Rolling Bearing Fault Feature Extraction Based on SVD-EEMD

Cheng Wen; Chuan De Zhou

doi:10.4028/www.scientific.net/AMM.411-414.1067

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 411-414Rolling Bearing Fault Feature Extraction Based on...

Rolling Bearing Fault Feature Extraction Based on SVD-EEMD

Abstract:

The novel method that singular value decomposition (SVD) is combined with ensemble empirical mode decomposition (EEMD) is proposed because of the mode mixing in empirical mode decomposition (EMD). The first step of this method is to reduce the random noise in fault signal by the SVD, and then does EEMD to restrain the mode mixing effectively. Finally, the intrinsic mode function (IMF) is done for envelope demodulation and as a result, the fault feature is extracted successfully. The implementation process was analyzed by simulation signal and this method has been successfully applied to in inner race and outer race of rolling bearing fault diagnosis. The results show that this method can extract the fault information of rolling bearing effectively and realize the precise fault diagnosis.

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Periodical:

Applied Mechanics and Materials (Volumes 411-414)

Pages:

1067-1071

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.411-414.1067

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Online since:

September 2013

Authors:

Cheng Wen, Chuan De Zhou

Keywords:

Ensemble Empirical Mode Decomposition, Fault Feature Extraction, Rolling Bearing, Singular Value Decomposition

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