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

 |  Login

Show simple item record

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


Files in this item

Files Size Format View

There are no files associated with this item.

This item appears in the following Collection(s)

Show simple item record

Search archive


Advanced Search

article-iconSubmit a publication

Browse

My Account