A new method for assessing the health status of aerospace equipment based on a belief rule base with balanced accuracy and complexity

The health status of aerospace equipment directly affects the operational capability of the entire system. Belief rule base (BRB) is an effective method for assessing health status that combines expert knowledge and historical data. However, in the actual assessment, the data provided by experts only form the basic framework of the model. Therefore, the BRB model with joint optimization of structure and parameters (BRB-SPO) is proposed to achieve a balance between the model's accuracy and complexity. First, to balance complexity and accuracy of the model, parameter structure backward stepwise selection method (BSS) and full factorial design (FFD) are introduced. BSS constructs the optimal parameter set, while FFD determines the best parameter values for the model. Subsequently, the constructed model is deduced using the evidential reasoning (ER) calculation procedure, the other parameters are optimized using the projection covariance matrix adaptive evolution strategy (P-CMA-ES). Finally, the practicality of the proposed method is validated through two examples.