Statistical Process Monitoring Using Advanced Data-Driven and Deep Learning Approaches : Theory and Practical Applications

Fouzi Harrou

ISBN 10: 0128193654 / ISBN 13: 9780128193655

Verlag: Elsevier Science & Technology, Elsevier, 2020

Beschreibung

nach der Bestellung gedruckt Neuware - Printed after ordering - 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.

Bestandsnummer des Verkäufers 9780128193655

Bibliografische Details

• Titel: Statistical Process Monitoring Using Advanced Data-Driven and Deep Learning Approaches : Theory and Practical Applications
• Verlag: Elsevier Science & Technology, Elsevier
• Erscheinungsjahr: 2020
• Sprache: Englisch
• ISBN-10: 0128193654
• ISBN-13: 9780128193655
• Einband: Taschenbuch
• Zustand: Neu
• Abmessungen: 229B x 152 H x 15 T Millimeter Breite: 229 mm, Höhe: 152 mm, Tiefe: 15 mm
• Gewicht: 520 Gramm

Über diesen Titel

Inhaltsangabe

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

Über die Autorinnen und Autoren

Fouzi Harrou received the M.Sc. degree in telecommunications and networking from the University of Paris VI, France, and the Ph.D. degree in systems optimization and security from the University of Technology of Troyes (UTT), France. He was an Assistant Professor with UTT for one year and with the Institute of Automotive and Transport Engineering, Nevers, France, for one year. He was also a Postdoctoral Research Associate with the Systems Modeling and Dependability Laboratory, UTT, for one year. He was a Research Scientist with the Chemical Engineering Department, Texas A&M University at Qatar, Doha, Qatar, for three years. He is actually a Research Scientist with the Division of Computer, Electrical and Mathematical Sciences and Engineering, King Abdullah University of Science and Technology. He is the author of more than 150 refereed journals and conference publications and book chapters. He is co-author of the book "Statistical Process Monitoring Using Advanced Data-Driven and Deep Learning Approaches: Theory and Practical Applications" (Elsevier, 2020). Dr. Harrou’s research interests are in the area of statistical anomaly detection and process monitoring with a particular emphasis on data-driven, machine learning/deep learning methods. The algorithms developed in Dr. Harrou’s research are utilized in many applications to improve the operation of various environmental, chemical, and electrical systems.

Professor Ying Sun received her Ph.D. in Statistics from Texas A&M in 2011 followed by a two-year postdoctoral research position at the Statistical and Applied Mathematical Sciences Institute and at the University of Chicago. She was an Assistant Professor at the Ohio State University for a year before joining KAUST in 2014. At KAUST, Professor Sun established and leads the Environmental Statistics research group which works on developing statistical models and methods for complex data to address important environmental problems. She has made original contributions to environmental statistics, in particular in the areas of spatio-temporal statistics, functional data analysis, visualization, computational statistics, with an exceptionally broad array of applications. Professor Sun won two prestigious awards: the Early Investigator Award in Environmental Statistics presented by the American Statistical Association, and the Abdel El-Shaarawi Young Research Award from the International Environmetrics Society

Professor Amanda Hering obtained her Ph.D. from Texas A&M University in Statistics in 2009. She joined the Department of Applied Mathematics and Statistics at Colorado School of Mines in Golden, Colorado in 2009 as an Assistant Professor and was promoted to Associate Professor in 2016. She joined the Department of Statistical Science at Baylor University in the fall of 2016 as an Associate Professor. Her research interests are in modeling big, multivariate, spatial datasets; developing methods for categorical spatial data; and detecting outliers and faults for process and data control. She works with researchers whose data structures generate new statistical methodologies because either the goals or the size of the data presents a new challenge. She is an Associate Editor of Technometrics, Environmetrics, and Stat. She received the American Statistical Association’s Section on Statistics in the Environment Early Investigator Award in 2017.

Muddu Madakyaru is an Associate professor of Department of Chemical Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, India. He received B.E. degree in Chemical Engineering and M.Tech. in Chemical plant Design from the R.V.C.E and National Institute of Technology Karnataka, India respectively. In the year 2010 he obtained his Ph.D degree in process control from Indian Institute of Technology, Bombay, India. Later he was involved in post-doctoral research at Texas A&M University, Doha, Qatar for four years. His research interests are in advanced process control, including, system identification, Fault detection and diagnosis, model predictive control and latent variable regression modeling using wavelets. He has published more than 20 papers in peer reviewed journals and 10 international conference proceedings papers. He is fellow of Institution of Engineers (India), Life Member of Indian Society for Technical Education and Indian Society of Systems for Science and Engineering (ISSE).

Dr. Abdelkader Dairi received the Engineer degree in computer science from the University of Oran 1 Ahmed Ben Bella, Algeria, in 2003. He also received the Magister degree in computer science from the National Polytechnic School of Oran, Algeria, in 2006. From 2007 to 2013 he was a senior Oracle database administrator (DBA) and enterprise resource planning (ERP) manager. He has over 20 years of programming experience in different languages and environments. In 2018 he received the Ph.D. degree in computer sciences from Ben Bella Oran1 University. His research interests include deep learning approach for autonomous robot navigation, computer vision, image processing, and mobile robotics.