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

Nikus, Mats; Vermasvuori, Mikko; Vatanski, Nikolai; Jämsä-Jounela, Sirkka-Liisa

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dc.contributor	Aalto-yliopisto	fi
dc.contributor	Aalto University	en
dc.contributor.author	Nikus, Mats
dc.contributor.author	Vermasvuori, Mikko
dc.contributor.author	Vatanski, Nikolai
dc.contributor.author	Jämsä-Jounela, Sirkka-Liisa
dc.contributor.editor	Leiviskä, L.
dc.date.accessioned	2016-09-23T08:29:35Z
dc.date.issued	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: https://research.aalto.fi/en/publications/support-vector-machines-for-detection-of-analyzer-faults-a-case-study(fa86912a-cd66-418f-82b7-534b523d7c03).html
dc.identifier.other	PURE FILEURL: https://research.aalto.fi/files/6634010/support_vector_machines_for_detection_of_analyzer_faults.pdf
dc.identifier.uri	https://aaltodoc.aalto.fi/handle/123456789/22387
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