Hybrid Kalman filter and physics-informed long short-term memory network for enhanced fault diagnosis of aircraft angle-of-attack sensors

Angle-of-attack (AoA) sensor faults have historically contributed to several aviation accidents. To address this issue, a sensor fusion-based fault diagnosis framework is introduced that integrates first principles of physics with sensor data. A reduced-order aircraft model that incorporates sensor biases and gains is first developed using first principles of physics. This model is used to propose a two-stage fault detection scheme: an extended Kalman filter (EKF) innovation monitoring with covariance regularization (IMCR) for initial fault detection and Euclidean bias/gain deviations (EBGD) for fault confirmation. A physics-informed long short-term memory (PILSTM) network is then trained as a virtual sensor for use in the case of dual AoA faults. Finally, a fault mitigation method is developed to determine the appropriate actions in the event of a fault. It uses the IMCR-EBGD fault detection, aircraft sensor redundancy, and the PILSTM virtual sensor. The full framework is validated using flight data from the Advanced Technologies Testing Aircraft System (ATTAS) under both fault-free and fault-injected conditions. The proposed method detects ramp-type AoA faults within 0.18 s and yields a lower 95% Clopper–Pearson false alarm upper bound under fault-free scenarios. A sensitivity analysis incorporating Gaussian noise shows the proposed method achieves a lower false alarm rate. Furthermore, under the dual AoA fault test, the best estimation performance was obtained when the PILSTM loss function included a 15% contribution from the physics-informed loss function. These results demonstrate that hybridizing data-driven learning with first principles of physics improves fault diagnosis in complex sensor environments.