A risk-based fuzzy evidential framework for FMEA analysis under uncertainty: An interval-valued DS approach

Failure Modes and Effects Analysis (FMEA) is a common technique used in several manufacturing and service industries for eliminating failures and potential problems using the evaluation of failure modes of a new or an existing product, process, or system. The risk analysis is carried out by calculating the Risk Priority Number (RPN), which is a product of three factors, Occurrence (O), Severity (S), and Detection (D). In the literature, modeling uncertainties are used to improve the FMEA process and overcome the inefficiencies of traditional RPN. One of the common uncertainties in FMEA is the epistemic uncertainty that is essentially modeled using the Dempster-Shafer theory (DST). In this study, a novel risk-based fuzzy evidential approach is proposed by using interval-valued DST and fuzzy axiomatic design (FAD) to assess the risk of failure modes with fuzzy belief structures. The efficiency of the proposed model was investigated with the help of an example and the results are compared with riskless evaluations. Reviewing the results shows the information content of failure modes decrease relatively when risk is taken into account, in fact failure modes become relatively more critical than those in the case where no risk is considered.