Expectation-maximization approach to fault diagnosis with missing data

IEEE Transactions on Industrial Electronics

Volumn 62, Issue 2, 2015, Pages 1231-1240

  Zhang, Kangkang ^a,b   Gonzalez, Ruben ^b   Huang, Biao ^b   Ji, Guoli ^a  

^a XIAMEN UNIVERSITY   (China)

^b UNIVERSITY OF ALBERTA   (Canada)

Author keywords

Data driven approach; expectation maximization (EM) algorithm; fault diagnosis; missing data

Indexed keywords

ALGORITHMS; FAILURE ANALYSIS; MAXIMUM LIKELIHOOD; MAXIMUM PRINCIPLE;

DATA-DRIVEN APPROACH; EM ALGORITHMS; EXPECTATION-MAXIMIZATION ALGORITHMS; EXPECTATION-MAXIMIZATION APPROACHES; MISSING DATA; TUBE SYSTEMS;

FAULT DETECTION;

EID: 84920895410     PISSN: 02780046     EISSN: None     Source Type: Journal    
DOI: 10.1109/TIE.2014.2336635     Document Type: Article

References (35)

1. J. J. Gertler, "Survey of model-based failure detection and isolation in complex plants," IEEE Control Syst. Mag., vol. 8, no. 6, pp. 3-11, Dec. 1988.
2. I. Samy, I. Postlethwaite, and D.-W. Gu, "Survey and application of sensor fault detection and isolation schemes," Control Eng. Practice, vol. 19, no. 7, pp. 658-674, Jul. 2011.
3. R. Isermann, "Model-based fault-detection and diagnosis-Status and applications," Annu. Rev. Control, vol. 29, no. 1, pp. 71-85, 2005.
4. R. J. Patton, R. N. Clark, and P. M. Frank, Issues of Fault Diagnosis for Dynamic Systems. New York, NY, USA: Springer-Verlag, 2000.
5. C. H. De Angelo et al., "Online model-based stator-fault detection and identification in induction motors," IEEE Trans. Ind. Electron., vol. 56, no. 11, pp. 4671-4680, Nov. 2009.
6. G. Foo, X. Zhang, and D. Vilathgamuwa, "A sensor fault detection and isolation method in interior permanent-magnet synchronous motor drives based on an extended Kalman filter," IEEE Trans. Ind. Electron., vol. 60, no. 8, pp. 3485-3495, Aug. 2013.
7. S. Huang, K. K. Tan, and T. H. Lee, "Fault diagnosis and fault-tolerant control in linear drives using the Kalman filter," IEEE Trans. Ind. Electron., vol. 59, no. 11, pp. 4285-4292, Nov. 2012.
8. S. A. Arogeti, D. Wang, C. B. Low, and M. Yu, "Fault detection isolation and estimation in a vehicle steering system," IEEE Trans. Ind. Electron., vol. 59, no. 12, pp. 4810-4820, Dec. 2012.
9. P. Zhang, H. Ye, S. X. Ding, G. Wang, and D. Zhou, "On the relationship between parity space and H2 approaches to fault detection," Syst. Control Lett., vol. 55, no. 2, pp. 94-100, Feb. 2006.
10. F. Alrowaie, R. Gopaluni, and K. Kwok, "Fault detection and isolation in stochastic nonlinear state-space models using particle filters," Control Eng. Practice, vol. 20, no. 10, pp. 1016-1032, Oct. 2012.
11. T. Boukra, A. Lebaroud, and G. Clerc, "Statistical and neural-network approaches for the classification of induction machine faults using the ambiguity plane representation," IEEE Trans. Ind. Electron., vol. 60, no. 9, pp. 4034-4042, Sep. 2013.
12. K. Patan and T. Parisini, "Identification of neural dynamic models for fault detection and isolation: The case of a real sugar evaporation process," J. Process Control, vol. 15, no. 1, pp. 67-79, Feb. 2005.
13. Y. Shatnawi and M. Al-khassaweneh, "Fault diagnosis in internal combustion engines using extension neural network," IEEE Trans. Ind. Electron., vol. 61, no. 3, pp. 1434-1443, Mar. 2014.
14. Z. Han, W. Li, and S. L. Shah, "Fault detection and isolation in the presence of process uncertainties," Control Eng. Practice, vol. 13, no. 5, pp. 587-599, May 2005.
15. I. Fagarasan, S. Ploix, and S. Gentil, "Causal fault detection and isolation based on a set-membership approach," Automatica, vol. 40, no. 12, pp. 2099-2110, Dec. 2004.
16. W. Li and S. Shah, "Structured residual vector-based approach to sensor fault detection and isolation," J. Process Control, vol. 12, no. 3, pp. 429-443, Apr. 2002.
17. A. Soualhi, G. Clerc, and H. Razik, "Detection and diagnosis of faults in induction motor using an improved artificial ant clustering technique," IEEE Trans. Ind. Electron., vol. 60, no. 9, pp. 4053-4062, Sep. 2013.
18. R. Muradore and P. Fiorini, "A PLS-based statistical approach for fault detection and isolation of robotic manipulators," IEEE Trans. Ind. Electron., vol. 59, no. 8, pp. 3167-3175, Aug. 2012.
19. S. Ding, S. Yin, K. Peng, H. Hao, and B. Shen, "A novel scheme for key performance indicator prediction and diagnosis with application to an industrial hot strip mill," IEEE Trans. Ind. Informat., vol. 9, no. 4, pp. 2239-2247, Nov. 2013.
20. A. Bouzida et al., "Fault diagnosis in industrial induction machines through discrete wavelet transform," IEEE Trans. Ind. Electron., vol. 58, no. 9, pp. 4385-4395, Sep. 2011.
21. M. Nyberg, "Model based fault diagnosis using structured hypothesis tests," in Proc. IFAC Symp. SAFEPROCESS, 2000, pp. 1026-1031.
22. R. Levy, S. Arogeti, and D. Wang, "An integrated approach to mode tracking and diagnosis of hybrid systems," IEEE Trans. Ind. Electron., vol. 61, no. 4, pp. 2024-2040, Apr. 2014.
23. S. Yin, S. X. Ding, A. Haghani, H. Hao, and P. Zhang, "A comparison study of basic data-driven fault diagnosis and process monitoring methods on the benchmark Tennessee Eastman process," J. Process Control, vol. 22, no. 9, pp. 1567-1581, Oct. 2012.
24. S. Yin, S. X. Ding, A. H. Abandan Sari, and H. Hao, "Data-driven monitoring for stochastic systems and its application on batch process," Int. J. Syst. Sci., vol. 44, no. 7, pp. 1366-1376, Jul. 2013.
25. S. Yin, H. Luo, and S. Ding, "Real-time implementation of fault-tolerant control systems with performance optimization," IEEE Trans. Ind. Electron., vol. 61, no. 5, pp. 2402-2411, May 2014.
26. A. Pernestål, "A Bayesian approach to fault isolation with application to diesel engine diagnosis," Ph.D. dissertation, Royal Inst. Technol. (KTH), Stockholm, Sweden, 2007.
27. B. Huang, "Bayesian methods for control loop monitoring and diagnosis," J. Process Control, vol. 18, no. 9, pp. 829-838, Oct. 2008.
28. F. Qi and B. Huang, "Bayesian methods for control loop diagnosis in the presence of temporal dependent evidences," Automatica, vol. 47, no. 7, pp. 1349-1356, Jul. 2011.
29. R. Gonzalez and B. Huang, "Control loop diagnosis with ambiguous historical operating modes: Part 1. A proportional parametrization approach," J. Process Control, vol. 23, no. 4, pp. 585-597, Apr. 2013.
30. F. Qi, B. Huang, and E. C. Tamayo, "A Bayesian approach for control loop diagnosis with missing data," AIChE J., vol. 56, no. 1, pp. 179-195, Jan. 2010.
31. A. P. Dempster, N.M. Laird, and D. B. Rubin, "Maximum likelihood from incomplete data via the EM algorithm," J. R. Stat. Soc. B., vol. 39, no. 1, pp. 1-38, 1977.
32. G. J. McLachlan and T. Krishnan, The EM Algorithm and Extensions, vol. 382. Hoboken, NJ, USA: Wiley, 2007.
33. N. Quijano, A. E. Gil, and K. M. Passino, "Experiments for dynamic resource allocation, scheduling, and control: New challenges from information technology-enabled feedback control," IEEE Control Syst. Mag., vol. 25, no. 1, pp. 63-79, Feb. 2005.
34. Y. Zhu, "Multivariable process identification for MPC: The asymptotic method and its applications," J. Process Control, vol. 8, no. 2, pp. 101-115, Apr. 1998.
35. G. Ji, K. Zhang, and Y. Zhu, "A method of MPC model error detection," J. Process Control, vol. 22, no. 3, pp. 635-642, Mar. 2012.