Fault Diagnosis of Rolling Bearing Based on Improved Independent Component Analysis and Cepstrum Theory

Abstract:

Based on the advantages of independent component analysis (ICA) and cepstrum, this paper adopts a novel feature extraction scheme for rolling bearing fault diagnosis utilizing improved independent component analysis and cepstrum analysis. Firstly, the fast fixed-point algorithm (FastICA) based on negative entropy was used here as the ICA approach to separate the mixed observation signals of rolling bearing vibration. Then, the largest spectral kurtosis value was used to confirm the characteristic separated signal associated with the Rolling bearing faults. Finally, cepstrum analysis was employed to deal with the selected signal to extract the original fault feature. The experimental results show that sensitive fault feature can be extracted prominently after the presented processing, and the proposed diagnostic method is effective for the fault diagnosis of rolling bearing. In addition, the proposed method provides an effective technical means for weak fault diagnosis.

• Periodical: Advanced Materials Research (Volume 823)
• Pages: 188-192
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.823.188
• Online since: October 2013
• Authors: Pei Xin Zhu, Guo Yong Jin, Yu Quan Yan, Si Yang Gao
• Keywords: Cepstrum, Fault Diagnosis, Independent Component Analysis (ICA), Rolling Bearing

References

[1] J. Zarei and J. Poshtan: Bearing fault detectionusing wavelet packet transform of induction motorstator current, Tribology International Vol. 5 (2007), p.40.

DOI: 10.1016/j.triboint.2006.07.002

[2] J. Cheng, D. Yu and Y. Yang: A fault diagnosis approach for roller bearings based on EMD method and AR model, Mechanical Systems and SignalProcessing Vol. 2 (2006), p.2.

DOI: 10.1016/j.ymssp.2004.11.002

[3] A. Hyvärinen and E. Oja: A fast fixed-point algorithm for independent component analysis, Neural Computat (1997).

DOI: 10.1162/neco.1997.9.7.1483

[4] B. Bogert, M. Healy and J. Tukey: The frequency analysis of time series for echoes, Proc. of Symp. On Time Series Analysis, New York (1963).

[5] A. Hyvärinen, J. Karhunen, and E. Oja: Independent Component Analysis. New York (2001).

[6] E. Oja: Convergence of the symmetrical FastICA algorithm, Neural Inf. Process (2002).

[7] Badaoui M E, Guillet F and Daniere J: New applications of the real cepstrum to gear signals including definition of a robust fault indicator. Mechanical systems and Signal Processing (2004).

DOI: 10.1016/j.ymssp.2004.01.005

[8] Information on http: /csegroups. case. edu/bearingdatacenter/home.