Feature extraction for rolling bearing based on adaptive wavelet filter

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Journal of Vibration and Shock ›› 2015, Vol. 34 ›› Issue (23) : 25-30.

ZHANG Yan1，TANG Bao-ping1，DENG Lei1，YAN Bin-sheng2

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Abstract

A new method based on the filter characteristics of wavelet transform and autocorrelation analysis is proposed for feature extraction form rolling bearing vibration signal. Aiming at the wavelet parameter optimization problem, local mean decomposition is used to produce appropriate production functions (PFs), and the center frequency of the wavelet filter is then adaptively and efficiently determined using the PFs which takes advantage of the statistical information contained within them. The bandwidth of the wavelet filter is optimized according to the relationship between the modified Shannon entropy of the filtered signal and the filter bandwidth. The analysis results of the experimental signal and rolling bearing vibration signal with inner-race and outer-race faults show that the filter parameters can be optimized adaptively, and the fault feature of rolling bearing can be extracted by the proposed method.

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feature extraction / wavelet filtering / local mean decomposition / Shannon entropy

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ZHANG Yan1，TANG Bao-ping1，DENG Lei1，YAN Bin-sheng2. Feature extraction for rolling bearing based on adaptive wavelet filter[J]. Journal of Vibration and Shock, 2015, 34(23): 25-30

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