A risk assessment approach for failure mode and effects analysis based on intuitionistic fuzzy sets and evidence theory

Abstract

Failure mode and effects analysis (FMEA) is an extensively used engineering technique for defining, identifying and prioritizing the potential failure modes. Traditionally the Risk Priority Number (RPN) is used to rank the potential failure modes. In this paper, a new risk assessment methodology which combines intuitionistic fuzzy sets (IFSs) with evidence theory is proposed to analyze the potential failure modes. The risk factors S, O, and D are evaluated by using linguistic variables and intuitionistic fuzzy numbers, which can be transformed into the basic probability assignment functions. The Jousselme distance is used to compute the weights of decision makers in order to efficiently combine highly conflicting evidence. The weighted average of evidence is obtained and the classical Dempster's combination rule is used to combine the mass functions modified. The proposed approach is applied thereafter to the prioritization of failure modes. A numerical example for risk assessment is given to illustrate the effectiveness and rationality of the proposed method finally.