Article(id=1308454758218490161, tenantId=1146029695717560320, journalId=1146123302524792850, issueId=1308454712513156008, articleNumber=null, orderNo=null, doi=10.3969/j.issn.1672-6073.2026.04.022, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1751731200000, receivedDateStr=2025-07-06, revisedDate=1769702400000, revisedDateStr=2026-01-30, acceptedDate=null, acceptedDateStr=null, onlineDate=1789889524273, onlineDateStr=2026-09-20, pubDate=null, pubDateStr=null, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1789889524273, onlineIssueDateStr=2026-09-20, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1789889524273, creator=13701087609, updateTime=1789889524273, updator=13701087609, issue=Issue{id=1308454712513156008, tenantId=1146029695717560320, journalId=1146123302524792850, year='2026', volume='39', issue='4', pageStart='1', pageEnd='197', issueExtLink='null', onlineDate='null', pubDate='1786291200000', pubDateStr='2026-08-10', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=1, specialIssue=null, createTime=1789889513376, creator='13701087609', updateTime=1789889839088, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1308456079029986178, tenantId=1146029695717560320, journalId=1146123302524792850, issueId=1308454712513156008, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1308456079029986179, tenantId=1146029695717560320, journalId=1146123302524792850, issueId=1308454712513156008, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=188, endPage=197, ext={EN=ArticleExt(id=1308454758436593970, articleId=1308454758218490161, tenantId=1146029695717560320, journalId=1146123302524792850, language=EN, title=Metro Rail Corrugation Identification Method Based on WPD-1DCNN, columnId=1153381654128218984, journalTitle=Urban Rapid Rail Transit, columnName=Operation Management, runingTitle=null, highlight=null, articleAbstract=

To achieve accurate identification and intelligent monitoring of metro rail corrugation, this study proposes a novel method combining WPD and 1DCNN for rail corrugation identification. Based on the vertical axle-box acceleration signals collected from in-service metro trains, sixteen sub-band energy features were extracted through four-level WPD and normalized to construct energy vectors. A lightweight 1DCNN architecture was designed to perform multiscale feature learning using the energy vectors as input. Three corrugation types—no corrugation, long-wavelength corrugation, and short-wavelength corrugation—were selected as classification labels for model training and testing. Based on the differences in energy distribution among different corrugation types in low- and high-frequency sub-bands, the feature extraction results were analyzed. The results demonstrated that the proposed method achieved an average recognition accuracy of 97.60%, with a recall rate of 100% for long-wavelength corrugation. Compared with SVM, LSTM, and 2DCNN models, the 1DCNN model exhibited higher accuracy and stronger generalization capability. It enables intelligent rail corrugation identification under in-service operating conditions without reliance on specialized inspection systems. The consistency between the wavelength-domain evaluation framework of conventional rail corrugation testing and the identification results of this study was analyzed, and a complementary engineering application workflow integrating online identification with on-site precision measurement was proposed. This study verified the effectiveness of the WPD-1DCNN fusion method in metro rail corrugation recognition and provided a new approach for online monitoring and intelligent maintenance of track conditions.

, authors=Tiesheng Liu¹, Yankun Yu¹, Junjie Liang², Yuexin Sun³, Jingrong Hao¹, Chunlei Jiang¹, authorsList=Tiesheng Liu, Yankun Yu, Junjie Liang, Yuexin Sun, Jingrong Hao, Chunlei Jiang, authorCompany=null, correspAuthors=Junjie Liang, 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=1308454758726000947, articleId=1308454758218490161, tenantId=1146029695717560320, journalId=1146123302524792850, language=CN, title=基于WPD-1DCNN的地铁钢轨波磨识别方法, columnId=1153381654287602537, journalTitle=都市快轨交通, columnName=运营管理, runingTitle=null, highlight=null, articleAbstract=

为实现地铁钢轨波磨状态的准确识别与智能监测，提出一种结合小波包分解（WPD）与一维卷积神经网络（1DCNN）的波磨识别方法。基于现场实测列车轴箱垂向加速度信号，采用4层小波包分解提取16个子带能量特征，并进行能量归一化构建能量向量；设计轻量化1DCNN结构，以能量向量为输入，实现多尺度特征学习。选取无波磨、长波波磨和短波波磨3种典型波磨类型作为分类标签，构建训练集与测试集进行模型训练与验证。结合不同波磨类型在低频与高频子带中的能量分布差异，对特征提取结果进行分析。结果表明：该方法平均识别准确率达97.60%，长波波磨的召回率为100%。与SVM、LSTM和2DCNN等模型相比，1DCNN表现出更高的精度与更强的泛化能力，可在不依赖专用检测设备的条件下实现列车运行过程中的波磨智能识别。结合传统波磨测试的波长域评价体系，分析其与本研究识别结果的一致性，提出在线识别与现场精测互补的工程应用流程。本研究验证了WPD与1DCNN融合方法在地铁钢轨波磨识别中的有效性，为轨道运行状态在线监测和智能维护提供了新思路。

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刘铁生，男，硕士，工程师，从事城市轨道交通线路安全研究，lubin_0_0@163.com

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