Support vector machines for detection of analyzer faults- a case study

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dc.contributor Aalto-yliopisto fi
dc.contributor Aalto University en Nikus, Mats Vermasvuori, Mikko Vatanski, Nikolai Jämsä-Jounela, Sirkka-Liisa
dc.contributor.editor Leiviskä, L. 2016-09-23T08:29:35Z 2006
dc.identifier.citation Nikus , M , Vermasvuori , M , Vatanski , N & Jämsä-Jounela , S-L 2006 , Support vector machines for detection of analyzer faults- a case study . in L Leiviskä (ed.) , ALSIS 2006, Finland, 2006 . Suomen Automaatioseura , Helsinki . en
dc.identifier.isbn 952-5183-28-9
dc.identifier.other PURE UUID: fa86912a-cd66-418f-82b7-534b523d7c03
dc.identifier.other PURE ITEMURL:
dc.identifier.other PURE FILEURL:
dc.description.abstract The aim of the work presented in this paper is to assess the ability of support vector machines (SVM) for detecting measurement faults. Two different support vector machine approaches for detecting faults are tested and compared to neural networks. The first method is based on a SVM regression model together with an analysis of the residuals whereas the second method is based on a SVM classifier. The methods were applied to a rigorous first principles based dynamic simulator of a dearomatization process. en
dc.format.mimetype application/pdf
dc.language.iso en en
dc.relation.ispartofseries ALSIS 2006, Finland, 2006 en
dc.rights openAccess en
dc.title Support vector machines for detection of analyzer faults- a case study en
dc.type A4 Artikkeli konferenssijulkaisussa fi
dc.description.version Peer reviewed en
dc.contributor.department Department of Biotechnology and Chemical Technology en
dc.subject.keyword fault detection
dc.subject.keyword monitoring
dc.subject.keyword support vector machines
dc.subject.keyword classification
dc.subject.keyword regression
dc.subject.keyword dearomatization process
dc.identifier.urn URN:NBN:fi:aalto-201609234391
dc.type.version acceptedVersion

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