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| Study on Simulation Data-Driven Fault Diagnosis Technology for Unmanned Aerial Vehicles |
| RONG Guang1，2，3， ZHANG Yexin4， TANG Chao1，
CHEN Jinbao1， ZHOU Yiling1， WANG Jianyuan1 |
| 1. College of Astronautics， Nanjing University of Aeronautics and Astronautics， Nanjing 210016， Jiangsu，
China； 2. China Logistics Group Co.， Ltd.， Beijing 100073， China； 3. China Logistics Group Digital
Technology Co.， Ltd， Beijing 100073，China； 4. Shanghai Electro-Mechanical
Engineering Institute， Shanghai 201109， China |
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Abstract This paper addresses the increasingly complex fault diagnosis requirements of UAV (Unmanned Aerial Vehicle) systems by proposing a data-driven multi-model fusion fault diagnosis method. A deep learning model library incorporating CNNs, LSTMs, and RNNs was constructed to perform modular fault diagnosis across four key subsystems: guidance control, electromechanical, power, and airframe structure. Experiments employed a self-built simulation dataset, and model performance was evaluated using metrics including the confusion matrix, accuracy, recall, and F1 score. The results demonstrate that all subsystem models exhibit robust diagnostic capabilities, with the power subsystem achieving the highest accuracy, followed by the avionics subsystem. This technical approach offers a systematic solution for intelligent UAV fault diagnosis and can be applied to fault prediction in other complex equipment.
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Received: 01 September 2025
Published: 13 January 2026
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2025, Vol. 8 
