DocumentCode :
2202811
Title :
A model updating approach of multivariate statistical process monitoring
Author :
He, Bo ; Yang, Xianhui
Author_Institution :
Dept. of Autom., Tsinghua Univ., Beijing, China
fYear :
2011
fDate :
6-8 June 2011
Firstpage :
400
Lastpage :
405
Abstract :
Multivariate statistical process control based on conventional principal component analysis (PCA) has been used widely in practice. The slow and normal changes in the processes often lead to false alarm since the conventional PCA algorithm is static. In this paper, we proposed a model updating approach of multivariate statistical process monitoring. By the proposed approach, the PCA model which presents the norm operation condition has been remodeled every N samples. Those remodeling data are chosen by quality information and engineer experience. Furthermore, the method of calculating the updating interval has been discussed. Finally, this model updating approach has been evaluated by a mathematic example and CSTR process simulation. The results show the effectiveness of this method.
Keywords :
multivariable control systems; principal component analysis; process monitoring; statistical process control; CSTR process; model updating approach; multivariate statistical process control; multivariate statistical process monitoring; principal component analysis; quality information; Adaptation models; Data models; Mathematical model; Monitoring; Principal component analysis; Process control; Temperature measurement; Adaptive process monitoring; fault detection; model updating; principal component analysis; update interval;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Information and Automation (ICIA), 2011 IEEE International Conference on
Conference_Location :
Shenzhen
Print_ISBN :
978-1-4577-0268-6
Electronic_ISBN :
978-1-4577-0269-3
Type :
conf
DOI :
10.1109/ICINFA.2011.5949025
Filename :
5949025
Link To Document :
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