中国机械工程2017，Vol.28Issue(11)：1339-1346,8.DOI:10.3969/j.issn.1004-132X.2017.11.014
基于复合多尺度熵与拉普拉斯支持向量机的滚动轴承故障诊断方法
Rolling Bearing Fault Diagnosis Method Based on Composite Multiscale Entropy and Laplacian SVM
摘要
Abstract
Since the unclear of early fault of rolling bearings and it was difficult to extract the fea-tures from the mechanical systems,a new j udging time series complexity testing method called com-posite multiscale entropy (CMSE)was applied to extract the fault features from the vibration signals of rolling bearings.CMSE overcome the defects of coarse-graining in MSE and was an effective meth-od for measuring the complexity of time series with better consistency and stability.Besides,as it was easy to collect a large number of samples,but difficult to label them in mechanical fault intelligent di-agnosis,the LapSVM was applied to the intelligent fault diagnosis of rolling bearings.Then a new fault diagnosis method for rolling bearings was proposed based on the CMSE,sequential forward se-lection and LapSVM.Finally,the experimental data were analyzed based on the proposed method. The results show that the fault features of rolling bearings are extracted effectively by CMSE,com-pared with SVM that may only be trained by the labeled samples,the LapSVM combining with se-quential forward selection for feature selection and studying from a large number of unlabeled samples may significantly improve the accuracy of fault diagnosis for fewer number of labeled samples.关键词
多尺度熵/复合多尺度熵/支持向量机/拉普拉斯支持向量机/故障诊断Key words
multiscale entropy (MSE)/composite multiscale entropy/support vector machine (SVM)/Laplacian support vector machine(LapSVM)/fault diagnosis分类
信息技术与安全科学引用本文复制引用
代俊习,郑近德,潘海洋,潘紫微..基于复合多尺度熵与拉普拉斯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(11):1339-1346,8.基金项目
国家自然科学基金资助项目(51505002) （51505002）
安徽省高校自然科学研究重点资助项目(KJ2015A080) （KJ2015A080）
安徽工业大学研究生创新研究基金资助项目(2016061) （2016061）
