空天防御2025，Vol.8Issue(6)：73-84,12.
基于仿真数据驱动的无人飞行器故障诊断技术研究
Study on Simulation Data-Driven Fault Diagnosis Technology for Unmanned Aerial Vehicles
摘要
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.关键词
无人飞行器/故障诊断/数据驱动/深度学习/系统分析/性能评估Key words
unmanned aerial vehicle/fault diagnosis/data-driven/deep learning/system analysis/performance evaluation分类
航空航天引用本文复制引用
荣光,张业鑫,唐朝,陈金宝,周奕玲,王建园..基于仿真数据驱动的无人飞行器故障诊断技术研究[J].空天防御,2025,8(6):73-84,12.基金项目
江苏省自然科学基金项目(BK20241395) （BK20241395）
