A Novel Belief Rule-Based Fault Diagnosis Method with Interpretability

Fault diagnosis plays an irreplaceable role in the normal operation of equipment. A fault diagnosis model is often required to be interpretable for increasing the trust between humans and the model. Due to the understandable knowledge expression and transparent reasoning process, the belief rule base (BRB) has extensive applications as an interpretable expert system in fault diagnosis. Optimization is an effective means to weaken the subjectivity of experts in BRB, where the interpretability of BRB may be weakened. Hence, to obtain a credible result, the weakening factors of interpretability in the BRB-based fault diagnosis model are firstly analyzed, which are manifested in deviation from the initial judgement of experts and over-optimization of parameters. For these two factors, three indexes are proposed, namely the consistency index of rules, consistency index of the rule base and over-optimization index, to measure the interpretability of the optimized model. Considering both the accuracy and interpretability of a model, an improved coordinate ascent (I-CA) algorithm is proposed to fine-tune the parameters of the fault diagnosis model based on BRB. In I-CA, the algorithm combined with the advance and retreat method and the golden section method is employed to be one-dimensional search algorithm. Furthermore, the random optimization sequence and adaptive step size are proposed to improve the accuracy of the model. Finally, a case study of fault diagnosis in aerospace relays based on BRB is carried out to verify the effectiveness of the proposed method.