A multichannel fusion approach based on coupled hidden Markov models for rolling element bearing fault diagnosis

Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science

Volumn 226, Issue 1, 2012, Pages 202-216

  Xiao, W B ^a   Chen, J ^a   Dong, G M ^a   Zhou, Y ^a   Wang, Z Y ^a  

^a SHANGHAI JIAO TONG UNIVERSITY   (China)

Author keywords

Coupled hidden Markov model; Fault diagnosis; Multichannel fusion; Rolling element bearing

Indexed keywords

CLASSIFICATION RATES; COUPLED HIDDEN MARKOV MODELS; MULTI-CHANNEL; PARAMETER ESTIMATION ALGORITHM; PROBABILISTIC INFERENCE; ROLLING ELEMENT BEARING; STATE SEQUENCES; TWO-CHANNEL; VERTICAL VIBRATIONS; VIBRATION SIGNAL; WAVELET PACKET ENERGIES;

FACE RECOGNITION; FAILURE ANALYSIS; INFERENCE ENGINES; PARAMETER ESTIMATION; VIDEO SIGNAL PROCESSING;

HIDDEN MARKOV MODELS;

EID: 84863065420     PISSN: 09544062     EISSN: 20412983     Source Type: Journal    
DOI: 10.1177/0954406211412015     Document Type: Article

References (49)

1. Du, Q. and Yang, S. Application of the EMD method in the vibration analysis of ball bearings. Mech. Systems and Signal Processing., 2007, 21(6), 2634-2644. (Pubitemid 46829763)
2. Al-Ghamd, A. M. and Mba, D. A comparative experimental study on the use of acoustic emission and vibration analysis for bearing defect identification and estimation of defect size. Mech. Systems and Signal Processing., 2006, 20(7), 1537-1571.
3. Kiral, Z. and Karagülle, H. Vibration analysis of rolling element bearings with various defects under the action of an unbalanced force. Mech. Systems and Signal Processing., 2006, 20(8), 1967-1991. (Pubitemid 44205411)
4. Orhan, S., Aktürk, N., and Celik, V. Vibration monitoring for defect diagnosis of rolling element bearings as a predictive maintenance tool: Comprehensive case studies. NDT&E Int., 2006, 39(4), 293-298. (Pubitemid 43061170)
5. De Silva, C. W. Vibration and shock handbook, 2005 (CRC Press, Boca Raton, Florida).
6. Ocak, H., Loparo, K. A., and Discenzo, F. M. Online tracking of bearing wear using wavelet packet decomposition and probabilistic modeling: A method for bearing prognostics. J. Sound and Vibr., 2007, 302(4-5), 951-961. (Pubitemid 46435577)
7. Jardine, A. K. S., Lin, D., and Banjevic, D. A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mech. Systems and Signal Processing., 2006, 20(7), 1483-1510.
8. McFadden, P. D. and Smith, J. D. Vibration monitoring of rolling element bearings by the high-frequency resonance technique - a review. Tribology Int., 1984, 17(1), 3-10. (Pubitemid 14561780)
9. Zhang, Y. and Randall, R. B. Rolling element bearing fault diagnosis based on the combination of genetic algorithms and fast kurtogram. Mech. Systems and Signal Processing., 2009, 23(5), 1509-1517.
10. Pan, M.-C., Sas, P., and Van Brussel, H. Machine condition monitoring using signal classification techniques. J. Vibr. and Control., 2003, 9(10), 1103-1120.
11. Nikolaou, N. G. and Antoniadis, I. A. Rolling element bearing fault diagnosis using wavelet packets. NDT&E Int., 2002, 35(3), 197-205. (Pubitemid 33143517)
12. Lin, J. and Qu, L. Feature extraction based on Morlet wavelet and its application for mechanical fault diagnosis. J. Sound and Vibr., 2000, 234(1), 135-148.
13. Liu, B., Ling, S.-F., and Meng, Q. Machinery diagnosis based on wavelet packets. J. Vibr. and Control., 1997, 3(1), 5-17. (Pubitemid 127561510)
14. Gade, S. and Gram-Hansen, K. Non-stationary signal analysis using wavelet transform, short-time Fourier transform and Wigner-Ville distribution. In. Brüel & Kjær Technical Review No. 2, 1996 (Brüel & Kjær).
15. Castejón, C., Lara, O., and Garc?́a-Prada, J. C. Automated diagnosis of rolling bearings using MRA and neural networks. Mech. Systems and Signal Processing., 2010, 24(1), 289-299.
16. Samanta, B. and Al-Balushi, K. R. Artificial neural network based fault diagnostics of rolling element bearings using time-domain features. Mech. Systems and Signal Processing., 2003, 17(2), 317-328.
17. Crupi, V., Guglielmino, E., and Milazzo, G. Neuralnetwork-based system for novel fault detection in rotating machinery. J. Vibr. and Control., 2004, 10(8), 1137-1150.
18. Ben?́tez-Pérez, H. and Ben?́tez-Pérez, A. The use of ARMAX strategy and self organizing maps for feature extraction and classification for fault diagnosis. Int. J. Innovative Computing, Inf. and Control., 2009, 5(12(B)), 4787-4796.
19. Jack, L. B. and Nandi, A. K. Support vector machines for detection and characterization of rolling element bearing faults. Proc. IMechE, Part C: J. Mechanical Engineering Science., 2001, 215(C9), 1065-1074. (Pubitemid 33056129)
20. Zhang, L., Xiong, G., Liu, H., Zou, H., and Guo, W. Applying improved multi-scale entropy and support vector machines for bearing health condition identification. Proc. IMechE, Part C: J. Mechanical Engineering Science., 2010, 224(C6), 1315-1325.
21. Abbasion, S., Rafsanjani, A., Farshidianfar, A., and Irani, N. Rolling element bearings multi-fault classification based on the wavelet denoising and support vector machine. Mech. Systems and Signal Processing., 2007, 21(7), 2933-2945. (Pubitemid 47284786)
22. Widodo, A. and Yang, B.-S. Support vector machine in machine condition monitoring and fault diagnosis. Mech. Systems and Signal Processing., 2007, 21(6), 2560-2574. (Pubitemid 46829756)
23. Mechefske, C. K. Objective machinery fault diagnosis using fuzzy logic. Mech. Systems and Signal Processing., 1998, 12(6), 855-862. (Pubitemid 128398632)
24. Liu, J. F., Wang, W., Xiao, Q. G., and Yang, Z. Fault diagnosis for flight control system of unmanned aerial vehicle using fuzzy Petri nets. ICIC Express Lett., 2010, 4(4), 1319-1324.
25. Xue, W., Guo, Y. Q., and Zhang, X. D. Application of a bank of Kalman filters and a robust Kalman filter for aircraft engine sensor/actuator fault diagnosis. Int. J. Innovative Computing, Inf. and Control., 2008, 4(12), 3161-3168.
26. Wang, J. F., Jiang, B., and Shi, P. Adaptive observer based fault diagnosis for satellite attitude control systems. Int. J. Innovative Computing, Inf. And Control., 2008, 4(8), 1921-1929.
27. Takahashi, M., Takagi, T., and Miyawaki, Y. Fault diagnosis of joint-sensors in parallel manipulators. ICIC Express Lett., 2009, 3(4(B)), 1309-1314.
28. Purushotham, V., Narayanan, S., and Prasad, S. A. N. Multi-fault diagnosis of rolling bearing elements using wavelet analysis and hidden Markov model based fault recognition. NDT&E Int., 2005, 38(8), 654-664. (Pubitemid 41261408)
29. Lee, J. M., Kim, S.-J., Hwang, Y., and Song, C.-S. Diagnosis of mechanical fault signals using continuous hidden Markov model. J. Sound and Vibr., 2004, 276(3-5), 1065-1080.
30. Li, Z., Han, J., Sun, J., He, Y., and Chu, F. Fault recognition method based on independent component analysis and hidden Markov model. J. Vibr. And Control., 2007, 13(2), 125-137. (Pubitemid 46345458)
31. Li, Z., Wu, Z., He, Y., and Chu, F. Hidden Markov model-based fault diagnostics method in speed-up and speed-down process for rotating machinery. Mech. Systems and Signal Processing., 2005, 19(2), 329-339.
32. Ocak, H. and Loparo, K. A. HMM-based fault detection and diagnosis scheme for rolling element bearings. J. Vibr. and Acoustics., 2005, 127(4), 299-306. (Pubitemid 41263726)
33. Ge, M., Du, R., and Xu, Y. Hidden Markov model based fault diagnosis for stamping processes. Mech. Systems and Signal Processing., 2004, 18(2), 391-408.
34. Rabiner, L. R. A tutorial on hidden Markov models and selected applications in speech recognition. Proc. IEEE., 1989, 77(2), 257-286.
35. Baruah, P. and Chinnam, R. B. HMMs for diagnostics and prognostics in machining processes. Int. J. Prod. Res., 2005, 43(6), 1275-1293. (Pubitemid 41633077)
36. Brand, M. Coupled hidden Markov models for modeling interacting processes. Learning and Common Sense Technical Report 405, MIT Media Lab Perceptual Computing, 1996.
37. Chu, S. M. and Huang, T. S. Audio-visual speech modeling using coupled hidden Markov models. In IEEE International Conference on Acoustic, Speech and Signal Processing, IEEE, Orlando, Florida, 2002.
38. Nefian, A. V., Liang, L., Pi, X., Liu, X., Mao, C., and Murphy, K. A coupledHMMfor audio-visual speech recognition. In IEEE International Conference on Acoustics, Speech and Signal Processing, IEEE, Orlando, Florida, 2002, pp. 2013-2016.
39. Xie, L. and Liu, Z.-Q. peech animation using coupled hidden Markov models. In Proceedings of the 18th International Conference on Pattern Recognition, IEEE, Hong Kong, 2006, pp. 1128-1131. (Pubitemid 46532098)
40. Brand, M., Oliver, N., and Pentland, A. Coupled hidden Markov models for complex action recognition. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition, IEEE, San Juan, Puerto Rico, 1997, pp. 994-999.
41. Pernkopf, F. 3D surface inspection using coupled HMMs. In Proceedings of the 17th International Conference on Pattern Recognition, IEEE, Cambridge, UK, 2004, pp. 223-226.
42. Zhong, S. and Ghosh, J. HMMs and coupled HMMs for multi-channel EEG classification. In International Joint Conference on Neural Networks, IEEE, Honolulu, Hawaii, 2002, pp. 1154-1159. (Pubitemid 34647790)
43. Çetin, Ö., Ostendorf, M., and Bernard, G. D. Multirate coupled hidden Markov models and their application to machining tool-wear classification. IEEE Trans. on Signal Processing., 2007, 55(6), 2885-2896. (Pubitemid 46845679)
44. Smyth, P., Heckerman, D., and Jordan, M. I. Probabilistic independence networks for hidden Markov probability models. Neural Computation., 1997, 9(2), 227-269. (Pubitemid 127635390)
45. Dempster, A. P., Laird, N. M., and Rubin, D. B. Maximum likelihood from incomplete data via the EM algorithm. J. R. Statist. Soc., Series B (Methodological)., 1977, 39(1), 1-38.
46. Liporace, L. A. Maximum likelihood estimation for multivariate observations of Markov sources. IEEE Trans. on Inf. Theory., 1982, 28(5), 729-734.
47. Redner, R. A. and Walker, H. F. Mixture densities, maximum likelihood and the EM algorithm. SIAM Rev., 1984, 26(2), 195-239.
48. Yen, G. G. and Lin, K. C. Wavelet packet feature extraction for vibration monitoring. IEEE Trans. On Ind. Electronics., 2000, 47(3), 650-667.
49. Fan, X. F. and Zuo, M. J. Gearbox fault detection using Hilbert and wavelet packet transform. Mech. Systems and Signal Processing., 2006, 20(4), 966-982. (Pubitemid 43121169)