A Novel Fault Diagnosis for Rolling Element Bearings Based on Continuous Wavelet Transform and Hidden Markov Model

Abstract:

Article Preview

This paper presents a novel approach to detect the fault categories for rolling element bearings based on the continuous wavelet transform and hidden Markov models. With the Morlet wavelet transform, the effective fault information is extracted from the time-frequency domain of the vibration signals. Then, the wavelet coefficients are divided into multi-segments, and the infinity-norms of each segment is applied as the features to construct the observation vector of the hidden Markov models. Finally, the experimental results on bearing faults identification and isolation are illustrated.

Info:

Periodical:

Edited by:

Yongping Zhang, Linhua Zhou and Elwin Mao

Pages:

313-317

DOI:

10.4028/www.scientific.net/AMM.109.313

Citation:

R. G. Zhang and Y. H. Tan, "A Novel Fault Diagnosis for Rolling Element Bearings Based on Continuous Wavelet Transform and Hidden Markov Model", Applied Mechanics and Materials, Vol. 109, pp. 313-317, 2012

Online since:

October 2011

Export:

Price:

$35.00

[1] J. Lin, L. Qu: Feature extraction based on Morlet wavelet and its application for mechanical fault diagnosis, Journal of Sound and Vibration, Vol. 234 (2000), pp.135-148.

DOI: 10.1006/jsvi.2000.2864

[2] H. Qiu, J. Lee, J. Lin, and G. Yu: Robust performance degradation assessment methods for enhanced rolling element bearing prognostics, Advanced Engineering Informatics, vol. 17(2003), pp.127-140.

DOI: 10.1016/j.aei.2004.08.001

[3] H. Qiu, J. Lee, J. Lin, and G. Yu: Wavelet filter-based weak signature detection method and its application on rolling element bearing prognostics, Journal of Sound and Vibration, Vol. 289 (2006), pp.1066-1090.

DOI: 10.1016/j.jsv.2005.03.007

[4] L. Li, L. Qu and X. Liao: Haar wavelet for machine fault diagnosis, Mechanical Systems and Signal Processing, vol. 21(2007), pp.1773-1786.

DOI: 10.1016/j.ymssp.2006.07.006

[5] S. Abbasion, A. Rafsanjani, A. Farshidianfar, and N. Irani: Rolling element bearings multi-fault classification based on the wavelet denoising and support vector machine, Mechanical System and Signal Processing, Vol. 21 (2007), pp.2933-2945.

DOI: 10.1016/j.ymssp.2007.02.003

[6] J. M. Lee, S. J. Kim, Y. Hwang, and C. S. Song: Diagnosis of mechanical fault signals using continuous hidden Markov model, Journal of Sound and Vibration, vol. 276(2004), pp.1065-1080.

DOI: 10.1016/j.jsv.2003.08.021

[7] V. Purushotham, S. Narayanan and S. Prasad: Multi-fault diagnosis of rolling bearing elements using wavelet analysis and hidden Markov model based fault recognition, NDT and E International, vol. 38(2005), pp.654-664.

DOI: 10.1016/j.ndteint.2005.04.003

[8] F. V. Nelwamondo, T. Marwala and U. Mahola: Early classifications of bearing faults using hidden Markov models, Gaussian mixture models, Mel-frequency cepstral coefficients and fractals, International Journal of Innovative Computing, Information and Control, vol. 2(2006).

DOI: 10.1109/icsmc.2006.384397

[9] H. Ocak, K. A. Loparo and F. M. Discenzo: Online tracking of bearing wear using wavelet packet decomposition and probabilistic modeling: A method for bearing prognostics, Journal of Sound and Vibration, Vol. 302 (2007), pp.951-961.

DOI: 10.1016/j.jsv.2007.01.001

[10] M. Dong and D. He: Hidden semi-Markov model-based methodology for multi-sensor equipment health diagnosis and prognosis, European Journal of Operational Research, Vol. 178 (2007), pp.858-878.

DOI: 10.1016/j.ejor.2006.01.041

[11] M. Dong and D. He: A segmental hidden semi-Markov model (HSMM)-based diagnostics and prognostics framework and methodology, Mechanical System and Signal Processing, Vol. 21 (2007), pp.2248-2266.

DOI: 10.1016/j.ymssp.2006.10.001

[12] B. Ling, M. Khonsari, A. Mesgarnejad, and R. Hathaway: Online coated ball bearing health monitoring using degree of randomness and Hidden Markov Model, IEEE Aerospace Conference, (2009), pp.1-10.

DOI: 10.1109/aero.2009.4839674

[13] H. Qiu, J. Lee, J. Lin, and G. Yu: Wavelet filter-based weak signature detection method and its application on rolling element bearing prognostics, Journal of Sound and Vibration, vol. 289(2006), pp.1066-1090.

DOI: 10.1016/j.jsv.2005.03.007

In order to see related information, you need to Login.