Crack fault diagnosis of vibration exciter rolling bearing based on genetic algorithm–optimized Morlet wavelet filter and empirical mode decomposition

Xiaoming Han, Jin Xu, Songnan Song and Jiawei Zhou

International Journal of Distributed Sensor Networks, 2022, vol. 18, issue 8, 15501329221114566

Abstract: The fault diagnosis of vibration exciter rolling bearing is of great significance to maintain the stability of vibration equipment. When the crack fault of the bearing occurs, the effective fault feature information cannot be extracted because the fault feature information of vibration signal is interfered by the noise around the vibrator. To solve this problem, a fault feature recognition method based on genetic algorithm–optimized Morlet wavelet filter and empirical mode decomposition is proposed. The Morlet wavelet filter optimized by genetic algorithm was used to filter the vibration signal, and then the empirical mode decomposition was applied to the filtered signal. In the envelope spectrum of the reconstructed signal, the characteristic frequency of the rolling bearing crack fault of the vibration exciter could be found accurately. Through simulation and experiment, it is proved that this method can provide theoretical and technical support for the crack fault diagnosis of vibration exciter rolling bearing.

Keywords: Vibration exciter; fault diagnosis; genetic algorithm; empirical mode decomposition; intrinsic mode functions (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:sae:intdis:v:18:y:2022:i:8:p:15501329221114566

DOI: 10.1177/15501329221114566

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