Title :
Applications of multivariate statistics at Dofasco
Author :
Dudzic, Michael ; Vaculik, Vit ; Miletic, Ivan
Author_Institution :
Dofasco, Hamilton, Ont., Canada
Abstract :
Multivariate statistical technologies, the principal components analysis and projection to latent structures, are data modeling technologies based on advanced multivariable statistical methods. These methods are capable of: analyzing process data; building predictive models and providing SPC functionality by extracting information from all process and quality data from an operation simultaneously. Multivariate statistical methods are especially powerful techniques for analyzing industrial data sets that have the following characteristics: higher dimensionality; high collinearity; noisy; and with some missing data. The application of these methods have been successfully done at Dofasco since 1993 to analyze data for a variety of purposes, develop online predictive models, and develop online process monitoring systems. An online application is described to illustrate the advantages of this technology
Keywords :
computerised monitoring; predictive control; principal component analysis; quality control; real-time systems; statistical process control; Dofasco; data modeling; latent structure; multivariate statistics; predictive models; principal components analysis; process monitoring; quality control; real time systems; statistical process control; Appropriate technology; Automation; Data analysis; Data engineering; Monitoring; Predictive models; Principal component analysis; Process control; Statistical analysis; Statistics;
Conference_Titel :
Advanced Process Control Applications for Industry Workshop, 1999. IEEE Industry Applications Society
Conference_Location :
Vancouver, BC
DOI :
10.1109/APCA.1999.805022