Integrating fuzzy logic and multi-criteria decision-making in a hybrid FMECA for robust risk prioritization

Failure mode effects and criticality analysis (FMECA) is widely employed across industries to recognize and reduce possible failures. Despite its extensive usage, FMECA encounters challenges in decision-making. In this paper, a new fuzzy resilience-based RPN model is created to develop the FMECA method. The fuzzy model transcends the limitations associated with traditional risk priority number calculations by incorporating factors beyond frequency, severity, and detection. This extension includes considerations impacting system cost, sustainability, and safety, providing a more comprehensive risk assessment. In addition, to create trust in decision-makers, a robust assessment approach is suggested, integrating three methodologies. In the initial phase, the fuzzy analytical hierarchy process and the grey relation analysis method are used to determine the subjective weights of different risk factors and resolve the flaws associated with the deficiency of constructed fuzzy inference rules. In the second phase, an entropy method is applied to handle the uncertainty of individual weightage calculated and capture different conflicting experts' views. The suggested approach is validated through a case study involving a gas turbine. The results demonstrate significant differences in failure mode prioritization between different approaches. The introduction of MTTR addresses critical shortcomings in traditional FMECA, enhancing predictive capabilities. Furthermore, the hybrid approach improved criticality assessment and failure mode ranking, classifying failure modes into fifteen categories, aiding decision-making, and applying appropriate risk mitigation measures. Overall, the findings validate the efficacy of the proposed approach in addressing uncertainties and divergent expert judgments for risk assessment in complex systems.