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

dc.contributorAalto-yliopistofi
dc.contributorAalto Universityen
dc.contributor.authorVermasvuori, Mikkoen_US
dc.contributor.authorVatanski, Nikolaien_US
dc.contributor.authorJämsä-Jounela, Sirkka-Liisaen_US
dc.contributor.departmentDepartment of Biotechnology and Chemical Technologyen
dc.date.accessioned2016-09-23T08:16:22Z
dc.date.issued2005en_US
dc.description.abstractFault 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.description.versionPeer revieweden
dc.format.mimetypeapplication/pdfen_US
dc.identifier.citationVermasvuori , 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 .en
dc.identifier.otherPURE UUID: 7d161113-e809-4434-97df-42b45fe0556cen_US
dc.identifier.otherPURE ITEMURL: https://research.aalto.fi/en/publications/7d161113-e809-4434-97df-42b45fe0556cen_US
dc.identifier.otherPURE FILEURL: https://research.aalto.fi/files/6634166/data_based_fault_detection_of_the_online_analysers_in_a_dearomatisation_process.pdfen_US
dc.identifier.urihttps://aaltodoc.aalto.fi/handle/123456789/22316
dc.identifier.urnURN:NBN:fi:aalto-201609234320
dc.language.isoenen
dc.relation.ispartofseries1st Workshop on Networked Control System and Fault Tolerant Control October 6-7th, 2005, Ajaccio, FRANCEen
dc.rightsopenAccessen
dc.subject.keywordData-based fault detection, Input variable selection, Feature construction.en_US
dc.subject.keywordfault detectionen_US
dc.subject.keywordinput variable selectionen_US
dc.subject.keywordfeature constructionen_US
dc.subject.keywordsubspace identificationen_US
dc.titleDATA BASED FAULT DETECTION OF THE ONLINE ANALYSERS IN A DEAROMATISATION PROCESSen
dc.typeConference article in proceedingsfi
dc.type.versionacceptedVersion
Files