基于EEMD和参数自适应VMD的高速列车轮对轴承故障诊断

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Abstract Aiming at the problem that the traditional parameter-adaptive VMD method can not extract the fault feature information of wheelset bearing accurately because the working environment of wheelset bearing of high-speed train is complex and the vibration signal is often accompanied by impact noise and cyclic stationary noise, an improved parameter-adaptive VMD method based on ensemble empirical mode decomposition preprocessing is proposed. First, the collected vibration signal is decomposed by EEMD, the original signal and the envelope kurtosis values of each component are calculated, and the components whose kurtosis values are greater than the original signal kurtosis value are selected for reconstruction to generate new vibration signal. Secondly, the local maximum envelope spectrum kurtosis is taken as the objective function, and the new signal is analyzed by parameter-adaptive VMD method based on particle swarm optimization to determine the optimal parameters. Finally, the optimized VMD is used to decompose the new signal, and the component with the largest envelope spectrum kurtosis value is selected for envelope demodulation analysis. Through simulation and experimental analysis, it is proved that this method still has good performance in fault feature extraction under strong noise interference. The research results have certain theoretical significance and application value for improving the effect of train wheelset bearing fault diagnosis.

Key words： wheelset bearing fault diagnosis VMD envelope kurtosis envelope spectrum kurtosis

Received: 09 June 2021 Published: 15 January 2022

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	Articles by authors
	LI Cuixing1
	2
	LIAO Yingying2
	LIU Yongqiang2

Cite this article:

LI Cuixing1,2,LIAO Yingying2, et al. Fault diagnosis of wheelset bearing of high-speed train based on EEMD and parameter adaptive VMD[J]. JOURNAL OF VIBRATION AND SHOCK, 2022, 41(1): 68-77.

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http://jvs.sjtu.edu.cn/EN/ OR http://jvs.sjtu.edu.cn/EN/Y2022/V41/I1/68

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