Fault Detection on Sensors of the Quadrotor System Using Bayesian Network and Two-Stage Kalman Filter †

In recent years, model-based fault techniques have become popular due to their capability to reduce calculation cost. Bayesian Network and two-stage Kalman filter-based methods have recently become quite popular due to their robustness. In this paper, a model-based fault diagnosis method is presented that uses a Bayesian network and two-stage Kalman filter (TSKF) together to robustly determine the sensor faults in an Unmanned Aerial Vehicle (UAV) system. By using these two approaches together, the robustness of the fault detection in the sensor improved. For demonstrating the behavior of the proposed method, numerical simulations were performed in MATLAB/Simulink^TM environment. The results show that the proposed method is capable of detecting faults more robustly.