Abstract
In multi-sensor fusion, the Dempster–Shafer theory is frequently used for fault diagnosis and other decision-making problems. However, if the information collected from various sensors exhibits uncertainty and high conflict, the classical Dempster’s Combination Rule may produce a counter-intuitive result. Various studies were conducted by Ghosh and other scholars to solve this problem, and good results were achieved. This work provides a novel idea which uses the Markov chain to model the uncertainty information in evidence theory, explore the ordered rules in random evidence, and then obtain the evidence support degree, thereby reducing the effect of high-conflict evidence. Considering the information amount of evidence combined with the evidence support degree, the final credibility of evidence is obtained, and the weighted summation is used to obtain new evidence. Finally, the classical Dempster combination rule is used to process new evidence and make the final decision. Through numerical examples and case studies, the proposed method’s superior efficiency and robustness over the existing multisensor fault diagnosis methods are illustrated.
Published Version
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