Turbofan Engine Fault Prediction Based on Evidential Reasoning and Belief Rule Base

Aiming at the fault prediction problem of a turbofan engine, a fault prediction model based on evidential reasoning (ER) and belief rule base (BRB) is proposed. In order to describe the health state of turbofan engine, ER algorithm is adopted to fuse the state information. Combined with prior knowledge, a hybrid driven simulation prediction of BRB model is established. Projection covariance matrix adaptive evolution strategy (P-CMA-ES) is used to optimize the model parameters. The validity of the model is verified by experiments. Experimental results show that the proposed method not only accurately predicts the probability of failure risk of the turbofan engine, but also provides strong support for fault diagnosis and maintenance support.