Article(id=1294717617739161646, tenantId=1146029695717560320, journalId=1185652524569653253, issueId=1294717591537345363, 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=1765814400000, receivedDateStr=2025-12-16, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1786614334759, onlineDateStr=2026-08-13, pubDate=1780588800000, pubDateStr=2026-06-05, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1786614334759, onlineIssueDateStr=2026-08-13, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1786614334759, creator=13701087609, updateTime=1786614334759, updator=13701087609, issue=Issue{id=1294717591537345363, tenantId=1146029695717560320, journalId=1185652524569653253, year='2026', volume='52', issue='3', pageStart='1', pageEnd='98', issueExtLink='null', onlineDate='null', pubDate='1780588800000', pubDateStr='2026-06-05', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=1, specialIssue=null, createTime=1786614328512, creator='13701087609', updateTime=1786616758423, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1294727783402459675, tenantId=1146029695717560320, journalId=1185652524569653253, issueId=1294717591537345363, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1294727783402459676, tenantId=1146029695717560320, journalId=1185652524569653253, issueId=1294717591537345363, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=70, endPage=75, ext={EN=ArticleExt(id=1294717617948876847, articleId=1294717617739161646, tenantId=1146029695717560320, journalId=1185652524569653253, language=EN, title=Fault Diagnosis Method of CNN for Gear System Based on Reconstruction of Two-dimensional Array, columnId=1187100786392711543, journalTitle=Rare Earth Steel, columnName=Equipment and Automation, runingTitle=null, highlight=null, articleAbstract=

In the face of a bulk of vibration signal data of gear system, fault features have been difficult to be accurately extracted and efficient fault classification could not be completed with conventional signal analysis methods. In the paper, a method of reconstruction from one-dimensional sample data to two-dimensional data is proposed aiming at the problems and the reconstructed two-dimensional data are input into the convolutional neural network (CNN) for fault feature extraction and fault classification to solve the problem of end-to-end fault diagnosis. Eventually, the high accuracy rate of 99.7% is achieved. The experimental results showed that the method of reconstructing two-dimensional data and inputting them into the CNN was suitable for fault diagnosis of gear system.

, authors=Lin Chen¹, Yunting Ma¹, Lihong Guo¹, Bin Wei¹, Wenqi Wang², authorsList=Lin Chen, Yunting Ma, Lihong Guo, Bin Wei, Wenqi Wang, 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=1294717618187952176, articleId=1294717617739161646, tenantId=1146029695717560320, journalId=1185652524569653253, language=CN, title=基于重构二维数组的CNN齿轮系统故障诊断方法, columnId=1185882040256840459, journalTitle=稀土钢, columnName=设备与自动化, runingTitle=null, highlight=null, articleAbstract=

面对海量的齿轮系统振动信号数据，常规信号分析方法已难以精准提取故障特征，也无法完成高效的故障分类工作。针对这一问题，文章提出一种重构一维样本数据为二维数据的方法，并将重构之后的二维数据输入到CNN网络中进行故障提取与故障分类，解决端到端的故障诊断问题，最终取得了99.7%的高准确率，实验结果表明重构二维数据并输入到CNN网络的方法适用于齿轮系统的故障诊断。

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陈琳（1971-），男，内蒙古包头市人，硕士，高级工程师，现从事设备专业技术管理工作。

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陈琳（1971-），男，内蒙古包头市人，硕士，高级工程师，现从事设备专业技术管理工作。

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