Author(s)

Xiukun Wei, Kun Guo, Limin Jia, Guangwu Liu, Minzheng Yuan

Guangzhou Metro Co., Guangzhou, China.
School of Traffic and Transportation, Beijing Jiaotong University, Beijing, China.
State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing, China.

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.

Suspension System; Fault Isolation; D-S Evidence Theory; Information Fusion; Similarity Measurement

X. Wei, K. Guo, L. Jia, G. Liu and M. Yuan, "Fault Isolation of Light Rail Vehicle Suspension System Based on D-S Evidence Theory and Improvement Application Case," Journal of Intelligent Learning Systems and Applications, Vol. 5 No. 4, 2013, pp. 245-253. doi: 10.4236/jilsa.2013.54029.