Improving Risk Assessment from the Perspective of Multi-Criteria Decision-Making

Failure mode and effect analysis (FMEA) is one of the most powerful reliability analysis techniques for identifying and preventing potential risks across various fields. Current FMEA methods, while effective, still present several shortcomings. First, the crisp numbers cannot effectively elicit experts' fuzzy evaluations. Second, the weight of risk factors (i.e., occurrence, severity and detection) are assumed to be equal, which cannot reflect their different importance. Third, the traditional calculation method for risk priority is coarse and lack practical implications. There may exist the situation that two failure modes have the same risk priority number but with different risk levels. To improve the performance of FMEA, multi-criteria decision-making (MCDM) methods have been employed to support risk evaluation and prioritization in recent years. This book proposes a novel FMEA method by exploring several MCDM techniques. Benefiting from these techniques, the traditional FMEA method can be improved from the above mentioned aspects. First, the ELICIT linguistic model is employed to generate group risk assessments under uncertainty, which can better model experts' uncertain expressions. Then, grey relation analysis (GRA) is incorporated into the decision-making trial and evaluation laboratory (DEMATEL) method to objectively determine the weight of risk factors. Afterward, the traditional outranking method is generalized into the ELICIT environment to prioritize the failure modes. Finally, a case study of FMEA for electro-mechanical actuators is presented to illustrate the effectiveness of the proposed method.