Springer Monitoring Multimode Continuous Processes: A Data-Driven Approach: 309 (Studies in Systems, Decision and Control, 309)

Product Description This book examines recent methods for data-driven fault diagnosis of multimode continuous processes. It formalizes, generalizes, and systematically presents the main concepts, and approaches required to design fault diagnosis methods for multimode continuous processes. The book provides both theoretical and practical tools to help readers address the fault diagnosis problem by drawing data-driven methods from at least three different areas: statistics, unsupervised, and supervised learning. From the Back Cover This book examines recent methods for data-driven fault diagnosis of multimode continuous processes. It formalizes, generalizes, and systematically presents the main concepts, and approaches required to design fault diagnosis methods for multimode continuous processes. The book provides both theoretical and practical tools to help readers address the fault diagnosis problem by drawing data-driven methods from at least three different areas: statistics, unsupervised, and supervised learning. About the Author Marcos Quiñones Grueiro is graduated Summa Cum Laude of Automation in 2012 (Universidad Tecnológica de la Habana “José Antonio Echeverría”). He obtained the M.S. degree in Industrial Informatics and Automation, and in 2017 the Ph.D. degree in Technical Sciences with specialization in Automation. Since 2012 he is a Professor at the Automation and Computing Department, Universidad Tecnológica de La Habana José Antonio Echeverría, CUJAE, Cuba. He won the Excellence Scholarship from Mexican Government for Foreign Students in 2016, and made a one-year exchange program in Mexico (Universidad Nacional Autónoma de Mexico, UNAM) from 2016 to 2017. He authored 10 journal papers, 3 book chapters, and 15 full conference papers. His research interest areas are process safety, fault detection and diagnosis of industrial processes, leak detection and location in water distribution networks, and reinforcement learning with applications to control.   Orestes Llanes Santiago obtained the Degree of Electrical Engineer from Instituto Superior Politécnico José A. Echeverría, CUJAE, in La Habana-Cuba in 1981. He pursued graduate studies from 1989 to 1994 at the Universidad de Los Andes, Mérida-Venezuela, where he obtained the degree of Master of Science in Control Engineering in 1990, and the degree of Doctor in Applied Sciences in 1994. He is currently a Full Professor and Researcher in the Automation and Computing Department of CUJAE, where he has also held the positions of Head of the Automation and Computing Department from 1996 to 2002, Dean of the Electrical Engineering Faculty from 2002 to 2007, Director of Postgraduates Studies in 2008, and Vice Rector of Research and Postgraduate Studies from 2009 to 2015. Also, he is currently the Coordinator of the Master Program in Mathematical Modeling applied to Engineering, Coordinator of the Doctoral Program in Automation, and the Head of the Research Laboratory in Parameter Estimation and Fault Diagnosis in Industrial Systems. He was the General Coordinator of the National Member Organization of Cuba in IFAC (Red de Automática de Cuba) since 1998 to 2016. Furthermore, he is a Titular Member of the Sciences Academy of Cuba since 2012, and currently Vice-Coordinator of the Technical Sciences Section of this Institution. He has authored, or coauthored, 163 full conference papers, 114 full technical papers,  27 book chapters and 3 books (2 as editor/organizer). His areas of interest are fault diagnosis in industrial systems, nonlinear control, inverse problems, and computational intelligence with applications to control.   Antônio José da Silva Neto is a Mechanical / Nuclear Engineer (Universidade Federal do Rio de Janeiro - UFRJ, Brazil, 1983), M.Sc. in Nuclear Engineering (UFRJ, 1989), and Ph.D. in Mechanical Engineering, with a minor in Computational Mathematics (North Carolina State University, USA, 1993). He worked as engineer/researcher