Fault feature enhancement method for rolling bearing based on adaptive probabilistic principal component analysis

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Journal of Vibration and Shock ›› 2017, Vol. 36 ›› Issue (19) : 145-150.

HU Aijun, NAN Bing

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Abstract

Aiming at the difficulty of extracting weak fault signal feature of rolling element bearings in practical engineering, a new method named adaptive probabilistic principal component analysis (APPCA) is proposed to enhance feature of bearing fault. PPCA is able to extract main fault feature and remove background noise interference, but easily affected by the number of principal components and the dimension of original variables. in order to adaptively achieve the best analysis result, the particle swarm optimization algorithm with multi-parameter optimization characteristic is applied to search for the optimal combination of influencing parameters of PPCA based on the maximum kurtosis criterion. After the original signal is processed by the APPCA method, the background noise is effectively suppressed, and the fault feature is enhanced, finally, the signal envelope spectrum is analyzed to identify fault feature. The simulation and experiment results show the effectiveness of this method.

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rolling bearing / probabilistic principal component analysis / fault diagnosis

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HU Aijun, NAN Bing. Fault feature enhancement method for rolling bearing based on adaptive probabilistic principal component analysis[J]. Journal of Vibration and Shock, 2017, 36(19): 145-150

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