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Data Mining for Building Rule-based Fault Diagnosis Systems
This paper aims at developing rule-based fault diagnosis (RBFD) systems using data mining techniques, where we address a problem of generating rules for faults with low probability of occurrence but considerable conceptual importance. Main technical contributions include a multilayer structure of ru...
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| creator | Dianhui Wang |
| description | This paper aims at developing rule-based fault diagnosis (RBFD) systems using data mining techniques, where we address a problem of generating rules for faults with low probability of occurrence but considerable conceptual importance. Main technical contributions include a multilayer structure of rule generation and use, and a regularization model embedding some information on recognition rate, coverage rate and generalization capability for rule optimization. A case study is carried out by an engine diagnostics to illustrate effectiveness of our methodology. |
| doi_str_mv | 10.1109/CHICC.2006.280946 |
| format | conference_proceeding |
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Main technical contributions include a multilayer structure of rule generation and use, and a regularization model embedding some information on recognition rate, coverage rate and generalization capability for rule optimization. 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Main technical contributions include a multilayer structure of rule generation and use, and a regularization model embedding some information on recognition rate, coverage rate and generalization capability for rule optimization. A case study is carried out by an engine diagnostics to illustrate effectiveness of our methodology.</description><subject>Computer science</subject><subject>Control systems</subject><subject>Data engineering</subject><subject>Data mining</subject><subject>Diagnostic expert systems</subject><subject>Electronic mail</subject><subject>Engines</subject><subject>Fault diagnosis</subject><subject>multilayer classifiers</subject><subject>Nonhomogeneous media</subject><subject>Power engineering and energy</subject><subject>rule-based expert systems</subject><issn>1934-1768</issn><issn>2161-2927</issn><isbn>9787810778022</isbn><isbn>7810778021</isbn><isbn>7900669884</isbn><isbn>9787900669889</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2006</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><recordid>eNotjMtOAjEUQOsrcUQ-wLiZHxi8vb32tnGlgwgJxkTZk860JTUDGDos-HswujknZ3OEuJMwkhLsQz2d1fUIAfQIDVjSZ-KG7Sm1NYbORYFSywot8oUYWjZsJDAbQLwUhbSKKsnaXIthzt8AIK1mQizE09j1rnxPm7RZlXG7K1_2qfO_8bnvQtW4HHw5cfuuL8fJrTbbnHL5dch9WOdbcRVdl8Pw3wOxmLwu6mk1_3ib1c_zKlnoK0dtwz42baCoMbJpvPGBDCs0MujopG_QKE0GPZLmE1Cp2LK1j9QGVANx_7dNIYTlzy6t3e6wJNBAltQRvSVLqw</recordid><startdate>200608</startdate><enddate>200608</enddate><creator>Dianhui Wang</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>200608</creationdate><title>Data Mining for Building Rule-based Fault Diagnosis Systems</title><author>Dianhui Wang</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i90t-a4cb7dfbce4f62f78bd8de4873281e6fa1db2836482d2467d24233fc79954ce23</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2006</creationdate><topic>Computer science</topic><topic>Control systems</topic><topic>Data engineering</topic><topic>Data mining</topic><topic>Diagnostic expert systems</topic><topic>Electronic mail</topic><topic>Engines</topic><topic>Fault diagnosis</topic><topic>multilayer classifiers</topic><topic>Nonhomogeneous media</topic><topic>Power engineering and energy</topic><topic>rule-based expert systems</topic><toplevel>online_resources</toplevel><creatorcontrib>Dianhui Wang</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 Xplore Digital 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>Dianhui Wang</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Data Mining for Building Rule-based Fault Diagnosis Systems</atitle><btitle>2006 Chinese Control Conference</btitle><stitle>CHICC</stitle><date>2006-08</date><risdate>2006</risdate><spage>2206</spage><epage>2211</epage><pages>2206-2211</pages><issn>1934-1768</issn><eissn>2161-2927</eissn><isbn>9787810778022</isbn><isbn>7810778021</isbn><eisbn>7900669884</eisbn><eisbn>9787900669889</eisbn><abstract>This paper aims at developing rule-based fault diagnosis (RBFD) systems using data mining techniques, where we address a problem of generating rules for faults with low probability of occurrence but considerable conceptual importance. Main technical contributions include a multilayer structure of rule generation and use, and a regularization model embedding some information on recognition rate, coverage rate and generalization capability for rule optimization. A case study is carried out by an engine diagnostics to illustrate effectiveness of our methodology.</abstract><pub>IEEE</pub><doi>10.1109/CHICC.2006.280946</doi><tpages>6</tpages></addata></record> |
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| identifier | ISSN: 1934-1768 |
| ispartof | 2006 Chinese Control Conference, 2006, p.2206-2211 |
| issn | 1934-1768 2161-2927 |
| language | eng |
| recordid | cdi_ieee_primary_4060494 |
| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Computer science Control systems Data engineering Data mining Diagnostic expert systems Electronic mail Engines Fault diagnosis multilayer classifiers Nonhomogeneous media Power engineering and energy rule-based expert systems |
| title | Data Mining for Building Rule-based Fault Diagnosis Systems |
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