Mechanical Engineering

Fault Feature Extraction Based on Morlet Wavelet Transform and Singular Value Decomposition

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  • School of Mechanical and Automotive Engineering,South China University of Technology,Guangzhou 510640,Guangdong,China
耿宇斌(1990-),男,博士生,主要从事信号处理与故障诊断研究.

Received date: 2013-12-27

  Revised date: 2014-04-23

  Online published: 2014-06-01

Supported by

国家自然科学基金资助项目(51375178);广东省自然科学基金资助项目(S2012010008789)

Abstract

Aiming at the feature extraction of Morlet wavelet transform results,the wavelet coefficient matrixobtained by the continuous Morlet wavelet transform is decomposed by singular value decomposition (SVD).Therelationship among the singular value,the feature signal and the noise in the Morlet wavelet transform results isanalyzed.Based on this relationship,the effective feature information of wavelet transform results can be clearlyextracted by selecting appropriate singular values for SVD reconstruction.Further calculation is carried out to obtainthe frequency- energy spectrum,and the shock feature can be extracted according to the peak position of this spec-trum.Finally,the proposed method is applied to the fault feature extraction of bearing vibration signals and is com-pared with other methods.The results show that the proposed method can extarct the distinct fault waveforms andachieve a very good effect on fault feature extraction at a low signal- to- noise ratio(SNR).

Cite this article

Geng Yu- bin Zhao Xue- zhi . Fault Feature Extraction Based on Morlet Wavelet Transform and Singular Value Decomposition[J]. Journal of South China University of Technology(Natural Science), 2014 , 42(7) : 55 -61 . DOI: 10.3969/j.issn.1000-565X.2014.07.009

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