Fouzi Harrou, Ying Sun, Amanda S. Hering, Muddu Madakyaru, Abdelkader Dairi

Statistical Process Monitoring Using Advanced Data-Driven and Deep Learning Approaches (eBook, ePUB)

Theory and Practical Applications

• Format: ePub

Produktbeschreibung

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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Produktdetails

• Verlag: Elsevier Science & Techn.
• Seitenzahl: 328
• Erscheinungstermin: 3. Juli 2020
• Englisch
• ISBN-13: 9780128193662
• Artikelnr.: 61688100

Autorenporträt

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 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.

Inhaltsangabe

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