Safety Assessment of Dynamic Systems: An Evidential Group Interaction-Based Fusion Design

Abstract

Considering the inherent unknown risks in the operation of complex dynamic system, it is very important to assess its safety accurately. Within the framework of human-in-the-loop, we propose a novel evidential group interaction-based (EGIB) safety assessment approach based on the evidence group interaction. With the analysis of safety assessment levels (SALs), the basic belief assignment (BBA) distribution of multiple safety indicators can be obtained. Fuzzy ranking technology is used to collect the individual’s cognition of these indicators. Using Shapley function in the framework of fuzzy measure and considering the cooperation effect, the peer importance is modeled. The collective group opinions of these indicators are obtained after the convergence process. The fusion procedure with weighted evidence combination is used to identify the safety state. To illustrate the effectiveness and practicability of the proposed method, we carry out simulation using real data obtained from electrical and observation communication system of deep-sea manned submersible, and comparsion is conducted between our approach and the baseline methods. Results show that the proposed approach can be applied to solve the safety assessment problems of complex dynamic systems in practice.