TITLE:
Fault Isolation of Light Rail Vehicle Suspension System Based on D-S Evidence Theory and Improvement Application Case
AUTHORS:
Xiukun Wei, Kun Guo, Limin Jia, Guangwu Liu, Minzheng Yuan
KEYWORDS:
Suspension System; Fault Isolation; D-S Evidence Theory; Information Fusion; Similarity Measurement
JOURNAL NAME:
Journal of Intelligent Learning Systems and Applications,
Vol.5 No.4,
November
26,
2013
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.