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

Statistical Process Monitoring Using Advanced Data Driven and Deep Learning Approaches


  • Author : Fouzi Harrou
  • Publisher : Elsevier
  • Release Date : 2020-07-18
  • Genre: Technology & Engineering
  • Pages : 328
  • ISBN 10 : 9780128193655
  • Total Read : 89
  • File Size : 10,6 Mb

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

Statistical Process Monitoring Using Advanced Data Driven and Deep Learning Approaches


  • Author : Fouzi Harrou
  • Publisher : Elsevier
  • Release Date : 2020-07-03
  • Genre: Technology & Engineering
  • Pages : 328
  • ISBN 10 : 9780128193662
  • Total Read : 64
  • File Size : 5,7 Mb

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Statistical Process Monitoring Using Advanced Data Driven and Deep Learning Approaches 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

Road Traffic Modeling and Management Book

Road Traffic Modeling and Management


  • Author : Fouzi Harrou
  • Publisher : Elsevier
  • Release Date : 2021-10-05
  • Genre: Transportation
  • Pages : 268
  • ISBN 10 : 9780128234334
  • Total Read : 61
  • File Size : 6,8 Mb

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Road Traffic Modeling and Management Summary:

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 machine learning algorithms and how to apply them to traffic modeling, estimation, forecasting and traffic congestion monitoring. Providing both a theoretical framework along with practical technical solutions, this book is ideal for researchers and practitioners who want to improve the performance of intelligent transportation systems. Provides integrated, up-to-date and complete coverage of the key components for intelligent transportation systems: traffic modeling, forecasting, estimation and monitoring Uses methods based on video and time series data for traffic modeling and forecasting Includes case studies, key processes guidance and comparisons of different methodologies

Advanced Systems for Biomedical Applications Book

Advanced Systems for Biomedical Applications


  • Author : Olfa Kanoun
  • Publisher : Springer Nature
  • Release Date : 2021-07-19
  • Genre: Technology & Engineering
  • Pages : 286
  • ISBN 10 : 9783030712211
  • Total Read : 85
  • File Size : 7,9 Mb

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Advanced Systems for Biomedical Applications Summary:

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 under pandemic conditions. They provide a solid basis for supporting people and improving their health care. As such, the book offers a key reference guide about novel medical systems for students, engineers, designers, and technicians.

Unsupervised Process Monitoring and Fault Diagnosis with Machine Learning Methods Book

Unsupervised Process Monitoring and Fault Diagnosis with Machine Learning Methods


  • Author : Chris Aldrich
  • Publisher : Springer Science & Business Media
  • Release Date : 2013-06-15
  • Genre: Computers
  • Pages : 374
  • ISBN 10 : 9781447151852
  • Total Read : 64
  • File Size : 14,9 Mb

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Unsupervised Process Monitoring and Fault Diagnosis with Machine Learning Methods Summary:

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 enhance unsupervised learning in tree-based methods, the extension of kernel methods to multiple kernel learning for feature extraction from data, and the incremental training of multilayer perceptrons to construct deep architectures for enhanced data projections. Topics and features: discusses machine learning frameworks based on artificial neural networks, statistical learning theory and kernel-based methods, and tree-based methods; examines the application of machine learning to steady state and dynamic operations, with a focus on unsupervised learning; describes the use of spectral methods in process fault diagnosis.

Optimal State Estimation for Process Monitoring  Fault Diagnosis and Control Book

Optimal State Estimation for Process Monitoring Fault Diagnosis and Control


  • Author : Ch. Venkateswarlu
  • Publisher : Elsevier
  • Release Date : 2022-01-31
  • Genre: Computers
  • Pages : 366
  • ISBN 10 : 9780323900683
  • Total Read : 75
  • File Size : 8,9 Mb

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Optimal State Estimation for Process Monitoring Fault Diagnosis and Control Summary:

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 a number of applications and case studies concerning to various real chemical and biochemical processes. The book starts with the introduction of basic concepts, extending to classical methods and successively leading to advances in this field. Design and implementation of various classical and advanced state estimation methods to solve a wide variety of problems makes this book immensely useful for the audience working in different disciplines in academics, research and industry in areas concerning to process monitoring, fault diagnosis, control and related disciplines. • Describes various classical and advanced versions of mechanistic model based state estimation algorithms. • Describes various data-driven model based state estimation techniques. • Highlights a number of real applications of mechanistic model based and data-driven model based state estimators/soft sensors. • Beneficial to those associated with process monitoring, fault diagnosis, online optimization, control and related areas.

Multivariate Statistical Process Control Book

Multivariate Statistical Process Control


  • Author : Zhiqiang Ge
  • Publisher : Springer Science & Business Media
  • Release Date : 2012-11-28
  • Genre: Technology & Engineering
  • Pages : 194
  • ISBN 10 : 9781447145134
  • Total Read : 66
  • File Size : 12,6 Mb

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Multivariate Statistical Process Control 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. 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.

Road Traffic Modeling and Management Book

Road Traffic Modeling and Management


  • Author : Fouzi Harrou
  • Publisher : Elsevier
  • Release Date : 2021-10-08
  • Genre: Transportation
  • Pages : 268
  • ISBN 10 : 9780128234327
  • Total Read : 81
  • File Size : 17,5 Mb

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Road Traffic Modeling and Management Summary:

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 machine learning algorithms and how to apply them to traffic modeling, estimation, forecasting and traffic congestion monitoring. Providing both a theoretical framework along with practical technical solutions, this book is ideal for researchers and practitioners who want to improve the performance of intelligent transportation systems. Provides integrated, up-to-date and complete coverage of the key components for intelligent transportation systems: traffic modeling, forecasting, estimation and monitoring Uses methods based on video and time series data for traffic modeling and forecasting Includes case studies, key processes guidance and comparisons of different methodologies