Published: 30 June 2012

An adaptive lifting scheme and its application in rolling bearing fault diagnosis

Hongkai Jiang1
Chendong Duan2
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Vibration signals of rolling bearings usually are corrupted by heavy noise and it is very important to extract fault features from such signals. In this paper, an adaptive lifting scheme is proposed for fault diagnosis of rolling bearings. The kurtosis indexes of scale decomposition signals are used as the optimization indicator to select the prediction operator and update operator, which can adapt to the dominant signal characteristics, and reveal the fault feature. Fourier transform is adopted to remove the overlapping signal frequency components at every scale decomposition signal. Experimental results confirm the advantage of the adaptive lifting scheme over lifting scheme for feature extraction, and the typical features of rolling bearing in time domain are successfully extracted by adaptive lifting scheme.

About this article

04 March 2012
14 May 2012
30 June 2012
adaptive lifting scheme
kurtosis index
rolling bearing
fault diagnosis