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 |
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dc.subject.keyword |
monitoring |
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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 |
|