An Airborne Sensor Fault Diagnosis Method Based on Analytic Model Parameter Identification

Airborne sensors play an important role in flight control system acquisition of flight status, internal and external loop control law solution and so on. In addition to increasing the hardware redundancy to improve the system reliability, it can also increase the analytical redundancy of the model to improve the system fault tolerance. In this paper, based on the aerodynamic parameters of UAV, fault modeling and analysis of sensor system are carried out, and a state estimator is designed using Kalman-Bussy filter principle. According to the system residual, the improved residual detection algorithm is used to estimate and evaluate the system fault state. Under the condition of joint voting based on cycle time and residual value, an adaptive reference model is designed to compare and modify the analytical model in real time, so as to improve the fault tolerance ability of the system. The simulation results show proposed method is feasible and effective. The process of adding iterative residuals detection can respond quickly to sudden fault and detect the slow-changing characteristics of soft fault obviously, so it is suitable for typical fault diagnosis of airborne sensor system. The process of adaptive model adjustment plays a key role in reducing the influence of noise and correcting the uncertainty error of the system, which makes the identification process of aerodynamic parameter model more efficient.