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

In recent years model-based fault techniques become really popular due to reducing calculation cost. Bayesian Network and Two Stage Kalman Filter based methods have recently become quite popular due to their robustness. In this paper, model-based fault diagnosis method is presented that uses Bayesian Network and Two Stage Kalman Filter(TSKF) together to determine the sensor faults robustly in the Unmanned Aerial Vehicle (UAV) system. In the fault detection algorithm, six residual values are calculated. The threshold values of all the calculated residuals are determined using synthetic dataset. Depending on whether the residuals exceed the threshold value or not, the fault generation coefficients in the Bayesian Network are also dynamically updated to provide precise information regarding which sensor has a fault. By using these two approaches together, the robustness of the detection of the fault in the sensor improved. For demonstrating the behavior of the proposed method, numerical simulations are performed in MATLAB/Simulink^TM environment. The results show that the proposed method is capable of detecting the faults more robustly.