NDLI: Applications of multivariate statistics at Dofasco

Please wait, while we are loading the content...

Applications of multivariate statistics at Dofasco

Content Provider	IEEE Xplore Digital Library
Author	Dudzic, M. Vaculik, V. Miletic, I.
Copyright Year	1999
Description	Author affiliation: Dofasco, Hamilton, Ont., Canada (Dudzic, M.)
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.
Starting Page	27
Ending Page	29
File Size	203729
Page Count	3
File Format	PDF
DOI	10.1109/APCA.1999.805022
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1999-04-29
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Data analysis Automation Statistical analysis Process control Predictive models Data engineering Appropriate technology Statistics Monitoring Principal component analysis
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Industrial use of multivariate statistical analysis for process monitoring and control

A model updating approach of multivariate statistical process monitoring

Review of multivariate statistical process monitoring

Multivariate fault detection (MVFD) EP/FDC implementation

Using multivariate regression techniques to analyse the performance of a steam heated drying process

Multivariate Process Monitoring Using the Dynamic Biplot

Nonlinear multivariate statistical process monitoring of a water treatment plant

Multivariate statistical modeling and monitoring of SBR wastewater treatment using double moving window MPCA

Dynamic process monitoring based on probabilistic principle component regression

Applications of multivariate statistics at Dofasco

Similar Documents

Industrial use of multivariate statistical analysis for process monitoring and control

A model updating approach of multivariate statistical process monitoring

Review of multivariate statistical process monitoring

Multivariate fault detection (MVFD) EP/FDC implementation

Using multivariate regression techniques to analyse the performance of a steam heated drying process

Multivariate Process Monitoring Using the Dynamic Biplot

Nonlinear multivariate statistical process monitoring of a water treatment plant

Multivariate statistical modeling and monitoring of SBR wastewater treatment using double moving window MPCA

Dynamic process monitoring based on probabilistic principle component regression

Applications of multivariate statistics at Dofasco