Research on Uncrewed Aerial Vehicle Rotor Fault Diagnosis Method Based on Ordered Quadruplet Loss

Unmanned aerial vehicles are increasingly used across various applications, but their vulnerability to faults under complex operating conditions poses significant risks to safety and mission success. Fault diagnosis in UAVs is essential for ensuring reliable and safe operations. This paper proposes a novel framework for UAV fault diagnosis, integrating a Multi-Scale Residual Network (MSR) with an Ordered Quadruplet Loss (OQL). The MSR leverages multi-scale feature extraction to capture intricate fault patterns, while the OQL addresses the challenge of modeling fault severity progression within fault categories, thus improving fault classification and severity ordering. The framework is designed to handle both known and unknown fault states, using deep metric learning to structure the embedding space based on fault similarities. Through a series of experiments, including rotor faults simulated on a DJI Phantom 4 Pro+ V2.0 quadrotor, we demonstrate that the proposed model achieves high classification accuracy (100%) and robust fault severity ordering (99.6%). Additionally, it shows excellent generalization capability, performing well even with partially unseen fault states. This work enhances the diagnostic performance and practical applicability of UAV fault detection, offering a significant step forward in ensuring UAV safety and reliability in real-world conditions.