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Statistical Process Monitoring Using Advanced Data-Driven and Deep Learning Approaches - 1st Edition - ISBN: 9780128193655, 9780128193662

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

1st Edition

Theory and Practical Applications

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Authors: Fouzi Harrou Ying Sun Amanda Hering Muddu Madakyaru abdelkader Dairi
Paperback ISBN: 9780128193655
eBook ISBN: 9780128193662
Imprint: Elsevier
Published Date: 4th July 2020
Page Count: 328
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Description

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.

Key Features

  • 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

Readership

Practitioners and researchers in academia and industry working in chemical and environmental engineering

Table of Contents

1. Introduction

2. Linear Latent Variable Regression (LVR)-Based Process Monitoring

3. Fault Isolation

4. Nonlinear latent variable regression methods

5. Multiscale latent variable regression-based process monitoring methods

6. Unsupervised deep learning-based process monitoring methods

7. Unsupervised recurrent deep learning schemes for process monitoring

8. Case studies

9. Conclusions and future perspectives

Details

No. of pages:
328
Language:
English
Copyright:
© Elsevier 2021
Published:
4th July 2020
Imprint:
Elsevier
Paperback ISBN:
9780128193655
eBook ISBN:
9780128193662

About the Authors

Fouzi Harrou

Dr. Fouzi Harrou received the Ph.D. degree in systems optimization and security from the University of Technology of Troyes (UTT), France. He was an Assistant Professor at UTT for one year and was an Assistant Professor at the Institute of Automotive and Transport Engineering, Nevers, France, for one-year. He was also a Post-Doctoral Research Associate at the Systems Modelling 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. Since 2015, he has been Post-Doctoral Fellow with the Division of Computer, Electrical, and Mathematical Sciences and Engineering, King Abdullah University of Science and Technology. Dr. Fouzi Harrou is the author of more than 100 refereed journal and conference publications and book chapters. His current research interests include statistical decision theory and its applications, fault detection and diagnosis, and signal processing

Affiliations and Expertise

King Abdullah University of Science and Technology, Saudi Arabia

Ying Sun

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

Affiliations and Expertise

King Abdullah University of Science and Technology, Saudi Arabia

Amanda Hering

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.

Affiliations and Expertise

Baylor University, Dept of Statistical Sciences, Waco, Texas, USA

Muddu Madakyaru

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).

Affiliations and Expertise

Department of Chemical Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India

abdelkader Dairi

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.

Affiliations and Expertise

Computer Science Department, University of Oran 1 Ahmed Ben Bella, Oran, Algeria

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