Article(id=1309640052876079966, tenantId=1146029695717560320, journalId=1189621681917173762, issueId=1309640041656316677, articleNumber=null, orderNo=null, doi=10.19620/j.cnki.1000-3703.20240116, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=null, receivedDateStr=null, revisedDate=1764086400000, revisedDateStr=2025-11-26, acceptedDate=null, acceptedDateStr=null, onlineDate=1790172120541, onlineDateStr=2026-09-23, pubDate=1782230400000, pubDateStr=2026-06-24, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1790172120541, onlineIssueDateStr=2026-09-23, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1790172120541, creator=13701087609, updateTime=1790172120541, updator=13701087609, issue=Issue{id=1309640041656316677, tenantId=1146029695717560320, journalId=1189621681917173762, year='2026', volume='', issue='6', pageStart='1', pageEnd='62', issueExtLink='null', onlineDate='null', pubDate='1782230400000', pubDateStr='2026-06-24', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1790172117866, creator='13701087609', updateTime=1790232797347, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1309894549917495577, tenantId=1146029695717560320, journalId=1189621681917173762, issueId=1309640041656316677, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1309894549917495578, tenantId=1146029695717560320, journalId=1189621681917173762, issueId=1309640041656316677, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=53, endPage=62, ext={EN=ArticleExt(id=1309640053094183775, articleId=1309640052876079966, tenantId=1146029695717560320, journalId=1189621681917173762, language=EN, title=Diagnosis of Motor Inter-Turn Short Circuit Fault Based on Improved Transfer-Convolutional Neural Network, columnId=1309640043162075259, journalTitle=Automobile Technology, columnName=Selected Academic Papers of the 28th CAST Annual Meeting, runingTitle=null, highlight=null, articleAbstract=
To address the issue of poor accuracy and low efficiency in diagnosing inter turn short circuit faults of motors in new energy vehicles under variable operating conditions due to lack of data, this paper proposes a fault diagnosis method based on Attention Mechanism Optimized Multi-Scale Convolutional Neural Networks (Attention-MCNN) combined with transfer learning. Firstly, an inter- turn short circuit experimental platform for permanent magnet synchronous motors is established to collect raw three-phase current data, and construct a labeled dataset. Secondly, the Attention-MCNN network structure and parameters are determined through ablation experiments, and the network is trained to obtain a pre-trained network model. Finally, transfer learning is introduced to freeze the pre-trained model and fine tune the network by inputting labeled data under other operating conditions. A network model with the ability to recognize the characteristics of target domain data is obtained to achieve the diagnosis of inter turn short circuit faults in new energy vehicle motors. The experimental results show that the proposed method has an average fault diagnosis accuracy of 95.67%, with a detection time of about 5 s, demonstrating lower data dependency compared with traditional methods.
, authors=Jiujian Chang, Rui Huang, Yuhao Ding, authorsList=Jiujian Chang, Rui Huang, Yuhao Ding, authorCompany=null, correspAuthors=null, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1309640053350036320, articleId=1309640052876079966, tenantId=1146029695717560320, journalId=1189621681917173762, language=CN, title=基于改进迁移-卷积神经网络的电机匝间短路故障诊断, columnId=1309640043359207549, journalTitle=汽车技术, columnName=第二十八届中国科协年会学术论文选登, runingTitle=null, highlight=null, articleAbstract=
针对数据缺乏导致新能源汽车在变工况下电机匝间短路故障诊断精度差、效率低的问题,提出了一种基于注意力机制优化多尺度卷积神经网络(Attention-MCNN)结合迁移学习的故障诊断方法。首先,搭建永磁同步电机匝间短路试验平台,采集三相电流原始数据,制作带标签数据集;其次,通过消融试验确定Attention-MCNN的结构和参数,对网络进行训练,得到预训练后的网络模型;最后,引入迁移学习,冻结经预训练的模型,输入其他工况下带标签数据对网络进行微调,得到具有识别目标域数据特征能力的网络模型,实现新能源汽车电机匝间短路故障诊断。试验结果表明,所提出的方法故障诊断平均精度达到95.67%,检测时间约为5 s,与传统方法相比,具有更低的数据依赖性。
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