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, 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

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  • Author : Fouzi Harrou
  • Publisher : Elsevier
  • Pages : 328 pages
  • ISBN : 0128193662
  • Rating : 4/5 from 21 reviews
CLICK HERE TO GET THIS BOOKStatistical Process Monitoring Using Advanced Data Driven and Deep Learning Approaches

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 BOOKStatistical 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 BOOKStatistical 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 BOOKRoad 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

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 BOOKUnsupervised 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

Data-Driven and Model-Based Methods for Fault Detection and Diagnosis

Data-Driven and Model-Based Methods for Fault Detection and Diagnosis
  • Author : Majdi Mansouri,Mohamed-Faouzi Harkat,Hazem Nounou,Mohamed N. Nounou
  • Publisher : Elsevier
  • Release : 05 February 2020
GET THIS BOOKData-Driven and Model-Based Methods for Fault Detection and Diagnosis

Data-Driven and Model-Based Methods for Fault Detection and Diagnosis covers techniques that improve the quality of fault detection and enhance monitoring through chemical and environmental processes. The book provides both the theoretical framework and technical solutions. It starts with a review of relevant literature, proceeds with a detailed description of developed methodologies, and then discusses the results of developed methodologies, and ends with major conclusions reached from the analysis of simulation and experimental studies. The book is an indispensable resource

Data-Driven Modeling for Additive Manufacturing of Metals

Data-Driven Modeling for Additive Manufacturing of Metals
  • Author : National Academies of Sciences, Engineering, and Medicine,Division on Engineering and Physical Sciences,National Materials and Manufacturing Board,Board on Mathematical Sciences and Analytics
  • Publisher : National Academies Press
  • Release : 09 October 2019
GET THIS BOOKData-Driven Modeling for Additive Manufacturing of Metals

Additive manufacturing (AM) is the process in which a three-dimensional object is built by adding subsequent layers of materials. AM enables novel material compositions and shapes, often without the need for specialized tooling. This technology has the potential to revolutionize how mechanical parts are created, tested, and certified. However, successful real-time AM design requires the integration of complex systems and often necessitates expertise across domains. Simulation-based design approaches, such as those applied in engineering product design and material design, have

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 BOOKPerformance 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

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 : 24 January 2022
GET THIS BOOKAdvanced 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

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 BOOKMachine 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

Multivariate Statistical Process Control

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

Fault Diagnosis and Detection

Fault Diagnosis and Detection
  • Author : Mustafa Demetgul,Muhammet Ünal
  • Publisher : BoD – Books on Demand
  • Release : 31 May 2017
GET THIS BOOKFault Diagnosis and Detection

Mass production companies have become obliged to reduce their production costs and sell more products with lower profit margins in order to survive in competitive market conditions. The complexity and automation level of machinery are continuously growing. This development calls for some of the most critical issues that are reliability and dependability of automatic systems. In the future, machines will be monitored remotely, and computer-aided techniques will be employed to detect faults in the future, and also there will be

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 BOOKData-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.

Advanced Mathematical And Computational Tools In Metrology And Testing Xii

Advanced Mathematical And Computational Tools In Metrology And Testing Xii
  • Author : Franco Pavese,Alistair B Forbes,Nien Fan Zhang,Anna G Chunovkina
  • Publisher : World Scientific
  • Release : 13 January 2022
GET THIS BOOKAdvanced Mathematical And Computational Tools In Metrology And Testing Xii

This volume contains original, refereed contributions by researchers from national metrology institutes, universities and laboratories across the world involved in metrology and testing. The volume has been produced by the International Measurement Confederation Technical Committee 21, Mathematical Tools for Measurements and is the twelfth in the series. The papers cover topics in numerical analysis and computational tools, statistical inference, regression, calibration and metrological traceability, computer science and data provenance, and describe applications in a wide range of application domains. This volume

Big Data Application in Power Systems

Big Data Application in Power Systems
  • Author : Reza Arghandeh,Yuxun Zhou
  • Publisher : Elsevier
  • Release : 27 November 2017
GET THIS BOOKBig Data Application in Power Systems

Big Data Application in Power Systems brings together experts from academia, industry and regulatory agencies who share their understanding and discuss the big data analytics applications for power systems diagnostics, operation and control. Recent developments in monitoring systems and sensor networks dramatically increase the variety, volume and velocity of measurement data in electricity transmission and distribution level. The book focuses on rapidly modernizing monitoring systems, measurement data availability, big data handling and machine learning approaches to process high dimensional, heterogeneous

Fault Detection and Diagnosis in Industrial Systems

Fault Detection and Diagnosis in Industrial Systems
  • Author : L.H. Chiang,E.L. Russell,R.D. Braatz
  • Publisher : Springer Science & Business Media
  • Release : 06 December 2012
GET THIS BOOKFault Detection and Diagnosis in Industrial Systems

Early and accurate fault detection and diagnosis for modern chemical plants can minimize downtime, increase the safety of plant operations, and reduce manufacturing costs. This book presents the theoretical background and practical techniques for data-driven process monitoring. It demonstrates the application of all the data-driven process monitoring techniques to the Tennessee Eastman plant simulator, and looks at the strengths and weaknesses of each approach in detail. A plant simulator and problems allow readers to apply process monitoring techniques.