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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: Rolling bearing fault signals often contain a lot of noise and exist in the form of modulation, so it is difficult to extract fault feature information. At the same time, when fast empirical wavelet transform (FEWT) is used to decompose the fault signal, the fault feature is weakened. Therefore, combining with the advantages of FEWT and fast independent component analysis (FastICA), a fault feature recognition method of rolling bearing based on FEWT-FastICA was proposed.Firstly, the FEWT algorithm was used to decompose the bearing fault signals to obtain a set of intrinsic modal components (IMF). According to the kurtosis criterion, IMF components with kurtosis greater than 3 were reconstructed as vibration impact signals, while IMF components with kurtosis less than 3 were reconstructed as virtual channel signals. Then, the reconstructed signal was denoised and unmixed by FastICA algorithm, and the best estimated signal was obtained.The envelope spectrum analysis of the best estimated signal was carried out to complete the fault diagnosis and analysis of rolling bearing.Finally,in order to verify the effectiveness of FEWT-FastICA algorithm, simulation signals and real bearing fault signals were used for experimental verification. At the same time, in order to verify the superiority of FEWT-FastICA algorithm, it was compared with FEWT. The research results show that the FEWT-FastICA method can effectively extract fault feature information, and the signal-to-noise ratio of the results obtained by FEWT method is 1.55 times higher than that obtained by FEWT method, which provides a new method for bearing fault diagnosis.
Key words: bearing fault diagnosis; fast empirical wavelet transform(FEWT); fast independent component analysis(FastICA); denoised and unmixed; fault feature extract; signal-to-noise ratio
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