Physics-informed transformer with residual analysis and adaptive threshold Kalman filter for angle-of-attack sensor fault detection

The angle-of-attack (AoA) sensor is essential to modern aircraft systems; however, its vulnerability to faults has contributed to several aviation accidents. This study proposes two novel sensor fusion-based fault detection methods that combine data-driven techniques with physics principles: the Physics-Informed Transformer with Residual Analysis (PITRA) and the Adaptive Threshold Kalman Filter (ATKF). PITRA uses a physics-informed encoder-decoder transformer to construct a virtual AoA sensor. By incorporating reduced-order aircraft dynamics into the loss function and applying an adaptive weight (γ_adaptive) to balance physics-informed and data-driven losses, PITRA enhances AoA estimation. The ATKF augments a reduced-order aircraft model with sensor bias and gain parameters for fault detection using adaptive thresholding and Kalman filtering. Both methods are evaluated using using flight data from the Advanced Technologies Testing Aircraft System (ATTAS) under fault-free, faulty, and noisy sensor conditions. Sensitivity analyses with Gaussian and non-Gaussian sensor noise showed that PITRA (γ_adaptive) is more robust in noisy environments. During elevator maneuver with a 10% Gaussian noise-to-signal power ratio (NSPR) AoA sensor, PITRA achieved an average false positive rate (FPR) of 0, compared to 12.8 ± 19.8 for ATKF. PITRA (γ _{adaptive, Noisy}), trained on 70% of the dataset with 10% NSPR Gaussian noise, performed better with only minimal degradation. However, ATKF outperformed PITRA in terms of detection delay, identifying ramp faults in 0.18 seconds, compared to 0.44 seconds. The research shows that while physics-informed machine learning models require significant computational resources, they excel in noisy environments. In contrast, physics-based fault detection methods using Kalman filters are less resource-intensive and more effective.