Development and Sharing of a Multi-Modal Indoor UAV Dataset for PHM Research
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Abstract
With the rapid expansion of unmanned aerial vehicle (UAV) applications, ensuring reliable and safe operation has become a pressing challenge. In this paper, we present a modular quadrotor-based data acquisition platform designed to capture rich, high-fidelity data under motion capture guidance. Our system integrates conventional flight telemetry with detailed vibration and temperature measurements, user input logs, and precise 6D motion tracking. This offers an comprehensive view into the UAV’s physical and control state. We describe our systematic process for data cleaning, organization, and exploratory analysis, laying the groundwork for robust prognostics and health management (PHM) research. To illustrate the platform’s potential, we implement supervised and semi-supervised models for anomaly detection and fault identification. We release the dataset, synchronized flight videos, and analysis code to accelerate UAV health-monitoring research and collaboration.
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UAV (Unmanned Aerial Vehicle), Quadrotor, Data Acquisition Platform, Prognostics and Health Management (PHM), Anomaly Detection, Fault Identification, Vibration Measurement, Motion Capture
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