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

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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.

Details

Original languageEnglish
Title of host publicationALSIS 2006, Finland, 2006
EditorsL. Leiviskä
Publication statusPublished - 2006
MoE publication typeA4 Article in a conference publication

    Research areas

  • fault detection, monitoring, support vector machines, classification, regression, dearomatization process

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ID: 2369193