Article(id=1281616679708897923, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1281616655499366465, articleNumber=null, orderNo=null, doi=10.16265/j.cnki.issn1003-3033.2026.02.0599, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1757088000000, receivedDateStr=2025-09-06, revisedDate=1764777600000, revisedDateStr=2025-12-04, acceptedDate=null, acceptedDateStr=null, onlineDate=1783490827728, onlineDateStr=2026-07-08, pubDate=1772208000000, pubDateStr=2026-02-28, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1783490827728, onlineIssueDateStr=2026-07-08, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1783490827728, creator=13701087609, updateTime=1783490827728, updator=13701087609, issue=Issue{id=1281616655499366465, tenantId=1146029695717560320, journalId=1146031787341344770, year='2026', volume='36', issue='2', pageStart='1', pageEnd='270', issueExtLink='null', onlineDate='null', pubDate='1772208000000', pubDateStr='2026-02-28', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1783490821957, creator='13701087609', updateTime=1783588693138, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1282027157069218127, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1281616655499366465, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1282027157069218128, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1281616655499366465, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=136, endPage=144, ext={EN=ArticleExt(id=1281616679901835908, articleId=1281616679708897923, tenantId=1146029695717560320, journalId=1146031787341344770, language=EN, title=Defect diagnosis method for oil and gas pipelines based on fusion of Welch power spectrum and multi-scale residual network, columnId=1277328337617941059, journalTitle=China Safety Science Journal, columnName=Safety Technology and Engineering, runingTitle=null, highlight=null, articleAbstract=

To address the problem of reduced defect classification accuracy caused by noise contamination in the bend detection signals of oil and gas pipelines, this paper proposes an oil and gas elbow defect diagnosis model based on Welch power spectrum feature enhancement and multi-head attention improved dual-branch multi-scale-residual collaborative network. Firstly, the Welch method was used to convert the collected time domain signal into a feature-enhanced power spectrum, showing the energy distribution of the defect signals at different frequencies. Secondly, the multi-scale network branch composed of parallel stacked convolutional layers was responsible for extracting the multi-dimensional features of the signal power spectrum, and the multi-head attention mechanism was used to establish long-term associations between features. Simultaneously, the residual network branch captured the detail information of the signal power spectrum. Finally, the deep concatenation layer fused the features extracted by the dual-branch network to achieve defect classification. The experiment results show that in a high-noise environment, the test accuracy of the proposed model is 91.6%. Compared with the models based on Kaiser windows and flat-top windows, the classification accuracy is improved by 1%~7.9%; compared with convolutional neural network (CNN) and long short-term memory network (LSTM), the accuracy is improved by 36.9% and 10.3% respectively.

, authors=Chunyan LIAO^{1, 2}, Wei LIANG^{**, 1, 2}, Shuanglei LIU^{1, 2}, Tianchang HUANG^{1, 2}, authorsList=Chunyan LIAO, Wei LIANG, Shuanglei LIU, Tianchang HUANG, authorCompany=null, correspAuthors=Wei 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=1281616682137399943, articleId=1281616679708897923, tenantId=1146029695717560320, journalId=1146031787341344770, language=CN, title=融合Welch功率谱和多尺度残差网络的油气管道缺陷诊断, columnId=1277328337940902469, journalTitle=中国安全科学学报, columnName=安全技术与工程, runingTitle=null, highlight=null, articleAbstract=

为解决油气管道弯管检测信号受噪声污染导致缺陷分类准确率下降的问题，提出一种基于Welch功率谱特征增强和多头注意力改进型双分支多尺度-残差协同网络的油气弯管缺陷诊断模型。首先，利用基于汉明窗的Welch方法将采集到的时域信号转换为特征增强的功率谱，展示缺陷信号在不同频率处能量的分布情况；然后，并行堆叠的卷积层构成的多尺度网络分支负责提取信号功率谱的多维度特征，并利用多头注意力机制建立特征之间长期的关联，同时，残差网络分支捕捉信号功率谱的细节信息；最后，深度串联层融合双分支网络提取的特征，以实现缺陷分类。结果表明：在高噪声环境下，该模型的测试准确率为91.6%，相比于基于凯塞窗和平顶窗的模型，分类准确率提高1%~7.9%；相比于卷积神经网络（CNN）和长短时记忆网络（LSTM），准确率分别提高36.9%和10.3%。

, authors=廖春燕^{1, 2}, 梁伟教授^{**, 1, 2}, 刘双磊^{1, 2}, 黄天长^{1, 2}, authorsList=廖春燕, 梁伟教授, 刘双磊, 黄天长, authorCompany=null, correspAuthors=梁伟教授, authorNote=, correspAuthorsNote=, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=n+3KMif0iONvmtEpxJLGgA==, magXml=fc+qsfIo05nDSYr8sxXoug==, pdfUrl=null, pdf=tPC6UTb+qmI9Rjy1fPQ7QA==, pdfFileSize=10984959, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=DB/rSeGTP0aKahTdVevAEA==, mapNumber=null, fund=null)}, authors=[Author(id=1281616682544247439, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1281616679708897923, orderNo=0, firstName=null, middleName=null, lastName=null, nameCn=null, orcid=null, stid=null, country=null, authorPic=null, dead=0, email=2023310522@student.cup.edu.cn, emailSecond=null, emailThird=null, correspondingAuthor=0, authorType=1, ext={EN=AuthorExt(id=1281616682615550610, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1281616679708897923, authorId=1281616682544247439, language=EN, stringName=Chunyan LIAO, firstName=Chunyan, middleName=null, lastName=LIAO, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=^{1, 2}, address=¹College of Safety and Ocean Engineering, China University of Petroleum (Beijing), Beijing 102249, China
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²应急管理部 油气生产安全与应急技术重点实验室，北京 102249, bio={"content":"

廖春燕（1994—），女，广东肇庆人，博士研究生，主要研究方向为油气生产安全监测与设备智能诊断、运维等。E-mail: 2023310522@student.cup.edu.cn。

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廖春燕（1994—），女，广东肇庆人，博士研究生，主要研究方向为油气生产安全监测与设备智能诊断、运维等。E-mail: 2023310522@student.cup.edu.cn。

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