机械科学与技术2017，Vol.36Issue(6)：915-918,4.DOI:10.13433/j.cnki.1003-8728.2017.0615
LMD能量熵和SVM相结合的滚动轴承故障诊断
Fault Diagnosis of Rolling Bearing Combined LMD Energy Entropy and SVM
摘要
Abstract
To achieve the fault detection and failure analysis of rolling bearing for small samples,a rolling bearing fault diagnosis method is proposed based on the local mean decomposition (LMD) energy entropy and the support vector machines (SVM).In this method,the rolling bearing vibration signals are decomposed into several production functions (PF) by using the LMD signal processing method.Then the energy entropy of the PF components for fault feature extraction is calculated and the features are input into the SVM classifiers for training and testing.Finally,the fault diagnosis of rolling bearing is performed.The experimental results show that the proposed method can be used effectively to identify and classify the type of rolling bearing fault accuratelyfor small samples.关键词
滚动轴承/故障诊断/局部均值分解/能量熵/支持向量机Key words
rolling bearing/fault diagnosis/ocal mean decomposition/energy entropy/support vector machines分类
机械制造引用本文复制引用
徐乐,邢邦圣,郎超男,高钦武..LMD能量熵和SVM相结合的滚动轴承故障诊断[J].机械科学与技术,2017,36(6):915-918,4.基金项目
江苏省“六大人才高峰”高层次人才项目(2012-ZBZZ-038)、江苏省普通高校研究生科研创新计划项目(SJLX_0656)、江苏师范大学博士科研支持项目(14XLR033)及江苏师范大学研究生科研创新计划重点项目(2015YZD018)资助 （2012-ZBZZ-038）
