A Labeled UAV Fault Diagnosis Dataset from Real Flight Experiments for AI-Based Anomaly Detection

This study introduces a real-world dataset designed to support fault diagnosis in unmanned aerial vehicles (UAVs) through artificial intelligence–based anomaly detection techniques. To construct the dataset, common failure types were identified through analysis of historical UAV accident reports, followed by the design and execution of flight tests simulating faults in structural, propulsion, and sensor subsystems. A total of 25 flight trials were conducted under both nominal and fault-induced conditions. During each test, multimodal flight data—including positioning, attitude, barometric pressure, control signals, and vibration—were recorded.The collected data were preprocessed and categorized into three distinct segments: original, normal, and error. This structured dataset supports both supervised and unsupervised learning models. To validate its effectiveness, two approaches were evaluated: a supervised model based on a long short-term memory (LSTM) network, and an unsupervised, transformer-based model for time-series anomaly detection. The transformer model achieved an F1-score of 98.12%, precision of 96.31%, recall of 100.00%, and accuracy of 99.18%, outperforming the supervised baseline.These results demonstrate the high reliability and applicability of the proposed dataset for real-time fault detection. The methodology introduced in this study can be extended to other cyber-physical systems requiring high-integrity operational diagnostics. The proposed dataset and labeling framework provide a practical foundation for future research on autonomous safety monitoring and predictive maintenance in UAVs and other intelligent systems.