An improved FMEA model based on EDAS method within fermatean fuzzy linguistic environment

Failure mode and effect analysis (FMEA) is a systematic and proactive risk prevention and management tool used to identify and assess risks of potential or known failure modes within a system, product or service. However, the traditional FMEA has been criticized for a variety of drawbacks, which limit its efficiency and potential of applications. Although numerous approaches have been proposed to address these drawbacks, most of them fail to accommodate complex and uncertain decision-making environments, and neglect the conflicting criteria of failure modes. To address these issues, this study proposes an improved FMEA model that integrates Fermatean fuzzy linguistic sets (FFLSs) with the evaluation based on distance from average solution (EDAS) method to assess and rank the risk of failure modes. First, the FFLSs are employed to represent the complex and uncertain risk assessments from experts. Then, a modified EDAS method is adopted to derive the risk ranking of failure modes. Additionally, the entropy weight method is improved to determine the weights of risk factors. Finally, a practical case is presented to illustrate the feasibility and reliability of the proposed FMEA model. The findings show that the proposed FMEA model can not only addresses most of the limitations in the traditional FMEA, but also yield reliable risk ranking of failure modes.