine failure data, Quality and Reliability Engineering International, 3, 77, 10.1002/qre.4680030204 Vlok, 2004, Utilising statistical residual life estimates of bearings to quantify the influence of preventive maintenance actions, Mechanical Systems and Signal Processing, 18, 833, 10.1016/j.ymssp.2003.09.003 Moubray, 1997 Goode, 2000, Plant machinery working life prediction method utilizing reliability and condition-monitoring data, Proceedings of the Institution of Mechanical Engineers Part E—Journal of Process Mechanical Engineering, 214, 109, 10.1243/0954408001530146 Rabiner, 1989, Tutorial on hidden Markov models and selected applications in speech recognition, Proceedings of the IEEE, 77, 257, 10.1109/5.18626 Elliott, 1995 Bunks, 2000, Condition-based maintenance of machines using hidden Markov models, Mechanical Systems and Signal Processing, 14, 597, 10.1006/mssp.2000.1309 M. Dong, D. He, Hidden semi-Markov models for machinery health diagnosis and prognosis, in: Papers Presented at NAMRC 32, vol. 32, Charlotte, NC, USA, 2004, pp. 199–206. Lin, 2003, Recursive filters for a partially observable system subject to random failure, Advances in Applied Probability, 35, 207, 10.1239/aap/1046366106 Lin, 2004, On-line parameter estimation for a failure-prone system subject to condition monitoring, Journal of Applied Probability, 41, 211, 10.1239/jap/1077134679 Wang, 2002, A model to predict the residual life of rolling element bearings given monitored condition information to date, IMA Journal of Management Mathematics, 13, 3, 10.1093/imaman/13.1.3 Wang, 2000, On the application of a model of condition-based maintenance, Journal of the Operational Research Society, 51, 1218, 10.1057/palgrave.jors.2601042 A.K.S. Jardine, Optimizing condition based maintenance decisions, in: Proceedings of the Annual Reliability and Maintainability Symposium, 2002, pp. 90–97. Wang, 2002, Modelling condition-based maintenance decision support, 79 Williams, 1994 Korbicz, 2004 Ma, 1995, Detection of localized defects in rolling element bearings via composite hypothesis test, Mechanical Systems and Signal Processing, 9, 63, 10.1006/mssp.1995.0005 Kim, 2001, Developing a fault tolerant power-train control system by integrating design of control and diagnostics, International Journal of Robust and Nonlinear Control, 11, 1095, 10.1002/rnc.646 Sohn, 2002, Statistical damage classification under changing environmental and operational conditions, Journal of Intelligent Material Systems and Structures, 13, 561, 10.1106/104538902030904 M. Nyberg, A general framework for fault diagnosis based on statistical hypothesis testing, in: Twelfth International Workshop on Principles of Diagnosis (DX 2001), Via Lattea, Italian Alps, 2001, pp. 135–142. Fugate, 2001, Vibration-based damage detection using statistical process control, Mechanical Systems and Signal Processing, 15, 707, 10.1006/mssp.2000.1323 V.A. Skormin, L.J. Popyack, V.I. Gorodetski, M.L. Araiza, J.D. Michel, Applications of cluster analysis in diagnostics-related problems, in: Proceedings of the 1999 IEEE Aerospace Conference, vol. 3, Snowmass at Aspen, CO, USA, 1999, pp. 161–168. M. Artes, L. Del Castillo, J. Perez, Failure prevention and diagnosis in machine elements using cluster, in: Proceedings of the Tenth International Congress on Sound and Vibration, Stockholm, Sweden, 2003, pp. 1197–1203. Schurmann, 1996 Ding, 1991, An approach to state recognition and knowledge-based diagnosis for engines, Mechanical Systems and Signal Processing, 5, 257, 10.1016/0888-3270(91)90027-3 Staszewski, 1997, Time–frequency analysis in gearbox fault detection using the Wigner–Ville distribution and pattern recognition, Mechanical Systems and Signal Processing, 11, 673, 10.1006/mssp.1997.0102 Goumas, 2002, Classification of washing machines vibration signals using discrete wavelet analysis for feature extraction, IEEE Transactions on Instrumentation and Measurement, 51, 497, 10.1109/TIM.2002.1017721 Lou, 2004, Bearing fault diagnosis based on wavelet transform and fuzzy inference, Mechanical Systems and Signal Processing, 18, 1077, 10.1016/S0888-3270(03)00077-3 Pan, 2003, Machine condition monitoring using signal classification techniques, Journal of Vibration and Control, 9, 1103, 10.1177/107754603030683 Webb, 2002 Mechefske, 1992, Fault detection and diagnosis in low speed rolling element bearing. Part II: The use of nearest neighbour classification, Mechanical Systems and Signal Processing, 6, 309, 10.1016/0888-3270(92)90033-F Sun, 2004, Pattern recognition for automatic machinery fault diagnosis, Journal of Vibration and Acoustics, Transactions of the ASME, 126, 307, 10.1115/1.1687391 M. Guo, L. Xie, S.-Q. Wang, J.-M. Zhang, Research on an integrated ICA-SVM based framework for fault diagnosis, in: Proceedings of the 2003 IEEE International Conference on Systems, Man and Cybernetics, vol. 3, Washington, DC, USA, 2003, pp. 2710–2715. Ying, 2000, A hidden Markov model-based algorithm for fault diagnosis with partial and imperfect tests, IEEE Transactions on Systems, Man and Cybernetics, Part C (Applications and Reviews), 30, 463, 10.1109/5326.897073 Ge, 2004, Hidden Markov model based fault diagnosis for stamping processes, Mechanical Systems and Signal Processing, 18, 391, 10.1016/S0888-3270(03)00076-1 Li, 2005, Hidden Markov model-based fault diagnostics method in speed-up and speed-down process for rotating machinery, Mechanical Systems and Signal Processing, 19, 329, 10.1016/j.ymssp.2004.01.001 Xu, 2004, Hidden Markov model-based process monitoring system, Journal of Intelligent Manufacturing, 15, 337, 10.1023/B:JIMS.0000026572.03164.64 D. Ye, Q. Ding, Z. Wu, New method for faults diagnosis of rotating machinery based on 2-dimension hidden Markov model, in: Proceedings of the International Symposium on Precision Mechanical Measurement, vol. 4, Hefei, China, 2002, pp. 391–395. A. Siddique, G.S. Yadava, B. Singh, Applications of artificial intelligence techniques for induction machine stator fault diagnostics: Review, in: Proceedings of the IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives, New York, 2003, pp. 29–34. Roemer, 1996, Machine health monitoring and life management using finite element-based neural networks, Journal of Engineering for Gas Turbines and Power—Transactions of the ASME, 118, 830, 10.1115/1.2817002 E.C. Larson, D.P. Wipf, B.E. Parker, Gear and bearing diagnostics using neural network-based amplitude and phase demodulation, in: Proceedings of the 51st Meeting of the Society for Machinery Failure Prevention Technology, Virginia Beach, VA, 1997, pp. 511–521. Li, 2000, Neural-network-based motor rolling bearing fault diagnosis, IEEE Transactions on Industrial Electronics, 47, 1060, 10.1109/41.873214 Fan, 2002, Diagnostic rule extraction from trained feedforward neural networks, Mechanical Systems and Signal Processing, 16, 1073, 10.1006/mssp.2001.1396 Paya, 1997, Artificial neural network based fault diagnostics of rotating machinery using wavelet transforms as a preprocessor, Mechanical Systems and Signal Processing, 11, 751, 10.1006/mssp.1997.0090 Samanta, 2003, Artificial neural network based fault diagnostics of rolling element bearings using time-domain features, Mechanical Systems and Signal Processing, 17, 317, 10.1006/mssp.2001.1462 Spoerre, 1997, Application of the cascade correlation algorithm (CCA) to bearing fault classification problems, Computers in Industry, 32, 295, 10.1016/S0166-3615(96)00080-2 D.W. Dong, J.J. Hopfield, K.P. Unnikrishnan, Neural networks for engine fault diagnostics, in: Neural Networks for Signal Processing VII, New York, 1997, pp. 636–644. Li, 1999, Automatic structure and parameter training methods for modeling of mechanical systems by recurrent neural networks, Applied Mathematical Modelling, 23, 933, 10.1016/S0307-904X(99)00020-7 Deuszkiewicz, 2003, On-line condition monitoring of a power transmission unit of a rail vehicle, Mechanical Systems and Signal Processing, 17, 1321, 10.1006/mssp.2002.1578 Tallam, 2002, Self-commissioning training algorithms for neural networks with applications to electric machine fault diagnostics, IEEE Transactions on Power Electronics, 17, 1089, 10.1109/TPEL.2002.805611 Y.H. Yoon, E.S. Yoon, K.S. Chang, Process fault diagnostics using the integrated graph model, in: On-Line Fault Detection and Supervision in the Chemical Process Industries, Oxford, 1993, pp. 89–94. Hansen, 1994, Expert systems for machine fault diagnosis, Acoustics Australia, 22, 85 Baig, 1998, Model-based reasoning for fault diagnosis of twin-spool turbofans, Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering, 212, 109, 10.1243/0954410981532171 Z.Y. Wen, J. Crossman, J. Cardillo, Y.L. Murphey, Case base reasoning in vehicle fault diagnostics, in: Proceedings of the International Joint Conference on Neural Networks 2003, vols. 1–4, New York, 2003, pp. 2679–2684. M. Bengtsson, E. Olsson, P. Funk, M. Jackson, Technical design of condition based maintenance system—A case study using sound analysis and case-based reasoning, in: Maintenance and Reliability Conference—Proceedings of the Eighth Congress, Knoxville, USA, 2004. M.L. Araiza, R. Kent, R. Espinosa, Real-time, embedded diagnostics and prognostics in advanced artillery systems, in: 2002 IEEE Autotestcon Proceedings, Systems Readiness Technology Conference, New York, 2002, pp. 818–841. Hall, 1997, The negative information problem in mechanical diagnostics, Journal of Engineering for Gas Turbines and Power—Transactions of the ASME, 119, 370, 10.1115/1.2815584 Stanek, 2001, Model-aided diagnosis: An inexpensive combination of model-based and case-based condition assessment, IEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews, 31, 137, 10.1109/5326.941838 Silva, 1998, Tool wear monitoring of turning operations by neural network and expert system classification of a feature set generated from multiple sensors, Mechanical Systems and Signal Processing, 12, 319, 10.1006/mssp.1997.0123 DePold, 1999, The application of expert systems and neural networks to gas turbine prognostics and diagnostics, Journal of Engineering for Gas Turbines and Power, 121, 607, 10.1115/1.2818515 Yang, 2004, Integration of ART-Kohonen neural network and case-based reasoning for intelligent fault diagnosis, Expert Systems with Applications, 26, 387, 10.1016/j.eswa.2003.09.009 Mechefske, 1998, Objective machinery fault diagnosis using fuzzy logic, Mechanical Systems and Signal Processing, 12, 855, 10.1006/mssp.1998.0173 G.C. Collins, J.R. Bourne, A.J. Brodersen, C.F. Lo, Comparison of rule-based and belief-based systems for diagnostic problems, in: Proceedings of the Second International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems (IEA/AIE—89), vol. 2, New York, USA, 1989, pp. 785–793. Du, 2004, Fuzzy transition probability: A new method for monitoring progressive faults. Part 1: The theory, Engineering Applications of Artificial Intelligence, 17, 457, 10.1016/j.engappai.2004.04.019 Zhang, 2003, Fault diagnosis system for rotary machine based on fuzzy neural networks, JSME International Journal. Series C: Mechanical Systems, Machine Elements and Manufacturing, 46, 1035, 10.1299/jsmec.46.1035 Liu, 1996, Detection of roller bearing defects using expert system and fuzzy logic, Mechanical Systems and Signal Processing, 10, 595, 10.1006/mssp.1996.0041 Chang, 1995, Development of the on-line operator aid system OASYS using a rule-based expert system and fuzzy logic for nuclear power plants, Nuclear Technology, 112, 266, 10.13182/NT95-A35179 A.K. Garga, K.T. McClintic, R.L. Campbell, C.C. Yang, M.S. Lebold, T.A. Hay, C.S. Byington, Hybrid reasoning for prognostic learning in CBM systems, in: 2001 IEEE Aerospace Conference Proceedings, vols. 1–7, New York, 2001, pp. 2957–2969. Fogel, 1994, An introduction to simulated evolutionary optimization, IEEE Transactions on Neural Networks, 5, 3, 10.1109/72.265956 Sampath, 2002, Engine-fault diagnostics: An optimisation procedure, Applied Energy, 73, 47, 10.1016/S0306-2619(02)00051-X Chen, 2003, Evolutionary strategy for classification problems and its application in fault diagnostics, Engineering Applications of Artificial Intelligence, 16, 31, 10.1016/S0952-1976(03)00027-7 Huang, 2002, Evolving wavelet networks for power transformer condition monitoring, IEEE Transactions on Power Delivery, 17, 412, 10.1109/61.997908 G.-T. Yan, G.-F. Ma, Fault diagnosis of diesel engine combustion system based on neural networks, in: Proceedings of the 2004 International Conference on Machine Learning and Cybernetics, vol. 5, Shanghai, China, 2004, pp. 3111–3114. Gertler, 1998 Simani, 2003 Howard, 2001, The dynamic modelling of a spur gear in mesh including friction and a crack, Mechanical Systems and Signal Processing, 15, 831, 10.1006/mssp.2001.1414 W.Y. Wang, Towards dynamic model-based prognostics for transmission gears, in: Component and Systems Diagnostics, Prognostics, and Health Management II, vol. 4733, Bellingham, 2002, pp. 157–167. D.C. Baillie, J. Mathew, Nonlinear model-based fault diagnosis of bearings, in: Proceedings of an International Conference on Condition Monitoring, Swansea, UK, 1994, pp. 241–252. Loparo, 2000, Fault detection and diagnosis of rotating machinery, IEEE Transactions on Industrial Electronics, 47, 1005, 10.1109/41.873208 K.A. Loparo, A.H. Falah, M.L. Adams, Model-based fault detection and diagnosis in rotating machinery, in: Proceedings of the Tenth International Congress on Sound and Vibration, Stockholm, Sweden, 2003, pp. 1299–1306. C.H. Oppenheimer, K.A. Loparo, Physically based diagnosis and prognosis of cracked rotor shafts, in: Component and Systems Diagnostics, Prognostics, and Health Management II, vol. 4733, Bellingham, 2002, pp. 122–132. Sekhar, 2004, Model-based identification of two cracks in a rotor system, Mechanical Systems and Signal Processing, 18, 977, 10.1016/S0888-3270(03)00041-4 Choi, 1996, Application of minimum cross entropy to model-based monitoring in diamond turning, Mechanical Systems and Signal Processing, 10, 615, 10.1006/mssp.1996.0042 Bartelmus, 2001, Mathematical modelling and computer simulations as an aid to gearbox diagnostics, Mechanical Systems and Signal Processing, 15, 855, 10.1006/mssp.2001.1411 Bartelmus, 2003, Diagnostic information on gearbox condition for mechatronic systems, Transactions of the Institute of Measurement and Control, 25, 451, 10.1191/0142331203tm0099oa Hansen, 1995, A new approach to the challenge of machinery prognostics, Journal of Engineering for Gas Turbines and Power, 117, 320, 10.1115/1.2814097 Vania, 2004, Experimental and theoretical application of fault identification measures of accuracy in rotating machine diagnostics, Mechanical Systems and Signal Processing, 18, 329, 10.1016/S0888-3270(03)00014-1 David, 1994, Petri nets for modeling of dynamic systems—A survey, Automatica, 30, 175, 10.1016/0005-1098(94)90024-8 N.C. Propes, A fuzzy Petri net based mode identification algorithm for fault diagnosis of complex systems, in: System Diagnosis and Prognosis: Security and Condition Monitoring Issues III, vol. 5107, Bellingham, 2003, pp. 44–53. Yang, 2003, A condition-based failure-prediction and processing-scheme for preventive maintenance, IEEE Transactions on Reliability, 52, 373, 10.1109/TR.2003.816402 Yang, 2004, Case-based reasoning system with Petri nets for induction motor fault diagnosis, Expert Systems with Applications, 27, 301, 10.1016/j.eswa.2004.02.004 C.R. Farrar, F. Hemez, G. Park, A.N. Robertson, H. Sohn, T.O. Williams, A coupled approach to developing damage prognosis solutions, in: Damage Assessment of Structures—The Fifth International Conference on Damage Assessment of Structures (DAMAS 2003), Southampton, UK, 2003. Yan, 2004, A prognostic algorithm for machine performance assessment and its application, Production Planning and Control, 15, 796, 10.1080/09537280412331309208 E. Phelps, P. Willett, T. Kirubarajan, A statistical approach to prognostics, in: Component and Systems Diagnostics, Prognosis and Health Management, vol. 4389, Bellingham, 2001, pp. 23–34. D. Banjevic, A.K.S. Jardine, Calculation of reliability function and remaining useful life for a Markov failure time process, IMA Journal of Management Mathematics (2005), to appear, doi:10.1093/imaman/dpi029. R.B. Chinnam, P. Baruah, Autonomous diagnostics and prognostics through competitive learning driven HMM-based clustering, in: Proceedings of the International Joint Conference on Neural Networks 2003, vols. 1–4, New York, 2003, pp. 2466–2471. C. Kwan, X. Zhang, R. Xu, L. Haynes, A novel approach to fault diagnostics and prognostics, in: Proceedings of the 2003 IEEE International Conference on Robotics and Automation, vols. 1–3, New York, 2003, pp. 604–609. Lin, 2004, Filters and parameter estimation for a partially observable system subject to random failure with continuous-range observations, Advances in Applied Probability, 36, 1212, 10.1239/aap/1103662964 Zhang, 1997, Multivariable trend analysis using neural networks for intelligent diagnostics of rotating machinery, Transactions of the ASME. Journal of Engineering for Gas Turbines and Power, 119, 378, 10.1115/1.2815585 Wang, 2001, Fault prognostics using dynamic wavelet neural networks, AI EDAM-Artificial Intelligence for Engineering Design Analysis and Manufacturing, 15, 349, 10.1017/S0890060401154089 Yam, 2001, Intelligent predictive decision support system for condition-based maintenance, International Journal of Advanced Manufacturing Technology, 17, 383, 10.1007/s001700170173 Y.-L. Dong, Y.-J. Gu, K. Yang, W.-K. Zhang, A combining condition prediction model and its application in power plant, in: Proceedings of the 2004 International Conference on Machine Learning and Cybernetics, vol. 6, Shanghai, China, 2004, pp. 3474–3478. Wang, 2004, Prognosis of machine health condition using neuro-fuzzy systems, Mechanical Systems and Signal Processing, 18, 813, 10.1016/S0888-3270(03)00079-7 Chinnam, 2004, A neuro-fuzzy approach for estimating mean residual life in condition-based maintenance systems, International Journal of Materials and Product Technology, 20, 166, 10.1504/IJMPT.2004.003920 Ray, 1996, Stochastic modeling of fatigue crack dynamics for on-line failure prognostics, IEEE Transactions on Control Systems Technology, 4, 443, 10.1109/87.508893 Li, 1999, Adaptive prognostics for rolling element bearing condition, Mechanical Systems and Signal Processing, 13, 103, 10.1006/mssp.1998.0183 Li, 2000, Stochastic prognostics for rolling element bearings, Mechanical Systems and Signal Processing, 14, 747, 10.1006/mssp.2000.1301 Chelidze, 2004, A dynamical systems approach to failure prognosis, Journal of Vibration and Acoustics, 126, 2, 10.1115/1.1640638 J. Luo, A. Bixby, K. Pattipati, L. Qiao, M. Kawamoto, S. Chigusa, An interacting multiple model approach to model-based prognostics, in: System Security and Assurance, vol. 1, Washington, DC, USA, 2003, pp. 189–194. Kacprzynski, 2004, Predicting remaining life by fusing the physics of failure modeling with diagnostics, Journal of Metal, 56, 29 Cempel, 1997, A passive diagnostic experiment with ergodic properties, Mechanical Systems and Signal Processing, 11, 107, 10.1006/mssp.1996.0064 Qiu, 2002, Damage mechanics approach for bearing lifetime prognostics, Mechanical Systems and Signal Processing, 16, 817, 10.1006/mssp.2002.1483 G.A. Lesieutre, L. Fang, U. Lee, Hierarchical failure simulation for machinery prognostics, in: Critical Link: Diagnosis to Prognosis, Haymarket, 1997, pp. 103–110. S.J. Engel, B.J. Gilmartin, K. Bongort, A. Hess, Prognostics, the real issues involved with predicting life remaining, in: 2000 IEEE Aerospace Conference Proceedings, vol. 6, New York, 2000, pp. 457–469. Scarf, 1997, On the application of mathematical models in maintenance, European Journal of Operational Research, 99, 493, 10.1016/S0377-2217(96)00316-5 Lugtigheid, 2004, Modelling repairable system reliability with explanatory variables and repair and maintenance actions, IMA Journal Management Mathematics, 15, 89, 10.1093/imaman/15.2.89 J.E. Campbell, B.M. Thompson, L.P. Swiler, Consequence analysis in predictive health monitoring systems, in: Proceedings of Probabilistic Safety Assessment and Management, vols. I and II, Amsterdam, 2002, pp. 1353–1358. Wang, 2000, A model to determine the optimal critical level and the monitoring intervals in condition-based maintenance, International Journal of Production Research, 38, 1425, 10.1080/002075400188933 Grall, 2002, A condition-based maintenance policy for stochastically deteriorating systems, Reliability Engineering and System Safety, 76, 167, 10.1016/S0951-8320(01)00148-X Castanier, 2003, A sequential condition-based repair/replacement policy with non-periodic inspections for a system subject to continuous wear, Applied Stochastic Models in Business and Industry, 19, 327, 10.1002/asmb.493 Dieulle, 2003, Sequential condition-based maintenance scheduling for a deteriorating system, European Journal of Operational Research, 150, 451, 10.1016/S0377-2217(02)00593-3 S.V. Amari, L. McLaughlin, Optimal design of a condition-based maintenance model, in: Proceedings of the Annual Reliability and Maintainability Symposium, Los Angeles, CA, USA, 2004, pp. 528–533. C. Berenguer, A. Grall, B. Castanier, Simulation and evaluation of condition-based maintenance policies for multi-component continuous-state deteriorating systems, in: Foresight and Precaution, vol. 1–2, Rotterdam, 2000, pp. 275–282. Barata, 2002, Simulation modelling of repairable multi-component deteriorating systems for ‘on condition’ maintenance optimisation, Reliability Engineering and System Safety, 76, 255, 10.1016/S0951-8320(02)00017-0 Marseguerra, 2002, Condition-based maintenance optimization by means of genetic algorithms and Monte Carlo simulation, Reliability Engineering and System Safety, 77, 151, 10.1016/S0951-8320(02)00043-1 Hosseini, 2000, An inspection model with minimal and major maintenance for a system with deterioration and Poisson failures, IEEE Transactions on Reliability, 49, 88, 10.1109/24.855541 Ohnishi, 1994, Optimal minimal-repair and replacement problem of discrete-time Markovian deterioration system under incomplete state information, Computers and Industrial Engineering, 27, 409, 10.1016/0360-8352(94)90321-2 Hontelez, 1996, Optimum condition-based maintenance policies for deteriorating systems with partial information, Reliability Engineering and System Safety, 51, 267, 10.1016/0951-8320(95)00087-9 Aven, 1996, Condition based replacement policies-a counting process approach, Reliability Engineering and System Safety, 51, 275, 10.1016/0951-8320(95)00057-7 Barbera, 1996, A condition based maintenance model with exponential failures and fixed inspection intervals, Journal of the Operational Research Society, 47, 1037, 10.1057/jors.1996.130 Barbera, 1999, A condition based maintenance model for a two-unit series system, European Journal of Operational Research, 116, 281, 10.1016/S0377-2217(98)00189-1 Christer, 1997, A state space condition monitoring model for furnace erosion prediction and replacement, European Journal of Operational Research, 101, 1, 10.1016/S0377-2217(97)00132-X Kumar, 1997, Maintenance scheduling under age replacement policy using proportional hazards model and TTT-plotting, European Journal of Operational Research, 99, 507, 10.1016/S0377-2217(96)00317-7 Makis, 1992, Optimal replacement in the proportional hazards model, INFOR, 30, 172 Banjevic, 2001, A control-limit policy and software for condition-based maintenance optimization, INFOR, 39, 32 Makis, 1998, A condition-based maintenance model, IMA Journal of Mathematics Applied in Business and Industry, 9, 201 Makis, 2003, Optimal replacement under partial observations, Mathematics of Operations Research, 28, 382, 10.1287/moor.28.2.382.14484 W.B. Wang, A stochastic control model for on line condition based maintenance decision support, in: Sixth World Multiconference on Systematics, Cybernetics and Informatics, vol. VI, Proceedings—Industrial Systems and Engineering I, Orlando, 2002, pp. 370–374. Okumura, 2003, Optimisation of inspection time vector and warning level in CBM considering residual life loss and constraint on preventive replacement probability, International Journal of COMADEM, 6, 10 A. Barros, A. Grall, C. Berenguer, A maintenance policy optimized with imperfect and/or partial monitoring, in: Proceedings of the Annual Reliability and Maintainability Symposium, New York, 2003, pp. 406–411. Christer, 1995, A simple condition monitoring model for a direct monitoring process, European Journal of Operational Research, 82, 258, 10.1016/0377-2217(94)00262-B Okumura, 1997, An inspection policy for deteriorating processes using delay-time concept, International Transactions in Operational Research, 4, 365, 10.1111/j.1475-3995.1997.tb00092.x Goode, 2000, Development of model to predict condition monitoring interval times, Ironmaking and Steelmaking, 27, 63, 10.1179/030192300677381 Wang, 2003, Modelling condition monitoring intervals: A hybrid of simulation and analytical approaches, Journal of the Operational Research Society, 54, 273, 10.1057/palgrave.jors.2601508 Hall, 2001 Hall, 2004 Liu, 2001, A case study on multisensor data fusion for imbalance diagnosis of rotating machinery, (AI EDAM) Artificial Intelligence for Engineering Design, Analysis and Manufacturing, 15, 203, 10.1017/S0890060401153011 Wang, 2000, Fault diagnosis theory: Method and application based on multisensor data fusion, Journal of Testing and Evaluation, 28, 513, 10.1520/JTE12143J J.D. Kozlowski, C.S. Byington, A.K. Garga, M.J. Watson, T.A. Hay, Model-based predictive diagnostics for electrochemical energy sources, in: 2001 IEEE Aerospace Conference, vol. 6, Big Sky, MT, 2001, pp. 63149–63164. C.S. Byington, T.A. Merdes, J.D. Kozlowski, Fusion techniques for vibration and oil debris/quality in gearbox failure testing, in: Proceedings of the Condition Monitoring ’99, Chipping Norton, 1999, pp. 113–128. Mannan, 2000, Application of image and sound analysis techniques to monitor the condition of cutting tools, Pattern Recognition Letters, 21, 969, 10.1016/S0167-8655(00)00050-7 P. Hannah, A. Starr, P. Bryanston-Cross, Condition monitoring and diagnostic engineering—A data fusion approach, in: Condition Monitoring and Diagnostic Engineering Management, Amsterdam, 2001, pp. 275–282. R. Willetts, A.G. Starr, D. Banjevic, A.K.S. Jardine, A. Doyle, Optimising complex CBM decisions using hybrid fusion methods, in: Condition Monitoring and Diagnostic Engineering Management, Amsterdam, 2001, pp. 909–918. Starr, 2002, Data fusion applications in intelligent condition monitoring, 110 M.J. Roemer, G.J. Kacprzynski, R.F. Orsagh, Assessment of data and knowledge fusion strategies for prognostics and health management, in: 2001 IEEE Aerospace Conference Proceedings, vol. 6, Big Sky, MT, USA, 2001, pp. 2979–2988. Z.B. Zhang, J. Wang, Y. Tian, H.Q. Zheng, Assessment of information fusion strategies for diagnostics and prognostics, in: Proceedings of the ISTM/2003: Fifth International Symposium on Test and Measurement, vol. 1–6, Beijing, 2003, pp. 1901–1903. Wang, 2003, The reliability and self-diagnosis of sensors in a multisensor data fusion diagnostic system, Journal of Testing and Evaluation, 31, 370 Haykin, 2000 Hyvarinen, 1999, Survey on independent component analysis, Neural Computing Surveys, 2, 94 L. Li, L. Qu, Machine diagnosis with independent component analysis and envelope analysis, in: International Conference on Industrial Technology: ‘Productivity Reincarnation through Robotics and Automation’, vol. 2, Bangkok, Thailand, 2002, pp. 1360–1364. J.P. Barnard, C. Aldrich, Diagnostic monitoring of internal combustion engines by use of independent component analysis and neural networks, in: 2003 International Joint Conference on Neural Networks, vol. 2, Portland, OR, USA, 2003, pp. 869–872. Z.S. Chen, Y.M. Yang, G.J. Shen, X.S. Wen, Early diagnosis of helicopter gearboxes based on independent component analysis, in: Proceedings of the ISTM/2003: Fifth International Symposium on Test and Measurement, vols. 1–6, Beijing, 2003, pp. 3383–3386. X.J. Ma, Z.H. Hao, Multisensor data fusion based on independent component analysis for fault diagnosis of rotor, in: Advances in Neural Networks—ISNN 2004, Part 1, vol. 3173, Berlin, 2004, pp. 744–749. X.H. Tian, J. Lin, K.R. Fyfe, M.J. Zuo, Gearbox fault diagnosis using independent component analysis in the frequency domain and wavelet filtering, in: Proceedings of the 2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, vol. II, Speech II; Industry Technology Tracks; Design and Implementation of Signal Processing Systems; Neural Networks for Signal Processing, New York, 2003, pp. 245–248. H.J. Zhang, L.S. Qu, B.G. Xu, G.G. Wen, Partially blind source separation of the diagnostic signals with prior knowledge, in: Condition Monitoring and Diagnostic Engineering Management, Amsterdam, 2001, pp. 177–184. G. Gelle, M. Colas, C. Serviere, BSS for fault detection and machine monitoring—Time or frequency domain approach? in: Proceedings of the International Workshop on Independent Component Analysis and Blind Signal Separation (ICA 2000), Helsinki, Finland, 2000, pp. 555–560. Gelle, 2003, Blind source separation: A new pre-processing tool for rotating machines monitoring?, IEEE Transactions on Instrumentation and Measurement, 52, 790, 10.1109/TIM.2003.814356 P.W. Tse, J. Zhang, The use of blind-source-separation algorithm for mechanical signal separation and machine fault diagnosis, in: 2003 ASME International Mechanical Engineering Congress, vol. 24, Washington, DC, USA, 2003, pp. 57–63. R.M. Vilela, J.C. Metrolho, J.C. Cardoso, Machine and industrial monitorization system by analysis of acoustic signatures, in: Proceedings of the 12th IEEE Mediterranean Electrotechnical Conference (MELECON 2004), vol. 1, Dubrovnik, Croatia, 2004, pp. 277–279. Serviere, 2004, Blind source separation of noisy harmonic signals for rotating machine diagnosis, Journal of Sound and Vibration, 272, 317, 10.1016/S0022-460X(03)00774-0 Collacott, 1977 Allen, 2004 Discenzo, 1999, Self-diagnosing intelligent motors: A key enabler for next generation manufacturing systems, IEE Colloquium (Digest), 15