A multidimensional reliability-enhanced belief rule base model for fault diagnosis

Fault diagnosis is essential for the safe and stable operation of industrial equipment and for improving maintenance efficiency. The belief rule base (BRB) is well suited to fault diagnosis under complex conditions because of its strengths in knowledge representation and uncertainty reasoning. However, traditional BRB still faces three limitations in multi-class diagnosis: rule-scale expansion, insufficient use of feature reliability during inference, and inadequate exploitation of output reliability from multiple submodels. To address these issues, this study proposes a reliability-enhanced one-versus-rest BRB method, termed RE-OvR-BRB. First, the OvR strategy decomposes the multi-class task into binary subtasks and constructs parallel BRB submodels to reduce rule-combination complexity. Second, a multidimensional reliability modeling scheme is proposed, which incorporates feature-level reliability into matching calculation, rule activation, and evidence fusion, and further integrates submodel output-level reliability into the final decision fusion. Third, a decision fusion mechanism considering submodel output reliability is introduced to improve the robustness of multi-class decisions. Experiments on the CWRU bearing dataset and the UConn gear dataset show that the proposed method outperforms competing methods in classification accuracy, decision confidence, and noise robustness, demonstrating its effectiveness under complex industrial conditions.