Identification of Critical Components of Complex Product Based on Hybrid Intuitionistic Fuzzy Set and Improved Mahalanobis-Taguchi System

To avoid the decrease of system reliability due to insufficient component maintenance and the resource waste caused by excessive component maintenance, identifying the critical components of complex products is an effective way to improve the efficiency of maintenance activities. Existing studies on identifying critical components of complex products are mainly from two aspects i.e., topological properties and functional properties, respectively. In this paper, we combine these two aspects to establish a hybrid intuitionistic fuzzy set to incorporate the different types of attribute values. Considering the mutual correlation between attributes, a combination of AHP (Analytic Hierarchy Process) and Improved Mahalanobis-Taguchi System (MTS) is used to determine the λ-Shapley fuzzy measures for attributes. Then, the λ-Shapley Choquet integral intuitionistic fuzzy TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution) method is proposed for calculating the closeness degrees of components to generate their ranking order. Finally, a case study which is about the right gear of airbus 320 is taken as an example to verify the feasibility and effectiveness of this method. This novel methodology can effectively solve the critical components identification problem with different types of evaluation information and completely unknown weight information of attributes, which provides the implementation of protection measures for the system reliability of complex products.