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

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Conference article in proceedings
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Date
2006
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Language
en
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ALSIS 2006, Finland, 2006
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.
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Keywords
fault detection, monitoring, support vector machines, classification, regression, dearomatization process
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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 .