A new performance evaluation model based on approximate belief rule base with local uncertainty

Abstract

Performance evaluation is of vital significance in guaranteeing the reliable operation of complex systems. During the process of performance evaluation, the limitations of expert knowledge and insufficient observation data pose challenges in differentiating adjacent performance states of complex systems. As such, a new performance evaluation model based on the approximate belief rule base with local uncertainty (ABRB-LU) is proposed in this paper. Regarding the model inference, the local uncertainty is assigned to the predefined vague state, which can effectively address the difficulty of distinguishing adjacent performance states. Subsequently, the multiple belief rules incorporating local uncertainty are fused by employing the evidential reasoning (ER) rule, contributing to establishing the evaluation model based on ABRB-LU. Meanwhile, an optimization objective is set to improve the evaluation accuracy. Regarding the model analysis, starting from two belief rules, a rigorous mathematical derivation is carried out to obtain the sensitivity factor of the evaluation results concerning the local uncertainty. On this basis, the analysis process is extended to multiple belief rules, forming a generalized method for sensitivity analysis. This can provide a scientific basis for decision-makers to locate weak links. An engineering example of servo mechanism is carried out to verify the validity of the proposed model.