无人机故障模拟数据集构建与评测方法  

Construction and evaluation method of unmanned aerialvehicle faults simulation dataset

作　　者：王怡澄 柴梦娟 余道杰[1] 白艺杰 梁丽月 李涛[1] 周佳乐 杜剑平[1] 姚振宁 Wang Yicheng;Chai Mengjuan;Yu Daojie;Bai Yijie;Liang Liyue;Li Tao;Zhou Jiale;Du Jianping;Yao Zhenning(School of Information Systems Engineering,Information Engineering University,Zhengzhou 450002,China)

机构地区：[1]网络空间部队信息工程大学信息系统工程学院,郑州450002

出　　处：《强激光与粒子束》2025年第4期37-44,共8页High Power Laser and Particle Beams

基　　金：国家自然科学基金项目(61871405)。

摘　　要：无人机系统复杂且故障模式多样,对其可靠性、稳定性和安全性提出了一定的挑战。针对无人机故障数据样本集缺乏且不完备的问题,采用预设故障注入法构建了无人机故障模拟数据集。故障模拟数据集基于偏差故障、漂移故障、锁死故障和缩放故障四种故障描述模型,实现了无人机正常状态、执行器故障和传感器故障的等效模拟,并进一步通过深度学习网络评测数据集。仿真结果表明:WDCNN、ResNet和QCNN三种深度学习网络均验证了本文故障模拟数据集构建方法及数据集的有效性和完备性。从故障诊断精确度指标来看,WDCNN达到82%以上,ResNet达到90%以上,QCNN达到92%以上,提出的方法为基于数据驱动的无人机故障诊断研究提供了一个较为完备的数据集及评测方法。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 

分 类 号：TN972[电子电信—信号与信息处理]