强激光与粒子束2025，Vol.37Issue(4)：37-44,8.DOI:10.11884/HPLPB202537.240340
无人机故障模拟数据集构建与评测方法
Construction and evaluation method of unmanned aerial vehicle faults simulation dataset
摘要
Abstract
The complexity of unmanned aerial vehicle(UAV)systems and the diversity of their fault modes present significant challenges to their reliability,stability,and safety.To address the issue of incomplete fault UAV data samples,a fault simulation dataset was constructed using a predefined fault injection method.This dataset is based on four models of faults:bias faults,drift faults,lock faults,and scale faults,allowing equivalent simulation of the drone in fault-free states,actuator failures,and sensor failures.Furthermore,the dataset was evaluated using deep learning networks.Simulation results demonstrate that the three deep learning architectures—WDCNN,ResNet,and QCNN—validate the completeness and effectiveness of the construction method and the fault simulation dataset in this paper.In terms of precision,WDCNN achieved over 82%,ResNet exceeded 90%,and QCNN surpassed 92%.The methods proposed in this study provides a complete dataset and evaluation method for data-driven research on UAV fault diagnosis.关键词
故障诊断/无人机系统/故障数据集/数据驱动/深度学习Key words
fault diagnosis/unmanned aerial vehicle system/fault dataset/data driven/deep learning分类
信息技术与安全科学引用本文复制引用
王怡澄,柴梦娟,余道杰,白艺杰,梁丽月,李涛,周佳乐,杜剑平,姚振宁..无人机故障模拟数据集构建与评测方法[J].强激光与粒子束,2025,37(4):37-44,8.基金项目
国家自然科学基金项目(61871405) （61871405）
