改进多元层次波动色散熵及其在滚动轴承故障诊断中的应用

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摘要针对滚动轴承振动信号故障特征难以提取以及单通道振动信号分析易存在故障信息缺漏的问题，提出一种新的衡量多通道时间序列动态特征的方法——改进多元层次波动色散熵(MMHFDE)，将其用于提取滚动轴承多通道振动信号中的故障特征，在此基础上提出一种基于MMHFDE，最大相关最小冗余(mRMR)和粒子群优化核极限学习机（PSO-KELM）的滚动轴承故障诊断新方法。首先使用MMHFDE提取滚动轴承不同状态的故障特征，而后采用mRMR从得到的故障特征中筛选敏感特征构成敏感特征向量，最后将敏感特征向量输入到基于PSO-KELM构建的故障分类器中进行故障识别。由实验结果可知，提出的方法可以有效识别滚动轴承不同故障状态。

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	周付明
	杨小强
	申金星
	刘武强
	刘小林

关键词 ：改进多元层次波动色散熵 (MMHFDE), 最大相关最小冗余 (mRMR), 粒子群优化核极限学习机（PSO-KELM）, 滚动轴承, 故障诊断

Abstract：Aiming at the problems that the fault features of rolling bearings vibration signals are difficult to extract and the single-channel vibration signals analysis is prone to fault information missing, a new method for measuring the dynamic features of multi-channel time series, which is called MMHFDE is proposed to extract the fault features of the multi-channel vibration signals of rolling bearings. On this basis, a new method for rolling bearings fault diagnosis based on MMHFDE, mRMR and PSO-KELM is proposed. Firstly, MMHFDE is applied to extract the fault features of rolling bearings under different states, and then mRMR is applied to screen the sensitive features from the obtained fault features to form sensitive feature vectors. Finally，the sensitive feature vectors are input into fault classifier based on PSO-KELM for fault identification. Experimental results show that the proposed method can effectively identify different fault states of rolling bearings.

Key words： modified multivariate hierarchical fluctuation dispersion entropy (MMHFDE) max-relevance and min-redundancy(mRMR) particle swarm optimization kernel extreme learning machine (PSO-KELM） rolling bearings fault diagnosis

收稿日期: 2020-08-20 出版日期: 2021-11-28

引用本文:

周付明，杨小强，申金星，刘武强，刘小林. 改进多元层次波动色散熵及其在滚动轴承故障诊断中的应用[J]. 振动与冲击, 2021, 40(22): 167-174.
ZHOU Fuming,YANG Xiaoqiang,SHEN Jinxing,LIU Wuqiang,LIU Xiaolin. Modified multivariate hierarchical fluctuation dispersion entropy and its application to the fault diagnosis of rolling bearings. JOURNAL OF VIBRATION AND SHOCK, 2021, 40(22): 167-174.

链接本文:

http://jvs.sjtu.edu.cn/CN/ 或 http://jvs.sjtu.edu.cn/CN/Y2021/V40/I22/167

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