Research on obtaining dynamic, robust fault diagnosis rules
This paper is about fault diagnosis rules. Firstly, a redundancy-based rule model is presented, compared with the reduced rule model without redundancy by Rough Set Theory (RST). This model has a robusticity to resist the data loss by sensor failure, as well as the inaccuracy data by sensor error. F...
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| creator | Chang Feng Tingdi Zhao Nuo Zhao Shuyue Yin |
| description | This paper is about fault diagnosis rules. Firstly, a redundancy-based rule model is presented, compared with the reduced rule model without redundancy by Rough Set Theory (RST). This model has a robusticity to resist the data loss by sensor failure, as well as the inaccuracy data by sensor error. Furthermore, considering the constraint of fault diagnosis cost, a dynamic optimization method on the redundancy-based rule model is proposed. The principle of the dynamic optimization model is to maximize the robusticity of rule model under cost constraint. An approach using Genetic Algorithm (GA) is expressed to execute the optimization. Finally, case study on hydraulic pump of civil aeroplane is presented to demonstrate the utility of the proposed model. |
| doi_str_mv | 10.1109/RAMS.2009.4914659 |
| format | Conference Proceeding |
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Firstly, a redundancy-based rule model is presented, compared with the reduced rule model without redundancy by Rough Set Theory (RST). This model has a robusticity to resist the data loss by sensor failure, as well as the inaccuracy data by sensor error. Furthermore, considering the constraint of fault diagnosis cost, a dynamic optimization method on the redundancy-based rule model is proposed. The principle of the dynamic optimization model is to maximize the robusticity of rule model under cost constraint. An approach using Genetic Algorithm (GA) is expressed to execute the optimization. Finally, case study on hydraulic pump of civil aeroplane is presented to demonstrate the utility of the proposed model.</description><identifier>ISSN: 0149-144X</identifier><identifier>ISBN: 9781424425082</identifier><identifier>ISBN: 1424425085</identifier><identifier>EISSN: 2577-0993</identifier><identifier>EISBN: 1424425093</identifier><identifier>EISBN: 9781424425099</identifier><identifier>DOI: 10.1109/RAMS.2009.4914659</identifier><identifier>LCCN: 78-132873</identifier><language>eng</language><publisher>IEEE</publisher><subject>Aerodynamics ; Constraint optimization ; Cost function ; decision rule ; Fault diagnosis ; genetic algorithm ; Genetic algorithms ; optimization ; Optimization methods ; Redundancy ; Resists ; Robustness ; rough set theory ; Set theory</subject><ispartof>2009 Annual Reliability and Maintainability Symposium, 2009, p.106-111</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/4914659$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,780,784,789,790,2058,27925,54920</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/4914659$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Chang Feng</creatorcontrib><creatorcontrib>Tingdi Zhao</creatorcontrib><creatorcontrib>Nuo Zhao</creatorcontrib><creatorcontrib>Shuyue Yin</creatorcontrib><title>Research on obtaining dynamic, robust fault diagnosis rules</title><title>2009 Annual Reliability and Maintainability Symposium</title><addtitle>RAMS</addtitle><description>This paper is about fault diagnosis rules. Firstly, a redundancy-based rule model is presented, compared with the reduced rule model without redundancy by Rough Set Theory (RST). This model has a robusticity to resist the data loss by sensor failure, as well as the inaccuracy data by sensor error. Furthermore, considering the constraint of fault diagnosis cost, a dynamic optimization method on the redundancy-based rule model is proposed. The principle of the dynamic optimization model is to maximize the robusticity of rule model under cost constraint. An approach using Genetic Algorithm (GA) is expressed to execute the optimization. Finally, case study on hydraulic pump of civil aeroplane is presented to demonstrate the utility of the proposed model.</description><subject>Aerodynamics</subject><subject>Constraint optimization</subject><subject>Cost function</subject><subject>decision rule</subject><subject>Fault diagnosis</subject><subject>genetic algorithm</subject><subject>Genetic algorithms</subject><subject>optimization</subject><subject>Optimization methods</subject><subject>Redundancy</subject><subject>Resists</subject><subject>Robustness</subject><subject>rough set theory</subject><subject>Set theory</subject><issn>0149-144X</issn><issn>2577-0993</issn><isbn>9781424425082</isbn><isbn>1424425085</isbn><isbn>1424425093</isbn><isbn>9781424425099</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2009</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNo1kM1KAzEURuNPwbHOA4ibPIBTb3KTyQRXpfgHFaEquCt3JkmNtDMymVn07RWs3-YsDpzFx9ilgJkQYG9W8-fXmQSwM2WFKrU9YudCSaWkBovHLJPamAKsxROWW1P9u0qesgyEsoVQ6mPCMlMVAmVl8IzlKX3B75SWqCFjtyufPPXNJ-9a3tUDxTa2G-72Le1ic837rh7TwAON24G7SJu2SzHxftz6dMEmgbbJ5wdO2fv93dvisVi-PDwt5ssiCqOHwlpryAQXau-wrJ1GXyKR0U2DynlJpSEpESuSAghF8CbUogxSaAcVAE7Z1V83eu_X333cUb9fHz7BH9pcTrU</recordid><startdate>200901</startdate><enddate>200901</enddate><creator>Chang Feng</creator><creator>Tingdi Zhao</creator><creator>Nuo Zhao</creator><creator>Shuyue Yin</creator><general>IEEE</general><scope>6IE</scope><scope>6IH</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIO</scope></search><sort><creationdate>200901</creationdate><title>Research on obtaining dynamic, robust fault diagnosis rules</title><author>Chang Feng ; Tingdi Zhao ; Nuo Zhao ; Shuyue Yin</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i175t-9997a7fdfbed36bd53e63aa75cc34de2a67a22338a210a31fe7fb16f215d08003</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2009</creationdate><topic>Aerodynamics</topic><topic>Constraint optimization</topic><topic>Cost function</topic><topic>decision rule</topic><topic>Fault diagnosis</topic><topic>genetic algorithm</topic><topic>Genetic algorithms</topic><topic>optimization</topic><topic>Optimization methods</topic><topic>Redundancy</topic><topic>Resists</topic><topic>Robustness</topic><topic>rough set theory</topic><topic>Set theory</topic><toplevel>online_resources</toplevel><creatorcontrib>Chang Feng</creatorcontrib><creatorcontrib>Tingdi Zhao</creatorcontrib><creatorcontrib>Nuo Zhao</creatorcontrib><creatorcontrib>Shuyue Yin</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan (POP) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP) 1998-present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Chang Feng</au><au>Tingdi Zhao</au><au>Nuo Zhao</au><au>Shuyue Yin</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Research on obtaining dynamic, robust fault diagnosis rules</atitle><btitle>2009 Annual Reliability and Maintainability Symposium</btitle><stitle>RAMS</stitle><date>2009-01</date><risdate>2009</risdate><spage>106</spage><epage>111</epage><pages>106-111</pages><issn>0149-144X</issn><eissn>2577-0993</eissn><isbn>9781424425082</isbn><isbn>1424425085</isbn><eisbn>1424425093</eisbn><eisbn>9781424425099</eisbn><abstract>This paper is about fault diagnosis rules. Firstly, a redundancy-based rule model is presented, compared with the reduced rule model without redundancy by Rough Set Theory (RST). This model has a robusticity to resist the data loss by sensor failure, as well as the inaccuracy data by sensor error. Furthermore, considering the constraint of fault diagnosis cost, a dynamic optimization method on the redundancy-based rule model is proposed. The principle of the dynamic optimization model is to maximize the robusticity of rule model under cost constraint. An approach using Genetic Algorithm (GA) is expressed to execute the optimization. Finally, case study on hydraulic pump of civil aeroplane is presented to demonstrate the utility of the proposed model.</abstract><pub>IEEE</pub><doi>10.1109/RAMS.2009.4914659</doi><tpages>6</tpages></addata></record> |
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| identifier | ISSN: 0149-144X |
| ispartof | 2009 Annual Reliability and Maintainability Symposium, 2009, p.106-111 |
| issn | 0149-144X 2577-0993 |
| language | eng |
| recordid | cdi_ieee_primary_4914659 |
| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Aerodynamics Constraint optimization Cost function decision rule Fault diagnosis genetic algorithm Genetic algorithms optimization Optimization methods Redundancy Resists Robustness rough set theory Set theory |
| title | Research on obtaining dynamic, robust fault diagnosis rules |
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