A social network analysis-based model for failure mode and effect analysis under linguistic preference relation environment

Failure mode and effect analysis (FMEA) is a proactive risk analysis technique widely used to improve the reliability and safety of complex systems in various industries. Generally, FMEA is a multidisciplinary team activity in which the trust relationships among experts have an influence on the risk assessment process and the risk priority of failure items. Furthermore, it is significant to obtain the risk ranking result of failure items with a high consistency degree in the group FMEA. Therefore, in this study, we aim to develop a new FMEA model based on double hierarchy hesitant linguistic preference relations to derive the risk priority of failure items considering experts' social network. First, the risk assessments of experts are described by using the double hierarchy hesitant linguistic preference relations via pairwise comparisons of failure items. Second, the relative weights of FMEA experts are derived based on their trust relationships in social network and the consistency of failure risk evaluations. Third, consistency checking and repairing algorithm are performed to address experts' selfcontradictory opinions, and the risk priority of failure items is determined in line with their risk degrees. Finally, a practical risk evaluation case is provided to demonstrate the applicability of the proposed FMEA model, and a comparative analysis is conducted to illustrate its effectiveness and advantages.