Article(id=1322594169198068735, tenantId=1146029695717560320, journalId=1189918454225211397, issueId=1322594153020641347, articleNumber=null, orderNo=null, doi=10.20104/j.cnki.1674-6546.20250304, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=null, receivedDateStr=null, revisedDate=1765900800000, revisedDateStr=2025-12-17, acceptedDate=null, acceptedDateStr=null, onlineDate=1793260622547, onlineDateStr=2026-10-29, pubDate=1791993600000, pubDateStr=2026-10-15, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793260622547, onlineIssueDateStr=2026-10-29, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793260622547, creator=13701087609, updateTime=1793260622547, updator=13701087609, issue=Issue{id=1322594153020641347, tenantId=1146029695717560320, journalId=1189918454225211397, year='2026', volume='', issue='10', pageStart='1', pageEnd='48', issueExtLink='null', onlineDate='null', pubDate='1791993600000', pubDateStr='2026-10-15', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=1, specialIssue=null, createTime=1793260618691, creator='13701087609', updateTime=1793261887162, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1322599473793060887, tenantId=1146029695717560320, journalId=1189918454225211397, issueId=1322594153020641347, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1322599473793060888, tenantId=1146029695717560320, journalId=1189918454225211397, issueId=1322594153020641347, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=34, endPage=41, ext={EN=ArticleExt(id=1322594169525224448, articleId=1322594169198068735, tenantId=1146029695717560320, journalId=1189918454225211397, language=EN, title=Research on Open-Circuit Fault Diagnosis for Inverters in Permanent Magnet Synchronous Motor Drive Systems for Electric Vehicles Based on IWOA-CNN-LSTM, columnId=1322594154186657860, journalTitle=Automotive Engineer, columnName=Selected Papers of SAECCE 2026, runingTitle=null, highlight=null, articleAbstract=

To address the issue of insufficient accuracy in diagnosing open-circuit faults in inverters of Permanent Magnet Synchronous Motor (PMSM) drive systems, a fault identification method integrating One-Dimensional Convolutional Neural Networks (1D-CNN) and Long Short-Term Memory (LSTM) networks is proposed. First, MATLAB/Simulink simulations are employed to model various types of open-circuit faults in three-phase inverters. Three-phase stator currents were selected as the fault feature extraction targets. The 1D-CNN-LSTM model is enhanced with the Improved Whale Optimization Algorithm (IWOA) and utilized to identify open-circuit faults in three-phase inverters. The feasibility of the algorithm is validated by comparing it with different fault diagnosis algorithms. Experimental results demonstrate that the IWOA-1D-CNN-LSTM model achieves effective fault classification with an average recognition accuracy of 98.73%.

, authors=Hao Zhou, Jingsheng Cao, Yadong Liang, authorsList=Hao Zhou, Jingsheng Cao, Yadong Liang, 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=1322594169999179776, articleId=1322594169198068735, tenantId=1146029695717560320, journalId=1189918454225211397, language=CN, title=基于IWOA-CNN-LSTM网络的车用永磁同步电机驱动系统逆变器开路故障诊断研究, columnId=1322594154534785094, journalTitle=汽车工程师, columnName=2026中国汽车工程学会年会优秀论文（选登）, runingTitle=null, highlight=null, articleAbstract=

为解决电动汽车永磁同步电机（PMSM）驱动系统逆变器开路故障诊断中准确率不足的问题，提出了一种融合一维卷积神经网络（1D-CNN）和长短时记忆（LSTM）网络的故障识别方法。首先利用MATLAB/Simulink对不同类型的三相逆变器开路故障进行仿真，以三相定子电流作为故障特征提取对象，并利用改进鲸鱼优化算法（IWOA）的1D-CNN-LSTM模型实现对三相逆变器开路故障的识别。不同故障诊断算法对比验证结果表明，所提出的方法可有效进行故障分类识别，平均识别准确率达98.73%。

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