AeroGuard: Towards Real-Time UAV Fault Detection With Hybrid Models

Unmanned Aerial Vehicles (UAVs) are increasingly deployed in safety-critical applications, yet their operations in complex environments make them vulnerable to diverse faults. This paper presents AeroGuard, a lightweight hybrid frame work for real-time UAV fault detection. AeroGuard combines Long Short-Term Memory (LSTM) and AutoRegressive with eXogenous input (ARX) models, with residual-driven adaptive weighting to balance their strengths. Faults are identified through Z-score and Sequential Probability Ratio Test (SPRT) applied to prediction residuals, ensuring accurate and timely detection. Extensive experiments on public datasets, real UAV flight logs, and outdoor flights confirm AeroGuard's robustness, particularly in detecting drift and bias faults where existing methods degrade. AeroGuard achieves up to 95.8% precision, representing about 10% improvement over prior work, while maintaining sub-5ms latency on Raspberry Pi 4B with modest resource usage, and sub second detection on Pi Zero for low-speed UAVs. We also discuss current limitations, noting that evaluation on hardware-induced faults (e.g., motor seizure) will be pursued in future work.