Study on Hankel matrix-based SVD and its application in rolling element bearing fault diagnosis

Mechanical Systems and Signal Processing

Volumn 52-53, Issue 1, 2015, Pages 338-359

  Jiang, Huiming ^a   Chen, Jin ^a   Dong, Guangming ^a   Liu, Tao ^a   Chen, Gang ^a  

^a SHANGHAI JIAO TONG UNIVERSITY   (China)

Author keywords

Continuous hidden Markov model; Hankel matrix based SVD; Performance degradation assessment; Ratio of neighboring singular values; Rolling element bearing fault diagnosis; Singular value

Indexed keywords

EXTRACTION; FAILURE ANALYSIS; FAULT DETECTION; FEATURE EXTRACTION; HIDDEN MARKOV MODELS; SINGULAR VALUE DECOMPOSITION; BEARINGS (MACHINE PARTS); MARKOV PROCESSES; MATRIX ALGEBRA;

CONTINUOUS HIDDEN MARKOV MODEL; HANKEL MATRIX; PERFORMANCE DEGRADATION ASSESSMENT; ROLLING ELEMENT BEARING; SINGULAR VALUES;

ROLLER BEARINGS;

EID: 84922898022     PISSN: 08883270     EISSN: 10961216     Source Type: Journal    
DOI: 10.1016/j.ymssp.2014.07.019     Document Type: Article

References (51)

1. N. Tandon, A. Choudhury, A review of vibration and acoustic measurement methods for the detection of defects in rolling element bearings, Tribol. Int. 32 (1999) 469-480.
2. R.B. Randall, J. Antoni, Rolling element bearing diagnostics - a tutorial, Mech. Syst. Signal Process. 25 (2011) 485-520.
3. D. Wang, Q. Miao, R. Sun, H.-Z. Huang, Bearing fault diagnosis using singular value decomposition and hidden Markov modeling, in: Proceedings of the ASME 2009 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, American Society of Mechanical Engineers, 2009, pp. 811-817.
4. Z. Peng, F. Chu, Application of the wavelet transform in machine condition monitoring and fault diagnostics: a review with bibliography, Mech. Syst. Signal Process. 18 (2004) 199-221.
5. Y. Pan, J. Chen, X. Li, Bearing performance degradation assessment based on lifting wavelet packet decomposition and fuzzy c-means, Mech. Syst. Signal Process. 24 (2010) 559-566.
6. P. Kankar, S.C. Sharma, S. Harsha, Fault diagnosis of ball bearings using continuous wavelet transform, Appl. Soft Comput. 11 (2011) 2300-2312.
7. X. Zhao, T.H. Patel, M.J. Zuo, Multivariate EMD and full spectrum based condition monitoring for rotating machinery, Mech. Syst. Signal Process. 27 (2012) 712-728.
8. Y. Lei, J. Lin, Z. He, M.J. Zuo, A review on empirical mode decomposition in fault diagnosis of rotating machinery, Mech. Syst. Signal Process. 35 (2013) 108-126.
9. G. Bin, J. Gao, X. Li, B. Dhillon, Early fault diagnosis of rotating machinery based on wavelet packets - empirical mode decomposition feature extraction and neural network, Mech. Syst. Signal Process. 27 (2012) 696-711.
10. M. Žvokelj, S. Zupan, I. Prebil, Multivariate and multiscale monitoring of large-size low-speed bearings using ensemble empirical mode decomposition method combined with principal component analysis, Mech. Syst. Signal Process. 24 (2010) 1049-1067.
11. R. Yan, R.X. Gao, Approximate entropy as a diagnostic tool for machine health monitoring, Mech. Syst. Signal Process. 21 (2007) 824-839.
12. H. Ocak, Automatic detection of epileptic seizures in EEG using discrete wavelet transform and approximate entropy, Expert Syst. Appl. 36 (2009) 2027-2036.
13. J.S. Richman, J.R. Moorman, Physiological time-series analysis using approximate entropy and sample entropy, Am. J. Physiol. - Heart Circ. Physiol. 278 (2000) H2039-H2049.
14. L. Zhang, G. Xiong, H. Liu, H. Zou, W. Guo, Bearing fault diagnosis using multi-scale entropy and adaptive neuro-fuzzy inference, Expert Syst. Appl. 37 (2010) 6077-6085.
15. X. Zhao, B. Ye, Similarity of signal processing effect between Hankel matrix-based SVD and wavelet transform and its mechanism analysis, Mech. Syst. Signal Process. 23 (2009) 1062-1075.
16. T. Liu, J. Chen, G. Dong, Singular spectrum analysis and continuous hidden Markov model for rolling element bearing fault diagnosis, J. Vib. Control (2013) (in press).
17. P. Kanjilal, S. Palit, Modelling and prediction of time series using singular value decomposition and neural networks, Comput. Electr. Eng. 21 (1995) 299-309.
18. P.P. Kanjilal, S. Palit, On multiple pattern extraction using singular value decomposition, IEEE Trans. Signal Process. 43 (1995) 1536-1540.
19. F. Cong, J. Chen, G. Dong, F. Zhao, Short-time matrix series based singular value decomposition for rolling bearing fault diagnosis, Mech. Syst. Signal Process. 34 (2013) 218-230.
20. L. Rabiner, A tutorial on hidden Markov models and selected applications in speech recognition, Proc. IEEE 77 (1989) 257-286.
21. J.P. Campbell, Speaker recognition: a tutorial, Proc. IEEE 85 (1997) 1437-1462.
22. T. Varga, H. Bunke, Generation of synthetic training data for an HMM-based handwriting recognition system, document analysis and recognition, in: Proceedings of the Seventh IEEE International Conference, 2003, pp. 618-622.
23. A. Cohen, Hidden Markov models in biomedical signal processing, in: Proceedings of the 20th Annual International Conference of the IEEE, Engineering in Medicine and Biology Society, 1998, pp. 1145-1150.
24. T. Williams, X. Ribadeneira, S. Billington, T. Kurfess, Rolling element bearing diagnostics in run-to-failure lifetime testing, Mech. Syst. Signal Process. 15 (2001) 979-993.
25. S.S.H. Zaidi, S. Aviyente, M. Salman, K.K. Shin, E.G. Strangas, Failure prognosis of DC starter motors using hidden Markov models, in: Proceedings of the 2009 IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives, SDEMPED 2009, 2009, 7 pp.
26. Z. Li, Y. He, F. Chu, J. Han, W. Hao, Fault recognition method for speed-up and speed-down process of rotating machinery based on independent component analysis and Factorial Hidden Markov Model, J. Sound Vib. 291 (2006) 60-71.
27. M. Dong, D. He, A segmental hidden semi-Markov model (HSMM)-based diagnostics and prognostics framework and methodology, Mech. Syst. Signal Process. 21 (2007) 2248-2266.
28. M. Dong, D. He, Hidden semi-Markov model-based methodology for multi-sensor equipment health diagnosis and prognosis, Eur. J. Oper. Res. 178 (2007) 858-878.
29. F. Cartella, T. Liu, S. Meganck, J. Lemeire, H. Sahli, Online adaptive learning of Left-Right Continuous HMM for bearings condition assessment, J. Phys.: Conf. Ser. 364 (2012) 012031.
30. B. Muruganatham, M. Sanjith, B. Krishnakumar, S. Satya Murty, Roller element bearing fault diagnosis using singular spectrum analysis, Mech. Syst. Signal Process. 35 (2013) 150-166.
31. W. Wang, J. Chen, X. Wu, Z. Wu, The application of some non-linear methods in rotating machinery fault diagnosis, Mech. Syst. Signal Process. 15 (2001) 697-705.
32. B. Muruganatham, M. Sanjith, B.K. Kumar, S.S. Murty, P. Swaminathan, Inner race bearing fault detection using singular spectrum analysis, in: Proceedings of the 2010 IEEE International Conference on Communication Control and Computing Technologies (ICCCCT), 2010, pp. 573-579.
33. F. Alonso, D. Salgado, Analysis of the structure of vibration signals for tool wear detection, Mech. Syst. Signal Process. 22 (2008) 735-748.
34. N. Golyandina, V. Nekrutkin, A.A. Zhigljavsky, Analysis of Time Series Structure: SSA and Related Techniques, CRC Press, New York, 2001.
35. H. Ocak, K.A. Loparo, A new bearing fault detection and diagnosis scheme based on hidden Markov modeling of vibration signals, in: Proceedings of the 2001 IEEE International Conference on Acoustics, Speech, and Signal Processing, vols. I-VI, Proceedings: vol I: Speech Processing 1; vol II: Speech Processing 2 Ind Technol Track Design & Implementation of Signal Processing Systems Neural Networks for Signal Processing; vol III: Image & Multidimensional Signal Processing Multimedia Signal Processing, 2001, pp. 3141-3144.
36. H. Ocak, K.A. Loparo, HMM-based fault detection and diagnosis scheme for rolling element bearings, J. Vib. Acoust. 127 (2005) 299-306.
37. H. Ocak, K.A. Loparo, F.M. Discenzo, Online tracking of bearing wear using wavelet packet decomposition and probabilistic modeling: a method for bearing prognostics, J. Sound Vib. 302 (2007) 951-961.
38. C. Bunks, D. McCarthy, T. Al-Ani, Condition-based maintenance of machines using hidden Markov models, Mech. Syst. Signal Process. 14 (2000) 597-612.
39. Z. Li, Z. Wu, Y. He, C. Fulei, Hidden Markov model-based fault diagnostics method in speed-up and speed-down process for rotating machinery, Mech. Syst. Signal Process. 19 (2005) 329-339.
40. Q. Miao, D. Wang, M. Pecht, A probabilistic description scheme for rotating machinery health evaluation, J. Mech. Sci. Technol. 24 (2010) 2421-2430.
41. Q. Miao, V. Makis, Condition monitoring and classification of rotating machinery using wavelets and hidden Markov models, Mech. Syst. Signal Process. 21 (2007) 840-855.
42. LR. Rabiner, A tutorial on hidden Markov models and selected applications in speech recognition, Proc. IEEE 77 (1989) 257-286.
43. Y. Pan, J. Chen, L Guo, Robust bearing performance degradation assessment method based on improved wavelet packet - support vector data description, Mech. Syst. Signal Process. 23 (2009) 669-681.
44. D. Wang, Q. Miao, R. Kang, Robust health evaluation of gearbox subject to tooth failure with wavelet decomposition, J. Sound Vib. 324 (2009) 1141-1157.
45. D. Wang, WT. Peter, W Guo, Q. Miao, Support vector data description for fusion of multiple health indicators for enhancing gearbox fault diagnosis and prognosis, Meas. Sci. Technol. 22 (2011) 025102.
46. B.-S. Yang, T. Han, J.L An, ART-KOHONEN neural network for fault diagnosis of rotating machinery, Mech. Syst. Signal Process. 18 (2004) 645-657.
47. Y Lei, Z. He, Y Zi, Q. Hu, Fault diagnosis of rotating machinery based on multiple ANFIS combination with GAs, Mech. Syst. Signal Process. 21 (2007) 2280-2294.
48. Y Lei, Z. He, Y Zi, X. Chen, New clustering algorithm-based fault diagnosis using compensation distance evaluation technique, Mech. Syst. Signal Process. 22 (2008) 419-435.
49. WT. Peter, Y Peng, R. Yam, Wavelet analysis and envelope detection for rolling element bearing fault diagnosis - their effectiveness and flexibilities, J. Vib. Acoust. 123 (2001) 303-310.
50. J.M. Lee, S.-J. Kim, Y Hwang, C.-S. Song, Diagnosis of mechanical fault signals using continuous hidden Markov model, J. Sound Vib. 276 (2004) 1065-1080.
51. LE. Baum, T. Petrie, G. Soules, N. Weiss, A maximization technique occurring in the statistical analysis of probabilistic functions of Markov chains, Ann. Math. Stat. 41 (1970) 164-171.