Offline and online fault detection and diagnosis of induction motors using a hybrid soft computing model

Applied Soft Computing Journal

Volumn 13, Issue 12, 2013, Pages 4493-4507

  Seera, Manjeevan ^a   Lim, Chee Peng ^b   Ishak, Dahaman ^c   Singh, Harapajan ^d  

^a Intel Technology Malaysia Sdn Bhd   (Malaysia)

^b DEAKIN UNIVERSITY   (Australia)

^c SCHOOL OF PHYSICS   (Malaysia)

^d UNIVERSITI TEKNOLOGI MARA   (Malaysia)

Author keywords

Classification and Regression Tree; Fault detection and diagnosis; Fuzzy Min Max neural network; Induction motor; Motor Current Signature Analysis

Indexed keywords

CLASSIFICATION AND REGRESSION TREE; FAULT DETECTION AND DIAGNOSIS; FUZZY MIN-MAX NEURAL NETWORKS; MOTOR CURRENT SIGNATURE ANALYSIS; MOTOR FAULT DETECTION AND DIAGNOSIS; ON-LINE FAULT DETECTION; POWER SPECTRAL DENSITIES (PSD); STATOR WINDING FAULTS;

FORESTRY; INDUCTION MOTORS; NEURAL NETWORKS; POWER SPECTRAL DENSITY; SOFT COMPUTING; STATORS;

FAULT DETECTION;

CLASSIFICATION; COMPUTATION; ELECTRIC MOTORS; FORESTRY; INDUCTANCE; NEURAL NETWORKS; REGRESSION ANALYSIS;

EID: 84885095960     PISSN: 15684946     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.asoc.2013.08.002     Document Type: Article

References (62)

1. I. Aydin, M. Karakose, and E. Akin A new method for early fault detection and diagnosis of broken rotor bars Energy Conversion and Management 52 2011 1790 1799
2. E. Zio, P. Baraldi, and G. Gola Feature-based classifier ensembles for diagnosing multiple faults in rotating machinery Applied Soft Computing 8 2008 1365 1380
3. L.H. Chiang, R.D. Braatz, and E.L. Russel Fault Detection and Diagnosis in Industrial Systems: A Data-Driven Approach, Advanced Textbooks in Control and Signal Processing Series 2001 Springer-Verlag London
4. H. Han, B. Gu, T. Wang, and Z.R. Li Important sensors for chiller fault detection and diagnosis (FDD) from the perspective of feature selection and machine learning International Journal of Refrigeration 34 2011 586 599
5. S.N. Huang, K.K. Tan, and T.H. Lee Automated fault detection and diagnosis in mechanical systems IEEE Transactions Systems, Man, and Cybernetics Part C: Applications and Reviews 37 2007 1360 1364 (Pubitemid 350130725)
6. J. Ma, and J. Jiang Applications of fault detection and diagnosis methods in nuclear power plants: a review Progress in Nuclear Energy 53 2011 255 266
7. K. Salahshoor, M. Kordestani, and M.S. Khoshro Fault detection and diagnosis of an industrial steam turbine using a distributed configuration of adaptive neuro-fuzzy inference systems Energy 35 2010 5472 5482
8. R. Saidur A review on electrical motors energy use and energy savings Renewable and Sustainable Energy Reviews 14 2010 877 898
9. P.V.J. Rodriguez, and A. Arkkio Detection of stator winding fault in induction motor using fuzzy logic Applied Soft Computing 8 2008 1112 1120 (Pubitemid 350266505)
10. A.T. Almeida, Energy Using Product (EuP) Directive Preparatory Study, Lot 11: Motors, Analysis of Existing Technical and Market Information, 2006.
11. Commission of the European Communities, Full Impact Assessment, C(2009) 5675, SEC(2009) 1014, Brussels.
12. P. Konar, and P. Chattopadhyay Bearing fault detection of induction motor using wavelet and Support Vector Machines (SVMs) Applied Soft Computing 11 2011 4203 4211
13. V.N. Ghate, and S.V. Dudul Cascade neural network based fault classifier for three phase induction motor IEEE Transactions on Industrial Electronics 58 2010 1555 1563
14. J. Penman, H.G. Sedding, B.A. Lloyd, and W.T. Fink Detection and location of interturn short circuits in the stator windings of operating motors IEEE Transactions Energy Conversion 9 1994 652 658
15. A.H. Bonnett, and G.C. Soukup Analysis of rotor failures in squirrel cage induction motors IEEE Transactions Industry Applications 24 1988 1124 1130
16. 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 2011 321 328
17. L.C. Jain, S.C. Tan, and C.P. Lim An introduction to computational intelligence paradigms Studies in Computational Intelligence 137 2008 1 23
18. H. ArabacI, and O. Bilgin Automatic detection and classification of rotor cage faults in squirrel cage induction motor Neural Computing and Applications 19 2010 713 723
19. A. Sadeghian, Z. Ye, and B. Wu Online detection of broken rotor bars in induction motors by wavelet packet decomposition and artificial neural networks IEEE Transactions Instrumentation and Measurement 58 2009 2253 2263
20. 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 2005 336 343 (Pubitemid 40843733)
21. Q. Guo, X. Li, H. Yu, W. Hu, and J. Hu Broken rotor bars fault detection in induction motors using park's vector modulus and FWNN approach Lecture Notes in Computer Science 5264/2008 2008 809 821
22. M.F.S.V. D'Angelo, R.M. Palhares, R.H.C. Takahashi, R.H. Loschi, L.M.R. Baccarini, and W.M. Caminhas Incipient fault detection in induction machine stator-winding using a fuzzy-Bayesian change point detection approach Applied Soft Computing 11 2011 179 192
23. O. Ondel, E. Boutleuxa, G. Clercb, and E. Blanco FDI based on pattern recognition using Kalman prediction: application to an induction machine Engineering Applications of Artificial Intelligence 21 2008 961 973
24. A. Abraham Intelligent systems: architectures and perspectives A. Abraham, L. Jain, J. Kacprzyk, Recent Advances in Intelligent Paradigms and Applications 2002 Springer-Verlag Germany 1 35
25. S. Osowski, and T.H. Linh ECG beat recognition using fuzzy hybrid neural network IEEE Transactions on Biomedical Engineering 48 11 2001 1265 1271 (Pubitemid 32988567)
26. C.-C. Yang, and N.K. Bose Landmine detection and classification with complex-valued hybrid neural network using scattering parameters dataset IEEE Transactions on Neural Networks 16 3 2005 743 753 (Pubitemid 40718017)
27. T. Kohonen Self-Organization and Associative Memory 1984 Springer-Verlag Berlin
28. P.K. Simpson Fuzzy min-max neural networks - part 1: classification IEEE Transactions Neural Networks 3 1992 776 786
29. 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 2008 985 995 (Pubitemid 350266493)
30. G.A. Carpenter, and A. Tan Rule extraction from neural architecture to symbolic representation Connection Science 7 1995 3 27
31. K. Jaber, R. Abdullah, and N.A. Rashid Indexing protein sequence/structure databases using decision tree: a preliminary study International Symposium Information Technology 2 2010 844 849
32. 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 2009 1840 1849
33. A.N. Pathak, M. Sehgal, and D. Christopher A study on fraud detection based on data mining using decision tree International Journal of Computer Science Issues 8 2011 258 261
34. J. Han, M. Kamber, and J. Pei Data Mining: Concepts and Techniques 3rd ed. 2012 Morgan Kaufmann MA, USA
35. Y. Yohannes, and P. Webb Classification and Regression Trees, CART 1999 International Food Policy Research Institute USA
36. A. Siddique, G.S. Yadava, and B. Singh A review of stator fault monitoring techniques of induction motors IEEE Transactions Energy Conversion 20 2005 106 114 (Pubitemid 40377529)
37. 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
38. J.G. Proakis, and D.K. Manolakis Digital Signal Processing 3rd ed. 1995 Prentice-Hall New Jersey
39. R. Sharifi, and M. Ebrahimi Detection of stator winding faults in induction motors using three-phase current monitoring ISA Transactions 50 2011 14 20
40. S. Nandi, and H.A. Toliyat Novel frequency-domain-based technique to detect stator interturn faults in induction machines using stator-induced voltages after switch-off IEEE Transactions on Industrial Applications 38 2002 101 109 (Pubitemid 34491223)
41. Y. Amara, and G. Barakat Modeling and diagnostic of stator faults in induction machines using permeance network method Progress in Electromagnetics Research Symposium 2011 1550 1559
42. 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 2010 1432 1441
43. J. Faiz, and M. Ojaghi Different indexes for eccentricity faults diagnosis in three-phase squirrel-cage induction motors: a review Mechatronics 19 2009 2 13
44. J. Cusidó, J. Rosero, E. Aldabas, J.A. Ortega, and L. Romeral Fault detection techniques for induction motors IEEE Compatibility Power Electronics 2005 85 90
45. P.V.J. Rodríguez, M. Negrea, and A. Arkkioa A simplified scheme for induction motor condition monitoring Mechanical Systems and Signal Processing 22 2008 1216 1236
46. Energy Efficiency & Renewable Energy, Energy Tips: Motor Systems 2012 U.S. Department of Energy
47. S.R. Kolla, and S.D. Altman Artificial neural network based fault identification scheme implementation for a three-phase induction motor ISA Transactions 46 2 2007 261 266 (Pubitemid 46435865)
48. B. Efron Bootstrap methods: another look at the Jackknife Annals of Statistics 7 1 1979 1 26
49. B. Efron, and R. Tibshirani An Introduction to the Bootstrap 1993 Chapman and Hall New York
50. R. Wehrens, and W.E. van der Linden Bootstrapping principal component regression models Journal of Chemometricls 11 1997 157 171 (Pubitemid 127486004)
51. 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 2003 245 251
52. M.R. Mehrjou, N. Mariun, M.H. Marhaban, and N. Misron Rotor fault condition monitoring techniques for squirrel-cage induction machine - a review Mechanical System and Signal Processing 25 8 2011 2827 2848
53. S. Nandi, R. Bharadwaj, H.A. Toliyat, and A.G. Parlos Study of three phase induction motors with incipient rotor cage faults under different supply conditions Proceedings of the IEEE Industry Applications Conference, 34th IAS Annual Meeting 1999 1922 1928 (Pubitemid 30544028)
54. A. Garcia-Perez, R.J. Romero-Troncoso, E. Cabal-Yepez, R.A. Osornio-Rios, J. Rangel-Magdaleno, and H. Miranda Startup current analysis of incipient broken rotor bar in induction motors using high-resolution spectral analysis IEEE Symposium on Diagnostics for Electric Machines, Power Electronics & Drives 2011 657 663
55. C. Bruzzese, and E. Santini Experimental performances of harmonic current sideband based broken bar indicators IEEE International Symposium Diagnostics for Electric Machines, Power Electronics and Drives 2007 226 230
56. G.M. Joksimovic, and J. Penman The detection of inter-turn short circuits in the stator windings of operating motors IEEE Transactions on Industrial Electronics 47 5 2000 1078 1084
57. M.S. Ballal, Z.J. Khan, H.M. Suryawanshi, and R.L. Sonolikar Adaptive neural fuzzy inference system for the detection of inter-turn insulation and bearing wear faults in induction motor IEEE Transactions on Industrial Electronics 54 1 2007 250 258 (Pubitemid 46444022)
58. P.V.J. Rodríguez, and A. Arkkio Detection of stator winding fault in induction motor using fuzzy logic Applied Soft Computing 8 2008 1112 1120 (Pubitemid 350266505)
59. NEMA Standards Publication MG 1-2009: Motors and Generators 2009 National Electrical Manufacturers Association Virgina
60. G.K. Singh, and S.A.S. Al Kazzaz Development of an intelligent diagnostic system for induction machine health monitoring IEEE Systems Journal 2 1 2008 273 288
61. H. Singh, M.Z. Abdullah, and A. Qutieshat Detection and classification of electrical supply voltage quality to electrical motors using the fuzzy-min-max neural network IEEE International Electric Machines & Drives Conference 2011 961 965
62. B.M. Ebrahimi, J. Faiz, and M.J. Roshtkhari Static-, dynamic-, and mixed-eccentricity fault diagnoses in permanent-magnet synchronous motors IEEE Transactions on Industrial Electronics 56 11 2009 4727 4739