A new framework of simultaneous-fault diagnosis using pairwise probabilistic multi-label classification for time-dependent patterns

IEEE Transactions on Industrial Electronics

Volumn 60, Issue 8, 2013, Pages 3372-3385

  Vong, Chi Man ^a   Wong, Pak Kin ^a   Ip, Weng Fai ^a  

^a UNIVERSITY OF MACAU   (Macao)

Author keywords

Automotive applications; fault diagnosis; feature extraction; genetic algorithms (GA); ignition; internal combustion engines; multiple signal classification; principal component analysis (PCA); wavelet packets

Indexed keywords

FAILURE ANALYSIS; GENETIC ALGORITHMS; IGNITION; IGNITION SYSTEMS; INTERNAL COMBUSTION ENGINES; PRINCIPAL COMPONENT ANALYSIS; WAVELET ANALYSIS;

AUTOMOTIVE APPLICATIONS; DECISION THRESHOLD; MULTI-LABEL CLASSIFICATIONS; MULTIPLE SIGNAL CLASSIFICATION; SIMULTANEOUS FAULTS; TIME FREQUENCY FEATURES; TIME INDEPENDENTS; WAVELET PACKET;

FEATURE EXTRACTION;

EID: 84876277688     PISSN: 02780046     EISSN: None     Source Type: Journal    
DOI: 10.1109/TIE.2012.2202358     Document Type: Article

References (44)

1. A. Lig?eza and J. Kóscielny, "A new approach to multiple fault diagnosis: A combination of diagnostic matrices, graphs, algebraic and rule-based models. The case of two-layer models," Int. J. Appl. Math. Comput. Sci., vol. 18, no. 4, pp. 465-476, Dec. 2008.
2. C. M. Vong and P. K. Wong, "Engine ignition signal diagnosis with wavelet packet transform and multi-class least squares support vector machines," Expert Syst. Appl., vol. 38, no. 7, pp. 8563-8570, Jul. 2011.
3. I. Yélamos, M. Graells, L. Puigjaner, and G. Escudero, "Simultaneous fault diagnosis in chemical plants using a multilabel approach," Amer. Inst. Chem. Eng. J., vol. 53, no. 11, pp. 2871-2884, Nov. 2007. (Pubitemid 350074580)
4. K. Watanabe, S. Hirota, L. Hou, and D. M. Himmeblau, "Diagnosis of multiple simultaneous fault via hierarchical artificial neural networks," Amer. Inst. Chem. Eng. J., vol. 40, no. 5, pp. 839-848, May 1994.
5. C. M. Vong, P. K. Wong, and W. F. Ip, "Simultaneous faults diagnosis for automotive ignition patterns," in Proc. ICMLC, Guilin, China, 2011, pp. 1324-1330.
6. R. Eslamloueyan, "Designing a hierarchical neural network based on fuzzy clustering for fault diagnosis of the Tennessee-Eastman process," Appl. Soft Comput., vol. 11, no. 1, pp. 1407-1415, Jan. 2011.
7. H. Rafaralahy, H. Richard, M. Boutayeb, and M. Zasadzinski, "Simultaneous observer based sensor diagnosis and speed estimation of unmanned aerial vehicle," in Proc. 47th IEEE Conf. Decision Control, 2008, pp. 2938-2943.
8. D. U. Campos-Delgado and D. R. Espinoza-Trejo, "An observer-based diagnosis scheme for single and simultaneous open-switch faults in induction motor drives," IEEE Trans. Ind. Electron., vol. 58, no. 2, pp. 671-679, Feb. 2011.
9. Z. Gao, R. S. Colby, L. Turner, and B. Leprettre, "Filter design for estimating parameters of induction motors with time-varying loads," IEEE Trans. Ind. Electron., vol. 58, no. 5, pp. 1518-1529, May 2011.
10. L. Romeral, J. C. Urresty, J.-R. R. Ruiz, and A. G. Espinosa, "Modeling of surface-mounted permanent magnet synchronous motors with stator winding interturn faults," IEEE Trans. Ind. Electron., vol. 58, no. 5, pp. 1576-1585, May 2011.
11. A. Raich and A. Çinar, "Diagnosis of process disturbances by statistical distance and angle measures," Comput. Chem. Eng., vol. 21, no. 6, pp. 661-673, Feb. 1997.
12. V. N. Ghate and S. V. Dudul, "Cascade neural-network-based fault classifier for three-phase induction motor," IEEE Trans. Ind. Electron., vol. 58, no. 5, pp. 1555-1563, May 2011.
13. A. Garcia-Perez, R. d. J. Romero-Troncoso, E. Cabal-Yepez, and R. A. Osornio-Rios, "The application of high-resolution spectral analysis for identifying multiple combined faults in induction motors," IEEE Trans. Ind. Electron., vol. 58, no. 5, pp. 2002-2010, May 2011.
14. M. Yu, D. W. Wang, and M. Luo, "Sequential prognosis of multiple failures for hybrid systems based on dynamic fault isolation and computational intelligence," in Proc. IECON-37th Annu. Conf. IEEE Ind. Electron. Soc., 2011, pp. 2417-2422.
15. R. Eslamloueyan, M. Shahrokhi, and R. Bozorgmehri, "Multiple simultaneous fault diagnosis via hierarchical and single artificial neural networks," Scientia Iranica, vol. 10, no. 3, pp. 300-310, Jul. 2003. (Pubitemid 37453690)
16. P. M. Ciarelli, E. Oliveira, C. Baude, and A. F. D. Souza, "Multi-label text categorization using a probabilistic neural network," Int. J. Comput. Inf. Syst. Ind. Manage. Appl. (IJCISIM), vol. 1, pp. 133-144, 2009.
17. R. Vullings, B. de Vries, and J. W. M. Bergmans, "An adaptive Kalman filter for ECG signal enhancement," IEEE Trans. Biomed. Eng., vol. 58, no. 4, pp. 1094-1103, Apr. 2011.
18. Z. Y. He, L. Fu, S. Lin, and Z. Q. Bo, "Fault detection and classification in EHV transmission line based on wavelet singular entropy," IEEE Trans. Power Del., vol. 25, no. 4, pp. 2156-2163, Oct. 2010.
19. P. K.Wong, L. M. Tam, K. Li, and C. M. Vong, "Engine idle-speed system modelling and control optimization using artificial intelligence," Proc. Inst. Mech. Eng. Part D, J. Automobile Eng., vol. 224, no. 1, pp. 55-72, Jan. 2010.
20. S. Choi, B. Akin, M. Rahimian, and H. Toliyat, "Performance-oriented electric motors diagnostics in modern energy conversion systems," IEEE Trans. Ind. Electron., vol. 59, no. 2, pp. 1266-1277, Feb. 2012.
21. A. Ceban, R. Pusca, and R. Romary, "Study of rotor faults in induction motors using external magnetic field analysis," IEEE Trans. Ind. Electron., vol. 59, no. 5, pp. 2082-2093, May 2012.
22. M. Drif and A. Cardoso, "Discriminating the simultaneous occurrence of three-phase induction motor rotor faults and mechanical load oscillations by the instantaneous active and reactive power media signature analyses," IEEE Trans. Ind. Electron., vol. 59, no. 3, pp. 1630-1639, Mar. 2012.
23. C. Bruzzese and G. Joksimovic, "Harmonic signatures of static eccentricities in the stator voltages and in the rotor current of no-load salientpole synchronous generators," IEEE Trans. Ind. Electron., vol. 58, no. 5, pp. 1606-1624, May 2011.
24. J. Pons-Llinares, J. A. Antonino-Daviu, M. Riera-Guasp, M. Pineda-Sanchez, and V. Climente-Alarcon, "Induction motor diagnosis based on a transient current analytic wavelet transform via frequency B-splines," IEEE Trans. Ind. Electron., vol. 58, no. 5, pp. 1530-1544, May 2011.
25. E. H. Bouchikhi, V. Choqueuse, M. E. H. Benbouzid, J. F. Charpentier, and G. Barakat, "A comparative study of time-frequency representations for fault detection in wind turbine," in Proc. IECON-37th Annu. Conf. IEEE Ind. Electron. Soc., 2011, pp. 3584-3589.
26. H. Henao, S. H. Kia, and G. Capolino, "Torsional-vibration assessment and gear-fault diagnosis in railway traction system," IEEE Trans. Ind. Electron., vol. 58, no. 5, pp. 1707-1717, May 2011.
27. S. Cruz, A.Mendes, and A. Cardoso, "A new fault diagnosis method and a fault-tolerant switching strategy formatrix converters operating with Optimum Alesina-Venturini modulation," IEEE Trans. Ind. Electron., vol. 59, no. 1, pp. 269-280, Jan. 2012.
28. K. Nguyen-Duy, T. H. Liu, D. F. Chen, and J. Y. Hung, "Improvement of matrix converter drive reliability by online fault detection and a faulttolerant switching strategy," IEEE Trans. Ind. Electron., vol. 59, no. 1, pp. 244-256, Jan. 2012.
29. T. Hastie and R. Tibshirani, "Classification by pairwise coupling," Ann. Statist., vol. 26, no. 2, pp. 451-471, Apr. 1998.
30. T. F.Wu, C. J. Lin, and R. C.Weng, "Probability estimates for multi-class classication by pairwise coupling," J. Mach. Learn. Res., vol. 5, pp. 975-1005, Aug. 2004.
31. S. Mallat, A Wavelet Tour of Signal Processing, 3rd ed. New York: Academic, 2008.
32. M. Aminghafaria, N. Chezea, and J. M. Poggi, "Multivariate denoising using wavelets and principal component analysis," Comput. Statist. Data Anal., vol. 50, no. 9, pp. 2381-2398, May 2006.
33. X. Jiang, "Asymmetric principal component and discriminant analyses for pattern classification," IEEE Trans. Pattern Anal. Mach. Intell., vol. 31, no. 5, pp. 931-937, May 2009.
34. J. Smith, III, Mathematics of the Discrete Fourier Transform (DFT), With Audio Applications, 2nd ed. Charleston, SC:W3K Publ., 2007. [Online]. Available: http://books.w3k.org
35. Y. Gang, C. Li, and S. Kamarthi, "Machine fault diagnosis using a clusterbased wavelet feature extraction and probabilistic neural networks," Int. J. Adv. Manuf. Technol., vol. 42, no. 1/2, pp. 145-151, May 2009.
36. M. E. Tipping, "Sparse Bayesian learning and the relevance vector machine," J. Mach. Learn. Res., vol. 1, no. 3, pp. 211-244, Sep. 2001. (Pubitemid 33687203)
37. W. Caesarendra, A. Widodo, and B. S. Yang, "Application of relevance vector machine and logistic regression for machine degradation assessment," Mech. Syst. Signal Process., vol. 24, no. 4, pp. 1161-1171, May 2010.
38. C. J. v. Rijsbergen, Information Retrieval. London, U.K.: Butterworths, 1979.
39. C. M. Vong, P. K. Wong, and W. F. Ip, "Case-based expert system using wavelet packet transform and kernel-based feature manipulation for engine ignition system diagnosis," Eng. Appl. Artif. Intell., vol. 24, no. 7, pp. 1281-1294, Oct. 2011.
40. C. C. Liu and J. Chu, (in Chinese), Automotive Computer Control Engine's Waveform Analysis and Inspection Technology. Taiwan, China: Sunlex, 2005.
41. A. Santini, Automotive Electricity & Electronics, 2nd ed. Stamford, CT: Delmar Cengage Learning, 2012.
42. S. Narashimhan and S. Shah, "Model identification and error covariance matrix estimation from noisy data using PCA," Control Eng. Pract., vol. 16, no. 1, pp. 146-155, Jan. 2008. (Pubitemid 350029028)
43. Matlab Wavelet Toolbox R2008a, Mathworks, 2008.
44. S. Abe, Support Vector Machines for Pattern Classification, 2nd ed. London: Springer-Verlag, 2010.