Robust Real-Time Thrust Fault Diagnosis for UAVs: A Physics-Informed Framework DecouplingWind Disturbances
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Abstract
Operational reliability of multi-rotor Unmanned Aerial Vehicles (UAVs) is frequently compromised by the ambiguity between external wind disturbances and internal thrust faults. This paper proposes a physics-informed fault diagnosis (PIFDI) framework that explicitly decouples wind-induced effects from total observed disturbances. By integrating an Extended Kalman Filter (EKF) for real-time wind estimation and a Disturbance Observer (DOB) for total torque monitoring, the framework isolates a clean fault residual through physical coefficient mapping. High-fidelity 6-DOF simulations involving Dryden turbulence and non-stationary discrete gusts demonstrate a rapid detection latency of 0.18 s for a 20% thrust loss, maintaining near-zero false alarms even during peak gust periods. Furthermore, a 300-trial Monte Carlo simulation confirmed high fault isolation accuracy, demonstrating
superior statistical robustness across varying wind intensities and randomized fault modes. The proposed physicsinformed
decoupling approach significantly enhances diagnostic resilience, providing a critical foundation for real-time fault-tolerant control in mission-critical UAV operations.
How to Cite
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Thrust fault diagnosis, Multi-rotor UAV, Wind disturbance decoupling, Extended Kalman Filter (EKF), Disturbance observer (DOB), Fault Detection and Isolation (FDI)
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