Enhancing risk-based engineering design: a hybrid fuzzy failure analysis with empirical validation

Introduction Precise risk-based design is essential for accurately identifying and assessing threats, improving reliability, and ensuring the overall safety of safety-critical systems. Failure Mode and Effect Analysis (FMEA) is a widely employed technique for the evaluation of risk of components, systems, services, and processes. To address subjectivity and ambiguity in decision-makers’ judgments in traditional FMEA, several methodological improvements have been proposed; however, a state-of-the-art review shows that several research avenues are still open in this domain. Reducing the variation in priority ranking within failure analysis remains a mostly underexplored area. This significant gap serves as the main motivation for investigating whether the synergy between different aggregation methods and normalization techniques, when combined with a fuzzy reference-based approach, can effectively decrease the distinct rankings. Methodology This study proposes an improved FMEA methodology that combines the Fuzzy Analytic Hierarchy Process (Fuzzy AHP), Fuzzy Elimination Et Choix Traduisant la REalité (Fuzzy ELECTRE III), and Entropy methods to derive a logical ranking of FMEA failure modes, thereby enhancing the effectiveness of FMEA. The proposed approach employs linguistic variables to set S, O, and D weights, FMEA using the Entropy and Fuzzy AHP methods, integrates these weights using Fuzzy ELECTRE III, and finally analyzes the priority of the options. To validate the practical applicability of the proposed framework, a real-world case study on a safety-critical machine component, the clutch system, which is a suitable case for risk-based engineering design, is conducted. Results and discussion The results are compared with those obtained by the integration of TOPSIS and VIKOR with FMEA, showing that the proposed method provides fewer priority rankings while delivering more effective results. Such clustering provides a more realistic representation of risk, acknowledging that minor distinctions between failure modes are often statistically insignificant. This ensures that resources are not diverted to minor issues at the expense of catastrophic but rare failure modes.