Operation Condition Monitoring using Temporal Weighted Dempster-Shafer Theory



Xiaoyun Wang Tingdi Zhao


System operation is a real time, dynamic decision process, a continuous observation should be implemented to support timely decision. Real time condition monitoring and diagnosis is featured with ongoing event sequence. The more recent observation, the much detailed, accurate information, and the more obsolete observations with much weak correlation to current faults and errors vise versa.

Dempster-Shafer evidence theory is best suitable for the problem of redundant sensors, insufficient data reasoning. However, D-S base applications largely focused on causational relationship between symptoms and effects, and the fusion process of evidences was performed regardless whatever order observed. As an improvement to the frame of discernment of the D-S theory, we purposed a time weighted evidence combination method. Observed events were extracted from multiple time points to form a temporal evidence sequence. Basic probability assignment was altered by temporal weights in accordance with the time proximity between the observed events and current time. The temporal weights value set was in accordance with its occurring time point. Evidences with same timestamps should be allocated with the same temporal weights. An example was discussed to illustrate the temporal weight, D-S rule based assessment framework. In the framework, latest observed evidences stream were combined into the framework to improving fault recognition.

How to Cite

Wang, X. ., & Zhao, T. . (2014). Operation Condition Monitoring using Temporal Weighted Dempster-Shafer Theory. Annual Conference of the PHM Society, 6(1). https://doi.org/10.36001/phmconf.2014.v6i1.2345
Abstract 39 | PDF Downloads 51



Dempster-Shafer theory, fault recognition, timed weight evidence combination

Yang, B.S., Kim, K. J., (2006). Application of Dempster- Shafer theory in fault diagnosis of induction motors using vibration and current signals. Mechanical Systems and Signal Processing, 20:403–420.

Parikh,C.R., Pont, M.J., Jones, N.B., (2001). Application of Dempster-Shafer theory in condition monitoring systems: A case study, Pattern Recognition Letters, 22 (6-7): 777-785.

Fang, L., Wang, C., et.al, (2010). A Framework for Network Security Situation Awareness Based on Knowledge. 2nd International Conference on Computer Engineering and Technology, Chengdu, China

Zomlot, L., Sundaramu rthy. S., (2011). Prioritizing Intrusion Analysis Using Dempster-Shafer Theory, Proceedings of the 4th ACM workshop on Security and artificial intelligence, Chicago, Illinois, USA

McKeever, S., (2009). Recognising Situations Using Extended Dempster-Shafer Theory, Doctoral dissertation. National University of Ireland, Dublin

Beranek, L., Knizek, J., (2013).The Use of Contextual Information to Detection of Fraud on On-line Auctions. Journal of Internet Banking and Commerce, vol. 18, no.3

Dempster, A., (1968). A Generalization of Bayesian inference. Journal of the Royal Statistical Society, pp. 205–247.

Shafer, G., (1976). A Mathematical Theory of Evidence, New Jersey, Princeton University Press.

Sentz, K., (2002). Combination of Evidence in Dempster- Shafer Theory, Binghamton University, Binghamton, NY

Garvin, D., (1988). Managing quality. NY: Free Press.

Yu, D,. Frincke, D., (2005). Alert Confidence Fusion in Intrusion Detection Systems with Extended Dempster-Shafer Theory, 43rd ACM Southeast Conference,Kennesaw, GA

Kay, R.U., (2007). Fundamentals of the Dempster-Shafer theory and its applications to system safety and reliability modeling. Reliability: Theory and Applications. vol 2(3-4), pp. 173-185
Poster Presentations