中国机械工程Issue(19)：2634-2640,2641,8.DOI:10.3969/j.issn.1004-132X.2014.19.015
基于 DLMD 样本熵和模糊聚类的滚动轴承故障诊断
Fault Diagnosis of Rolling Bearings Based on DLMD Sample Entropy and Fuzzy Clustering
摘要
Abstract
In view of the problem that the traditional local mean decomposition (LMD)was diffi-cult to effectively extract the weak high frequency signal components,a method of DLMD was put for-ward.A new approach for rolling bearing fault diagnosis based on the combination of DLMD,sample entropy and fuzzy clustering was proposed.Firstly,rolling bearing vibration signals were decomposed with DLMD to obtain a certain number of product function(PF)components which had physical mean-ing.Then the sample entropies of the PF components were calculated and used as the eigenvectors. Finally,the eigenvectors were recognized and classified through the fuzzy clustering.The experimen-tal results show that the method based on the combination of DLMD,sample entropy and fuzzy clus-tering can be used to recognize and classify rolling bearing fault signals accurately and effectively.关键词
故障诊断/滚动轴承/微分局部均值分解/样本熵/模糊聚类Key words
fault diagnosis/rolling bearing/differential local mean decomposition(DLMD)/sam-ple entropy/fuzzy clustering分类
信息技术与安全科学引用本文复制引用
孟宗,王亚超,王晓燕..基于 DLMD 样本熵和模糊聚类的滚动轴承故障诊断[J].中国机械工程,2014,(19):2634-2640,2641,8.基金项目
国家自然科学基金资助项目(51105323) （51105323）
河北省自然科学基金资助项目(E2012203166) （E2012203166）
