Article(id=1318999935069040986, tenantId=1146029695717560320, journalId=1146123302524792850, issueId=1318999770132234870, articleNumber=null, orderNo=null, doi=10.3969/j.issn.1672-6073.2025.05.018, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1751644800000, receivedDateStr=2025-07-05, revisedDate=1755187200000, revisedDateStr=2025-08-15, acceptedDate=null, acceptedDateStr=null, onlineDate=1792403690353, onlineDateStr=2026-10-19, pubDate=1760025600000, pubDateStr=2025-10-10, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1792403690353, onlineIssueDateStr=2026-10-19, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1792403690353, creator=13701087609, updateTime=1792403690353, updator=13701087609, issue=Issue{id=1318999770132234870, tenantId=1146029695717560320, journalId=1146123302524792850, year='2025', volume='38', issue='5', pageStart='1', pageEnd='158', issueExtLink='null', onlineDate='null', pubDate='1760025600000', pubDateStr='2025-10-10', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1792403651030, creator='13701087609', updateTime=1792733338119, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1320382578062902170, tenantId=1146029695717560320, journalId=1146123302524792850, issueId=1318999770132234870, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1320382578062902171, tenantId=1146029695717560320, journalId=1146123302524792850, issueId=1318999770132234870, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=142, endPage=152, ext={EN=ArticleExt(id=1318999936767734107, articleId=1318999935069040986, tenantId=1146029695717560320, journalId=1146123302524792850, language=EN, title=Deep Learning-Based Monitoring of Fatigue Stress in Bogie Frame-End Suspended Components, columnId=1152669334582243706, journalTitle=Urban Rapid Rail Transit, columnName=Electrical and Mechanical Engineering, runingTitle=null, highlight=null, articleAbstract=

To address the difficulty of directly monitoring fatigue damage in bogie-end suspension components caused by vibration during long-term service, this study takes a track inspection device as an example to analyze its fatigue failure mechanism and proposes an indirect fatigue stress monitoring scheme based on deep learning. Hazardous measurement points were identified via finite element analysis, and data on vibration acceleration at the frame end and component stress were collected through field line tests. Three temporal regression models (RNN, LSTM, and TCN) were constructed to establish the mapping relationship between acceleration loads at the frame end and component stress. The attention mechanism was introduced to optimize model performance. Experimental results demonstrate that all three models effectively predict stress, with the LSTM model incorporating the attention mechanism (LSTM-Att) achieving the highest prediction accuracy. Validation on six stress measurement points of the track inspection device confirmed that LSTM-Att exhibits excellent predictive performance in both the time and frequency domains. This proves the effectiveness of the proposed method and offers a novel approach for monitoring fatigue damage in frame end-suspended components.

, authors=Xiaochao DAI¹, Jie HU², Yuhang JING³, authorsList=Xiaochao DAI, Jie HU, Yuhang JING, 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=1318999937170387292, articleId=1318999935069040986, tenantId=1146029695717560320, journalId=1146123302524792850, language=CN, title=基于深度学习方法的构架端部悬挂部件疲劳应力监测研究, columnId=1152669334955536763, journalTitle=都市快轨交通, columnName=机电工程, runingTitle=null, highlight=null, articleAbstract=

针对构架端部悬挂部件在长期服役中因振动导致的疲劳损伤难以直接监测的问题，以轨道检测装置为例，分析其疲劳失效机理，提出一种基于深度学习方法的疲劳应力间接监测方案。通过有限元分析识别危险测点位置，利用线路试验采集构架端部振动加速度和部件应力数据；构建循环神经网络（RNN）、长短时记忆网络（LSTM）和时序卷积神经网络（TCN）共3种时序回归模型，建立构架端部加速度载荷与部件应力的映射关系；引入注意力机制优化模型性能。试验结果表明：3种模型均能有效预测应力，其中结合注意力机制的LSTM模型（LSTM-Att）预测精度最优；将该模型应用于轨道检测装置6个应力测点进行验证，其在时域和频域均表现出良好的预测效果，证明该方法的有效性，为构架端部悬挂部件的疲劳损伤监测提供新途径。

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戴晓超，男，学士，高级工程师，主要从事转向架设计领域的研究工作，daixiaochao@cccar.com.cn

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戴晓超，男，学士，高级工程师，主要从事转向架设计领域的研究工作，daixiaochao@cccar.com.cn

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