A Novel Conflict Measurement in Decision-Making and Its Application in Fault Diagnosis

Fuyuan Xiao, Zehong Cao, Alireza Jolfaei

Research output: Contribution to journalArticlepeer-review

113 Citations (Scopus)


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 article, we propose a novel evidential correlation coefficient (ECC) for belief functions by measuring the conflict between two pieces of evidence in decision making. Then, we investigate 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 present several examples and comparisons to demonstrate the superiority of our proposed ECC method. Finally, we apply the proposed ECC in a decision-making application of motor rotor fault diagnosis, which verifies the practicability and effectiveness of our proposed novel measurement.

Original languageEnglish
Article number9117065
Pages (from-to)186-197
Number of pages12
JournalIEEE Transactions on Fuzzy Systems
Issue number1
Publication statusPublished - Jan 2021
Externally publishedYes


  • Basic belief assignments
  • belief function
  • conflict management
  • decision making
  • Dempster-Shafer evidence theory
  • evidential correlation coefficient
  • fault diagnosis
  • fuzzy measure


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