Article(id=1223190872016143225, tenantId=1146029695717560320, journalId=1205116964453384197, issueId=1223190866320278179, articleNumber=null, orderNo=null, doi=10.20040/j.cnki.1000-7709.2023.20222408, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1668441600000, receivedDateStr=2022-11-15, revisedDate=1671379200000, revisedDateStr=2022-12-19, acceptedDate=null, acceptedDateStr=null, onlineDate=1769561029676, onlineDateStr=2026-01-28, pubDate=1695571200000, pubDateStr=2023-09-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1769561029676, onlineIssueDateStr=2026-01-28, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1769561029676, creator=13701087609, updateTime=1769561029676, updator=13701087609, issue=Issue{id=1223190866320278179, tenantId=1146029695717560320, journalId=1205116964453384197, year='2023', volume='41', issue='9', pageStart='1', pageEnd='220', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1769561028318, creator=13701087609, updateTime=1769562015483, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1223195006870082478, tenantId=1146029695717560320, journalId=1205116964453384197, issueId=1223190866320278179, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1223195006870082479, tenantId=1146029695717560320, journalId=1205116964453384197, issueId=1223190866320278179, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=159, endPage=163, ext={EN=ArticleExt(id=1223190872494293912, articleId=1223190872016143225, tenantId=1146029695717560320, journalId=1205116964453384197, language=EN, title=Prediction of Vibration Trend of Hydroelectric Unit Based on VMD-CIMFs-TCN, columnId=1222925284869922957, journalTitle=Water Resources and Power, columnName=ELECTROMECHANICS AND CONTROL ENGINEERING, runingTitle=null, highlight=null, articleAbstract=

Aiming at the nonlinearity and non-stationary of vibration signals of hydropower units and the timeliness of prediction, this paper proposed a vibration prediction model of hydropower units based on VMD-CIMFs-TCN. Firstly, the VMD algorithm was used to decompose the vibration signal to obtain the IMFs component with the minimum signal distortion, which realizes the accurate decomposition of the vibration signal. Secondly, by calculating the power spectrum entropy and the permutation entropy of each IMF component, the aggregation of the IMF components was realized to reduce the computational load of the prediction model. Finally, the TCN network was used to realize the accurate prediction of CIMFs, and the final vibration signal prediction results were obtained by adding them. The analysis shows that this method shortens the time required for prediction on the premise of ensuring the prediction accuracy, and meets the timeliness of the prediction model.

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针对水电机组振动信号的非线性和非平稳性及预测的时效性要求，提出了基于VMD-CIMFs-TCN的水电机组振动预测模型。首先采用VMD算法对振动信号进行分解得到信号失真度最小的IMFs分量，实现对振动信号的精确分解；其次通过计算各IMFs分量的功率谱熵和排列熵，实现IMFs分量的聚合以减小预测过程模型的计算量；最后利用TCN网络实现CIMFs的精确预测，并将其相加得到最终的振动信号预测结果。分析表明，该方法在保证预测精度的前提下缩短了预测所需的时间，满足了预测模型的时效性。

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陈天涯（1998-），男，硕士研究生，研究方向为流体机械状态监测与故障诊断，E-mail：347963359@qq.com

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陈天涯（1998-），男，硕士研究生，研究方向为流体机械状态监测与故障诊断，E-mail：347963359@qq.com

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