An intuitionistic fuzzy cloud model-based risk assessment method of failure modes considering hybrid weight information

Abstract

Failure mode and effects analysis (FMEA) is an effective tool utilized in various fields for discovering and eliminating potential failures in products and services, which is usually implemented based on experts’ linguistic assessments. However, incomprehensive weigh information of risk factors and experts, lacking the consideration of experts’ randomness and hesitation, and incomplete risk factor system is essential challenges for the traditional FMEA model. Therefore, to properly handle these challenges and further enhance the performance of the traditional FMEA, this study develops a new FMEA strategy for assessing and ranking failures’ risks. First, a novel concept of intuitionistic fuzzy clouds (IFCs) is developed by combining the merits of the intuitionistic fuzzy set theory and the cloud model theory in manipulating uncertain information. Some basic operations and the Minkowski-type distance measure of IFCs are also presented and discussed. Further, in the proposed FMEA model, two combination weighting methods are developed to determine the synthetic weights of experts and risk factors, respectively, which consider subjectivity and objectivity simultaneously. In addition, maintenance (M) is considered as a new risk factor to enrich the assessment factor system and facilitate a more reasonable risk assessment result. Finally, a case study is implemented along with comparisons to demonstrate the feasibility and superiority of the presented FMEA model.