Statistical Process Monitoring Using Advanced Data Driven and Deep Learning Approaches

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  • Author : Fouzi Harrou
  • Publisher : Elsevier
  • Pages : 328 pages
  • ISBN : 0128193662
  • Rating : /5 from reviews
CLICK HERE TO GET THIS BOOK >>>Statistical Process Monitoring Using Advanced Data Driven and Deep Learning Approaches

Download or Read online Statistical Process Monitoring Using Advanced Data Driven and Deep Learning Approaches full in PDF, ePub and kindle. this book written by Fouzi Harrou and published by Elsevier which was released on 03 July 2020 with total page 328 pages. We cannot guarantee that Statistical Process Monitoring Using Advanced Data Driven and Deep Learning Approaches book is available in the library, click Get Book button and read full online book in your kindle, tablet, IPAD, PC or mobile whenever and wherever You Like. 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
  • Release : 03 July 2020
GET THIS BOOK Statistical Process Monitoring Using Advanced Data Driven and Deep Learning Approaches

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,

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
  • Release : 29 July 2020
GET THIS BOOK Statistical Process Monitoring Using Advanced Data Driven and Deep Learning Approaches

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

Road Traffic Modeling and Management

Road Traffic Modeling and Management
  • Author : Fouzi Harrou,Abdelhafid Zeroual,Mohamad Mazen Hittawe,Ying Sun
  • Publisher : Elsevier
  • Release : 05 October 2021
GET THIS BOOK Road Traffic Modeling and Management

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

Road Traffic Modeling and Management

Road Traffic Modeling and Management
  • Author : Fouzi Harrou,Abdelhafid Zeroual,Mohamad Mazen Hittawe,Ying Sun
  • Publisher : Elsevier
  • Release : 15 January 2022
GET THIS BOOK Road Traffic Modeling and Management

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

Advanced Systems for Biomedical Applications

Advanced Systems for Biomedical Applications
  • Author : Olfa Kanoun,Nabil Derbel
  • Publisher : Springer Nature
  • Release : 09 December 2021
GET THIS BOOK Advanced Systems for Biomedical Applications

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

Machine Learning for Cyber Physical Systems

Machine Learning for Cyber Physical Systems
  • Author : Jürgen Beyerer,Alexander Maier,Oliver Niggemann
  • Publisher : Springer
  • Release : 09 April 2019
GET THIS BOOK Machine Learning for Cyber Physical Systems

The work presents new approaches to Machine Learning for Cyber Physical Systems, experiences and visions. It contains some selected papers from the international Conference ML4CPS – Machine Learning for Cyber Physical Systems, which was held in Lemgo, October 25th-26th, 2017. Cyber Physical Systems are characterized by their ability to adapt and to learn: They analyze their environment and, based on observations, they learn patterns, correlations and predictive models. Typical applications are condition monitoring, predictive maintenance, image processing and diagnosis. Machine

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
  • Release : 15 June 2013
GET THIS BOOK Unsupervised Process Monitoring and Fault Diagnosis with Machine Learning Methods

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

Advanced Methods for Fault Diagnosis and Fault tolerant Control

Advanced Methods for Fault Diagnosis and Fault tolerant Control
  • Author : Steven X. Ding
  • Publisher : Springer Nature
  • Release : 09 December 2021
GET THIS BOOK Advanced Methods for Fault Diagnosis and Fault tolerant Control

After the first two books have been dedicated to model-based and data-driven fault diagnosis respectively, this book addresses topics in both model-based and data-driven thematic fields with considerable focuses on fault-tolerant control issues and application of machine learning methods. The major objective of the book is to study basic fault diagnosis and fault-tolerant control problems and to build a framework for long-term research efforts in the fault diagnosis and fault-tolerant control domain. In this framework, possibly unified solutions and methods

Multivariate Statistical Process Control

Multivariate Statistical Process Control
  • Author : Zhiqiang Ge,Zhihuan Song
  • Publisher : Springer Science & Business Media
  • Release : 28 November 2012
GET THIS BOOK Multivariate Statistical Process Control

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

Metal Additive Manufacturing

Metal Additive Manufacturing
  • Author : Ehsan Toyserkani,Dyuti Sarker,Osezua Obehi Ibhadode,Farzad Liravi,Paola Russo,Katayoon Taherkhani
  • Publisher : John Wiley & Sons
  • Release : 07 February 2022
GET THIS BOOK Metal Additive Manufacturing

METAL ADDITIVE MANUFACTURING A comprehensive review of additive manufacturing processes for metallic structures Additive Manufacturing (AM)—also commonly referred to as 3D printing—builds three-dimensional objects by adding materials layer by layer. Recent years have seen unprecedented investment in additive manufacturing research and development by governments and corporations worldwide. This technology has the potential to replace many conventional manufacturing processes, enable the development of new industry practices, and transform the entire manufacturing enterprise. Metal Additive Manufacturing provides an up-to-date review

Performance Assessment for Process Monitoring and Fault Detection Methods

Performance Assessment for Process Monitoring and Fault Detection Methods
  • Author : Kai Zhang
  • Publisher : Springer
  • Release : 04 October 2016
GET THIS BOOK Performance Assessment for Process Monitoring and Fault Detection Methods

The objective of Kai Zhang and his research is to assess the existing process monitoring and fault detection (PM-FD) methods. His aim is to provide suggestions and guidance for choosing appropriate PM-FD methods, because the performance assessment study for PM-FD methods has become an area of interest in both academics and industry. The author first compares basic FD statistics, and then assesses different PM-FD methods to monitor the key performance indicators of static processes, steady-state dynamic processes and general dynamic

Data driven Detection and Diagnosis of Faults in Traction Systems of High speed Trains

Data driven Detection and Diagnosis of Faults in Traction Systems of High speed Trains
  • Author : Hongtian Chen,Bin Jiang,Ningyun Lu,Wen Chen
  • Publisher : Springer Nature
  • Release : 25 April 2020
GET THIS BOOK Data driven Detection and Diagnosis of Faults in Traction Systems of High speed Trains

This book addresses the needs of researchers and practitioners in the field of high-speed trains, especially those whose work involves safety and reliability issues in traction systems. It will appeal to researchers and graduate students at institutions of higher learning, research labs, and in the industrial R&D sector, catering to a readership from a broad range of disciplines including intelligent transportation, electrical engineering, mechanical engineering, chemical engineering, the biological sciences and engineering, economics, ecology, and the mathematical sciences.

Advances in Production Management Systems Production Management for Data Driven Intelligent Collaborative and Sustainable Manufacturing

Advances in Production Management Systems  Production Management for Data Driven  Intelligent  Collaborative  and Sustainable Manufacturing
  • Author : Ilkyeong Moon,Gyu M. Lee,Jinwoo Park,Dimitris Kiritsis,Gregor von Cieminski
  • Publisher : Springer
  • Release : 24 August 2018
GET THIS BOOK Advances in Production Management Systems Production Management for Data Driven Intelligent Collaborative and Sustainable Manufacturing

The two-volume set IFIP AICT 535 and 536 constitutes the refereed proceedings of the International IFIP WG 5.7 Conference on Advances in Production Management Systems, APMS 2018, held in Seoul, South Korea, in August 2018. The 129 revised full papers presented were carefully reviewed and selected from 149 submissions. They are organized in the following topical sections: lean and green manufacturing; operations management in engineer-to-order manufacturing; product-service systems, customer-driven innovation and value co-creation; collaborative networks; smart production for mass customization; global supply chain management; knowledge based production

Risk Reliability and Safety Innovating Theory and Practice

Risk  Reliability and Safety  Innovating Theory and Practice
  • Author : Lesley Walls,Matthew Revie,Tim Bedford
  • Publisher : CRC Press
  • Release : 25 November 2016
GET THIS BOOK Risk Reliability and Safety Innovating Theory and Practice

Risk, Reliability and Safety contains papers describing innovations in theory and practice contributed to the scientific programme of the European Safety and Reliability conference (ESREL 2016), held at the University of Strathclyde in Glasgow, Scotland (25—29 September 2016). Authors include scientists, academics, practitioners, regulators and other key individuals with expertise and experience relevant to specific areas. Papers include domain specific applications as well as general modelling methods. Papers cover evaluation of contemporary solutions, exploration of future challenges, and exposition of concepts, methods and