Abstract
Evidence Theory (ET) is widely applied to handle uncertainty issues in fault diagnosis. However, when dealing with highly conflicting evidence, the use of Dempster's rule may result in outcomes that contradict reality. To address this issue, this paper proposes a fault diagnosis decision-making method. The method is primarily divided into two parts. First, a similarity measurement method is introduced to solve the conflict management problem. This method combines the belief and plausibility functions within ET. It not only considers the numerical similarity between pieces of evidence but also takes into account directional similarity, better capturing the differences between different pieces of evidence. The effectiveness of this method is validated through several complex numerical examples. Next, based on this measurement method, we propose a conflict management method, which is validated through comparative experiments. Then, considering the inherent uncertainty in real-world sensor data, we propose a basic belief assignment (BBA) generation method based on Student's t-distribution and fuzzy membership functions. Finally, by combining the proposed conflict management method based on similarity measurement with the BBA generation method, we derive the final fault diagnosis decision, and its effectiveness is demonstrated through an application.