L-kurtosis-based optimal wavelet filtering and its application to fault diagnosis of rolling element bearings

Anbo Ming, Wei Zhang, Chao Fu, Yongfeng Yang, Fulei Chu, Yajuan Liu

科研成果: 期刊稿件文章同行评审

5 引用 (Scopus)

摘要

Repetitive transients are a key symptom for the occurrence of incipient fault of rolling element bearings. Therefore, an optimal wavelet filtering method is developed by maximizing the L-kurtosis through the genetic algorithm to extract the weak repetitive transients buried in the heavy noise and disturbed by the outliers. First, the capability of L-kurtosis for characterizing the impulsiveness and cyclostationary of repetitive transients is numerically studied at different degrees of noise. Then, the center frequency and band width of morlet wave filter are adaptively determined by the genetic algorithm and the maximization of L-kurtosis. Finally, both simulation and experiments are performed to validate the efficacy of the proposed method. Results show that the proposed method is more powerful and reliable than the other commonly used indexes-based optimal wavelet filtering methods.

源语言英语
页(从-至)1594-1603
页数10
期刊JVC/Journal of Vibration and Control
30
7-8
DOI
出版状态已出版 - 4月 2024

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