Edge-Based Real-Time Fault Detection in UAV Systems via B-Spline Telemetry Reconstruction and Lightweight Hybrid AI

Unmanned aerial vehicles (UAVs) increasingly demand robust onboard diagnostic frameworks to ensure safe operation under irregular telemetry and mission-critical conditions. This paper presents a real-time fault detection framework for unmanned aerial vehicles (UAVs), optimized for deployment on edge devices and designed to handle irregular, nonuniform telemetry. The system reconstructs raw sensor data using compactly supported B-spline interpolation, ensuring stable recovery of flight dynamics under jitter, dropouts, and asynchronous sampling. A lightweight hybrid anomaly detection module-combining a Long Short-Term Memory (LSTM) autoencoder with an Isolation Forest-analyzes both temporal patterns and statistical deviations across reconstructed signals. The full pipeline operates entirely onboard embedded platforms such as the Raspberry Pi 4 and NVIDIA Jetson Nano, with end-to-end inference latency under 50 milliseconds. Experiments using real PX4 UAV flight logs and synthetic fault injection demonstrate a detection accuracy of 93.6% and strong resilience to telemetry disruptions. These results support the feasibility of autonomous, sensor-based health monitoring in UAV systems and broader real-time cyber-physical applications.