Multi-fault diagnosis of rolling bearing elements using wavelet analysis and hidden Markov model based fault recognition

NDT and E International

Volumn 38, Issue 8, 2005, Pages 654-664

  Purushotham, V ^a   Narayanan, S ^a   Prasad, Suryanarayana A N ^a  

^a INDIAN INSTITUTE OF TECHNOLOGY MADRAS   (India)

Author keywords

Bearing fault recognition; Discrete wavelet transform; Hidden Markov model; Impulses; Mel frequency complex cepstrum

Indexed keywords

FAILURE (MECHANICAL); FAILURE ANALYSIS; MARKOV PROCESSES; MATHEMATICAL MODELS; POINT DEFECTS; WAVELET TRANSFORMS;

BEARING FAULT RECOGNITION; DISCRETE WAVELET TRANSFORMS (DWT); HIDDEN MARKOV MODELS (HMM); IMPULSES; MEL FREQUENCY COMPLEX CEPSTRUM; MULTI-FAULT DIAGNOSIS; ROLLING BEARING ELEMENTS;

ROLLER BEARINGS;

EID: 24344500897     PISSN: 09638695     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ndteint.2005.04.003     Document Type: Article

References (21)

1. P.D. Mc Fadden, and J.D. Smith Model for vibration produced by a single point defect in a rolling element bearing J Sound Vib 96 1 1984 69 82
2. P.D. Mc Fadden, and J.D. Smith The vibration produced by multiple point defects in a rolling element bearing J Sound Vib 98 2 1985 263 273
3. Braun S, Danter B. Analysis of roller/ball bearing vibrations, ASME paper. 77-WA/DE-5; 1977. p. 1-8.
4. Chaturvedi GK, Thomas DW. Bearing fault detection using adaptive noise canceling ASME paper, 81-DET-7; 1981. p. 1-10.
5. Prasad H, Ghosh M, Biswas S. Diagnostic monitoring of rolling element bearings by high frequency resonance technique ASLE paper 84-LC-3c3; 1984. p. 1-9.
6. K. Mori, N. Kasashima, T. Yoshioka, and Y. Ueno Prediction of spalling on a ball bearing by applying discrete wavelet transform to vibration signals Wear 195 1996 162 168
7. C. James, Li, and J. Ma Wavelet decomposition of vibrations for detection of bearing-localized defects NDT&E Int 30 3 1997 143 149
8. Jing Lin, and Liangsheng Qu Feature extraction based on morlet wavelet and its application for mechanical fault diagnosis J Sound Vib 234 1 2000 135 148
9. Serhat Seker, and Emini Ayaz Feature extraction related to bearing damage in electric motors by wavelet analysis J Franklin Inst 340 2003 125 134
10. S. Prabhakar, A.R. Mohanty, and A.S. Sekhar Application of discrete wavelet transform for detection of ball bearing race faults Tribol Int 35 2002 793 800
11. L.R. Rabiner A tutorial on hidden Markov models and selected applications in speech recognition Proc IEEE Ultrasonic Symp 77 2 1989 257 286
12. Kai-Fu Lee Automatic speech recognition 1989 Kluwer NewYork
13. L.P. Heck, and J.H. McClellan Mechanical system monitoring using hidden Markov models IEEE Int Conf Acoustics, Speech Signal Process Proceed 91 3 1991 1697 1700
14. Wongyu Cho, Lee, and Kim Modeling and recognition of cursive words with Hidden Markov Models Pattern Recogn 28 11 1991 1941 1953
15. S.G. Mallat A theory for multiresolution signal decomposition: the wavelet representation IEEE Trans Pattern Anal Machine Intelligence 11 7 1989 674 693
16. D.E. Newland Wavelet analysis of vibration, part I: theory J Vib Acoustics 116 1994 409 416
17. D.E. Newland Wavelet analysis of vibration, part II: wavelet maps J Vib Acoustics 116 1994 417 425
18. Litao Wang, G Mehrabi Mostaga, and Elijah Kannatey- Asibu Hidden Markov Model-based tool wear monitoring in turning J Manuf Sci Eng 124 2002 651 658
19. R. Rubin, and U. Meneghetti Application of the envelope and wavelet transform analyses for the diagnosis of incipient faults in ball bearings Mech Syst Signal Process 15 2 2001 287 302
20. P.W. Tse, Y.H. Peng, and R. Yam Wavelet analysis and envelope detection for rolling element fault diagnosis-their effectiveness and flexibilities J Vib Acoustics 123 2001 303 309
21. S.B. Davis, and P. Mermelstein Comparison of parametric representations for monosyllabic word recognition in continuously spoken sentences IEEE Trans Acoust, Speech, Signal Process ASSP-28 1980 357 366