Failure Mode and Effect Analysis (FMEA) is a widely applied preventive risk assessment approach to enhance product reliability and safety, yet its structural validity is frequently questioned. Existing improvement models generally overlook the interrelationships among failure modes and suffer from high uncertainty and instability in opinion aggregation, risk factor weight allocation, and Risk Priority Number (RPN) computation. To bridge these gaps, this study proposes an integrated decision-making model. First, the Decision Making and Trial Evaluation Laboratory (DEMATEL) method is employed to analyze interactions among failure modes, constructing an influential network diagram to identify critical items. Second, a Rough Dombi Aggregator is applied for opinion aggregation, minimizing data loss and handling uncertainties from experts’ diverse backgrounds. Third, the Full Consistency Method (FUCOM) is utilized to determine the relative weights of risk factors. Finally, four weighted aggregation methods are developed to calculate RPNs, mitigating the instability common in traditional methods. This vehicle power system case study, alongside model comparison and sensitivity analysis, demonstrates the model’s effectiveness and robustness. The results indicate that across 9 different weight fluctuation scenarios, the core high-risk item “FM7: Generator coil burned out due to short circuit” consistently ranks 1st. This highlights the exceptional stability of the proposed model in overcoming evaluation fluctuations. Ultimately, this integrated framework not only enhances the accuracy and robustness of failure mode prioritization but also serves as a valuable practical reference for engineers formulating preventive maintenance strategies under limited resources.
Liou et al. (Fri,) studied this question.