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.

In the field of aviation industry, flush air data sensing (FADS) is an advanced sensor for hypersonic aircraft. However, since the sensor has not been used in engineering practice for a long time, there is still much room for improvement. The main purpose of this paper is to improve the measurement accuracy of FADS from the perspective of fuzzy theory. First, the aerodynamic model of FADS is established based on the knowledge of aerodynamics under supersonic and subsonic conditions. Further, to describe the uncertainty and randomness of redundant signals, normal cloud model, a significant concept in fuzzy theory, is employed. Meanwhile, to reduce the influence of abnormal data on the final measurement accuracy, 3En principle is adopted to preprocess the data. In the process of data fusion using the aggregation operate, a new method of calculating the objective weight vector is derived based on Lagrange multiplier method and simplex evolutionary method of multi-objective programming (MOP). To illustrate the feasibility and validity of the proposed method, two FADS systems with six measurement taps and thirteen taps are adopted, respectively. The experimental results show that for the FADS with six measurement taps, the proposed method can improve the measurement accuracy by 4.55% and reduce the dispersion of data by 61.38%. For the latter, the proposed method can improve the measurement accuracy by 3.43% and reduce the dispersion of data by 50.14%. To further demonstrate the superiority of the proposed method, a detailed comparison with other six widely used methods is carried out.