期刊名称:Journal of Intelligent Learning Systems and Applications
印刷版ISSN:2150-8402
电子版ISSN:2150-8410
出版年度:2013
卷号:5
期号:4
页码:245-253
DOI:10.4236/jilsa.2013.54029
出版社:Scientific Research Publishing
摘要: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.