Statistical Process Monitoring Using Advanced Data-Driven and Deep Learning Approaches
  • Author : Fouzi Harrou
  • Release Date : 18 July 2020
  • Publisher : Elsevier
  • Genre : Technology & Engineering
  • Pages : 328
  • ISBN 13 : 9780128193655

Statistical Process Monitoring Using Advanced Data-Driven and Deep Learning Approaches Book Summary

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. Uses a data-driven based approach to fault detection and attribution Provides an in-depth understanding of fault detection and attribution in complex and multivariate systems Familiarises you with the most suitable data-driven based techniques including multivariate statistical techniques and deep learning-based methods Includes case studies and comparison of different methods

Statistical Process Monitoring Using Advanced Data Driven and Deep Learning Approaches

Statistical Process Monitoring Using Advanced Data Driven and Deep Learning Approaches

Author : Fouzi Harrou,Ying Sun,Amanda S. Hering,Muddu Madakyaru,abdelkader Dairi
Publisher : Elsevier
Genre : Technology & Engineering
Total View : 9457 Views
File Size : 44,7 Mb
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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 ...

Statistical Process Monitoring Using Advanced Data Driven and Deep Learning Approaches

Statistical Process Monitoring Using Advanced Data Driven and Deep Learning Approaches

Author : Fouzi Harrou,Ying Sun,Amanda S. Hering,Muddu Madakyaru,abdelkader Dairi
Publisher : Elsevier
Genre : Technology & Engineering
Total View : 1211 Views
File Size : 43,9 Mb
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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 ...

Road Traffic Modeling and Management

Road Traffic Modeling and Management

Author : Fouzi Harrou,Abdelhafid Zeroual,Mohamad Mazen Hittawe,Ying Sun
Publisher : Elsevier
Genre : Transportation
Total View : 9590 Views
File Size : 55,6 Mb
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Road Traffic Modeling and Management: Using Statistical Monitoring and Deep Learning provides a framework for understanding and enhancing road traffic monitoring and management. The book examines commonly used traffic analysis methodologies as well the emerging methods that use deep learning methods. Other sections discuss how to understand statistical models and ...

Advanced Systems for Biomedical Applications

Advanced Systems for Biomedical Applications

Author : Olfa Kanoun,Nabil Derbel
Publisher : Springer Nature
Genre : Technology & Engineering
Total View : 4588 Views
File Size : 48,7 Mb
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The book highlights recent developments in the field of biomedical systems covering a wide range of technological aspects, methods, systems and instrumentation techniques for diagnosis, monitoring, treatment, and assistance. Biomedical systems are becoming increasingly important in medicine and in special areas of application such as supporting people with disabilities and ...

Unsupervised Process Monitoring and Fault Diagnosis with Machine Learning Methods

Unsupervised Process Monitoring and Fault Diagnosis with Machine Learning Methods

Author : Chris Aldrich,Lidia Auret
Publisher : Springer Science & Business Media
Genre : Computers
Total View : 7493 Views
File Size : 49,8 Mb
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This unique text/reference describes in detail the latest advances in unsupervised process monitoring and fault diagnosis with machine learning methods. Abundant case studies throughout the text demonstrate the efficacy of each method in real-world settings. The broad coverage examines such cutting-edge topics as the use of information theory to ...

Optimal State Estimation for Process Monitoring  Fault Diagnosis and Control

Optimal State Estimation for Process Monitoring Fault Diagnosis and Control

Author : Ch. Venkateswarlu,Rama Rao Karri
Publisher : Elsevier
Genre : Computers
Total View : 1862 Views
File Size : 50,5 Mb
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Optimal State Estimation for Process Monitoring, Fault Diagnosis and Control presents various mechanistic model based state estimators and data-driven model based state estimators with a special emphasis on their development and applications to process monitoring, fault diagnosis and control. The design and analysis of different state estimators are highlighted with ...

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Control Charts and Machine Learning for Anomaly Detection in Manufacturing

Author : Kim Phuc Tran
Publisher : Springer Nature
Genre : Technology & Engineering
Total View : 9861 Views
File Size : 46,9 Mb
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This book introduces the latest research on advanced control charts and new machine learning approaches to detect abnormalities in the smart manufacturing process. By approaching anomaly detection using both statistics and machine learning, the book promotes interdisciplinary cooperation between the research communities, to jointly develop new anomaly detection approaches that ...

Multivariate Statistical Process Control

Multivariate Statistical Process Control

Author : Zhiqiang Ge,Zhihuan Song
Publisher : Springer Science & Business Media
Genre : Technology & Engineering
Total View : 4441 Views
File Size : 47,8 Mb
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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 ...