Research on the Fault Detection of FADS

During the flight, whether the fault of sensors is detected in time will directly affect the flight safety. The main contribution of the paper is to achieve the fault detection of flush air data sensing (FADS). First of all, a high-precision FADS model is established based on the aerodynamics data obtained from a CFD software and the aerodynamics knowledge under subsonic and supersonic conditions. Subsequently, the distribution characteristics of each group of signal under the fault condition are derived through strict formulas. Meanwhile, the threshold of alarm times is designed with statistical knowledge. Then, a representative fault case is adopted to demonstrate the method. For further confirming the validity and feasibility in the engineering practice, the comparison with other two existing methods, the parity equation and$$\chi ^2$$ distribution, is conducted.