In engineering scenarios, the performance of industrial systems varies continuously, making it necessary to develop a prediction model to track system performance . Recently, a modeling approach known as the concise belief rule base (CBRB) has provided an effective reference for performance prediction. However, CBRB ignores the decay phenomenon of information credibility during the prediction process, leading to suboptimal output accuracy. To address this limitation, a novel performance prediction model based on the concise belief rule base with credibility decay (CBRB-CD) is put forward. The proposed model incorporates a decay factor to reflect the property that the credibility of belief rules decays over time. Meanwhile, the decay factor is aggregated into the fusion process of belief rules, from which the prediction results are generated. Furthermore, a stability analysis of the prediction model is carried out by introducing external perturbations to validate the prediction results. The analysis results quantitatively reveal the changing patterns of prediction results under perturbed environments. Finally, real-world experiments on aerospace relays demonstrate the feasibility of the proposed model.

Remaining useful life (RUL) estimation of aero-engine is a significant part of the aircraft reliability improvement and operational cost reduction. In order to solve the waste of performance data, in this study, a belief rule-based inference methodology using the evidential reasoning (RIMER) based RUL estimation process is proposed. Firstly, we take the principal component analysis (PCA) and the differential evolution (DE) algorithm to optimize the structure and parameter of the belief rule base (BRB), and then estimate the RUL of aero-engine through evidential reasoning (ER) algorithm. The practical case is investigated to validate the feasibility and efficiency of the proposed method. The experimental results show the RIMER based RUL estimation of aero-engine can support decision-making in the fields of aircraft prognostic and health management.