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Fault Prediction Based on Data-Driven Technique
This paper presents principal component analysis (PCA), some improvement of PCA and the development of PCA. PCA does not depend on the accurate mathematical model, is able to implement the feature extraction of the complex process data, and establishes a principal component model of the correspondin...
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| creator | Lin Luhui Ma Jie |
| description | This paper presents principal component analysis (PCA), some improvement of PCA and the development of PCA. PCA does not depend on the accurate mathematical model, is able to implement the feature extraction of the complex process data, and establishes a principal component model of the corresponding process. It can achieve the extraction of the system information and eliminate the interference the system. So there is the existence of a good applications prospect in the complex process of fault diagnosis and prediction maintain. |
| doi_str_mv | 10.1109/iCECE.2010.253 |
| format | conference_proceeding |
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PCA does not depend on the accurate mathematical model, is able to implement the feature extraction of the complex process data, and establishes a principal component model of the corresponding process. It can achieve the extraction of the system information and eliminate the interference the system. So there is the existence of a good applications prospect in the complex process of fault diagnosis and prediction maintain.</description><identifier>ISBN: 1424468809</identifier><identifier>ISBN: 9781424468805</identifier><identifier>EISBN: 9781424468812</identifier><identifier>EISBN: 0769540317</identifier><identifier>EISBN: 9780769540313</identifier><identifier>EISBN: 1424468817</identifier><identifier>DOI: 10.1109/iCECE.2010.253</identifier><language>eng</language><publisher>IEEE</publisher><subject>Artificial neural networks ; Data models ; data-driven ; Fault diagnosis ; fault prediction ; improvement ; Mathematical model ; Monitoring ; Principal component analysis ; principal component analysis (PCA)</subject><ispartof>2010 International Conference on Electrical and Control Engineering, 2010, p.997-1001</ispartof><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/5630495$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,780,784,789,790,2058,27925,54920</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/5630495$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Lin Luhui</creatorcontrib><creatorcontrib>Ma Jie</creatorcontrib><title>Fault Prediction Based on Data-Driven Technique</title><title>2010 International Conference on Electrical and Control Engineering</title><addtitle>ICECE</addtitle><description>This paper presents principal component analysis (PCA), some improvement of PCA and the development of PCA. PCA does not depend on the accurate mathematical model, is able to implement the feature extraction of the complex process data, and establishes a principal component model of the corresponding process. It can achieve the extraction of the system information and eliminate the interference the system. So there is the existence of a good applications prospect in the complex process of fault diagnosis and prediction maintain.</description><subject>Artificial neural networks</subject><subject>Data models</subject><subject>data-driven</subject><subject>Fault diagnosis</subject><subject>fault prediction</subject><subject>improvement</subject><subject>Mathematical model</subject><subject>Monitoring</subject><subject>Principal component analysis</subject><subject>principal component analysis (PCA)</subject><isbn>1424468809</isbn><isbn>9781424468805</isbn><isbn>9781424468812</isbn><isbn>0769540317</isbn><isbn>9780769540313</isbn><isbn>1424468817</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2010</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><recordid>eNo1jr1Ow0AQhA8hJCBxS0PjF3ByP7vn2xIcB5AikcJ9dPatxaFgwHaQeHtOAqaZ-ZqZEeJGyZVSktaxqqt6pWVijeZMZFQ6BRrAOqf0ubj-B0mXIpumV5kEqLWiK7He-tNxzvcjh9jN8X3I7_3EIU9h42dfbMb4xUPecPcyxM8TL8VF748TZ3--EM22bqrHYvf88FTd7YpIci5Uj8GkCXImIKPtAaSRTjNyCx2xdVR2JaYHjoG97cm2aNvW9OAxqNIsxO1vbWTmw8cY3_z4fUBrJBCaH8NiQis</recordid><startdate>201006</startdate><enddate>201006</enddate><creator>Lin Luhui</creator><creator>Ma Jie</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>201006</creationdate><title>Fault Prediction Based on Data-Driven Technique</title><author>Lin Luhui ; Ma Jie</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i90t-1f5d3452983d5e56f4403082e5eb4c9e6897c752198e4ea6f96b56bb3f4a5d173</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2010</creationdate><topic>Artificial neural networks</topic><topic>Data models</topic><topic>data-driven</topic><topic>Fault diagnosis</topic><topic>fault prediction</topic><topic>improvement</topic><topic>Mathematical model</topic><topic>Monitoring</topic><topic>Principal component analysis</topic><topic>principal component analysis (PCA)</topic><toplevel>online_resources</toplevel><creatorcontrib>Lin Luhui</creatorcontrib><creatorcontrib>Ma Jie</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/IET Electronic Library</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>Lin Luhui</au><au>Ma Jie</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Fault Prediction Based on Data-Driven Technique</atitle><btitle>2010 International Conference on Electrical and Control Engineering</btitle><stitle>ICECE</stitle><date>2010-06</date><risdate>2010</risdate><spage>997</spage><epage>1001</epage><pages>997-1001</pages><isbn>1424468809</isbn><isbn>9781424468805</isbn><eisbn>9781424468812</eisbn><eisbn>0769540317</eisbn><eisbn>9780769540313</eisbn><eisbn>1424468817</eisbn><abstract>This paper presents principal component analysis (PCA), some improvement of PCA and the development of PCA. PCA does not depend on the accurate mathematical model, is able to implement the feature extraction of the complex process data, and establishes a principal component model of the corresponding process. It can achieve the extraction of the system information and eliminate the interference the system. So there is the existence of a good applications prospect in the complex process of fault diagnosis and prediction maintain.</abstract><pub>IEEE</pub><doi>10.1109/iCECE.2010.253</doi><tpages>5</tpages></addata></record> |
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| ispartof | 2010 International Conference on Electrical and Control Engineering, 2010, p.997-1001 |
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| language | eng |
| recordid | cdi_ieee_primary_5630495 |
| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Artificial neural networks Data models data-driven Fault diagnosis fault prediction improvement Mathematical model Monitoring Principal component analysis principal component analysis (PCA) |
| title | Fault Prediction Based on Data-Driven Technique |
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