LAUSR

As a powerful pre-accident risk evaluation method, the traditional failure mode and effect analysis (FMEA) is extensively used to identify and eliminate the potential failure modes of products or processes, and presents several limitations simultaneously. To improve the accuracy of risk evaluation, this paper proposes a novel FMEA approach considering consensus level between decision makers. First, linguistic variables are applied to express the decision makers’ evaluation information of failure modes, which can be transformed into the corresponding interval-valued intuitionistic fuzzy (IVIF) numbers. Second, an IVIF consensus model is constructed to confirm whether the consensus is achieved, and subsequently, the collective evaluation matrix is aggregated by the interval-valued intuitionistic fuzzy prioritized weighted averaging operator. Third, a deviation maximization model is used to calculate the weights of risk factors. Finally, the improved IVIF-MULTIMOORA method is implemented to determine the risk ranking of failure modes. This paper also provides a numerical example to illustrate the validity and rationality of the proposed method.