Condition monitoring of induction motors: A review and an application of an ensemble of hybrid intelligent models

Expert Systems with Applications

Volumn 41, Issue 10, 2014, Pages 4891-4903

  Seera, Manjeevan ^a   Lim, Chee Peng ^b   Nahavandi, Saeid ^b   Loo, Chu Kiong ^a  

^a UNIVERSITY OF MALAYA   (Malaysia)

^b DEAKIN UNIVERSITY   (Australia)

Author keywords

Condition monitoring; Fuzzy Min Max neural network; Induction motor; Motor Current Signature Analysis; Random Forest

Indexed keywords

CONDITION MONITORING; COST EFFECTIVENESS; DECISION TREES; FUZZY INFERENCE; FUZZY NEURAL NETWORKS; INDUCTION MOTORS; PATIENT MONITORING; RANDOM FORESTS;

BENCH-MARK PROBLEMS; CLASSIFICATION AND REGRESSION TREE; FUZZY MIN-MAX NEURAL NETWORKS; HYBRID INTELLIGENT MODEL; INTELLIGENT LEARNING; MOTOR CURRENT SIGNATURE ANALYSIS; ON CONDITION MONITORING; STATOR CURRENT SIGNAL;

ELECTRIC MACHINE THEORY;

EID: 84896754429     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2014.02.028     Document Type: Article

References (95)

1. Almeida, A. T. (2006). Energy using product (EuP) directive preparatory study, Lot 11: Motors, analysis of existing technical and market information. Brussels: DG TREN.
2. E. Alpaydin Introduction to machine learning 2004 MIT Press
3. I. Alsyouf The role of maintenance in improving companies' productivity and profitability International Journal of Production Economics 105 1 2007 70 78
4. Amara, Y.; & Barakat, G. (2011). Modeling and diagnostic of stator faults in induction machines using permeance network method. In Progress in electromagnetics research symposium (pp. 1550-1559).
5. M.A. Awadallah, and M.M. Morcos Application of AI tools in fault diagnosis of electrical machines and drives-an overview IEEE Transactions Energy Conversion 18 2 2003 245 251
6. B. Ayhan, M.Y. Chow, and M.H. Song Multiple signature processing-based fault detection schemes for broken rotor bar in induction motors IEEE Transactions Energy Conversion 20 2 2005 336 343
7. B. Ayhan, H.J. Trussell, M.Y. Chow, and M.H. Song On the use of a lower sampling rate for broken rotor bar detection with DTFT and AR-based spectrum methods IEEE Transactions Industrial Electronics 55 3 2008 1421 1434
8. Bache, K.; & Lichman, M. (2013). UCI machine learning repository [Online]. Available < http://archive.ics.uci.edu/ml/datasets/ Steel+Plates+Faults >.
9. M.B.K. Bouzid, G. Champenois, N.M. Bellaaj, L. Signac, and K. Jelassi An effective neural approach for the automatic location of stator interturn faults in induction motor IEEE Transactions on Industrial Electronics 55 12 2008 4277 4289
10. Breiman, L.; Friedman, J.; Olshen, R.; & Stone, C. (1984). Classification and regression trees. Belmont, CA.
11. F. Camci, and R.B. Chinnam Health-state estimation and prognostics in machining processes IEEE Transactions Automation Science and Engineering 7 3 2010 581 597
12. J.R. Cameron, W.T. Thomson, and A.B. Dow Vibration and current monitoring for detecting air gap eccentricity in large induction motors IEE Proceedings Electrical Power Applications 133 3 1986 155 163
13. G.A. Carpenter, and A. Tan Rule extraction: From neural architecture to symbolic representation Connection Science 7 1995 3 27
14. Y. Chen, T. Ding, J. Jin, and D. Ceglarek Integration of process-oriented tolerancing and maintenance planning in design of multistation manufacturing processes IEEE Transactions Automation Science and Engineering 3 4 2006 440 453
15. M.E. Cholette, J. Liu, D. Djurdjanovic, and K.A. Marko Monitoring of complex systems of interacting dynamic systems Applied Intelligence 37 1 2012 60 79
16. Commission of the European communities (2009). Full impact assessment C (2009) 5675, SEC (2009), 1014, Brussels.
17. J. Cooney, D.L. Mann, and B. Winkless Case study applying the TRIZ methodology to machine maintenance TRIZ Journal 2003
18. J. Cusido, J. Rosero, E. Aldabas, J.A. Ortega, and L. Romeral Fault detection techniques for induction motors IEEE Compatibility Power Electronics 2005 85 90
19. P. Davis, E. Sullivan, D. Marlow, and D. Marney A selection framework for infrastructure condition monitoring technologies in water and wastewater networks Expert Systems with Applications 40 6 2012 1947 1958
20. T.G. Dietterich Approximate statistical tests for comparing supervised classification learning algorithms Neural Computation 10 1998 1895 1923
21. T. Dietterich An experimental comparison of three methods for constructing ensembles of decision trees: Bagging, boosting and randomization Machine Learning 40 2 2000 139 157
22. M. Domínguez, J.J. Fuertes, I. Díaz, M.A. Prada, S. Alonso, and A. Morán Monitoring industrial processes with SOM-based dissimilarity maps Expert Systems with Applications 39 8 2012 7110 7120
23. N.M. Elkasabgy, A.R. Eastham, and G.E. Dawson Detection of broken bars in the cage rotor on an induction machine IEEE Transactions on Industrial Applications 28 1 1992 165 171
24. J. Faiz, B.M. Ebrahimi, H.A. Toliyat, and W.S. Abu-Elhaija Mixed-fault diagnosis in induction motors considering varying load and broken bars location Energy Conversion and Management 51 7 2010 1432 1441
25. J. Faiz, and M. Ojaghi Different indexes for eccentricity faults diagnosis in three-phase squirrel-cage induction motors: A review Mechatronics 19 1 2009 2 13
26. M. Fakhr, and A.M. Elsayad Steel plates faults diagnosis with data mining models Journal Computer Science 8 4 2012 506 514
27. M. Feldman, and S. Seibold Damage diagnosis of rotors: Application of Hilbert transform and multi-hypothesis testing Journal Vibration and Control 5 3 1999 421 442
28. R.F. Garcia, J.L.C. Rolle, M.R. Gomez, and A.D. Catoira Expert condition monitoring on hydrostatic self-levitating bearings Expert Systems with Applications 40 8 2012 2975 2984
29. M. Gerdes Decision trees and genetic algorithms for condition monitoring forecasting of aircraft air conditioning Expert Systems with Applications 40 12 2013 5021 5026
30. V.N. Ghate, and S.V. Dudul Cascade neural network based fault classifier for three phase induction motor IEEE Transactions on Industrial Electronics 58 5 2010 1555 1563
31. M. Haji, and H.A. Toliyat Pattern recognition-a technique for induction machines rotor broken bar detection IEEE Transactions on Energy Conversion 16 4 2001 312 317
32. J. Han, M. Kamber, and J. Pei Data mining: Concepts and techniques 2012 Morgan Kaufmann MA, USA (3rd ed)
33. T. Han, B.S. Yang, and Z.J. Yin Feature-based fault diagnosis system of induction motors using vibration signal Journal Quality in Maintenance Engineering 13 2 2007 163 175
34. L. Hansen, and P. Salamon Neural network ensembles IEEE Transactions Pattern Analysis and Machine Intelligence 12 10 1990 993 1001
35. P. Herman, G. Prasad, T.M. McGinnity, and D. Coyle Comparative analysis of spectral approaches to feature extraction for EEG-based motor imagery classification IEEE Transactions Neural Systems and Rehabilitation Engineering 16 2008 317 326
36. J.S. Hsu, H.H. Woodson, and W.F. Weldon Possible errors in measurement of air gap torque pulsations of induction motors IEEE Transactions on Energy Conversion 7 1 1992 202 208
37. X. Huang, T.G. Habetler, R.G. Harley, and E.J. Wiedenbrug Using a surge tester to detect rotor eccentricity faults in induction motors IEEE Transactions on Industrial Applications 43 5 2007 1183 1190
38. Y.R. Hwang, K.K. Jen, and Y.T. Shen Application of cepstrum and neural network to bearing fault detection Journal of Mechanics Science and Technology 23 10 2009 2730 2737
39. R.H. Kewley, M.J. Embrechts, and C. Breneman Data strip mining for the virtual design of pharmaceuticals with neural networks IEEE Transactions on Neural Networks 11 3 2000 668 679
40. Kohavi, R.; & Wolpert, D. (1996). Bias plus variance decomposition for zero-one loss functions. Machine learning. In Proceedings 13th international conference (pp. 275-283).
41. Kohler, J. L.; Sottile, J.; & Trutt, F. C. (1999). Condition-based maintenance of electric machines. In Proceedings of the IEEE industrial applications conference (pp. 205-211).
42. E. Kolman, and M. Margaliot Are artificial neural networks white boxes? IEEE Transactions on Neural Networks 16 4 2005 844 852
43. Kral, C.; Habetler, T. G.; Harley, R. G.; Pirker, F.; Pascoli, G.; Oberguggenberger, H.; & Fenz, C. J. M. (2003). A comparison of rotor fault detection techniques with respect to the assessment of fault severity. In IEEE international symposium diagnostics for electric machines, power electronics and drives (pp. 265-270).
44. Y.S. Lee, J.K. Nelson, H.A. Scarton, D. Teng, and S.A. Ghannad An acoustic diagnostic techniques for use with electric machine insulation IEEE Transactions on Dielectrics and Electrical Insulation 1 6 1994 1186 1193
45. Y. Lei, Z. He, and Y. Zi A new approach to intelligent fault diagnosis of rotating machinery Expert Systems with Applications 35 4 2008 1593 1600
46. Z. Li, H. Fang, and L. Xia Increasing mapping based hidden Markov model for dynamic process monitoring and diagnosis Expert Systems with Applications 41 2 2014 744 751
47. W. Li, and C.K. Mechefske Detection of induction motor faults: A comparison of stator current, vibration and acoustic methods Journal Vibration and Control 12 2 2006 165 188
48. X. Liu, L. Ma, and J. Mathew Machinery fault diagnosis based on fuzzy measure and fuzzy integral data fusion techniques Mechanical Systems and Signal Processing 23 2009 690 700
49. J.F. Martins, V.F. Pires, and T. Amaral Induction motor fault detection and diagnosis using a current state space pattern recognition Pattern Recognition Letters 32 2 2011 321 328
50. M.R. Mehrjou, N. Mariun, M.H. Marhaban, and N. Misron Rotor fault condition monitoring techniques for squirrel-cage induction machine - a review Mechanical Systems and Signal Processing 25 8 2011 2827 2848
51. Milenkovic, A.; Milenkovic, M.; Jovanov, E.; Hite, D.; & Raskovic, D. (2005). An environment for runtime power monitoring of wireless sensor network platforms. In IEEE proceedings of the thirty-seventh southeastern symposium on system theory SSST'05 (pp. 406-410).
52. S. Mitra, K.M. Konwar, and S.K. Pal Fuzzy decision tree, linguistic rules and fuzzy knowledge-based network: Generation and evaluation IEEE Transactions Systems, Man, and Cybernetics, Part C: Applications and Reviews 32 4 2002 328 339
53. M. Montanari, S.M. Peresada, C. Rossi, and A. Tilli Speed sensorless control of induction motors based on a reduced-order adaptive observer IEEE Transactions on Control Systems Technology 15 6 2007 1049 1064
54. M. Nadakatti, A. Ramachandra, and A.N. Santosh Kumar Artificial intelligence-based condition monitoring for plant maintenance Assembly Automation 28 2 2008 143 150
55. A. Nagornyy, A.K. Wallace, and A.V. Jouanne Stray load loss efficiency connections IEEE Industrial Applications Magazine 10 3 2004 62 69
56. S. Nandi, T.C. Ilamparithi, S.B. Lee, and D. Hyun Detection of eccentricity faults in induction machines based on nameplate parameters IEEE Transactions on Industrial Electronics 58 5 2011 1673 1683
57. N.T. Nguyen, H.H. Lee, and J.M. Kwon Optimal feature selection using genetic algorithm for mechanical fault detection of induction motor Journal of Mechanics Science and Technology 22 3 2008 490 496
58. Y. Önel, E. Ayçiçek, and I. Senol An experimental study, about detection of bearing defects in inverter fed small induction motors by Concordia transform Journal Intelligence Manufacturing 20 2009 243 247
59. S. Park, and S.R. Lee Red tides prediction system using fuzzy reasoning and the ensemble method Applied Intelligence 2013 10.1007/s10489-013-0457-1
60. D. Partridge, and W.J. Krzanowski Software diversity: Practical statistics for its measurement and exploitation Information and Software Technology 39 1997 707 717
61. H.W. Penrose, and J. Jette Static motor circuit analysis: An introduction to theory and application IEEE Electrical Insulation Magazine 16 4 2000 6 10
62. V.F. Pires, J.F. Martins, and A.J. Pires Eigenvector/eigenvalue analysis of a 3D current referential fault detection and diagnosis of an induction motor Energy Conversion and Management 51 5 2010 901 907
63. A. Portioli-Staudacher, and M. Tantardini Integrated maintenance and production planning: A model to include rescheduling costs Journal Quality in Maintenance Engineering 18 1 2012 42 59
64. J.G. Proakis, and D.K. Manolakis Digital signal processing 3rd ed. 1995 Prentice-Hall New Jersey
65. A. Quteishat, and C.P. Lim A modified fuzzy min-max neural network with rule extraction and its application to fault detection and classification Applied Soft Computing 8 2 2008 985 995
66. Rockwell Automation (2012). It does a machine good. The Journal (pp. 29-31).
67. P.V.J. Rodríguez, M. Negrea, and A. Arkkioa A simplified scheme for induction motor condition monitoring Mechanical Systems and Signal Processing 22 5 2008 1216 1236
68. Rosero, J. A.; Cusido, J.; Garcia, A.; Ortega, J. A.; & Romeral, L. (2006). Broken bearings and eccentricity fault detection for a permanent magnet synchronous motor. In Proceedings of 32nd annual conference of the IEEE industrial electron society (pp. 964-969).
69. M. Sahraoui, A. Ghoggal, S.E. Zouzou, and M.E.H. Benbouzid Dynamic eccentricity in squirrel cage induction motors - simulation and analytical study of its spectral signatures on stator-currents Simulation Modelling Practice and Theory 16 9 2008 1503 1513
70. B. Samanta, and C. Nataraj Application of particle swarm optimization and proximal support vector machines for fault detection Swarm Intelligence 3 4 2009 303 325
71. A.B. Sasi, F. Gu, Y. Li, and A.D. Ball A validated model for the prediction of rotor bar failure in squirrel-cage motors using instantaneous angular speed Mechanical Systems and Signal Processing 20 7 2006 1572 1589
72. M. Seera, and C.P. Lim A hybrid intelligent system for medical data classification Expert Systems with Applications 41 5 2014 2239 2249
73. R. Sharifi, and M. Ebrahimi Detection of stator winding faults in induction motors using three-phase current monitoring ISA Transactions 50 1 2011 14 20
74. J. Siau, A. Graff, W. Soong, and N. Ertugrul Broken bar detection in induction motors using current and flux spectral analysis Australian Journal of Electrical and Electronics Engineering 1 3 2004 171 177
75. A. Siddique, G.S. Yadava, and B. Singh A review of stator fault monitoring techniques of induction motors IEEE Transactions on Energy Conversion 20 1 2005 106 114
76. P.K. Simpson Fuzzy min-max neural networks-Part 1: Classification IEEE Transactions on Neural Networks 3 5 1992 776 786
77. P.K. Simpson Fuzzy min-max neural networks-Part 2: Clustering IEEE Transactions on Fuzzy Systems 1 1 1993 32 45
78. R.K. Singal, K. Williams, and S.P. Verma Vibration behavior of stators of electrical machines, Part II: Experimental study Journal of Sound and Vibration 115 1987 13 23
79. H. Su, and K.T. Chong Induction machine condition monitoring using neural network modeling IEEE Transactions on Industrial Electronics 54 1 2007 241 249
80. Supangat, R.; Grieger, J.; Ertugrul, N.; Soong, W. L.; Gray, D. A.; & Hansen, C. (2007). Detection of broken rotor bar faults and effects of loading in induction motors during rundown. In Proceedings of the IEEE international electric machines and drives conference (pp. 196-201).
81. R.M. Tallam, T.G. Habetler, and R.G. Harley Stator winding turn fault detection for closed-loop induction motor drives IEEE Transactions on Industry Application 39 3 2003 720 724
82. N. Tandon, and A. Choudhury A review of vibration and acoustic measurement methods for the detection of defects in rolling element bearings Tribology International 32 8 1999 469 480
83. H. Taplak, I. Uzmay, and S. YIldIrIm An artificial neural network application to fault detection of a rotor bearing system Industrial Lubrication and Tribology 58 1 2006 32 44
84. R. Teti, K. Jemielniak, G. O'Donnell, and D. Dornfeld Advanced monitoring of machining operations CIRP Annals-Manufacturing Technology 59 2 2010 717 739
85. Thorsen, O. V.; & Dalva, M. (1997). Condition monitoring methods, failure identification and analysis for high voltage motors in petrochemical industry. In Eighth international conference on electric machines and drives (pp. 109-113).
86. O.V. Thorsen, and M. Dalva Failure identification and analysis for high-voltage induction motor in the petrochemical industry IEEE Transactions on Industrial Applications 14 1 1999 810 818
87. V.T. Tran, B.S. Yang, M.S. Oh, and A.C.C. Tan Fault diagnosis of induction motor based on decision trees and adaptive neuro-fuzzy inference Expert Systems with Applications 36 2 2009 1840 1849
88. S. Venugopal, R.A. Wagstaff, and J.P. Sharma Exploiting phase fluctuations to improve machine performance monitoring IEEE Transactions Automation Science and Engineering 4 2 2007 153 166
89. A. Verikas, A. Gelzinis, and M. Bacauskiene Mining data with random forests: A survey and results of new tests Pattern Recognition 44 2 2011 330 349
90. J. Weber, and F. Wotawa Diagnosis and repair of dependent failures in the control system of a mobile autonomous robot Applied Intelligence 36 3 2012 511 528
91. G.M. West, S.D.J. McArthur, and D. Towle Industrial implementation of intelligent system techniques for nuclear power plant condition monitoring Expert Systems with Applications 39 8 2012 7432 7440
92. A. Widodo, and B.S. Yang Wavelet support vector machine for induction machine fault diagnosis based on transient current Expert Systems with Applications 35 1-2 2008 307 316
93. Z. Xu, J. Xuan, T. Shi, B. Wu, and Y. Hu A novel fault diagnosis method of bearing based on improved fuzzy ARTMAP and modified distance discriminant technique Expert Systems with Applications 36 9 2009 11801 11807
94. Y. Yohannes, and P. Webb Classification and regression trees 1999 International Food Policy Research Institute USA
95. Z.J. Zhou, C.H. Hu, W.B. Wang, B.C. Zhang, D.L. Xu, and J.F. Zheng Condition-based maintenance of dynamic systems using online failure prognosis and belief rule base Expert Systems with Applications 39 6 2012 6140 6149