Neural network application to comprehensive engine diagnostics
The authors examine the application of trainable classification systems to the problem of diagnosing faults in engines at the manufacturing plant. It is demonstrated how a combination of conventional statistical processing methods and neural networks can be combined to create a classifier system for...
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| container_end_page | 1022 vol.2 |
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| creator | Marko, K.A. Bryant, B. Soderborg, N. |
| description | The authors examine the application of trainable classification systems to the problem of diagnosing faults in engines at the manufacturing plant. It is demonstrated how a combination of conventional statistical processing methods and neural networks can be combined to create a classifier system for engine diagnostics. The most significant computational effort is required for the principal component analysis and to properly develop the hard-shell classifiers using data sets augmented with Monte Carlo methods. Once these procedures are carried out, the application of neural networks to the data set to obtain the trainable classifier is quite straightforward.< > |
| doi_str_mv | 10.1109/ICSMC.1992.271659 |
| format | Conference Proceeding |
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It is demonstrated how a combination of conventional statistical processing methods and neural networks can be combined to create a classifier system for engine diagnostics. The most significant computational effort is required for the principal component analysis and to properly develop the hard-shell classifiers using data sets augmented with Monte Carlo methods. Once these procedures are carried out, the application of neural networks to the data set to obtain the trainable classifier is quite straightforward.< ></description><identifier>ISBN: 0780307208</identifier><identifier>ISBN: 9780780307209</identifier><identifier>DOI: 10.1109/ICSMC.1992.271659</identifier><language>eng</language><publisher>IEEE</publisher><subject>Engines ; Fault detection ; Fault diagnosis ; Manufacturing processes ; Neural networks ; Process control ; Sensor fusion ; Statistical analysis ; Statistical distributions ; Training data</subject><ispartof>[Proceedings] 1992 IEEE International Conference on Systems, Man, and Cybernetics, 1992, p.1016-1022 vol.2</ispartof><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/271659$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,776,780,785,786,2051,4035,4036,27904,54898</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/271659$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Marko, K.A.</creatorcontrib><creatorcontrib>Bryant, B.</creatorcontrib><creatorcontrib>Soderborg, N.</creatorcontrib><title>Neural network application to comprehensive engine diagnostics</title><title>[Proceedings] 1992 IEEE International Conference on Systems, Man, and Cybernetics</title><addtitle>ICSMC</addtitle><description>The authors examine the application of trainable classification systems to the problem of diagnosing faults in engines at the manufacturing plant. It is demonstrated how a combination of conventional statistical processing methods and neural networks can be combined to create a classifier system for engine diagnostics. The most significant computational effort is required for the principal component analysis and to properly develop the hard-shell classifiers using data sets augmented with Monte Carlo methods. Once these procedures are carried out, the application of neural networks to the data set to obtain the trainable classifier is quite straightforward.< ></description><subject>Engines</subject><subject>Fault detection</subject><subject>Fault diagnosis</subject><subject>Manufacturing processes</subject><subject>Neural networks</subject><subject>Process control</subject><subject>Sensor fusion</subject><subject>Statistical analysis</subject><subject>Statistical distributions</subject><subject>Training data</subject><isbn>0780307208</isbn><isbn>9780780307209</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>1992</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNotj81KxDAURgMiqOM8gK7yAq03TZr0bgQp_gyMulDXw217M0Y7aWmq4ts7MH6bszhw4BPiQkGuFODVqn55rHOFWOSFU7bEI3EGrgINroDqRCxT-oD9TAmIeCqun_hrol5Gnn-G6VPSOPahpTkMUc6DbIfdOPE7xxS-WXLchsiyC7SNQ5pDm87Fsac-8fKfC_F2d_taP2Tr5_tVfbPOgtKAmVXWWjaVgo48thbQlJ1rSCMZAxYb5wmMr4rOa42tdg3sVePZGzJKa70Ql4duYObNOIUdTb-bw0P9B20eRzw</recordid><startdate>1992</startdate><enddate>1992</enddate><creator>Marko, K.A.</creator><creator>Bryant, B.</creator><creator>Soderborg, N.</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>1992</creationdate><title>Neural network application to comprehensive engine diagnostics</title><author>Marko, K.A. ; Bryant, B. ; Soderborg, N.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i1309-61666e4810daf9c60945d7ba39a44069b7fa04f82df339c37b039abfef4a41333</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>1992</creationdate><topic>Engines</topic><topic>Fault detection</topic><topic>Fault diagnosis</topic><topic>Manufacturing processes</topic><topic>Neural networks</topic><topic>Process control</topic><topic>Sensor fusion</topic><topic>Statistical analysis</topic><topic>Statistical distributions</topic><topic>Training data</topic><toplevel>online_resources</toplevel><creatorcontrib>Marko, K.A.</creatorcontrib><creatorcontrib>Bryant, B.</creatorcontrib><creatorcontrib>Soderborg, N.</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>Marko, K.A.</au><au>Bryant, B.</au><au>Soderborg, N.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Neural network application to comprehensive engine diagnostics</atitle><btitle>[Proceedings] 1992 IEEE International Conference on Systems, Man, and Cybernetics</btitle><stitle>ICSMC</stitle><date>1992</date><risdate>1992</risdate><spage>1016</spage><epage>1022 vol.2</epage><pages>1016-1022 vol.2</pages><isbn>0780307208</isbn><isbn>9780780307209</isbn><abstract>The authors examine the application of trainable classification systems to the problem of diagnosing faults in engines at the manufacturing plant. It is demonstrated how a combination of conventional statistical processing methods and neural networks can be combined to create a classifier system for engine diagnostics. The most significant computational effort is required for the principal component analysis and to properly develop the hard-shell classifiers using data sets augmented with Monte Carlo methods. Once these procedures are carried out, the application of neural networks to the data set to obtain the trainable classifier is quite straightforward.< ></abstract><pub>IEEE</pub><doi>10.1109/ICSMC.1992.271659</doi><oa>free_for_read</oa></addata></record> |
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| identifier | ISBN: 0780307208 |
| ispartof | [Proceedings] 1992 IEEE International Conference on Systems, Man, and Cybernetics, 1992, p.1016-1022 vol.2 |
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| language | eng |
| recordid | cdi_ieee_primary_271659 |
| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Engines Fault detection Fault diagnosis Manufacturing processes Neural networks Process control Sensor fusion Statistical analysis Statistical distributions Training data |
| title | Neural network application to comprehensive engine diagnostics |
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