于参数优化变分模态分解的滚动轴承故障特征提取方法

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摘要在滚动轴承早期故障阶段，代表轴承故障特征的冲击成分容易被较强的背景噪声淹没，针对这一问题提出相关峭度(Correlated Kurtosis, CK)优化变分模态分解(Variational Mode Decomposition, VMD)的滚动轴承故障特征提取方法。针对变分模态分解方法参数不确定问题，提出利用以相关峭度为适应度函数的蝗虫优化算法(Grasshopper optimization algorithm, GOA)对变分模态分解参数进行自适应选定。针对故障信号经优化变分模态分解处理后模态分量的筛选问题，以相关峭度为指标，挑选具有最大相关峭度指标的模态分量进行包络解调分析，提取轴承信号中的故障特征信息。仿真及实测信号处理结果证明，该方法能在强噪声背景下准确提取滚动轴承故障信号的微弱特征。

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	郑义
	岳建海
	焦静
	郭鑫源

关键词 ：滚动轴承, 强噪声, 变分模态分解, 相关峭度, 蝗虫优化算法

Abstract：In early fault stage of rolling bearing, impulse components to reflect bearing fault features are easy to be submerged by strong background noise. Here, a rolling bearing fault feature extraction method based on correlated kurtosis (CK) and variational mode decomposition (VMD) was proposed to solve this problem. Aiming at the uncertainty of parameters in VMD, the grasshopper optimization algorithm (GOA) with CK as fitness function was proposed to adaptively select parameters of VMD. Aiming at the problem of selecting mode components of the fault signal after processed with the optimized VMD, CK was taken as the index, the mode component with the maximum CK index was selected to do envelope demodulation analysis, and extract fault feature information in bearing signal. The processing results of simulated and really measured signals showed that the proposed method can accurately extract weak features of rolling bearing fault signal under strong background noise.

Key words： rolling bearing strong noise variational mode decomposition (VMD) correlated kurtosis (CK) grasshopper optimization algorithm (GOA)

收稿日期: 2019-07-30 出版日期: 2021-01-15

引用本文:

郑义，岳建海，焦静，郭鑫源. 于参数优化变分模态分解的滚动轴承故障特征提取方法[J]. 振动与冲击, 2021, 40(1): 86-94.
ZHENG Yi, YUE Jianhai, JIAO Jing, GUO Xinyuan. Fault feature extraction method of rolling bearing based on parameter optimized VMD. JOURNAL OF VIBRATION AND SHOCK, 2021, 40(1): 86-94.

链接本文:

http://jvs.sjtu.edu.cn/CN/ 或 http://jvs.sjtu.edu.cn/CN/Y2021/V40/I1/86

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