Rolling Bearing Fault Diagnosis Approach Based on PPCA Denoising and Cyclic Bispectrum Method


Article Preview

A new method for bearing fault diagnosis is proposed based on Probabilistic Principal Component Analysis (PPCA) and Cyclic Bispectrum (CB). The first procedure is signal de-noised using PPCA and the second procedure is the CB analysis. The effectiveness of the proposed method is demonstrated by numerical simulation and experimental investigation of a rolling bearing with outer race fault.



Edited by:

Prasad Yarlagadda




B. Z. Jiang and J. W. Xiang, "Rolling Bearing Fault Diagnosis Approach Based on PPCA Denoising and Cyclic Bispectrum Method", Applied Mechanics and Materials, Vols. 536-537, pp. 26-29, 2014

Online since:

April 2014




* - Corresponding Author

[1] TAN, Hongzhou. Based on higher-order statistics of linear and nonlinear system identification theory of South China University of Technology PhD thesis, (1997).

[2] M. E. Tipping, C. M. Bishop, Probabilistic Principal Component Analysis. Journal of the Royal Statistical Society. Series B (Statistical Methodology), Vol. 61, No. 3, 1999, p.611–622.


[3] S. Roweis, EM Algorithms for PCA and SPCA., In Proceedings of the 1997 Conference on Advances in Neural Information Processing Systems. Vol. 10 (NIPS 1997), Cambridge, MA, USA: MIT Press, 1998, p.626–632.

[4] A. Ilin, T. Raiko, Practical Approaches to Principal Component Analysis in the Presence of Missing Values., J. Mach. Learn. Res. Vol. 11, August, 2010, p.1957–(2000).

[5] ZHANG, Xianda non-stationary signal analysis and processing. Defense Industry Press, 1998, pp.339-354.

[6] C.T. Yiakopoulos, I.A. Antoniadis, Cyclic Bispectrum patterns of defective rolling element bearing vibration response. Forsch Ingenieurwes. 2006, Vol. 70, p.90–104.