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Article: Fault detection of systems with redundant sensors using constrained Kohonen networks

TitleFault detection of systems with redundant sensors using constrained Kohonen networks
Authors
KeywordsFault detection and isolation
Kohonen network
Redundant sensor system
Self-organizing map
Issue Date2001
PublisherPergamon. The Journal's web site is located at http://www.elsevier.com/locate/automatica
Citation
Automatica, 2001, v. 37 n. 10, p. 1671-1676 How to Cite?
AbstractThe Kohonen self-organizing map (KN) was developed for pattern recognition, and has been extended to fault classification. However, the KN cannot be applied to classify faults from the system output if it contains other factors, such as system state and sensor mounting errors. To overcome this problem, a constrained KN (CKN) is proposed. To eliminate the effect of the system state and the mounting errors, it is proposed that the weight vectors of the CKN are constrained in the parity space. The training algorithm of the CKN is derived, and its convergence discussed. Application of the CKN to fault classification is presented, and its performance is illustrated by an example involving a redundant sensor system with six sensors. © 2001 Elsevier Science Ltd. All rights reserved.
Persistent Identifierhttp://hdl.handle.net/10722/75933
ISSN
2015 Impact Factor: 3.635
2015 SCImago Journal Rankings: 4.315
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorChan, CWen_HK
dc.contributor.authorJin, Hen_HK
dc.contributor.authorCheung, KCen_HK
dc.contributor.authorZhang, HYen_HK
dc.date.accessioned2010-09-06T07:15:59Z-
dc.date.available2010-09-06T07:15:59Z-
dc.date.issued2001en_HK
dc.identifier.citationAutomatica, 2001, v. 37 n. 10, p. 1671-1676en_HK
dc.identifier.issn0005-1098en_HK
dc.identifier.urihttp://hdl.handle.net/10722/75933-
dc.description.abstractThe Kohonen self-organizing map (KN) was developed for pattern recognition, and has been extended to fault classification. However, the KN cannot be applied to classify faults from the system output if it contains other factors, such as system state and sensor mounting errors. To overcome this problem, a constrained KN (CKN) is proposed. To eliminate the effect of the system state and the mounting errors, it is proposed that the weight vectors of the CKN are constrained in the parity space. The training algorithm of the CKN is derived, and its convergence discussed. Application of the CKN to fault classification is presented, and its performance is illustrated by an example involving a redundant sensor system with six sensors. © 2001 Elsevier Science Ltd. All rights reserved.en_HK
dc.languageengen_HK
dc.publisherPergamon. The Journal's web site is located at http://www.elsevier.com/locate/automaticaen_HK
dc.relation.ispartofAutomaticaen_HK
dc.subjectFault detection and isolationen_HK
dc.subjectKohonen networken_HK
dc.subjectRedundant sensor systemen_HK
dc.subjectSelf-organizing mapen_HK
dc.titleFault detection of systems with redundant sensors using constrained Kohonen networksen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=0005-1098&volume=37&spage=1671&epage=1676&date=2001&atitle=Fault+detection+of+systems+with+redundant+sensors+using+constrained+Kohonen+networksen_HK
dc.identifier.emailChan, CW: mechan@hkucc.hku.hken_HK
dc.identifier.emailCheung, KC: kccheung@hkucc.hku.hken_HK
dc.identifier.authorityChan, CW=rp00088en_HK
dc.identifier.authorityCheung, KC=rp01322en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1016/S0005-1098(01)00126-1en_HK
dc.identifier.scopuseid_2-s2.0-0035480342en_HK
dc.identifier.hkuros68127en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-0035480342&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume37en_HK
dc.identifier.issue10en_HK
dc.identifier.spage1671en_HK
dc.identifier.epage1676en_HK
dc.identifier.isiWOS:000170616200018-
dc.publisher.placeUnited Kingdomen_HK
dc.identifier.scopusauthoridChan, CW=7404814060en_HK
dc.identifier.scopusauthoridJin, H=34770583400en_HK
dc.identifier.scopusauthoridCheung, KC=7402406698en_HK
dc.identifier.scopusauthoridZhang, HY=7409196387en_HK

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