中国机械工程2016，Vol.27Issue(22)：3075-3081,7.DOI:10.3969/j.issn.1004-132X.2016.22.016
基于局部均值分解与拉普拉斯特征映射的滚动轴承故障诊断方法
Fault Diagnosis Method of Bearings Based on LMD and LE
摘要
Abstract
A new diagnosis method for feature extraction of non-stationary vibration signals and fault classification of rolling bearings was proposed based on LMD and LE.Firstly,the non-stationary vibration signals of rolling bearings were decomposed into several product functions with LMD.Then, dimensional fault feature sets were established by the time-frequency domain features of product func-tion,instantaneous frequency and amplitude.Secondly,LE was introduced to extract the sensitive and stable characteristic parameters to describe the running states of rolling bearings effectively and accu-rately.Finally,support vector machine classification model was built to realize the classification of fault bearings.For test samples classification,the average prediction accuracy is as 9 1 .1 7%.It means that the fusion method of the LMD and LE is suitable and feasible for the bearing fault feature extrac-tion.关键词
非平稳信号/局部均值分解/拉普拉斯特征映射/故障诊断Key words
non-stationary signal/local mean decomposition(LMD)/Laplacian eigenmap(LE)/fault diagnosis分类
机械制造引用本文复制引用
徐倩倩,刘凯,侯和平,徐卓飞..基于局部均值分解与拉普拉斯特征映射的滚动轴承故障诊断方法[J].中国机械工程,2016,27(22):3075-3081,7.基金项目
国家自然科学基金资助项目(51275406) （51275406）
国家青年科学基金资助项目(51305340) （51305340）
