Article(id=1281608073324245636, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1281608054340829934, articleNumber=null, orderNo=null, doi=10.16265/j.cnki.issn1003-3033.2026.04.1571, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1762790400000, receivedDateStr=2025-11-11, revisedDate=1770220800000, revisedDateStr=2026-02-05, acceptedDate=null, acceptedDateStr=null, onlineDate=1783488775806, onlineDateStr=2026-07-08, pubDate=1777305600000, pubDateStr=2026-04-28, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1783488775806, onlineIssueDateStr=2026-07-08, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1783488775806, creator=13701087609, updateTime=1783488775806, updator=13701087609, issue=Issue{id=1281608054340829934, tenantId=1146029695717560320, journalId=1146031787341344770, year='2026', volume='36', issue='4', pageStart='1', pageEnd='286', issueExtLink='null', onlineDate='null', pubDate='1777305600000', pubDateStr='2026-04-28', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1783488771281, creator='13701087609', updateTime=1783588648031, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1282026967885124001, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1281608054340829934, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1282026967885124002, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1281608054340829934, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=168, endPage=175, ext={EN=ArticleExt(id=1281608073580098181, articleId=1281608073324245636, tenantId=1146029695717560320, journalId=1146031787341344770, language=EN, title=Fault detection for chemical process based on memory-weighted kernel principal component analysis, columnId=1277328337617941059, journalTitle=China Safety Science Journal, columnName=Safety Technology and Engineering, runingTitle=null, highlight=null, articleAbstract=

To enhance the detection ability of common faults in the chemical process and ensure the stable and reliable operation, a new fault detection method, termed M-WKPCA, was proposed. The method was based on historical fault data and KPCA, incorporating historical fault data through a weighting mechanism. Initially, kernel principal components(KPC) of normal data and historical fault data were calculated according to KPCA. KPC that can highlight the fault information were selected based on a comparison of the constructed indexes. A weighting matrix was then used to highlight the fault information, and a new statistic was constructed to establish an online fault detection model based on M-WKPCA method. Then, the M-WKPCA method was used to detect common faults in parallel. A parallel WKPCA fault detection strategy was proposed to achieve high precision detection of common faults. Finally, the proposed method was verified using simulated data from the Tennessee Eastman (TE) chemical process. The results show that the proposed method achieves an average accurate detection accuracy of 82.25%. This is much higher than that of the comparison methods, demonstrating its superiority in fault detection. At the same time, since fault information is incorporated during the selection of KPC, the detected fault data are significantly different from the normal data in terms of statistics.

, authors=Shuai Yuan^{1, 2}, Chunxi Yang^{**, 1, 2}, Xiufeng Zhang^{1, 2}, Xian Wang^{1, 2}, Gengen Li^{1, 2}, authorsList=Shuai Yuan, Chunxi Yang, Xiufeng Zhang, Xian Wang, Gengen Li, authorCompany=null, correspAuthors=Chunxi Yang, 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=1281608074406376072, articleId=1281608073324245636, tenantId=1146029695717560320, journalId=1146031787341344770, language=CN, title=基于记忆加权核主成分分析的化工过程故障检测, columnId=1277328337940902469, journalTitle=中国安全科学学报, columnName=安全技术与工程, runingTitle=null, highlight=null, articleAbstract=

为提高对化工过程中常见故障的检测能力，保证化工过程平稳可靠运行，基于历史故障数据信息和核主成分分析法（KPCA），提出一种利用历史故障数据加权的新型故障检测方法，即记忆加权核主成分分析法（M-WKPCA）。首先，根据KPCA计算正常数据和历史故障数据的核主成分（KPC），对比构建的指标选择出能够突出故障信息的KPC，并利用求取的加权矩阵突出故障信息，构建新的统计量，进而建立基于M-WKPCA方法的在线故障检测模型；然后，利用M-WKPCA方法，将检测不同常见故障的M-WKPCA方法并行处理，提出一种能够高精度检测常见故障的并行WKPCA故障检测策略；最后，采用田纳西伊斯曼（TE）化工过程数据仿真验证文中所提方法。结果表明：所提方法在测试数据中平均准确检测率为82.25%，远高于对比方法，在故障检测上更有优势；同时，由于KPC的选择引入了故障信息，检测的故障数据与正常数据在统计量上区分明显。

, authors=袁帅^{1, 2}, 杨春曦教授^{**, 1, 2}, 张秀峰^{1, 2}, 王娴^{1, 2}, 李根根^{1, 2}, authorsList=袁帅, 杨春曦教授, 张秀峰, 王娴, 李根根, authorCompany=null, correspAuthors=杨春曦教授, authorNote=, correspAuthorsNote=, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=6L2+V9f7G1KG4x2mTcff2g==, magXml=MIZXcIuwvGj8yuX4ZqVbkw==, pdfUrl=null, pdf=DdHQXERHuBYE+cvIZlDtQQ==, pdfFileSize=5528026, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=iGpgtqMV+phAaqhfZsKR1A==, mapNumber=null, fund=null)}, authors=[Author(id=1281608076310590096, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1281608073324245636, orderNo=0, firstName=null, middleName=null, lastName=null, nameCn=null, orcid=null, stid=null, country=null, authorPic=null, dead=0, email=2403091313@qq.com, emailSecond=null, emailThird=null, correspondingAuthor=0, authorType=1, ext={EN=AuthorExt(id=1281608076407059091, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1281608073324245636, authorId=1281608076310590096, language=EN, stringName=Shuai Yuan, firstName=Shuai, middleName=null, lastName=Yuan, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=^{1, 2}, address=¹Faculty of Mechanical and Electrical Engineering, Kunming University of Science and Technology, Kunming Yunnan 650500, China
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²云南省先进装备智能控制及应用国际联合实验室，云南 昆明 650500, bio={"content":"

袁帅（1998—），男，河南南阳人，硕士研究生，主要研究方向为化工过程故障检测和诊断、数据分析。E-mail:2403091313@qq.com。

"}, bioImg=null, bioContent=

袁帅（1998—），男，河南南阳人，硕士研究生，主要研究方向为化工过程故障检测和诊断、数据分析。E-mail:2403091313@qq.com。

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