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A novel conflict measurement in decision making and its application in fault diagnosis

Citation

Xiao, F and Cao, Z and Jolfaei, A, A novel conflict measurement in decision making and its application in fault diagnosis, IEEE Transactions on Fuzzy Systems pp. 1-13. ISSN 1063-6706 (2020) [Refereed Article]


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DOI: doi:10.1109/TFUZZ.2020.3002431

Abstract

Dempster-Shafer evidence (DSE) theory, which allows combining pieces of evidence from different data sources to derive a degree of belief function that is a type of fuzzy measure, is a general framework for reasoning with uncertainty. In this framework, how to optimally manage the conflicts of multiple pieces of evidence in DSE remains an open issue to support decision making. The existing conflict measurement approaches can achieve acceptable outcomes but do not fully consider the optimization at the decision-making level using the novel measurement of conflicts. In this paper, we proposed a novel evidential correlation coefficient (ECC) for belief functions by measuring the conflict between two pieces of evidence in decision making. Then, we investigated the properties of our proposed evidential correlation and conflict coefficients, which are all proven to satisfy the desirable properties for conflict measurement, including nonnegativity, symmetry, boundedness, extreme consistency, and insensitivity to refinement. We also presented several examples and comparisons to demonstrate the superiority of our proposed ECC method. Finally, we applied the proposed ECC in a decision-making application of motor rotor fault diagnosis, which verified the practicability and effectiveness of our proposed novel measurement.

Item Details

Item Type:Refereed Article
Keywords:Dempster-Shafer evidence theory, conflict management, evidential correlation coefficient, belief function, fuzzy measure, basic belief assignments, decision making, fault diagnosis, decision making
Research Division:Information and Computing Sciences
Research Group:Machine learning
Research Field:Neural networks
Objective Division:Defence
Objective Group:Defence
Objective Field:Intelligence, surveillance and space
UTAS Author:Cao, Z (Dr Zehong Cao)
ID Code:139493
Year Published:2020
Deposited By:Information and Communication Technology
Deposited On:2020-06-17
Last Modified:2020-09-10
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