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| Rolling Bearing Fault Diagnosis Based on Improved Adaptive Parameterless Empirical Wavelet Transform |
| LI Ji-meng,WANG Hui,LI Ming,YAO Xi-feng |
| School of Electrical Engineering, Yanshan University, Qinhuangdao, Hebei 066004, China |
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Abstract In order to realize the effective extraction of rolling bearing fault periodic impulse features and solve the problem of Fourier spectral segmentation in empirical wavelet transform, an improved adaptive parameterless empirical wavelet transform method is proposed. First, adaptive segmentation of the Fourier spectrum is performed by adaptive parameterless empirical wavelet transform. Then, the spectral boundaries are combined by using the kurtosis index, and the filter banks are reconstructed to decompose the signal; finally, the component corresponding to the largest kurtosis value is selected to extract fault features by the Hilbert transform. The simulation and engineering application verify the effectiveness of the proposed method. The analysis results show that the performance of the proposed method is better than ensemble empirical model decomposition and the classical empirical wavelet transform.
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Received: 10 August 2018
Published: 08 June 2020
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Corresponding Authors:
LI Ji-meng
E-mail: xjtuljm@163.com
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2020, Vol. 41 
