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International Standard Serial Number:
ISSN 1001-4551
Sponsor:
Zhejiang University;
Zhejiang Machinery and Electrical Group
Edited by:
Editorial of Journal of Mechanical & Electrical Engineering
Chief Editor:
ZHAO Qun
Vice Chief Editor:
TANG ren-zhong,
LUO Xiang-yang
Tel:
86-571-87041360,87239525
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86-571-87239571
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No.9 Gaoguannong,Daxue Road,Hangzhou,China
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meem_contribute@163.com
Abstract: In order to improve the accuracy of bearing fault diagnosis under strong interference, an improved empirical wavelet transform(EWT)algorithm based on spectral envelope segmentation was proposed. Firstly, aiming at the problems of modal similarity and signal distortion caused by redundant frequency band segmentation of traditional EWT algorithm, the frequency band was segmented based on the pole of cubic B-spline envelope, and the modal components of signals in different frequency bands were effectively extracted. Then, the sensitivity of modal components was analyzed by using the margin factor, and the highly sensitive modal components were separated. The arrangement entropy of highly sensitive modal components was calculated to form the eigenvector. Finally, the clustering method was used to analyze the spectrum envelope EWT feature, the traditional EWT feature and the wavelet information entropy feature. The spectrum envelope EWT feature did not possess the phenomenon of cross between classes, but the cohesion degree within classes was high. The above three fault features were input into support vector machine for pattern recognition. The research results show that the diagnostic accuracy of wavelet information entropy feature is 93.75%, that of classical EWT feature is 87.50%, and that of spectral envelope EWT feature is 98.75%, which show that the quality of spectrum envelope EWT feature is the best, which can effectively improve the diagnostic accuracy of bearing under the background of strong interference.
Key words: rolling vibration signal analysis; shock component of fault signature; feature vector extraction; empirical wavelet transform(EWT); clearance factor; sensitive modal selection; permutation entropy(PE)
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