广西科技大学学报2026，Vol.37Issue(4)：58-65,77,9.DOI:10.16375/j.cnki.cn45-1395/t.2026.04.008
基于深度学习的整车辐射骚扰故障诊断方法研究
A deep learning-based method for radiated emission fault diagnosis in complete vehicles
摘要
Abstract
In automotive electromagnetic compatibility(EMC)testing,excessive radiated emissions present a significant challenge,making the rapid localization and accurate diagnosis of fault sources a critical technical issue.To address this problem,this paper proposed a deep learning-based fault diagnosis method for radiated emissions in complete vehicles,aiming to identify radiated emission faults that exceed the limits specified under regulatory limit requirements.Four typical fault sources responsible for excessive electromagnetic radiated emissions were considered,namely air conditioning controllers,DC-DC converters,engine ignition systems,and switch tubes at high frequencies.First,the test signals were preprocessed to obtain the preprocessed signals.Subsequently,the short-time Fourier transform was used to convert the preprocessed signals into two-dimensional time-frequency image datasets,and the MobileNetV3 deep learning model was selected for fault classification.Experimental results show that the proposed method achieves 93%training accuracy in model training,showing good diagnostic effects and high robustness,which proves that the signal preprocessing method can effectively improve the classification accuracy of the model.关键词
电磁兼容/辐射骚扰/故障诊断/MobileNetV3网络/时频分析/深度学习Key words
electromagnetic compatibility/radiation emission/fault diagnosis/MobileNetV3 network/time-frequency analysis/deep learning分类
交通工程引用本文复制引用
赵金强,夏圣,张吉宇,张亚君,郑艺侃,梁程华..基于深度学习的整车辐射骚扰故障诊断方法研究[J].广西科技大学学报,2026,37(4):58-65,77,9.基金项目
广西重点研发计划项目(AB22035044)资助 （AB22035044）
