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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摘要针对高速列车轮对轴承工作环境复杂，振动信号中时常伴有冲击性噪声和循环平稳性噪声，使得传统的参数自适应VMD方法对轮对轴承的故障特征信息提取不准确的问题，提出了一种基于集成经验模态分解（EEMD）预处理的改进参数自适应VMD方法。首先利用EEMD对采集到的振动信号进行分解，计算原始信号以及各分量的包络峭度值，选取峭度值大于原始信号峭度值的分量进行重构，生成新的振动信号；其次以局部最大包络谱峭度为目标函数，利用基于粒子群（PSO）的参数自适应VMD方法分析新信号，从而确定最佳参数；最后将优化后的VMD用于新信号的分解，选取包络谱峭度值最大的分量进行包络解调分析。通过仿真和试验数据分析，证明了该方法在强噪声干扰下仍具有优良的故障特征提取效果。研究结果对提高列车轮对轴承故障诊断效果有一定的理论意义和应用价值。

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	李翠省1
	2
	廖英英2
	刘永强2

关键词 ：轮对轴承, 故障诊断, VMD, 包络峭度, 包络谱峭度

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

收稿日期: 2021-06-09 出版日期: 2022-01-15

引用本文:

李翠省1,2，廖英英2，刘永强2. 基于EEMD和参数自适应VMD的高速列车轮对轴承故障诊断[J]. 振动与冲击, 2022, 41(1): 68-77.
LI Cuixing1,2, LIAO Yingying2, LIU Yongqiang2. Fault diagnosis of wheelset bearing of high-speed train based on EEMD and parameter adaptive VMD. JOURNAL OF VIBRATION AND SHOCK, 2022, 41(1): 68-77.

链接本文:

http://jvs.sjtu.edu.cn/CN/ 或 http://jvs.sjtu.edu.cn/CN/Y2022/V41/I1/68

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