Failure Mode and Effect Analysis Using T-Spherical Fuzzy Maximizing Deviation and Combined Comparison Solution Methods

Abstract

Failure mode and effect analysis (FMEA) is a potent risk analytical instrument extensively utilized for enhancing systems’ quality. Because the classical FMEA model has some deficiencies, numerous fuzzy set-based enhanced FMEA techniques have been developed to improve risk evaluation results’ reasonability. However, most of them require experts to follow certain associated constraints when expressing preferences; otherwise, their preferences will be invalid, which limits experts’ flexibility. In addition, the previous methods rarely consider the reliability of weight allocation results and the stabilization of risk ranking results. Many previous methods usually emphasize the local difference between assessments of failure modes in calculating objective weights and merely depend on one compromise solution in ranking failure modes, both of which may affect the precision of their results. To overcome these limitations, this study applies T-spherical fuzzy sets, the recent generalization of fuzzy sets without strict constraints, to flexibly characterize experts’ preferences. Subsequently, a divergence-based maximizing deviation method is presented to determine the weights of experts and risk factors. A new consensus feedback mechanism is also introduced to achieve consensus among experts. Furthermore, a T-spherical fuzzy combined compromise solution method is presented to rank failure modes stably. Finally, a case study, sensitivity analysis, and comparisons show that the proposed model is effective and practically suitable.