Xiukun Wei, Kun Guo, L. Jia, Guangwu Liu, Minzheng Yuan
{"title":"Fault Isolation of Light Rail Vehicle Suspension System Based on D-S Evidence Theory and Improvement Application Case","authors":"Xiukun Wei, Kun Guo, L. Jia, Guangwu Liu, Minzheng Yuan","doi":"10.4236/JILSA.2013.54029","DOIUrl":null,"url":null,"abstract":"This paper presents an innovative \napproach for the fault isolation of Light Rail Vehicle (LRV) suspension system \nbased on the Dempster-Shafer (D-S) evidence theory and its improvement \napplication case. The considered LRV has three rolling stocks and each one \nequips three sensors for monitoring the suspension system. A Kalman filter is \napplied to generate the residuals for fault diagnosis. For the purpose of fault \nisolation, a fault feature database is built in advance. The Eros and the norm \ndistance between the fault feature of the new occurred fault and the one in the \nfeature database are applied to measure the similarity of the feature which is \nthe basis for the basic belief assignment to the fault, respectively. After the basic belief \nassignments are obtained, they are fused by using the D-S evidence theory. The \nfusion of the basic belief assignments increases the isolation accuracy \nsignificantly. The efficiency of the proposed method is demonstrated by two case studies.","PeriodicalId":69452,"journal":{"name":"智能学习系统与应用(英文)","volume":"5 1","pages":"245-253"},"PeriodicalIF":0.0000,"publicationDate":"2013-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"智能学习系统与应用(英文)","FirstCategoryId":"1093","ListUrlMain":"https://doi.org/10.4236/JILSA.2013.54029","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 5
Abstract
This paper presents an innovative
approach for the fault isolation of Light Rail Vehicle (LRV) suspension system
based on the Dempster-Shafer (D-S) evidence theory and its improvement
application case. The considered LRV has three rolling stocks and each one
equips three sensors for monitoring the suspension system. A Kalman filter is
applied to generate the residuals for fault diagnosis. For the purpose of fault
isolation, a fault feature database is built in advance. The Eros and the norm
distance between the fault feature of the new occurred fault and the one in the
feature database are applied to measure the similarity of the feature which is
the basis for the basic belief assignment to the fault, respectively. After the basic belief
assignments are obtained, they are fused by using the D-S evidence theory. The
fusion of the basic belief assignments increases the isolation accuracy
significantly. The efficiency of the proposed method is demonstrated by two case studies.