A New Adaptive Interpretable Fault Diagnosis Model for Complex System Based on Belief Rule Base

This paper develops a new adaptive interpretable fault detection and diagnosis model (AIFD ) based on belief rule base (BRB) for complex system. The developed AIFD model aims to solve three problems in engineering practice: few fault samples, complex system mechanism and loss of interpretability in modeling process. The first two problems can be addressed by fusing expert knowledge and observation data in the BRB expert system. Moreover, to address the problem of loss of interpretability of AIFD model in its modeling process, a new belief rule weight adjustment method is proposed based on its sensitivity coefficient. The new adjustment method can improve the confidence degree of the users to the FDD output. The developed adjustment method can guarantee the fault diagnosis accuracy and the model interpretability. Then, to further address the influence of uncertain expert knowledge, a new optimization model is developed for the BRB based fault diagnosis model. To illustrate the effectiveness of the developed model, a case study of electric servo mechanism is conducted in this paper.