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Multivariate Statistical Process Control

Process Monitoring Methods and Applications
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Zhiqiang Ge
Advances in Industrial Control
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Adobe DRM [Hard-DRM]

Highly developed and widely applicable, MSPC is popular in many industrial contexts. This book covers the most recent developments in the field, and includes a wealth of supplementary material and industrial insight, and anticipates likely future research.


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.

Multivariate Statistical Process Control reviews the developments and improvements that have been made to MSPC over the last decade, and goes on to propose a series of new MSPC-based approaches for complex process monitoring. These new methods are demonstrated in several case studies from the chemical, biological, and semiconductor industrial areas.


Control and process engineers, and academic researchers in the process monitoring, process control and fault detection and isolation (FDI) disciplines will be interested in this book. It can also be used to provide supplementary material and industrial insight for graduate and advanced undergraduate students, and graduate engineers.

Advances in Industrial Control aims to report and encourage the transfer of technology in control engineering. The rapid development of control technology has an impact on all areas of the control discipline. The series offers an opportunity for researchers to present an extended exposition of new work in all aspects of industrial control.

Introduction.- An Overview of Conventional MSPC Methods.- Non-Gaussian Process Monitoring.- Fault Reconstruction and Identification.- Nonlinear Process Monitoring: Part I.- Nonlinear Process Monitoring: Part 2.- Time-varying Process Monitoring.- Multimode Process Monitoring: Part 1.- Multimode Process Monitoring: Part 2.- Dynamic Process Monitoring.- Probabilistic Process Monitoring.- Plant-wide Process Monitoring: Multiblock Method.- Reference.- Index.