Article(id=1282037410942923695, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1282037390130790473, articleNumber=null, orderNo=null, doi=10.16265/j.cnki.issn1003-3033.2025.12.1597, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1749484800000, receivedDateStr=2025-06-10, revisedDate=1758988800000, revisedDateStr=2025-09-28, acceptedDate=null, acceptedDateStr=null, onlineDate=1783591137871, onlineDateStr=2026-07-09, pubDate=1766851200000, pubDateStr=2025-12-28, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1783591137871, onlineIssueDateStr=2026-07-09, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1783591137871, creator=13701087609, updateTime=1783591137871, updator=13701087609, issue=Issue{id=1282037390130790473, tenantId=1146029695717560320, journalId=1146031787341344770, year='2025', volume='35', issue='12', pageStart='1', pageEnd='259', issueExtLink='null', onlineDate='null', pubDate='1766851200000', pubDateStr='2025-12-28', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1783591132910, creator='13701087609', updateTime=1783593783109, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1282048505929777979, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1282037390130790473, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1282048505929777980, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1282037390130790473, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=88, endPage=95, ext={EN=ArticleExt(id=1282037412738085808, articleId=1282037410942923695, tenantId=1146029695717560320, journalId=1146031787341344770, language=EN, title=Baseline modeling method for aircraft air conditioning system based on improved MSET, columnId=1149733269173878863, journalTitle=China Safety Science Journal, columnName=Safety engineering technology, runingTitle=null, highlight=null, articleAbstract=

In order to effectively monitor the potential performance degradation of core components in aircraft air conditioning systems, a baseline modeling method for aircraft air conditioning systems based on modified MSET was proposed in this paper. Firstly, QAR data of key components was selected as the feature parameter. Secondly, using healthy historical data as training sample, the density peak clustering method was used to screen state data and construct memory matrices for both non-low-temperature and low-temperature datasets. Then, the adaptive diagonal loading technique was applied to MSET process to reduce the abnormal fluctuations caused by the pathological state of the memory matrix. Finally, a performance baseline was established between the multivariate feature variables and the system operating state, and real flight data from Airbus A320 aircrafts was used to analysis. Results show that the proposed method can simultaneously establish performance baselines for multiple key components such as primary heat exchanger, main heat exchanger and air cycle machine. It can effectively detect flight cycles with performance degradation before component failure, and the detection results are relatively accurate, providing a reference standard for airlines' condition based on maintenance and health management.

, authors=Yanfei HAN^{1, 2}, Yongzheng WU², Lunlong ZHONG², Zixuan BAI², Lizhong GAN³, Renbiao WU^{**, 2}, authorsList=Yanfei HAN, Yongzheng WU, Lunlong ZHONG, Zixuan BAI, Lizhong GAN, Renbiao WU, authorCompany=null, correspAuthors=Renbiao WU, 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=1282037413237207987, articleId=1282037410942923695, tenantId=1146029695717560320, journalId=1146031787341344770, language=CN, title=基于改进MSET的飞机空调系统基线建模方法, columnId=1149733269727526997, journalTitle=中国安全科学学报, columnName=安全工程技术, runingTitle=null, highlight=null, articleAbstract=

为有效监控飞机空调系统核心部件的潜在性能衰退，提出一种基于改进多元状态估计（MSET）的飞机空调系统基线建模方法。首先，选取关键部件的快速存取记录器（QAR）数据作为特征参数；其次，以健康的历史数据作为训练样本，利用密度峰聚类算法分别筛选非低温数据集和低温数据集的状态数据，并构造记忆矩阵；然后，将自适应对角加载技术应用于MSET过程，降低由于记忆矩阵病态引起的异常波动影响；最后，建立多特征变量与系统运行状态之间的性能基线，并利用空客A320机型实测飞行数据进行检测分析。结果表明：文中所提方法可同时建立初级热交换器、主级热交换器和空气循环机等多个关键部件的性能基线，能在部件失效前有效地检测出存在性能衰退的飞行循环，检测结果较为准确，能够为航空公司的基于状态的维修与健康管理提供参考。

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²中国民航大学 天津市智能信号与图像处理重点实验室，天津 300300, bio={"content":"

韩雁飞（1987—），女，新疆乌鲁木齐人，博士研究生，讲师，主要从事航空运输大数据与人工智能、雷达信号处理等方面的研究。E-mail: yfhan@cauc.edu.cn。

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韩雁飞（1987—），女，新疆乌鲁木齐人，博士研究生，讲师，主要从事航空运输大数据与人工智能、雷达信号处理等方面的研究。E-mail: yfhan@cauc.edu.cn。

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