基于改进LMD和综合特征指标的滚动轴承故障诊断  

Fault diagnosis of rolling bearing based on improved LMD and comprehensive characteristic index

作　　者：辜志强[1] 林月叠 GU Zhiqiang;LIN Yuedie(Hubei Key Laboratory of Advanced Technology for Automotive Components, Wuhan University of Technology, Wuhan 430070, China)

机构地区：[1]武汉理工大学现代汽车零部件技术湖北省重点实验室,湖北武汉430070

出　　处：《合肥工业大学学报（自然科学版）》2021年第2期145-150,181,共7页Journal of Hefei University of Technology：Natural Science

基　　金：教育部创新团队发展计划资助项目(IRT-17R83)。

摘　　要：局部均值分解(local mean decomposition,LMD)适用于分析非平稳的滚动轴承故障信号。文章针对LMD存在的端点效应以及敏感分量难以筛选的问题,提出一种基于匹配误差的四点波形延拓方法来改善端点效应,将综合特征指标与K-means聚类分析相结合筛选敏感分量;轴承故障信号经改进的LMD分解为若干个乘积函数(product function,PF)分量;计算所有PF分量的8个参数作为综合特征指标,再利用K-means聚类分析进行分类,区分出敏感分量与虚假分量,并重组敏感分量;最后利用包络分析成功提取到故障特征频率。结果表明该方法是一种有效的滚动轴承故障诊断方法。Local mean decomposition(LMD)is suitable for analyzing non-stationary rolling bearing fault signals.Aiming at the endpoint effect and the difficulty in distinguishing sensitive components in LMD,a four-point waveform continuation method based on matching error is proposed to improve endpoint effect,and the sensitive components are screened by combining comprehensive characteristic indexes with K-means clustering analysis.The improved LMD is used to decompose the rolling bearing fault signal to obtain several product function(PF)components,and eight parameters of all PF components are calculated as comprehensive characteristic indexes.Then K-means clustering analysis is used to classify and distinguish sensitive components from false components,then the sensitive components are reorganized.Finally,the fault feature frequency is successfully extracted by envelope analysis.The results show that this method is an effective fault diagnosis method for rolling bearings.

关 键 词：局部均值分解(LMD) 波形延拓 综合特征指标 K-means聚类分析 故障诊断 

分 类 号：TH133.3[机械工程—机械制造及自动化] TH165.3