Interactive Cost-Based Reliability Consensus Reaching Models for Multisource Imprecise Information Calibration of Multistate Systems

In the system reliability assessment by multisource information fusion, the various sources of information often display biases and may be conflicting with each other. Practical scenarios typically require a consensus on the reliability estimate, which indicates that the multisource information has to be properly refined. This study introduces a consensus-reaching process aiming at calibrating imprecise information from diverse experts' opinions for multistate systems. Specifically, two interactive consensus reaching models, i.e., interactive minimum-cost reliability consensus model and interactive maximum consensus degree model, are put forth to allow experts to make cost-effective preference modifications to reach a reliability consensus. The interactive consensus reaching models use the evidential network and evidential utility function to amalgamate multisource imprecise information to reach a nominal reliability consensus. A feedback mechanism based on the belief Jensen–Shannon divergence measure is, therefore, developed to foster interaction between the expert's imprecise information and the nominal reliability consensus. Through the interactive consensus reaching process, the unified reliability estimate is assessed via a feasibility-based particle swarm optimization. The effectiveness of the proposed method is demonstrated via a numerical example and a control rod drive mechanism in nuclear power plants.