A Reliable Fault Diagnosis Method Based on Belief Rule Base Consider Robustness Analysis

Abstract:In equipment operation, the implementation of fault diagnosis is essential to ensure the continuity and safety of production equipment, improve operational efficiency and reduce maintenance costs. Since sensor readings are widely used for fault diagnosis, their reliability directly affects the results of fault diagnosis. A new fault diagnosis method is proposed to address the two problems of robustness assessment and robustness optimization of fault diagnosis models. For this purpose, a reliable fault diagnosis method based on a belief rule base (BRB) considering robustness analysis is proposed. Firstly, the robustness analysis of the BRB model is carried out systematically. Secondly, three robustness constraint strategies are proposed to optimize the robustness of the BRB fault diagnosis model. Finally, the effectiveness of the proposed model is verified by taking the fault diagnosis of WD615 diesel engine and Case Western Reserve University bearings as an example, and the experiments show that the proposed model improves both accuracy and robustness.

Artificial Intelligence (cs.AI)

arXiv:2606.10500 [cs.AI]

(or arXiv:2606.10500v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2606.10500 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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