Please use this identifier to cite or link to this item: https://doi.org/10.1109/TSMCC.2007.900623
DC FieldValue
dc.titleAutomated fault detection and diagnosis in mechanical systems
dc.contributor.authorHuang, S.N.
dc.contributor.authorTan, K.K.
dc.contributor.authorLee, T.H.
dc.date.accessioned2014-06-17T02:39:56Z
dc.date.available2014-06-17T02:39:56Z
dc.date.issued2007-11
dc.identifier.citationHuang, S.N., Tan, K.K., Lee, T.H. (2007-11). Automated fault detection and diagnosis in mechanical systems. IEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews 37 (6) : 1360-1364. ScholarBank@NUS Repository. https://doi.org/10.1109/TSMCC.2007.900623
dc.identifier.issn10946977
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/55166
dc.description.abstractIn this work, a fault detection method is developed based on a neural network (NN) learning model. The robust observer is designed for monitoring fault, without NN learning, when the system of concern is operating in the normal healthy mode. By comparing appropriate states with their signatures, the fault diagnosis can be carried out and the NN learning is then triggered to identify the fault function. © 2007 IEEE.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1109/TSMCC.2007.900623
dc.sourceScopus
dc.subjectFault detection
dc.subjectFault diagnosis
dc.subjectNeural nets
dc.subjectNeural networks (NNs)
dc.subjectNonlinear model
dc.subjectNonlinear systems
dc.typeArticle
dc.contributor.departmentELECTRICAL & COMPUTER ENGINEERING
dc.description.doi10.1109/TSMCC.2007.900623
dc.description.sourcetitleIEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews
dc.description.volume37
dc.description.issue6
dc.description.page1360-1364
dc.description.codenITCRF
dc.identifier.isiut000250395300024
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