Article(id=1223196812069158985, tenantId=1146029695717560320, journalId=1205116964453384197, issueId=1223196803663778281, articleNumber=null, orderNo=null, doi=10.20040/j.cnki.1000-7709.2023.20221875, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1662566400000, receivedDateStr=2022-09-08, revisedDate=1665590400000, revisedDateStr=2022-10-13, acceptedDate=null, acceptedDateStr=null, onlineDate=1769562445894, onlineDateStr=2026-01-28, pubDate=1692892800000, pubDateStr=2023-08-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1769562445894, onlineIssueDateStr=2026-01-28, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1769562445894, creator=13701087609, updateTime=1769562445894, updator=13701087609, issue=Issue{id=1223196803663778281, tenantId=1146029695717560320, journalId=1205116964453384197, year='2023', volume='41', issue='8', pageStart='1', pageEnd='222', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1769562443888, creator=13701087609, updateTime=1769563793740, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1223202465391166440, tenantId=1146029695717560320, journalId=1205116964453384197, issueId=1223196803663778281, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1223202465391166441, tenantId=1146029695717560320, journalId=1205116964453384197, issueId=1223196803663778281, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=188, endPage=191, ext={EN=ArticleExt(id=1223196812463423587, articleId=1223196812069158985, tenantId=1146029695717560320, journalId=1205116964453384197, language=EN, title=Detection of Abnormal Sound of Hydroelectric Unit Based on Combination of Deep Convolutional Neural Network and Gaussian Mixture Model, columnId=1222925284869922957, journalTitle=Water Resources and Power, columnName=ELECTROMECHANICS AND CONTROL ENGINEERING, runingTitle=null, highlight=null, articleAbstract=

In order to realize the safe monitoring of the operation status of hydroelectric units and solve the problem of automated watch keeping, based on speech recognition technology, the normal status model of measurement points based on the operation monitoring information of generating units was established to implement abnormality detection. Firstly, the experimental data of the bearings of Western Reserve University were used to verify the correctness of the selected modeling method of deep convolutional neural network (CNN) and Gaussian mixture model (GMM). Secondly, a total of forty-two measurement points were arranged for the turbine set, and ten sensitive measurement points were selected for position classification based on the rise rate of RMS before and after overspeed. Then some data were selected as training data to get CNN model and unit sound features. The GMM model was obtained by further training. Finally, the scoring results of the test data were used to determine the machine operation status, i.e., the degree of deviation from the normal status was determined to achieve abnormal status detection. The experimental scheme was confirmed by manual annotation, thus verifying the feasibility of the method, which realizes the design of sound-based abnormality detection algorithm for hydropower units.

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为实现水电机组运行状态的安全监测，解决自动化值守问题，依据语音识别技术，基于发电机组运行监测信息对水轮机部分测点的正常状态建模，以实现异常检测。先使用西储大学轴承试验数据，验证深度卷积神经网络（CNN）与高斯混合模型（GMM）组合建模方法的正确性；其次针对水轮机组共布置了42个测点，根据过速前后RMS的上升率，选择10个敏感测点进行位置分类；然后选取部分数据作为训练数据，得到CNN模型及机组声音特征，进一步训练并得到GMM模型；最后利用测试数据的打分结果，判断机器运行状态———即偏离正常状态的程度，实现异常状态检测。该试验方案通过人工标注确认，验证了方法的可行性，实现了基于声音的水电机组异常检测算法设计。

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张勇（1979-），男，硕士、高级工程师，研究方向为信号处理，E-mail：zhang_yong@sdic.com.cn

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张勇（1979-），男，硕士、高级工程师，研究方向为信号处理，E-mail：zhang_yong@sdic.com.cn

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