Information Fusion-Based Fault Diagnosis Method Using Synthetic Indicator

Abstract

Multisensor information fusion technology plays an essential role in fault diagnosis. Uncertain reasoning is the core of information fusion, and the DS evidence theory (DSE) has founded a general and popular framework for uncertainty reasoning. Under this framework, a novel information fusion method is developed in this article, to solve the problems of counterintuitive results, poor robustness, and “one-vote veto” when fusing highly conflicting evidence using DSE. We proposed a new synthetic indicator, which can effectively eliminate the conflicts between evidences. This synthetic indicator is composed of two indexes: measurement index of indirect conflicts and that of the evidence’s information itself. First, the divergence is used to measure the degree of conflict between bodies of evidence, and the Decision-Making Trial and Evaluation Laboratory (DEMATEL) model is employed to deal with indirect conflicts. Second, the Deng entropy is utilized to quantitatively measure the uncertainty of evidences, and the relative credibility preference of the evidence is expressed by the fuzzy preference relation. Finally, the initial body of evidence frame is weighted and revised by synthetic indicator before adopting the Dempster Shafer (DS) fusion rule. The superiority of the developed method is illustrated by numerical examples and application cases. The results show that fault diagnosis using the developed information fusion method can have a higher diagnostic accuracy.