Purpose To permit the system safety and reliability analysts to evaluate the criticality or risk associated with item failure modes. Design/methodology/approach The factors considered in traditional failure mode and effect analysis (FMEA) for risk assessment are frequency of occurrence (Sf), severity (S) and detectability (Sd) of an item failure mode. Because of the subjective and qualitative nature of the information and to make the analysis more consistent and logical, an approach using fuzzy logic is proposed. In the proposed approach, these parameters are represented as members of a fuzzy set fuzzified by using appropriate membership functions and are evaluated in fuzzy inference engine, which makes use of well‐defined rule base and fuzzy logic operations to determine the criticality/riskiness level of the failure. The fuzzy conclusion is then defuzzified to get risk priority number. The higher the value of RPN, the greater will be the risk and lower the value of RPN, and the lesser will be the risk. The fuzzy linguistic assessment model was developed using toolbox platform of MATLAB 6.5 R.13. Findings The applicability of the proposed approach is investigated with the help of an illustrative case study from the paper industry. Fuzzy risk assessment is carried out for prioritizing failure causes of the hydraulic system, a primary element of the feeding system. The results provide an alternate ranking to that obtained by the traditional method. It is concluded from the study that the fuzzy logic‐based approach not only resolves the limitations associated with traditional methodology for RPN evaluation but also permits the experts to combine probability of occurrence (Sf), severity (S) and detectability (Sd) of failure modes in a more flexible and realistic manner by using their judgement, experience and expertise. Originality/value The paper integrates the use of fuzzy logic and expert database with FMEA and may prove helpful to system safety and reliability analysts while conducting failure mode and effect analysis to prioritize failures for taking corrective or remedial actions.
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