Condition-Based Monitoring of Unmanned Aerial Vehicle Systems: Application to Motor Failure Detection

Abstract Accurate fault detection and diagnosis are critical components of any fault-tolerant control system, especially for unmanned aerial vehicles (UAVs) where reliability is paramount. Traditionally, both model-based and data-driven approaches have been applied for fault diagnosis. However, the increasing complexity of high-dimensional UAV systems has shifted focus toward data-driven methods, which leverage advanced classification algorithms to enhance fault identification and isolation. This study builds on this evolution by developing a sophisticated condition-based monitoring (CBM) system specifically designed for multirotor UAVs. In contrast to earlier studies that primarily relied on raw data for classifier training, this work introduces advanced preprocessing techniques and multidomain feature extraction, significantly improving the robustness and accuracy of fault detection. A comparative analysis is performed between feature-selection methods, including recursive feature elimination with cross-validation (RFECV) and variational autoencoder (VAE), to extract critical insights into UAV operational behavior. Through testing and evaluating various classification models on data from a hexarotor UAV under diverse actuator fault conditions, this research identifies optimal approaches for real-time fault detection and diagnosis. Results demonstrate notable improvements across all evaluation metrics, establishing this approach as a substantial advancement in UAV fault tolerance.