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

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 andDeep 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

1. Introduction2. Linear Latent Variable Regression (LVR)-Based Process Monitoring3. Fault Isolation4. Nonlinear latent variable regression methods5. Multiscale latent variable regression-based process monitoring methods6. Unsupervised deep learning-based process monitoring methods7. Unsupervised recurrent deep learning schemes for process monitoring 8. Case studies9. Conclusions and future perspectives