Aircraft sensor fault detection using state and input estimation

This paper presents a method for detecting aircraft sensor faults using state and input estimation. We formulate the kinematics as a nonlinear state space system, which requires no modeling information, and thus is applicable to all aircraft. To illustrate the method, we investigate three fault-detection scenarios, namely, faulty pitot tube, angle-of-attack sensor, and accelerometers. We use the extended Kalman filter for pitot-tube and angle-of-attack sensor fault detection, and retrospective cost input estimation for accelerometer fault detection. For numerical illustration, we use the NASA Generic Transport Model to detect stuck, bias, drift, and deadzone sensor faults.