Multivariate statistical process control : process monitoring methods and applications /

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Bibliographic Details
Author / Creator:Ge, Zhiqiang.
Imprint:London ; New York : Springer, c2013.
Description:1 online resource.
Language:English
Series:Advances in industrial control, 1430-9491
Advances in industrial control.
Subject:Process control -- Statistical methods.
Multivariate analysis.
Multivariate analysis.
Process control -- Statistical methods.
Electronic books.
Electronic books.
Format: E-Resource Book
URL for this record:http://pi.lib.uchicago.edu/1001/cat/bib/9849077
Hidden Bibliographic Details
Other authors / contributors:Song, Zhihuan.
ISBN:9781447145134 (electronic bk.)
1447145135 (electronic bk.)
9781447145127
1447145127 (hbk)
9781447145127 (hbk)
Notes:Includes bibliographical references and index.
Summary:Given their key position in the process control industry, process monitoring techniques have been extensively investigated by industrial practitioners and academic control researchers. Multivariate statistical process control (MSPC) is one of the most popular data-based methods for process monitoring and is widely used in various industrial areas. Effective routines for process monitoring can help operators run industrial processes efficiently at the same time as maintaining high product quality.
Other form:Print version: 9781447145127
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505 0 0 |t Introduction --  |t An Overview of Conventional MSPC Methods --  |t Non-Gaussian Process Monitoring --  |t Fault Reconstruction and Identification --  |t Nonlinear Process Monitoring:  |g Part 1 --  |t Nonlinear Process Monitoring:  |g Part 2 --  |t Time-Varying Process Monitoring --  |t Multimode Process Monitoring:  |g Part 1 --  |t Multimode Process Monitoring:  |g Part 2 --  |t Dynamic Process Monitoring --  |t Probabilistic Process Monitoring --  |t Plant-Wide Process Monitoring: Multiblock Method. 
504 |a Includes bibliographical references and index. 
520 |a Given their key position in the process control industry, process monitoring techniques have been extensively investigated by industrial practitioners and academic control researchers. Multivariate statistical process control (MSPC) is one of the most popular data-based methods for process monitoring and is widely used in various industrial areas. Effective routines for process monitoring can help operators run industrial processes efficiently at the same time as maintaining high product quality. 
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