A safety assessment method for diesel engines based on interpretable belief rule base with metric
Zongjun Zhang, Wei He, Ning Ma, Hongyu Li, Guohui Zhou · Measurement Science and Technology · 2025
Abstract When making maintenance decisions for diesel engines, the safety assessment plays a crucial role. Belief rule base (BRB) is a rule-based model embedded in expert knowledge with the advantage of being interpretable. However, due to the complexity of diesel engine systems and the impact of potential failures, their safety assessment faces the following challenges: (1) The interpretability of BRBs is often weakened during the building and optimization process. (2) Existing BRB models for safety assessment lack effective interpretability quantification methods. To address the above challenges, a diesel engine safety assessment method based on an interpretable BRB with the metric is proposed in this paper. First, an interpretability metric method for the interpretable BRB model for diesel engine safety assessment is proposed to quantify the interpretability of the entire modeling process. Second, a new statistical-based method for calculating the credibility of expert knowledge is proposed. Third, on the basis of these, three new interpretability maintenance strategies are designed to correct behaviors that destroy the interpretability of the model. The validity of the proposed model is verified through a case study of the safety assessment of a WD615 diesel engine.