Neural network fault prediction and its application
In this paper, the forecasting algorithm employs wavelet function to replace sigmoid function in the hidden layer of Back-Propagation Neural Network. And a Wavelet Neural Network prediction model is established to predict Anode Effect (the most typical fault) through forecasting the change rate of c...
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| creator | Jiejia Li Feng Qiao Tongying Guo |
| description | In this paper, the forecasting algorithm employs wavelet function to replace sigmoid function in the hidden layer of Back-Propagation Neural Network. And a Wavelet Neural Network prediction model is established to predict Anode Effect (the most typical fault) through forecasting the change rate of cell resistance. The authors have developed forecasting software based on platform of Visual Basic 6.0. The simulation results show that the proposed method not only has greatly improved fault prediction precision and real-time, but also improved the operation efficiency. That means we can increase energy efficiency and the safety of aluminum production process. |
| doi_str_mv | 10.1109/WCICA.2010.5554056 |
| format | Conference Proceeding |
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And a Wavelet Neural Network prediction model is established to predict Anode Effect (the most typical fault) through forecasting the change rate of cell resistance. The authors have developed forecasting software based on platform of Visual Basic 6.0. The simulation results show that the proposed method not only has greatly improved fault prediction precision and real-time, but also improved the operation efficiency. 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That means we can increase energy efficiency and the safety of aluminum production process.</description><subject>Aluminum</subject><subject>Aluminum Electrolysis</subject><subject>Artificial neural networks</subject><subject>Energy Conservation</subject><subject>Faults Prediction</subject><subject>Forecasting</subject><subject>Mathematical model</subject><subject>Predictive models</subject><subject>Resistance</subject><subject>Software</subject><subject>Wavelet Neural Network</subject><isbn>9781424467129</isbn><isbn>142446711X</isbn><isbn>9781424467112</isbn><isbn>1424467128</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2010</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNotj81KxDAUheNCUMa-gG7yAh2TNL9LKf7BMG4cZjnc3txCtHZKmkF8eyvO6nAOH4dzGLuVYi2lCPf79rV9WCuxeGOMFsZesCo4L7XS2jqpwhWr5vlDCCGdtcqqa9Zs6ZRh4COV72P-5D2chsKnTDFhSceRwxh5KjOHaRoSwl92wy57GGaqzrpiu6fH9_al3rw9Lws2dZLOlLqh0EUCdEFasAHBo_PRW6TOQx-CiJEIZa_RdtbpLsJCIGqIzpCJrlmxu__eRESHKacvyD-H87XmFz2JRiU</recordid><startdate>201007</startdate><enddate>201007</enddate><creator>Jiejia Li</creator><creator>Feng Qiao</creator><creator>Tongying Guo</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>201007</creationdate><title>Neural network fault prediction and its application</title><author>Jiejia Li ; Feng Qiao ; Tongying Guo</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i175t-3e9bdeac7916a69ca8c78d86ceb8af990ddeec1f4c6b674bdaa8ccc4ad75e5d73</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2010</creationdate><topic>Aluminum</topic><topic>Aluminum Electrolysis</topic><topic>Artificial neural networks</topic><topic>Energy Conservation</topic><topic>Faults Prediction</topic><topic>Forecasting</topic><topic>Mathematical model</topic><topic>Predictive models</topic><topic>Resistance</topic><topic>Software</topic><topic>Wavelet Neural Network</topic><toplevel>online_resources</toplevel><creatorcontrib>Jiejia Li</creatorcontrib><creatorcontrib>Feng Qiao</creatorcontrib><creatorcontrib>Tongying Guo</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>Jiejia Li</au><au>Feng Qiao</au><au>Tongying Guo</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Neural network fault prediction and its application</atitle><btitle>2010 8th World Congress on Intelligent Control and Automation</btitle><stitle>WCICA</stitle><date>2010-07</date><risdate>2010</risdate><spage>740</spage><epage>743</epage><pages>740-743</pages><eisbn>9781424467129</eisbn><eisbn>142446711X</eisbn><eisbn>9781424467112</eisbn><eisbn>1424467128</eisbn><abstract>In this paper, the forecasting algorithm employs wavelet function to replace sigmoid function in the hidden layer of Back-Propagation Neural Network. And a Wavelet Neural Network prediction model is established to predict Anode Effect (the most typical fault) through forecasting the change rate of cell resistance. The authors have developed forecasting software based on platform of Visual Basic 6.0. The simulation results show that the proposed method not only has greatly improved fault prediction precision and real-time, but also improved the operation efficiency. That means we can increase energy efficiency and the safety of aluminum production process.</abstract><pub>IEEE</pub><doi>10.1109/WCICA.2010.5554056</doi><tpages>4</tpages></addata></record> |
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| ispartof | 2010 8th World Congress on Intelligent Control and Automation, 2010, p.740-743 |
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| language | eng |
| recordid | cdi_ieee_primary_5554056 |
| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Aluminum Aluminum Electrolysis Artificial neural networks Energy Conservation Faults Prediction Forecasting Mathematical model Predictive models Resistance Software Wavelet Neural Network |
| title | Neural network fault prediction and its application |
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