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.

In this article, probabilistic hesitant fuzzy linguistic preference relations (PHFLPRs) are proposed to present the qualitative pairwise preference information of decision makers (DMs) with hesitation and probability uncertainty assessments. The measurements and improvements of additive consistency and consensus of PHFLPRs are investigated in group decision making (GDM). First, a new concept of probabilistic hesitant fuzzy linguistic term sets is defined. Second, the consistency and consensus measurements are established to survey the additive consistency and consensus levels of PHFLPRs. Subsequently, an optimization model is developed to improve the unacceptably additive consistent PHFLPR. By optimizing the unacceptable consensual PHFLPRs with repeating additive consistency improvement, the acceptably additive consistent and consensual PHFLPRs are obtained, based on which DMs' weights are determined objectively and then, the collective PHFLPR is aggregated from individual PHFLPRs. Alternatives' priority weights are derived from the collective PHFLPR as GDM. Finally, an example about failure criticality analysis is given, and a comparison analysis is presented.

For a long time, the machinery's reliability allocation methods have followed the electronics', and thus all of the interactions among moving components and parts in machines are neglected. In fact, the allocated results are not reasonable for mechanical products; even the allocation granularity is not appropriate to guide the reliability design. To overcome these problems, this paper proposes an allocation method based on meta-action. By using the function-movement-action method, we decompose the mechanical product into meta-actions. An action is used as the basic unit of reliability control, and all the parts contributing to the action are treated as a whole. The interactions of the parts which contribute to the same action are treated as the internal forces of meta-actions, and others are considered as the interactions between meta-actions by the consideration of common parts in different units. An illustrative example shows the proposed method makes the results more reasonable and more convenient to guide mechanical product designs.