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

Loading...
Thumbnail Image
Journal Title
Journal ISSN
Volume Title
Conference article in proceedings
This publication is imported from Aalto University research portal.
View publication in the Research portal
View/Open full text file from the Research portal
Date
2005
Major/Subject
Mcode
Degree programme
Language
en
Pages
Series
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.
Description
Keywords
Data-based fault detection, Input variable selection, Feature construction., fault detection, input variable selection, feature construction, subspace identification
Other note
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 . European Union , Nancy, France .