Process fault diagnosis using one-class support vector machines

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dc.contributor.authorJemwa G.T.
dc.contributor.authorAldrich C.
dc.date.accessioned2011-10-13T16:59:17Z
dc.date.available2011-10-13T16:59:17Z
dc.date.issued2007
dc.description.abstractThe process fault diagnosis problem is usually considered in classification framework. Although used widely in diagnostic applications, artificial neural networks and other nonlinear classifiers perform poorly under nonideal conditions encountered in practice, owing to the arbitrary placement of decision boundaries in empty regions of the input space and unbounded normal class region. This is particularly problematic where few and noisy data are available. In this paper, the use of one-class support vector machines for the diagnosis of process operations is proposed and their performance under practical conditions assessed. One-class classifiers are shown to be superior to and more robust than competing approaches previously proposed for diagnostic applications. Copyright © 2007 IFAC.
dc.description.versionConference Paper
dc.identifier.citationIFAC Proceedings Volumes (IFAC-PapersOnline)
dc.identifier.citation12
dc.identifier.citationPART 1
dc.identifier.citationhttp://www.scopus.com/inward/record.url?eid=2-s2.0-79960925913&partnerID=40&md5=fd41aa680ab3febcb038e9663f4a38c5
dc.identifier.issn14746670
dc.identifier.urihttp://hdl.handle.net/10019.1/17051
dc.titleProcess fault diagnosis using one-class support vector machines
dc.typeConference Paper
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