Article(id=1282030014979870998, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1282029960869167928, articleNumber=null, orderNo=null, doi=10.16265/j.cnki.issn1003-3033.2025.07.0604, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1741881600000, receivedDateStr=2025-03-14, revisedDate=1747497600000, revisedDateStr=2025-05-18, acceptedDate=null, acceptedDateStr=null, onlineDate=1783589374536, onlineDateStr=2026-07-09, pubDate=1753632000000, pubDateStr=2025-07-28, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1783589374536, onlineIssueDateStr=2026-07-09, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1783589374536, creator=13701087609, updateTime=1783589374536, updator=13701087609, issue=Issue{id=1282029960869167928, tenantId=1146029695717560320, journalId=1146031787341344770, year='2025', volume='35', issue='7', pageStart='1', pageEnd='252', issueExtLink='null', onlineDate='null', pubDate='1753632000000', pubDateStr='2025-07-28', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1783589361636, creator='13701087609', updateTime=1783589710614, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1282031424651903975, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1282029960869167928, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1282031424656098280, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1282029960869167928, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=167, endPage=175, ext={EN=ArticleExt(id=1282030015185391895, articleId=1282030014979870998, tenantId=1146029695717560320, journalId=1146031787341344770, language=EN, title=Monitoring data early warning method of natural gas compressor unit based on VCW-Informer, columnId=1149733269173878863, journalTitle=China Safety Science Journal, columnName=Safety engineering technology, runingTitle=null, highlight=null, articleAbstract=

In order to further enhance the early abnormal warning capability of natural gas compressor units, a novel method was proposed based on the VMD (Variational Mode Decomposition) algorithm, Informer algorithm, 3σ criterion, and Correlation-Weight optimization. A predictive model was constructed using the Informer architecture, in which the monitoring data were decomposed by VMD algorithm into multi-scale features of different frequencies to serve as model inputs. During the training process, the weight coefficients between each decomposed component and the original signal were calculated to optimize and adjust the internal model parameters. Furthermore, the prediction reconstruction results were combined with the 3σ statistical criterion to further improve the warning performance. Two segments of normal and abnormal pressure differential monitoring data from field compressor units were collected for experimental validation. The results show that, compared with other prediction methods, the proposed warning method achieves the lowest prediction errors. For the prediction of normal box pressure differentials, the errors are reduced by 66.67%-71.43% (Mean Squared Error, MSE), 36.67%-45.45% (Mean Absolute Error, MAE), 40.17%-45.42% (Root Mean Squared Error, RMSE), and 36.57%-45.72% (Mean Absolute Percentage Error, MAPE). For the abnormal inlet pressure differentials, the errors are reduced by 64.43%-71.12% (MSE), 44.02%-52.27% (MAE), 40.36%-45.53% (RMSE), and 37.24%-47.79% (MAPE). The proposed method exhibits superior prediction accuracy in both detailed and trend features. In the test set, it provides anomaly warning 60 minutes in advance, thereby improving the reliability of safe and stable operation of the compressor units.

, authors=Junming YAO^{1, 2}, Wei LIANG^{**, 1, 2}, Zhiming ZHENG³, Tianchang HUANG^{1, 2}, Qianjun FU^{1, 2}, Chunyan LIAO^{1, 2}, authorsList=Junming YAO, Wei LIANG, Zhiming ZHENG, Tianchang HUANG, Qianjun FU, Chunyan LIAO, 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=1282030015504159000, articleId=1282030014979870998, tenantId=1146029695717560320, journalId=1146031787341344770, language=CN, title=基于VCW-Informer的天然气压缩机组监测数据预警方法, columnId=1149733269727526997, journalTitle=中国安全科学学报, columnName=安全工程技术, runingTitle=null, highlight=null, articleAbstract=

为进一步提升天然气压缩机组的早期异常预警能力，基于变分模态分解（VMD）算法、Informer算法、3σ准则判据与Correlation-Weight优化提出一个新的预警方法。基于Informer架构搭建预测模型，利用VMD算法将监测数据分解为不同频率的多维尺度特征作为模型输入。在训练过程中，计算各分量特征与原始信号的权重系数来优化调整模型内部参数。并利用预测重构结果与统计分析的3σ判据进一步提升预警性能。采集现场压缩机组2段正常与异常的压差监测数据进行实例验证，试验结果表明：相比于其他预测方法，所提出的预警方法具有最小的预测误差，正常箱体压差的预测结果同比降低66.67%~71.43%（均方误差（MSE）），36.67%~45.45%（平均绝对误差（MAE）），40.17%~45.42%（均方根误差（RMSE）），36.57%~45.72%（平均绝对百分比误差（MAPE））；异常进气压差的预测结果同比降低64.43%~71.12%（MSE），44.02%~52.27%（MAE），40.36%~45.53%（RMSE），37.24%~47.79%（MAPE）。该方法在细节特征与趋势特征上具有更好的预测精度，在测试集中能提前60 min时间为异常监测信号提供预警，从而提升机组安全稳定运行的可靠性。

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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":"

姚俊名（2000—），男，重庆人，博士研究生，主要研究方向为油气设备安全检测与智能诊断、人工智能算法。E-mail: 2022310517@student.cup.edu.cn。

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姚俊名（2000—），男，重庆人，博士研究生，主要研究方向为油气设备安全检测与智能诊断、人工智能算法。E-mail: 2022310517@student.cup.edu.cn。

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