A Novel Fault Detection Model Based on Atanassov's Interval-Valued Intuitionistic Fuzzy Sets, Belief Rule Base and Evidential Reasoning

In engineering practice, fault detection, with the fundamental characteristics of randomness, uncertainty, and great damage, has attracted much attention from academia. The chief aim of this paper is to research the problem of flush air data sensing (FADS) system, an advances airborne sensor, with Atanassov's interval-valued intuitionistic fuzzy sets (AIVIFSs), belief rule base (BRB) and evidential reasoning (ER). First of all, this paper proposes some relevant concepts and similarity calculation operators between AIVIFSs, meanwhile, extracts a new similarity calculation approach based on previous researches. Then, a new score function that is applied to quantify the information contained in AIVIFSs is presented with the idea of p-norm, which is defined on the basis of the information measure from the reference level, regarding the positive and negative information depicted in terms of the AIVIFSs. Next, AIVIFSs and BRB that are characteristic of describing the randomness and uncertainty are combined to set up the fault detection model with ER. Finally, the algorithm process is presented and the proposed model is applied to the fault detection of FADS for the first time to confirm the validity and feasibility.