A review of data driven-based incipient fault diagnosis

Zidonghua Xuebao/Acta Automatica Sinica

Volumn 42, Issue 9, 2016, Pages 1285-1299

  Wen, Cheng Lin ^a   Lv, Fei Ya ^b   Bao, Zhe Jing ^b   Liu, Mei Qin ^b  

^a HANGZHOU DIANZI UNIVERSITY   (China)

^b ZHEJIANG UNIVERSITY   (China)

Author keywords

Artificial intelligence; Data driven; Incipient fault diagnosis; Signal processing; Statistical analysis

Indexed keywords

ARTIFICIAL INTELLIGENCE; FAILURE ANALYSIS; LEARNING SYSTEMS; SIGNAL PROCESSING; STATISTICAL METHODS;

CORRELATION ANALYSIS; DATA DRIVEN; EXISTING PROBLEMS; INCIPIENT FAULT DIAGNOSIS; MATHEMATICAL TOOLS; MULTI-SOURCE INFORMATION FUSION; SYSTEM DISTURBANCES; TIME FREQUENCY TRANSFORM;

FAULT DETECTION;

EID: 84988420204     PISSN: 02544156     EISSN: None     Source Type: Journal    
DOI: 10.16383/j.aas.2016.c160105     Document Type: Review

References (117)

1. Himmelblau D M. Fault Detection and Diagnosis in Chemical and Petrochemical Processes. New York: Elsevier, 1978.
2. Ren L, Xu Z Y, Yan X Q. Single-sensor incipient fault detection. IEEE Sensors Journal, 2011, 11(9): 2102-2107
3. Amar M, Gondal I, Wilson C. Vibration spectrum imaging: a novel bearing fault classification approach. IEEE Transactions on Industrial Electronics, 2015, 62(1): 494-502
4. Li B, Chow M Y, Tipsuwan Y, Hung J C. Neural-network-based motor rolling bearing fault diagnosis. IEEE Transactions on Industrial Electronics, 2000, 47(5): 1060-1069
5. Demetriou M A, Polycarpou M M. Incipient fault diagnosis of dynamical systems using online approximators. IEEE Transactions on Automatic Control, 1998, 43(11): 1612-1617
6. Naderi M S, Gharehpetian G B, Abedi M, Blackburn T R. Modeling and detection of transformer internal incipient fault during impulse test. IEEE Transactions on Dielectrics and Electrical Insulation, 2008, 15(1): 284-291
7. Oda T, Katoh E. Fault Diagnosis Expert System, U. S. Patent 5127005, June 1992.
8. Yan R Q, Gao R X, Chen X F. Wavelets for fault diagnosis of rotary machines: a review with applications. Signal Processing, 2014, 96: 1-15
9. Chiang L H, Russell E L, Braatz R D. Fault diagnosis in chemical processes using Fisher discriminant analysis, discriminant partial least squares, and principal component analysis. Chemometrics and Intelligent Laboratory Systems, 2000, 50(2): 243-252
10. Yoon S, MacGregor J F. Fault diagnosis with multivariate statistical models, part I: using steady state fault signatures. Journal of Process Control, 2001, 11(4): 387-400
11. Wong P K, Yang Z X, Vong C M, Zhong J H. Real-time fault diagnosis for gas turbine generator systems using extreme learning machine. Neurocomputing, 2014, 128: 249-257
12. Li K, Zhang Y L, Li Z X. Application research of Kalman filter and SVM applied to condition monitoring and fault diagnosis. Applied Mechanics and Materials, 2012, 121-126: 268-272
13. Li Juan, Zhou Dong-Hua, Si Xiao-Sheng, Chen Mao-Yin, Xu Chun-Hong. Review of incipient fault diagnosis methods. Control Theory and Applications, 2012, 29(12): 1517-1529
14. Ren W, Beard R W. Consensus algorithms for double-integrator dynamics. Distributed Consensus in Multi-vehicle Cooperative Control: Theory and Application. London: Springer, 2008. 77-104
15. Venkatasubramanian V, Rengaswamy R, Yin K, Kavuri S N. A review of process fault detection and diagnosis: Part I: quantitative model-based methods. Computers and Chemical Engineering, 2003, 27(3): 293-311
16. Li Han, Xiao De-Yun. Survey on data driven fault diagnosis methods. Control and Decision, 2011, 26(1): 1-9
17. Pearson K. Principal components analysis. The London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science, 1901, 6(2): 559
18. Bartlett M S. Multivariate analysis. Supplement to the Journal of the Royal Statistical Society, 1947, 9(2): 176-197
19. Wiesel A, Hero A O. Decomposable principal component analysis. IEEE Transactions on Signal Processing, 2009, 57(11): 4369-4377
20. Wise B M, Ricker N L, Veltkamp D F, Kowalski B R. A theoretical basis for the use of principal component models for monitoring multivariate processes. Process Control and Quality, 1990, 1(1): 41-51
21. Kaistha N, Upadhyaya B R. Incipient fault detection and isolation in a PWR plant using principal component analysis. In: Proceedings of the 2001 American Control Conference. Arlington, VA, USA: IEEE, 2001. 2119-2120
22. Zhao J S, Huang J C, Sun W. On-line early fault detection and diagnosis of municipal solid waste incinerators. Waste Management, 2008, 28(11): 2406-2414
23. Ge Zhi-Qiang, Yang Chun-Jie, Song Zhi-Huan. Research and application of small shifts detection method based on MEWMA-PCA. Information and Control, 2007, 36(5): 650-656
24. Wen Cheng-Lin, Hu Jing, Wang Tian-Zhen, Chen Zhi-Guo. Relative PCA with applications of data compression and fault diagnosis. Acta Automatica Sinica, 2008, 34(9): 1128-1139
25. Zhou F N, Tang T H, Wen C L. DCA based multi-level small fault diagnosis. In: Proceedings of the 11th IEEE International Conference on Communication Technology. Hangzhou, China: IEEE, 2008. 486-489
26. Harmouche J, Delpha C, Diallo D. Incipient fault detection and diagnosis based on Kullback-Leibler divergence using principal component analysis: Part I. Signal Processing, 2014, 94: 278-287
27. Harmouche J, Delpha C, Diallo D. Incipient fault detection and diagnosis based on Kullback-Leibler divergence using principal component analysis: Part II. Signal Processing, 2015, 109: 334-344
28. Comon P. Independent component analysis. Higher-Order Statistics. Amsterdam: Elsevier, 1992. 29-38
29. Kano M, Tanaka S, Hasebe S, Hashimoto I, Ohno H. Monitoring independent components for fault detection. AIChE Journal, 2003, 49(4): 969-976
30. Ge Z Q, Song Z H. Process monitoring based on independent component analysis-principal component analysis (ICA-PCA) and similarity factors. Industrial and Engineering Chemistry Research, 2007, 46(7): 2054-2063
31. Tian X H, Lin J, Fyfe K R, Zuo M J. 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. Hong Kong, China: IEEE, 2003. II-245-II-248
32. He Q B, Du R X, Kong F R. Phase space feature based on independent component analysis for machine health diagnosis. Journal of Vibration and Acoustics, 2012, 134(2): 021014
33. Ge Z Q, Xie L, Kruger U, Song Z H. Local ICA for multi-variate statistical fault diagnosis in systems with unknown signal and error distributions. AIChE Journal, 2012, 58(8): 2357-2372
34. Guo Y, Wu X, Na J, Fung R F. Incipient faults identification in gearbox by combining Kurtogram and independent component analysis. Applied Mechanics and Materials, 2015, 764-765: 309-313
35. Abdi H. Partial least square regression. Encyclopedia for Research Methods for the Social Sciences. Thousand Oaks: Sage, 2003. 792-795
36. Kresta J V, Macgregor J F, Marlin T E. Multivariate statistical monitoring of process operating performance. Canadian Journal of Chemical Engineering, 1991, 69(1): 35-47
37. Vigneau E, Bertrand D, Qannari E M. Application of latent root regression for calibration in near-infrared spectroscopy. Comparison with principal component regression and partial least squares. Chemometrics and Intelligent Laboratory Systems, 1996, 35(2): 231-238
38. Komulainen T, Sourander M, Jämsä-Jounela S L. An online application of dynamic PLS to a dearomatization process. Computers and Chemical Engineering, 2004, 28(12): 2611-2619
39. Qin S J, Zheng Y Y. Quality-relevant and process-relevant fault monitoring with concurrent projection to latent structures. AIChE Journal, 2013, 59(2): 496-504
40. Kruger U, Dimitriadis G. Diagnosis of process faults in chemical systems using a local partial least squares approach. AIChE Journal, 2008, 54(10): 2581-2596
41. Zhang X D, Polycarpou M M, Parisini T. Fault diagnosis of a class of nonlinear uncertain systems with Lipschitz nonlinearities using adaptive estimation. Automatica, 2010, 46(2): 290-299
42. Harmouche J, Delpha C, Diallo D. Incipient fault amplitude estimation using KL divergence with a probabilistic approach. Signal Processing, 2016, 120: 1-7
43. Lee D D, Seung H S. Learning the parts of objects by non-negative matrix factorization. Nature, 1999, 401(6755): 788-791
44. Hoyer P O. Non-negative sparse coding. In: Proceedings of the 12th IEEE Workshop on Neural Networks for Signal Processing. Martigny, Switzerland: IEEE, 2002. 557-565
45. Tangirala A K, Kanodia J, Shah S L. Non-negative matrix factorization for detection and diagnosis of plantwide oscillations. Industrial and Engineering Chemistry Research, 2007, 46(3): 801-817
46. Wang Q H, Zhang Y Y, Cai L, Zhu Y S. Fault diagnosis for diesel valve trains based on non-negative matrix factorization and neural network ensemble. Mechanical Systems and Signal Processing, 2009, 23(5): 1683-1695
47. Li B, Zhang P L, Liu D S, Mi S S, Ren G Q, Tian H. Feature extraction for rolling element bearing fault diagnosis utilizing generalized S transform and two-dimensional nonnegative matrix factorization. Journal of Sound and Vibration, 2011, 330(10): 2388-2399
48. Kirchgässner G, Wolters J. Introduction to Modern Time Series Analysis. Berlin Heidelberg: Springer, 2007. 2-5
49. Cong F Y, Chen J, Dong G M, Zhao F G. Short-time matrix series based singular value decomposition for rolling bearing fault diagnosis. Mechanical Systems and Signal Processing, 2013, 34(1-2): 218-230
50. Menon S, Uluyol Ä, Kim K, Nwadiogbu E O. Incipient fault detection and diagnosis in turbine engines using hidden Markov models. In: Proceedings of the 2003 ASME Turbo Expo, Collocated with the 2003 International Joint Power Generation Conference. Arlington, Georgia, USA: ASME, 2003. 493-500
51. Purushotham V, Narayanan S, Prasad S A N. Multi-fault diagnosis of rolling bearing elements using wavelet analysis and hidden Markov model based fault recognition. NDT and E International, 2005, 38(8): 654-664
52. Li Z F, Fang H J, Xia L S. Increasing mapping based hidden Markov model for dynamic process monitoring and diagnosis. Expert Systems with Applications, 2014, 41(2): 744-751
53. Bhatnagar S, Rajagopalan V, Ray A. Incipient fault detection in mechanical power transmission systems. In: Proceedings of the 2005 American Control Conference. Portland, OR: IEEE, 2005. 472-477
54. D'Angelo M F S V, Palhares R M, Takahashi R H C, Loschi R H, Baccarini L M R, Caminhas W M. Incipient fault detection in induction machine stator-winding using a fuzzy-Bayesian change point detection approach. Applied Soft Computing, 2011, 11(1): 179-192
55. Barman D, Sarkar S, Das G, Das S, Purkait P. DFA and DWT based severity detection and discrimination of induction motor stator winding short circuit fault from incipient insulation failure. In: Proceedings of the 2015 International Conference on Electrical, Electronics, Signals, Communication, and Optimization. Visakhapatnam: IEEE, 2015. 1-6
56. Liu H C, Liu L, Bian Q H, Lin Q L, Dong N, Xu P C. Failure mode and effects analysis using fuzzy evidential reasoning approach and grey theory. Expert Systems with Applications, 2011, 38(4): 4403-4415
57. Wang M H. Grey-extension method for incipient fault forecasting of oil-immersed power transformer. Electric Power Components and Systems, 2004, 32(10): 959-975
58. Lin C H, Chen J L, Huang P Z. Dissolved gases forecast to enhance oil-immersed transformer fault diagnosis with grey prediction-clustering analysis. Expert Systems, 2011, 28(2): 123-137
59. Wang M H, Tsai H H. Fuel cell fault forecasting system using grey and extension theories. IET Renewable Power Generation, 2012, 6(6): 373-380
60. Qiao W, Lu D G. A survey on wind turbine condition monitoring and fault diagnosis - part II: signals and signal processing methods. IEEE Transactions on Industrial Electronics, 2015, 62(10): 6546-6557
61. Daubechies I. Ten Lectures on Wavelets. Philadelphia: Society for Industrial and Applied Mathematics, 1992.
62. Rubini R, Meneghetti U. Application of the envelope and wavelet transform analyses for the diagnosis of incipient faults in ball bearings. Mechanical Systems and Signal Processing, 2001, 15(2): 287-302
63. Saravanan N, Ramachandran K I. Incipient gear box fault diagnosis using discrete wavelet transform (DWT) for feature extraction and classification using artificial neural network (ANN). Expert Systems with Applications, 2010, 37(6): 4168-4181
64. Sun W, Yang G A, Chen Q, Palazoglu A, Feng K. Fault diagnosis of rolling bearing based on wavelet transform and envelope spectrum correlation. Journal of Vibration and Control, 2013, 19(6): 924-941
65. Luo Rong, Tian Fu-Qing, Li Ke-Yu, Ding Qing-Xi. Realization of convolution wavelet transformation and its application in mechanical incipient fault diagnosis. Journal of Vibration and Shock, 2013, 32(7): 64-69
66. Sugumaran V, Rao A V, Ramachandran K I. A comprehensive study of fault diagnostics of roller bearings using continuous wavelet transform. International Journal of Manufacturing Systems and Design, 2015, 1(1): 27-46
67. Kay S M, Marple S L Jr. Spectrum analysis-a modern perspective. Proceedings of the IEEE, 1981, 69(11): 1380-1419
68. Feng Z P, Chen X W, Liang M. Joint envelope and frequency order spectrum analysis based on iterative generalized demodulation for planetary gearbox fault diagnosis under non-stationary conditions. Mechanical Systems and Signal Processing, 2016, 76-77: 242-264
69. Drago R J. Incipient failure detection. Power Transmission Design, 1979, 21(2): 40-45
70. Nandi S, Toliyat H A, Li X. Condition monitoring and fault diagnosis of electrical motors-a review. IEEE Transactions on Energy Conversion, 2005, 20(4): 719-729
71. Oh J E, Kim W T, Sim H J, Lee J Y. Advanced multi-dimensional spectral analysis and its application for early fault detection. Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, 2006, 220(4): 435-444
72. Muruganatham B, Sanjith M A, Krishnakumar B, Satya Murty S A V. Roller element bearing fault diagnosis using singular spectrum analysis. Mechanical Systems and Signal Processing, 2013, 35(1-2): 150-166
73. Xie S L, Zhang Y H, Xie Q, Chen C H, Zhang X N. Identification of high frequency loads using statistical energy analysis method. Mechanical Systems and Signal Processing, 2013, 35(1-2): 291-306
74. Elsner J B, Tsonis A A. Singular Spectrum Analysis: A New Tool in Time Series Analysis. New York: Springer Science & Business Media, 1996.
75. Huang N E, Shen Z, Long S R, Wu M C, Shi H H, Zheng Q A, Yen N C, Tung C C, Liu H H. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proceedings of the Royal Society of London A: Mathematical, Physical and Engineering Sciences, 1998, 454(1971): 903-995
76. Flandrin P, Rilling G, Goncalves P. Empirical mode decomposition as a filter bank. IEEE Signal Processing Letters, 2004, 11(2): 112-114
77. Elbouchikhi E, Choqueuse V, Trachi Y, Benbouzid M. Induction machine bearing faults detection based on Hilbert-Huang transform. In: Proceedings of the 24th IEEE International Symposium on Industrial Electronics. Buzios: IEEE, 2015. 843-848
78. Liang J, Yang Z X. A novel wavelet transform-empirical mode decomposition based sample entropy and SVD approach for acoustic signal fault diagnosis. In: Proceedings of the 6th International Conference on Advances in Swarm and Computational Intelligence. Beijing, China: Springer International Publishing, 2015. 232-241
79. Yan J H, Lu L. Improved Hilbert-Huang transform based weak signal detection methodology and its application on incipient fault diagnosis and ECG signal analysis. Signal Processing, 2014, 98: 74-87
80. Lei Y G, Lin J, He Z J, Zuo M J. A review on empirical mode decomposition in fault diagnosis of rotating machinery. Mechanical Systems and Signal Processing, 2013, 35(1-2): 108-126
81. Giardina C R, Dougherty E R. Morphological Methods in Image and Signal Processing. Englewood Cliffs: Prentice Hall, 1988.
82. Zhang L J, Xu J W, Yang J H, Yang D B, Wang D D. Multiscale morphology analysis and its application to fault diagnosis. Mechanical Systems and Signal Processing, 2008, 22(3): 597-610
83. He W, Jiang Z N, Qin Q. A joint adaptive wavelet filter and morphological signal processing method for weak mechanical impulse extraction. Journal of Mechanical Science and Technology, 2010, 24(8): 1709-1716
84. Dong Y B, Liao M F, Zhang X L, Wang F Z. Faults diagnosis of rolling element bearings based on modified morphological method. Mechanical Systems and Signal Processing, 2011, 25(4): 1276-1286
85. Li B, Zhang P L, Wang Z J, Mi S S, Zhang Y T. Gear fault detection using multi-scale morphological filters. Measurement, 2011, 44(10): 2078-2089
86. Li B, Zhang P L, Wang Z J, Mi S S, Liu D S. A weighted multi-scale morphological gradient filter for rolling element bearing fault detection. ISA Transactions, 2011, 50(4): 599-608
87. McCulloch W S, Pitts W. A logical calculus of the ideas immanent in nervous activity. The Bulletin of Mathematical Biophysics, 1943, 5(4): 115-133
88. Watanabe K, Matsuura I, Abe M, Kubota M, Himmelblau D M. Incipient fault diagnosis of chemical processes via artificial neural networks. AIChE Journal, 1989, 35(11): 1803-1812
89. Chow M Y, Mangum P, Thomas R J. Incipient fault detection in DC machines using a neural network. In: Proceedings of the 22nd Asilomar Conference on Signals, Systems, and Computers. Pacific Grove, CA: IEEE, 1988. 706-709
90. Zakaria F, Johari D, Musirin I. Artificial neural network (ANN) application in dissolved gas analysis (DGA) methods for the detection of incipient faults in oil-filled power transformer. In: Proceedings of the 2012 IEEE International Conference on Control System, Computing, and Engineering. Penang, Malaysia: IEEE, 2012. 328-332
91. Bhalla D, Bansal R K, Gupta H O. Function analysis based rule extraction from artificial neural networks for transformer incipient fault diagnosis. International Journal of Electrical Power and Energy Systems, 2012, 43(1): 1196-1203
92. Bhowmik P S, Pradhan S, Prakash M, Roy S. Investigation of wavelets and radial basis function neural network for incipient fault diagnosis in induction motors. In: Proceedings of the 2013 International Conference on Circuits, Controls, and Communications. Bengaluru: IEEE, 2013. 1-5
93. Zhang Z Y, Wang Y, Wang K S. Fault diagnosis and prognosis using wavelet packet decomposition, Fourier transform and artificial neural network. Journal of Intelligent Manufacturing, 2013, 24(6): 1213-1227
94. Hornik K, Stinchcombe M, White H. Multilayer feed forward networks are universal approximators. Neural Networks, 1989, 2(5): 359-366
95. Cortes C, Vapnik V. Support-vector networks. Machine Learning, 1995, 20(3): 273-297
96. Widodo A, Yang B S. Support vector machine in machine condition monitoring and fault diagnosis. Mechanical Systems and Signal Processing, 2007, 21(6): 2560-2574
97. Wei C H, TangWH, Wu Q H. A hybrid least-square support vector machine approach to incipient fault detection for oil-immersed power transformer. Electric Power Components and Systems, 2014, 42(5): 453-463
98. Namdari M, Jazayeri-Rad H. Incipient fault diagnosis using support vector machines based on monitoring continuous decision functions. Engineering Applications of Artificial Intelligence, 2014, 28: 22-35
99. Fernández-Francos D, Martínez-Rego D, Fontenla-Romero O, Alonso-Betanzos A. Automatic bearing fault diagnosis based on one-class ν-SVM. Computers and Industrial Engineering, 2013, 64(1): 357-365
100. Huang G B, Zhu Q Y, Siew C K. Extreme learning machine: theory and applications. Neurocomputing, 2006, 70(1-3): 489-501
101. Hu X F, Zhao Z, Wang S, Wang F L, He D K, Wu S K. Multi-stage extreme learning machine for fault diagnosis on hydraulic tube tester. Neural Computing and Applications, 2008, 17(4): 399-403
102. Tian Y, Ma J, Lu C, Wang Z L. Rolling bearing fault diagnosis under variable conditions using LMD-SVD and extreme learning machine. Mechanism and Machine Theory, 2015, 90: 175-186
103. Zadeh L A. Fuzzy sets. Information and Control, 1965, 8(3): 338-353
104. Isermann R. On fuzzy logic applications for automatic control, supervision, and fault diagnosis. IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans, 1998, 28(2): 221-235
105. Chow M Y. Methodologies of Using Neural Network and Fuzzy Logic Technologies for Motor Incipient Fault Detection. River Edge, NJ, USA: World Scientific, 1997.
106. Lou X S, Loparo K A. Bearing fault diagnosis based on wavelet transform and fuzzy inference. Mechanical Systems and Signal Processing, 2004, 18(5): 1077-1095
107. Yang B S, Oh M S, Tan A C C. Fault diagnosis of induction motor based on decision trees and adaptive neuro-fuzzy inference. Expert Systems with Applications, 2009, 36(2): 1840-1849
108. Zhou Dong-Hua, Liu Yang, He Xiao. Review on fault diagnosis techniques for closed-loop systems. Acta Automatica Sinica, 2013, 39(11): 1933-1943
109. McNabb C A, Qin S J. Fault diagnosis in the feedback-invariant subspace of closed-loop systems. Industrial and Engineering Chemistry Research, 2005, 44(8): 2359-2368
110. Gao Jing-Li, Wen Cheng-Lin, Liu Mei-Qin. Low-speed small target detection based on SVD and superposition. Journal of Shanghai Jiaotong University, 2015, 49(6): 876-883
111. Lv F Y, Wen C L, Bao Z J, Liu M Q. Fault diagnosis based on deep learning. In: Proceedings of the 2016 American Control Conference (ACC2016), Boston, MA, USA: IEEE, 2016. 6851-6856
112. Li C L, Hu S Y, Gao S H, Tang J. Real-time grayscale-thermal tracking via Laplacian sparse representation. In: Proceedings of the 22nd International Conference on MultiMedia Modeling. Miami, FL, USA: Springer International Publishing, 2016. 54-65
113. Xu Y, Zhang Z, Lu G M, Yang J. Approximately symmetrical face images for image preprocessing in face recognition and sparse representation based classification. Pattern Recognition, 2016, 54: 68-82
114. Namias V. The fractional order Fourier transform and its application to quantum mechanics. IMA Journal of Applied Mathematics, 1980, 25(3): 241-265
115. Ozaktas H M, Ankan O, Kutay M A, Bozdagt G. Digital computation of the fractional Fourier transform. IEEE Transactions on Signal Processing, 1996, 44(9): 2141-2150
116. Gao Zhe, Liao Xiao-Zhong. A stability criterion for linear fractional order systems in frequency domain. Acta Automatica Sinica, 2011, 37(11): 1387-1394
117. Lin L F, Wang H Q, Lv W Y, Zhong S C. A novel parameter-induced stochastic resonance phenomena in fractional Fourier domain. Mechanical Systems and Signal Processing, 2016, 76-77: 771-779