Article(id=1309892223794246562, tenantId=1146029695717560320, journalId=1146119893612605453, issueId=1309639316763149213, 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=1732291200000, receivedDateStr=2024-11-23, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1790232242772, onlineDateStr=2026-09-24, pubDate=1766160000000, pubDateStr=2025-12-20, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1790232242772, onlineIssueDateStr=2026-09-24, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1790232242772, creator=13701087609, updateTime=1790232242772, updator=13701087609, issue=Issue{id=1309639316763149213, tenantId=1146029695717560320, journalId=1146119893612605453, year='2025', volume='43', issue='12', pageStart='1563', pageEnd='1704', issueExtLink='null', onlineDate='null', pubDate='1766160000000', pubDateStr='2025-12-20', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1790171945038, creator='13701087609', updateTime=1790231727008, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1309890060590346990, tenantId=1146029695717560320, journalId=1146119893612605453, issueId=1309639316763149213, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1309890060594541295, tenantId=1146029695717560320, journalId=1146119893612605453, issueId=1309639316763149213, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=1619, endPage=1629, ext={EN=ArticleExt(id=1309892224142373795, articleId=1309892223794246562, tenantId=1146029695717560320, journalId=1146119893612605453, language=EN, title=Wind turbine bearing fault diagnosis method via combined features adaptation, columnId=null, journalTitle=Renewable Energy Resources, columnName=null, runingTitle=null, highlight=null, articleAbstract=

In response to the weak generalization of cross-domain fault diagnosis for rolling bearings in wind turbine units, a Wind Turbine Bearing Fault Diagnosis Method via Combined Features Adaptation is proposed. Firstly, a data selection method based on one-dimensional convolutional neural networks is introduced to filter data from the source domain that is similar to the target domain, forming a new source domain to reconstruct the training dataset and mitigate the negative transfer effect. Secondly, the continuous wavelet transform is applied to obtain two-dimension time-frequency features of the training data, these features are afforded for the residual neural network, which generates one-dimensional feature vectors as the initial features for transfer learning, ensuring comprehensive extraction of the training data's features. Thirdly, a new combined feature adaptation loss function is presented. In it, not only the feature transfer loss of the source domain and the target domain, but also the classification loss of the source domain is considered. This enables effective feature transfer from the source domain to the target domain. Finally, the softmax classifier of the constructed residual neural network is used for the diagnosis classification of the fault. By the simulation under different kinds of migration tasks between source domain and target domain, the proposed model is of higher diagnosis performance in the cross-domain environment.

, authors=Na Wang^{1, 2}, Zicong Wang¹, Jialin Liu¹, authorsList=Na Wang, Zicong Wang, Jialin Liu, 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=1309892224662467492, articleId=1309892223794246562, tenantId=1146029695717560320, journalId=1146119893612605453, language=CN, title=基于联合特征匹配的风电机组轴承故障诊断方法, columnId=0, journalTitle=可再生能源, columnName=, runingTitle=null, highlight=null, articleAbstract=

针对风电机组中滚动轴承跨域诊断泛化性弱的问题，文章提出一种基于联合特征匹配的风电机组轴承故障诊断方法。首先，基于一维卷积神经网络设计了数据筛选方法，从源域中筛选出与目标域相似的数据组成新源域，重构训练集以减弱负迁移效应；其次，利用连续小波变换获取训练数据的二维时频特征，再通过残差神经网络获得一维特征向量作为迁移学习的初始特征，确保模型对训练数据特征的全面提取；再次，综合考虑源域和目标域的特征迁移损失，以及源域的分类损失，提出新的联合特征匹配损失函数，实现从源域到目标域特征的有效迁移；最后，利用所构建残差神经网络的softmax分类器实现故障的诊断分类。结果表明，通过多种源域与目标域的迁移任务仿真，验证了所提模型在跨域环境中具有较好的诊断性能。

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王娜（1977-），女，博士，讲师，研究方向为智能故障诊断、态势感知。E-mail: wangna@tiangong.edu.cn

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王娜（1977-），女，博士，讲师，研究方向为智能故障诊断、态势感知。E-mail: wangna@tiangong.edu.cn

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