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

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dc.contributor Aalto-yliopisto fi
dc.contributor Aalto University en
dc.contributor.author Vermasvuori, Mikko
dc.contributor.author Vatanski, Nikolai
dc.contributor.author Jämsä-Jounela, Sirkka-Liisa
dc.date.accessioned 2016-09-23T08:16:22Z
dc.date.issued 2005
dc.identifier.citation 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 . NeCST, EU-IST-2004-004303 , Nancy, France . en
dc.identifier.other PURE UUID: 7d161113-e809-4434-97df-42b45fe0556c
dc.identifier.other PURE ITEMURL: https://research.aalto.fi/en/publications/data-based-fault-detection-of-the-online-analysers-in-a-dearomatisation-process(7d161113-e809-4434-97df-42b45fe0556c).html
dc.identifier.other PURE FILEURL: https://research.aalto.fi/files/6634166/data_based_fault_detection_of_the_online_analysers_in_a_dearomatisation_process.pdf
dc.identifier.uri https://aaltodoc.aalto.fi/handle/123456789/22316
dc.description.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. en
dc.format.mimetype application/pdf
dc.language.iso en en
dc.relation.ispartofseries 1st Workshop on Networked Control System and Fault Tolerant Control October 6-7th, 2005, Ajaccio, FRANCE en
dc.rights openAccess en
dc.title DATA BASED FAULT DETECTION OF THE ONLINE ANALYSERS IN A DEAROMATISATION PROCESS 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 Data-based fault detection, Input variable selection, Feature construction.
dc.subject.keyword fault detection
dc.subject.keyword input variable selection
dc.subject.keyword feature construction
dc.subject.keyword subspace identification
dc.identifier.urn URN:NBN:fi:aalto-201609234320
dc.type.version acceptedVersion


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