Approach of Combination of Weighted Evidence Based on Evidential Closeness Degree and its Application

Abstract:

Since the information fusion based on Dempster-Shafer(D-S) evidence theory involves counter-intuitive behaviors when evidences highly conflict, a new approach of combination of weighted based on evidence closeness degree is proposed and applied in fault diagnosis．Firstly, the calculation method of evidence distance and evidential closeness degree as well as its revision are given according to the relevance and importance of various evidence sources; Then the closeness degree is normalized in order to obtain the weights of evidence sources. And the information fusion is realized with the base of weighted evidence theory. Finally, the approach is applied to fault diagnosis model of multi-evidences (multi-symptom domains or multi-sensors) in this paper. In the experiments, the proposed approach is compared with the existing combination rules and the results show that the reliability and accuracy of fault diagnosis are significantly improved and its uncertainty is decreased remarkably. The fusion problem given above is also solved effectively.

• Periodical: Advanced Materials Research (Volume 630)
• Pages: 377-382
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.630.377
• Online since: December 2012
• Authors: Zhuan Zhe Zhao, Min Ping Jia, Kang He, Hao Zhou, Yu Jie Ding
• Keywords: Closeness Degree, D-S Evidence Theory, Evidence Conflict, Fault Diagnosis, Weighted Balance

References

[1] O. Basir, X.H. Yuan. Engine fault diagnosis based on multisensor information fusion using Dempster evidence theory, Information fusion. 8(2007) 379-386.

DOI: 10.1016/j.inffus.2005.07.003

[2] L. Oukhellou,A. Debiolles,T. Denoeux, et al, Fault diagnosis in railway track circuits using Dempster-Shafer classifier fusion, Engineering Applications of Artificial Intelligence. 23(2010) 117-128.

DOI: 10.1016/j.engappai.2009.06.005

[3] R.R. Yager, On the Dempster-Shafer framework and new combination rules, Information Sciences, 41(1987) 93-138.

DOI: 10.1016/0020-0255(87)90007-7

[4] Y.S. Zhu C.D. Wang Y.Y. Zhang, Study of neural network based on method of weighted balance of evidence and its application to fault diagnosis, Chinese Journal of Mechanical Engineering. 38(2002) 66-71.

DOI: 10.3901/jme.2002.06.066

[5] P. Zhang X.D. Zhang, Fault diagnosis of rotating machinery based on evidence theory of evidence entropy. Journal of Vibration, Measurement & Diagnosis. 30(2010) 56-59.

DOI: 10.1109/icemi.2009.5274685

[6] G. Shafer, A Mathematical Theory of Evidence, Princeton University Press, Princeton, NJ, (1976).

[7] J.M. Xia J.A. Yang,Q. Zhang, A novel approach for calculation of the evidence distance and the evaluation of sensor credibility, Chinese Journal of Sensors and Actuators. 23(2010)1111-1117.

[8] F. Smarandache, J. Dezert, Advances and applications of DSmT for information fusion, American Research Press, Rehoboth, NM, (2004).

[9] C.K. Murphy, Combining belief functions when evidence conflicts, Decision Support Systems. 29 (2000) 1-9.

DOI: 10.1016/s0167-9236(99)00084-6

[10] H.W. Guo W.K. Shi,Y. Deng, Evaluating sensor reliability in classification problems based on evidence theory, IEEE Trans. Syst., Man, Cybern. B, Cybern. 36(2006) 970-981.

DOI: 10.1109/tsmcb.2006.872269