Article(id=1309639758247191099, tenantId=1146029695717560320, journalId=1146119893612605453, issueId=1309639744087229134, articleNumber=null, orderNo=null, doi=null, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1733587200000, receivedDateStr=2024-12-08, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1790172050296, onlineDateStr=2026-09-23, pubDate=1768838400000, pubDateStr=2026-01-20, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1790172050296, onlineIssueDateStr=2026-09-23, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1790172050296, creator=13701087609, updateTime=1790172050296, updator=13701087609, issue=Issue{id=1309639744087229134, tenantId=1146029695717560320, journalId=1146119893612605453, year='2026', volume='44', issue='1', pageStart='1', pageEnd='142', issueExtLink='null', onlineDate='null', pubDate='1768838400000', pubDateStr='2026-01-20', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=1, specialIssue=null, createTime=1790172046920, creator='13701087609', updateTime=1790231697005, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1309889934790590725, tenantId=1146029695717560320, journalId=1146119893612605453, issueId=1309639744087229134, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1309889934790590726, tenantId=1146029695717560320, journalId=1146119893612605453, issueId=1309639744087229134, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=60, endPage=69, ext={EN=ArticleExt(id=1309639758494655036, articleId=1309639758247191099, tenantId=1146029695717560320, journalId=1146119893612605453, language=EN, title=Fault diagnosis of wind turbines based on Chebshev Graph Convolutions and Gated Recurrent Unit, columnId=null, journalTitle=Renewable Energy Resources, columnName=null, runingTitle=null, highlight=null, articleAbstract=

Addressing the limitations of traditional feedforward neural networks and convolutional neural networks in effectively extracting nonlinear spatial and temporal features from wind turbine operational data, and the current wind turbine fault diagnosis methods that can only perform state monitoring without effective fault localization, this paper proposes a wind turbine fault diagnosis method based on Chebyshev Graph Convolutional Networks and Gated Recurrent Units. First, a graph structure is constructed based on Dynamic Time Warping algorithm; second, Chebyshev Graph Convolutional Networks are used to extract nonlinear spatial correlations from wind turbine operational data; then, Gated Recurrent Units are employed to extract temporal features from the operational data; finally, the wind turbine fault status and fault location are output through fully connected layers and Softmax activation function. Experimental validation shows that this method can not only diagnose potential faults in wind turbines but also effectively determine the specific components where faults occur, achieving an accuracy of 99.33%, with a low false alarm rate of 0.38% and a low missed detection rate of 0.41%.

, authors=Hongpu Liu^{1, 2}, Ming Yang¹, Zhiyong Dong³, Ning Tu⁴, Ping Zhang^{1, 5}, authorsList=Hongpu Liu, Ming Yang, Zhiyong Dong, Ning Tu, Ping Zhang, authorCompany=null, correspAuthors=Ping Zhang, 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=1309639758968611389, articleId=1309639758247191099, tenantId=1146029695717560320, journalId=1146119893612605453, language=CN, title=基于切比雪夫图卷积与门控循环单元的风电机组故障诊断方法, columnId=0, journalTitle=可再生能源, columnName=, runingTitle=null, highlight=null, articleAbstract=

针对传统前馈神经网络与卷积神经网络无法有效提取风电机组运行数据的非线性空间特征与时间特征，以及目前的风电机组故障诊断方法只能进行状态监测，无法有效进行故障定位等问题，文章提出一种基于切比雪夫图卷积网络与循环门控单元的风电机组故障诊断方法。首先，基于动态时间规整算法构建图结构；其次，通过切比雪夫图卷积网络提取风电机组运行数据的非线性空间相关性；再次，利用循环门控单元提取风电机组运行数据的时间特征；最后，通过全连接层以及Softmax激活函数输出风电机组故障状态以及故障部位。经实验验证，该方法不但能够实现风电机组潜在故障的诊断，同时也可有效判断故障发生的具体部件，准确率达到99.33%，故障误检率低至0.38%，故障漏检率低至0.41%。

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^2．河北省大数据计算重点实验室（河北工业大学），天津　300401, bio={"content":"

刘洪普（1977-），男，硕士，副教授，研究方向为机器学习与数据分析。E-mail: liuii@hebut.edu.cn

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刘洪普（1977-），男，硕士，副教授，研究方向为机器学习与数据分析。E-mail: liuii@hebut.edu.cn

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