Physics-based fault detection for aircraft angle of attack sensors: Bias–gain tracking and covariance-regularized innovation monitoring evaluated against recurrent neural network-based fault detection methods

Fault detection is critical to ensure the safety of modern aerospace systems. Most existing studies evaluate data-driven fault detection methods against other data-driven fault methods and physics-based model fault detection methods against physics-based model fault detection methods. This compartmentalized evaluation impedes a comprehensive understanding of the strengths and limitations of each approach. To address this gap, we propose two novel physics-based models for the angle of attack (AoA) sensor fault detection: Manhattan Bias and Gain Tracking (MBGT) and Innovation Monitoring with Covariance Regularization (IMCR), both utilizing the Extended Kalman Filter (EKF). The proposed methods use a reduced order fixed-wing aircraft physics model developed using the first principles of physics and sensor fusion. We benchmarked these methods against machine learning-based approaches, including Long Short-Term Memory (LSTM) with residual analysis. The MBGT and IMCR are validated using flight data from the ATTAS research aircraft. The fault detection methods are evaluated under fault and fault-free conditions. Sensitivity analyses using a noisy sensor test dataset are also conducted. The results indicate that the MBGT and IMCR achieve near-zero false positive rates (FPR) under fault-free conditions. For ramp faults, the detection delays are 0.2 s for the IMCR and 0.18 s for the MBGT, demonstrating high responsiveness. In contrast, machine learning-based methods gave 0.4 s delay for ramp faults. Although physics-based methods are efficient and computationally lightweight, data-driven approaches, particularly LSTM, offer superior performance in noisy sensor environments and achieve lower FPR. The results show that a hybrid method is effective for fault detection.