Risk assessment of failure mode and effects analysis (FMEA) under hesitant fuzzy information

Failure mode and effects analysis (FMEA) is considered as a widely used engineering tool for risk assessment to identify potential failures in various industries. The criticality or risk assessment of failure modes is traditionally established by using a risk priority number (RPN), which is determined by the multiplication of the occurrence (O), severity (S) and detection (D) of a failure mode. However, the use of RPNs has been criticised fora variety of reasons. Furthermore, it is not appropriate for handling problems with a set of values that are provided by decision-makers under a hesitant fuzzy environment. To overcome the shortcomings of the RPN, a novel FMEA approach, which permits values given by decision-makers with hesitant fuzzy information, is proposed in this study. First, the relative weight vector is calculated based on the maximising deviation method. Subsequently, the closeness coefficients are obtained based on the technique for order preference by similarity to an ideal solution (TOPSIS) theory. Finally, the risk priorities of failure modes are ranked. A large pendulum case study is used to demonstrate the applicability of this novel FMEA approach within a hesitant fuzzy environment.