Hydro-generator units operating condition forecasting and fault diagnosis based on BP neural network
In this paper, from the Angle to predict , take hydro generating operation condition parameters (head, power) as input sample, take vibration, shaft waggling and pulse pressure, bearings temperature and so on parameter as output sample, create neural network prediction model. Train the established m...
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| creator | Xinfeng Ge Luoping Pan Zhongxin Gao Shu Tang Dongdong Chu |
| description | In this paper, from the Angle to predict , take hydro generating operation condition parameters (head, power) as input sample, take vibration, shaft waggling and pulse pressure, bearings temperature and so on parameter as output sample, create neural network prediction model. Train the established models, through comparing a different designs scheme, chose one smaller error model. Predict through the trained neural network modes ,and compare with the measurement values. |
| doi_str_mv | 10.1109/CSSS.2011.5972027 |
| format | Conference Proceeding |
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Train the established models, through comparing a different designs scheme, chose one smaller error model. Predict through the trained neural network modes ,and compare with the measurement values.</description><subject>Artificial neural networks</subject><subject>condition forecasting</subject><subject>Fault diagnosis</subject><subject>Forecasting</subject><subject>hydro-generating units</subject><subject>Mathematical model</subject><subject>neural network</subject><subject>Presses</subject><subject>Temperature measurement</subject><subject>Vibrations</subject><isbn>9781424497621</isbn><isbn>1424497620</isbn><isbn>1424497639</isbn><isbn>9781424497638</isbn><isbn>9781424497614</isbn><isbn>1424497612</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2011</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNo1UM1KAzEYjIig1j6AeMkL7JrfzeaoRa1QUNjey9fNlxKtSUl2kb69rda5DDMMAzOE3HJWc87s_azrulowzmttjWDCnJFrroRS1jTSnpOpNe2_FvySTEv5YAc0TWuluiJuvnc5VRuMmGFImY4xDIWm3VGGuKF9ii4MIUXqU8Yeyq8L0VEP43agLsAmphIKXUNBRw_Bx3caccywPdDwnfLnDbnwsC04PfGELJ-flrN5tXh7eZ09LKpg2VBpyXrLjGiVw9YzlE570Ruje6u541xqZXTDWs4QpQLgay8aMC244xTn5YTc_dUGRFztcviCvF-dfpE_OEdXsg</recordid><startdate>201106</startdate><enddate>201106</enddate><creator>Xinfeng Ge</creator><creator>Luoping Pan</creator><creator>Zhongxin Gao</creator><creator>Shu Tang</creator><creator>Dongdong Chu</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>201106</creationdate><title>Hydro-generator units operating condition forecasting and fault diagnosis based on BP neural network</title><author>Xinfeng Ge ; Luoping Pan ; Zhongxin Gao ; Shu Tang ; Dongdong Chu</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i90t-530c907284de8f0e3d5f2c775c951d113547560810ee34aa1bf26a78ad8934df3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>chi ; eng</language><creationdate>2011</creationdate><topic>Artificial neural networks</topic><topic>condition forecasting</topic><topic>Fault diagnosis</topic><topic>Forecasting</topic><topic>hydro-generating units</topic><topic>Mathematical model</topic><topic>neural network</topic><topic>Presses</topic><topic>Temperature measurement</topic><topic>Vibrations</topic><toplevel>online_resources</toplevel><creatorcontrib>Xinfeng Ge</creatorcontrib><creatorcontrib>Luoping Pan</creatorcontrib><creatorcontrib>Zhongxin Gao</creatorcontrib><creatorcontrib>Shu Tang</creatorcontrib><creatorcontrib>Dongdong Chu</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Xinfeng Ge</au><au>Luoping Pan</au><au>Zhongxin Gao</au><au>Shu Tang</au><au>Dongdong Chu</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Hydro-generator units operating condition forecasting and fault diagnosis based on BP neural network</atitle><btitle>2011 International Conference on Computer Science and Service System (CSSS)</btitle><stitle>CSSS</stitle><date>2011-06</date><risdate>2011</risdate><spage>1315</spage><epage>1317</epage><pages>1315-1317</pages><isbn>9781424497621</isbn><isbn>1424497620</isbn><eisbn>1424497639</eisbn><eisbn>9781424497638</eisbn><eisbn>9781424497614</eisbn><eisbn>1424497612</eisbn><abstract>In this paper, from the Angle to predict , take hydro generating operation condition parameters (head, power) as input sample, take vibration, shaft waggling and pulse pressure, bearings temperature and so on parameter as output sample, create neural network prediction model. Train the established models, through comparing a different designs scheme, chose one smaller error model. Predict through the trained neural network modes ,and compare with the measurement values.</abstract><pub>IEEE</pub><doi>10.1109/CSSS.2011.5972027</doi><tpages>3</tpages></addata></record> |
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| language | chi ; eng |
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| subjects | Artificial neural networks condition forecasting Fault diagnosis Forecasting hydro-generating units Mathematical model neural network Presses Temperature measurement Vibrations |
| title | Hydro-generator units operating condition forecasting and fault diagnosis based on BP neural network |
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