A two-stage FMECA with game-theoretic FAHP–MEREC weighting for key component identification in high-speed EMU traction systems

As high-speed electric multiple units (EMU) speeds and mileage increase, the electronic traction system has become a prominent part requiring focused attention, as even minor failures can significantly impact the system performance. Consequently, identifying the key components becomes crucial. This study presents a novel Failure Mode, Effects, and Criticality Analysis (FMECA) approach specifically tailored for high-speed rail traction systems. It addresses limitations in conventional FMECA, notably the expert subjectivity involved in Severity (S), Occurrence (O), and Detectability (D) scores, as well as the ambiguity associated with Risk Priority Numbers (RPNs). A two-stage methodology is proposed: (1) An expert qualification process that filters and calibrates initial S, O, D assessments to mitigate individual bias. (2) A game-theoretic weighting scheme optimally integrates weights from Fuzzy Analytic Hierarchy Process (FAHP) and the Method based on the Removal Effects of Criteria (MEREC), objectively determining the relative importance of S, O, D while resolving RPN non-uniqueness and expert uncertainty. A case study conducted on a representative EMU traction system validates the proposed methodology, identifying the Four-Quadrant rectifier, TCU module, traction motor, ventilation system, and carbon skateboard as the key components. Comprehensive sensitivity analysis further demonstrates that the proposed approach exhibits improved robustness over individual methods (FAHP, MEREC, etc.), confirming its enhanced reliability for key component identification in complex engineering systems.