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 : 0128193654
  • 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 : 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

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,

Data Analytics Applied to the Mining Industry

Data Analytics Applied to the Mining Industry
  • Author : Ali Soofastaei
  • Publisher : CRC Press
  • Release : 13 November 2020
GET THIS BOOKData Analytics Applied to the Mining Industry

Data Analytics Applied to the Mining Industry describes the key challenges facing the mining sector as it transforms into a digital industry able to fully exploit process automation, remote operation centers, autonomous equipment and the opportunities offered by the industrial internet of things. It provides guidelines on how data needs to be collected, stored and managed to enable the different advanced data analytics methods to be applied effectively in practice, through use of case studies, and worked examples. Aimed at

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 N. 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

Lees' Process Safety Essentials

Lees' Process Safety Essentials
  • Author : Sam Mannan
  • Publisher : Butterworth-Heinemann
  • Release : 12 November 2013
GET THIS BOOKLees' Process Safety Essentials

Lees' Process Safety Essentials is a single-volume digest presenting the critical, practical content from Lees' Loss Prevention for day-to-day use and reference. It is portable, authoritative, affordable, and accessible — ideal for those on the move, students, and individuals without access to the full three volumes of Lees'. This book provides a convenient summary of the main content of Lees', primarily drawn from the hazard identification, assessment, and control content of volumes one and two. Users can access Essentials for day-to-day

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.

Advanced Mapping of Environmental Data

Advanced Mapping of Environmental Data
  • Author : Mikhail Kanevski
  • Publisher : John Wiley & Sons
  • Release : 10 May 2013
GET THIS BOOKAdvanced Mapping of Environmental Data

This book combines geostatistics and global mapping systems to present an up-to-the-minute study of environmental data. Featuring numerous case studies, the reference covers model dependent (geostatistics) and data driven (machine learning algorithms) analysis techniques such as risk mapping, conditional stochastic simulations, descriptions of spatial uncertainty and variability, artificial neural networks (ANN) for spatial data, Bayesian maximum entropy (BME), and more.

Digitalization and Analytics for Smart Plant Performance

Digitalization and Analytics for Smart Plant Performance
  • Author : Frank (Xin X.) Zhu
  • Publisher : John Wiley & Sons
  • Release : 06 April 2021
GET THIS BOOKDigitalization and Analytics for Smart Plant Performance

This book addresses the topic of integrated digitization of plants on an objective basis and in a holistic manner by sharing data, applying analytics tools and integrating workflows via pertinent examples from industry. It begins with an evaluation of current performance management practices and an overview of the need for a "Connected Plant" via digitalization followed by sections on "Connected Assets: Improve Reliability and Utilization," "Connected Processes: Optimize Performance and Economic Margin " and "Connected People: Digitalizing the Workforce and Workflows

Computational Science and Its Applications – ICCSA 2019

Computational Science and Its Applications – ICCSA 2019
  • Author : Sanjay Misra,Osvaldo Gervasi,Beniamino Murgante,Elena Stankova,Vladimir Korkhov,Carmelo Torre,Ana Maria A.C. Rocha,David Taniar,Bernady O. Apduhan,Eufemia Tarantino
  • Publisher : Springer
  • Release : 28 June 2019
GET THIS BOOKComputational Science and Its Applications – ICCSA 2019

The six volumes LNCS 11619-11624 constitute the refereed proceedings of the 19th International Conference on Computational Science and Its Applications, ICCSA 2019, held in Saint Petersburg, Russia, in July 2019. The 64 full papers, 10 short papers and 259 workshop papers presented were carefully reviewed and selected form numerous submissions. The 64 full papers are organized in the following five general tracks: computational methods, algorithms and scientific applications; high performance computing and networks; geometric modeling, graphics and visualization; advanced and emerging applications; and information systems and

Green Chemical Engineering

Green Chemical Engineering
  • Author : Anonim
  • Publisher : John Wiley & Sons
  • Release : 19 July 2018
GET THIS BOOKGreen Chemical Engineering

Green chemistry and chemical engineering belong together and this twelth volume in the successful Handbook of Green Chemistry series represents the perfect one-stop reference on the topic. Written by an international team of specialists with each section edited by international leading experts, this book provides first-hand insights into the field, covering chemical engineering process design, innovations in unit operations and manufacturing, biorefining and much more besides. An indispensable source for every chemical engineer in industry and academia.