FADS系统故障诊断方法研究

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.