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Conference Paper: Online fault detection and isolation of nonlinear systems

TitleOnline fault detection and isolation of nonlinear systems
Authors
KeywordsNonlinear system
Fault detection
Recurrent neural network
Observer
Issue Date1999
PublisherIEEE.
Citation
The 1999 American Control Conference (ACC'99), San Diego, CA., 2-4 June 1999. In Historic Title American Control Conference Proceedings, 1999, v. 6, p. 3980-3984 How to Cite?
AbstractThis paper describes an online fault detection scheme for a class of nonlinear dynamic systems with modelling uncertainty and inaccessible states. Only the inputs and outputs of the system can be measured. The faults are assumed to be functions of the state, instead of the output and the input of the system. A nonlinear online approximator using dynamic recurrent neural network is utilised to monitor the faults in the system. The construction and the learning algorithm of the online approximator are presented. The stability, robustness and sensitivity of the fault detection scheme under certain assumptions are analysed. An example demonstrates the efficiency of the proposed fault detection scheme.
Persistent Identifierhttp://hdl.handle.net/10722/46649
ISBN
ISSN
References

 

DC FieldValueLanguage
dc.contributor.authorChan, CWen_HK
dc.contributor.authorCheung, KCen_HK
dc.contributor.authorWang, Yen_HK
dc.contributor.authorChan, WCen_HK
dc.date.accessioned2007-10-30T06:55:04Z-
dc.date.available2007-10-30T06:55:04Z-
dc.date.issued1999en_HK
dc.identifier.citationThe 1999 American Control Conference (ACC'99), San Diego, CA., 2-4 June 1999. In Historic Title American Control Conference Proceedings, 1999, v. 6, p. 3980-3984en_HK
dc.identifier.isbn0-7803-4990-3en_HK
dc.identifier.issn0743-1619-
dc.identifier.urihttp://hdl.handle.net/10722/46649-
dc.description.abstractThis paper describes an online fault detection scheme for a class of nonlinear dynamic systems with modelling uncertainty and inaccessible states. Only the inputs and outputs of the system can be measured. The faults are assumed to be functions of the state, instead of the output and the input of the system. A nonlinear online approximator using dynamic recurrent neural network is utilised to monitor the faults in the system. The construction and the learning algorithm of the online approximator are presented. The stability, robustness and sensitivity of the fault detection scheme under certain assumptions are analysed. An example demonstrates the efficiency of the proposed fault detection scheme.en_HK
dc.languageengen_HK
dc.publisherIEEE.en_HK
dc.relation.ispartofAmerican Control Conference Proceedings-
dc.rights©1999 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.en_HK
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.subjectNonlinear systemen_HK
dc.subjectFault detectionen_HK
dc.subjectRecurrent neural networken_HK
dc.subjectObserveren_HK
dc.titleOnline fault detection and isolation of nonlinear systemsen_HK
dc.typeConference_Paperen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=0-7803-4990-3&volume=6&spage=3980&epage=3984&date=1999&atitle=Online+fault+detection+and+isolation+of+nonlinear+systemsen_HK
dc.identifier.emailChan, CW: mechan@hkucc.hku.hk-
dc.identifier.emailCheung, KC: kccheung@hkucc.hku.hk-
dc.identifier.authorityChan, CW=rp00088-
dc.identifier.authorityCheung, KC=rp01322-
dc.description.naturepublished_or_final_versionen_HK
dc.identifier.doi10.1109/ACC.1999.786267en_HK
dc.identifier.scopuseid_2-s2.0-0033284975-
dc.identifier.hkuros41244-
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-0033284975&selection=ref&src=s&origin=recordpage-
dc.identifier.volume6-
dc.identifier.spage3980-
dc.identifier.epage3984-
dc.publisher.placeUnited States-
dc.identifier.scopusauthoridChan, CW=7404814060-
dc.identifier.scopusauthoridCheung, KC=7402406698-
dc.identifier.scopusauthoridWang, Y=7601487533-
dc.identifier.scopusauthoridChan, WC=36503653500-
dc.customcontrol.immutablesml 151028 - merged-

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