Multivariate statistical monitoring of a continuous steel slab caster

Multivariate statistical monitoring of a continuous steel slab caster

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...

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Bibliographische Detailangaben
Hauptverfasser: Dudzic, M., Miletic, I.
Format: Tagungsbericht
Sprache:eng
Schlagworte:
Applied sciences
Casting
Computer science
control theory
systems
Control theory. Systems
Delay
Exact sciences and technology
Fault detection
Fault diagnosis
Instruments
Monitoring
Principal component analysis
Process control
Slabs
Steel
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Beschreibung
Zusammenfassung: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.
ISSN:0743-1619
2378-5861
DOI:10.1109/ACC.2002.1024872