DocumentCode
2087774
Title
Multivariate statistical monitoring of a continuous steel slab caster
Author
Dudzic, Michael ; Miletic, Ivan
Author_Institution
Dofasco Inc., Hamilton, Ont., Canada
Volume
1
fYear
2002
fDate
2002
Firstpage
600
Abstract
Principal components analysis (PCA) is a multivariate statistical (MVS) technology that can be applied in process monitoring and fault diagnosis. Dofasco has developed on-line multivariate statistical process control (SPC) monitoring systems for both its #1 and #2 continuous casters (#1CC & #2CC) based on this technology. The paper introduces the reader to the basic process of steel casting and the motivation for MVS modeling. As well, the paper highlights various experiences involved in implementing on-line multivariate monitoring strategies and resulting outcome.
Keywords
casting; principal component analysis; process monitoring; statistical process control; steel industry; Dofasco; continuous steel slab caster; fault diagnosis; multivariate statistical monitoring; principal components analysis; process monitoring; statistical process control; Casting; Delay; Fault detection; Fault diagnosis; Instruments; Monitoring; Principal component analysis; Process control; Slabs; Steel;
fLanguage
English
Publisher
ieee
Conference_Titel
American Control Conference, 2002. Proceedings of the 2002
ISSN
0743-1619
Print_ISBN
0-7803-7298-0
Type
conf
DOI
10.1109/ACC.2002.1024872
Filename
1024872
Link To Document