中国机械工程2017，Vol.28Issue(24)：3001-3012,12.DOI:10.3969/j.issn.1004-132X.2017.24.017
基于最小熵解卷积-变分模态分解和优化支持向量机的滚动轴承故障诊断方法
Fault Diagnosis Method Based on MED-VMD and Optimized SVM for Rolling Bearings
摘要
Abstract
A method of fault feature extraction was proposed based on MED,VMD and fuzzy approximate entropy,and the optimized SVM was used to identify faults.The MED method was used to reduce the noise interferences and to enhance the fault feature informations in the fault signals,and the signals after noise reduction by VMD were decomposed,then,the fuzzy approximation entropy was used to quantify the modal components of fault feature informations after VMD,and the feature vectors were constructed,Finally,the extended particle swarm optimization(EPSO) algorithm was used to optimize the penalty factors and the kernel function parameters of SVM to complete the fault recognition classification.The proposed method was applied to the experimental data of rolling bearings,and the effectiveness of the method was verified.Compared with the feature extraction method based on local mean decomposition(LMD),it is shown that the proposed method may extract the features of rolling bearing faults more accurately and may identify different faults more accurately.Compared with SVM based on grid search algorithm and the least square support vector machines(LSSVM) based on EPSO algorithm,the proposed method has better classification performance and better diagnosis performance.关键词
故障诊断/变分模态分解/最小熵解卷积/模糊近似熵/支持向量机Key words
fault diagnosis/variational mode decomposition(VMD)/minimum entropy deconvolution(MED)/fuzzy approximate entropy/support vector machine(SVM)分类
机械制造引用本文复制引用
姚成玉,来博文,陈东宁,孙飞,吕世君..基于最小熵解卷积-变分模态分解和优化支持向量机的滚动轴承故障诊断方法[J].中国机械工程,2017,28(24):3001-3012,12.基金项目
国家自然科学基金资助项目(51675460,51405426) （51675460,51405426）
河北省自然科学基金资助项目(E2016203306) （E2016203306）
中国博士后科学基金资助项目(2017M621101) （2017M621101）
