DATA BASED FAULT DETECTION OF THE ONLINE ANALYSERS IN A DEAROMATISATION PROCESS

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A4 Artikkeli konferenssijulkaisussa
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2005

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Department of Biotechnology and Chemical Technology

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en

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1st Workshop on Networked Control System and Fault Tolerant Control October 6-7th, 2005, Ajaccio, FRANCE

Abstract

Fault diagnosis methods based on process history data have been studied widely in recent years, and several successful industrial applications have been reported. In this paper a comparison of four monitoring methods, PCA, PLS, subspace identification and self-organising maps, for fault detection of the online analysers in a dearomatisation process is presented. The effectiveness of different statistical process monitoring methods in FDI of the online analysers is evaluated on the basis of a large number of simulation studies. Finally the results are presented and discussed.

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Data-based fault detection, Input variable selection, Feature construction., fault detection, input variable selection, feature construction, subspace identification

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Vermasvuori, M, Vatanski, N & Jämsä-Jounela, S-L 2005, DATA BASED FAULT DETECTION OF THE ONLINE ANALYSERS IN A DEAROMATISATION PROCESS. in 1st Workshop on Networked Control System and Fault Tolerant Control October 6-7th, 2005, Ajaccio, FRANCE. European Union, Nancy, France.

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https://urn.fi/URN:NBN:fi:aalto-201609234320

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[article-cris] Kemian tekniikan korkeakoulu / CHEM