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Failure mode and effect analysis (FMEA) is a risk management technique frequently applied to enhance the system performance and safety. In recent years, many researchers have shown an intense interest in improving FMEA due to inherent weaknesses associated with the classical risk priority number (RPN) method. In this study, we develop a new risk ranking model for FMEA based on soft set theory and COPRAS method, which can deal with the limitations and enhance the performance of the conventional FMEA. First, trapezoidal fuzzy soft set is adopted to manage FMEA team members' linguistic assessments on failure modes. Then, a modified COPRAS method is utilized for determining the ranking order of the failure modes recognized in FMEA. Especially, we treat the risk factors as interdependent and employ the Choquet integral to obtain the aggregate risk of failures in the new FMEA approach. Finally, a practical FMEA problem is analyzed via the proposed approach to demonstrate its applicability and effectiveness. The result shows that the FMEA model developed in this study outperforms the traditional RPN method and provides a more reasonable risk assessment of failure modes.

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Failure mode and effect analysis (FMEA) is an effective quality management technique widely used in various industries to improve the reliability and safety of systems, products, processes, and services. In traditional FMEA, the ranking of failure modes is carried out by the risk priority number (RPN), which is calculated by the product of severity (S), occurrence (O), and detection (D). Nevertheless, the normal FMEA has many inherent defects in assessing and ranking failure modes. Therefore, in this paper, we present a new FMEA model, which integrates probabilistic linguistic term sets (PLTSs) and fuzzy Petri nets (FPNs) for the risk assessment and prioritization of failure modes. Specifically, the PLTSs are used to capture the uncertainty of FMEA team members' subjective judgments, and the FPNs are established to acquire the risk priority of the identified failure modes. Besides, a technique for order preference by similarity to an ideal solution (TOPSIS)-based weighting method is proposed to determine the objective weight of each team member. Finally, a marine-ship system risk assessment example is provided to illustrate the suggested FMEA and a comparative analysis is conducted to assess its effectiveness and usefulness. The results show that the new FMEA approach can produce more reliable and reasonable risk ranking result of failure modes. INDEX TERMS Failure mode and effect analysis (FMEA), probabilistic linguistic term set (PLTS), TOPSIS method, fuzzy Petri net (FPN). The associate editor coordinating the review of this manuscript and approving it for publication was Zhiwu Li.