Abstract
In order to solve the fault detection problem of flush air data sensing (FADS), an advanced airborne sensor, a new method is proposed in this paper. First, the high-precision FADS model is established on the basis of the database obtained from the CFD software and aerodynamics knowledge. Then, the distribution characteristics of each group of signals under fault condition are derived through strict formulas. Meanwhile, the threshold of alarm times is designed with statistical knowledge. For verifying the effectiveness of the newly proposed method, a comparison with other two widely adopted methods, including the methods based on parity equation and Chi-square χ2 distribution, is conducted under different measurement noise. Simulation results show that the proposed fault detection method for FADS possess higher accuracy and stronger anti-interference.
Highlights
In order to solve the fault detection problem of flush air data sensing ( FADS), an advanced airborne sensor, a new method is proposed in this paper
The high⁃precision FADS model is established on the basis of the database obtained from the CFD software and aerodynamics knowledge
The distribution characteristics of each group of signals under fault condition are derived through strict formulas
Summary
西北工业大学学报 Journal of Northwestern Polytechnical University https: / / doi.org / 10.1051 / jnwpu / 20203861210 Γijcos2θk + Γjk cos2θi + Γki cos2θj = 0 (4) 假设 1 号孔发生故障,就 α1 而言,(9) 式中 A 可 写成如下形式 A1,3,5 = Γ15 sin2 λ3 + Γ31 sin2 λ5 + Γ53 sin2 λ1 (10) 考虑到 λ1 = λ5 = 20°,p1 = 0,λ3 = 0 所以(10) 可简化 为 A1,3,5 = p5 sin220°。
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