Fault Isolation of Light Rail Vehicle Suspension System Based on D-S Evidence Theory and Improvement Application Case

DOI: 10.4236/jilsa.2013.54029   PDF   HTML     3,748 Downloads   5,574 Views   Citations


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.

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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.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] S. Bruni, R. Goodall, T. X. Mei and H. Tsunashima, “Control and Monitoring for Railway Vehicle Dynamics,” Vehicle System Dynamics, Vol. 45, No. 7-8, 2007, pp. 765-771.
[2] R. Goodall and T. Mei, “Advanced Control and Monitoring for Railway Vehicle Suspensions,” International Symposium on Speed-Up and Service Technology for Railway and Maglev Systems (STECH’06), Chengdu, 13-16 July 2006, pp. 10-16.
[3] R. Goodall and C. Roberts, “Concepts and Techniques for Railway Condition Monitoring,” IET International Conference on Railway Condition Monitoring, Birmingham, 29-30 November 2006, pp. 90-95.
[4] H. Yang, “The Research and Application of Multi-Sensor Information Fusion,” Harbin Engineering University, Harbin, 2010.
[5] D.-Q. Zhu and Y.-A. Liu, “Information Fusion Method for Fault Diagnosis,” Control and Decision, Vol. 22, No. 12, 2007, pp. 1321-1328.
[6] X. Wei and H. Liu, “Fault Diagnosis of Rail Vehicle Suspension Systems by Using GLRT,” Chinese Control and Decision Conference, Mianyang, 23-25 May 2011, pp. 1932-1936.
[7] Y. C. Lin, C. L. Lin and N. C. Shieh, “A Hybird Evolutionary Approach for Robust Active Suspension Design If Light Rail Vehicles,” IEEE Transactions on Control Systems Technology, Vol. 14, No. 4, 2006, pp. 65-706.
[8] O. Basir and X. Yuan, “Engine Fault Diagnosis Based on Multi-Sensor Information Fusion Using Dempster-Shafer Evidence Theory,” Information Fusion, Vol. 8, No. 4, 2007, pp. 379-386.
[9] X. Fan and M. Zuo, “Fault Diagnosis of Machines Based on D-S Evidence Theory. Part 1: D-S Evidence Theory and Its Improvement,” Pattern Recognition Letters, Vol. 27, No. 5, 2006, pp. 366-376.
[10] J. M. Richardson and K. A. Marsch, “Fusion of Multisensor Data,” Information Journal of Robotic Research, Vol. 7, No. 6, 1988, pp. 78-96.
[11] B.-S. Yang and K. J. Kim, “Application of DempsterShafer Theory in Fault Diagnosis of Induction Motors Using Vibration and Current Signals,” Mechanical Systems and Signal Processing, Vol. 20, No. 2, 2006, pp. 403-420.

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