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Statistical Process Monitoring Using Advanced Data-Driven and Deep Learning Approaches: Theory and Practical Applications

AUTHOR Hering, Amanda S.; Hering, Amanda S.; Sun, Ying et al.
PUBLISHER Elsevier (07/04/2020)
PRODUCT TYPE Paperback (Paperback)
Description
Statistical Process Monitoring Using Advanced Data-Driven and Deep Learning Approaches tackles multivariate challenges in process monitoring by merging the advantages of univariate and traditional multivariate techniques to enhance their performance and widen their practical applicability. The book proceeds with merging the desirable properties of shallow learning approaches - such as a one-class support vector machine and k-nearest neighbours and unsupervised deep learning approaches - to develop more sophisticated and efficient monitoring techniques.

Finally, the developed approaches are applied to monitor many processes, such as waste-water treatment plants, detection of obstacles in driving environments for autonomous robots and vehicles, robot swarm, chemical processes (continuous stirred tank reactor, plug flow rector, and distillation columns), ozone pollution, road traffic congestion, and solar photovoltaic systems.
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Product Format
Product Details
ISBN-13: 9780128193655
ISBN-10: 0128193654
Binding: Paperback or Softback (Trade Paperback (Us))
Content Language: English
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Page Count: 328
Carton Quantity: 24
Product Dimensions: 6.00 x 0.69 x 9.00 inches
Weight: 0.97 pound(s)
Feature Codes: Bibliography, Index, Illustrated
Country of Origin: US
Subject Information
BISAC Categories
Technology & Engineering | Chemical & Biochemical
Technology & Engineering | Automation
Dewey Decimal: 629.895
Library of Congress Control Number: 2020938028
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publisher marketing
Statistical Process Monitoring Using Advanced Data-Driven and Deep Learning Approaches tackles multivariate challenges in process monitoring by merging the advantages of univariate and traditional multivariate techniques to enhance their performance and widen their practical applicability. The book proceeds with merging the desirable properties of shallow learning approaches - such as a one-class support vector machine and k-nearest neighbours and unsupervised deep learning approaches - to develop more sophisticated and efficient monitoring techniques.

Finally, the developed approaches are applied to monitor many processes, such as waste-water treatment plants, detection of obstacles in driving environments for autonomous robots and vehicles, robot swarm, chemical processes (continuous stirred tank reactor, plug flow rector, and distillation columns), ozone pollution, road traffic congestion, and solar photovoltaic systems.

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