Elsevier Data-Driven and Model-Based Methods for Fault Detection and Diagnosis

Product Description Data-Driven and Model-Based Methods for Fault Detection and Diagnosis covers techniques that improve the quality of fault detection and enhance monitoring through chemical and environmental processes. The book provides both the theoretical framework and technical solutions. It starts with a review of relevant literature, proceeds with a detailed description of developed methodologies, and then discusses the results of developed methodologies, and ends with major conclusions reached from the analysis of simulation and experimental studies. The book is an indispensable resource for researchers in academia and industry and practitioners working in chemical and environmental engineering to do their work safely. Review Comprehensive reference providing enhanced fault detection methods for improving reliable and safe process operation in industry About the Author Dr. Majdi Mansouri received the engineering degree in Electrical Engineering in 2006 from the Higher School of Communication of Tunisia (SUPCOM), Tunisia. He received his master degree of Electrical Engineering from the School of Electronic, Informatique and Radiocommunications in Bordeaux (ENSEIRB), France, in 2008. He received his PhD degree of Electrical Engineering from the University of Technology of Troyes (UTT), France, in 2011. In December 2019, he received the degree of HDR (Accreditation To Supervise Research) of Applied Mathematics and Statistics for Electrical Engineering from University of Orleans in France. He joined the Electrical Engineering Program at Texas A&M University at Qatar, in 2011, where he is currently an Associate Research Scientist. He has over ten years of research and practical experience in systems engineering and signal processing. His work focuses on the utilization of applied mathematics and statistics concepts to develop statistical data and model driven techniques and algorithms for modeling, estimation, fault detection, fault classification, monitoring and diagnosis, which aim to improve process operations and enhance the data validation. Dr. Majdi Mansouri is the author of more than 150 refereed journal and conference publications and book chapters, and has worked on several projects as lead principal investigator (LPI) and principal investigator (PI). Dr. Mansouri is a member of IEEE. Dr. Mohamed-Faouzi HARKAT received his Eng. degree in Automatic control from Annaba University, Algeria in 1996, his Ph.D. degree from Institut National Polytechnique de Lorraine (INPL), France in 2003. He is now Professor in the Department of Electronics at Annaba University, Algeria. His research interests include fault diagnosis, process modelling and monitoring, multivariate statistical approaches and neural networks. Dr. Harkat is the author of more than 100 refereed journal and conference publications and book chapters. Dr. Hazem N. Nounou (SM’08) is a professor in the Electrical and Computer Engineering Program and the Assistant Dean for Academic and Student Services at Texas A&M University at Qatar. From 20015-2017, he was the holder of Itochu Professorship. He received the B.S. degree (magna cum laude) from Texas A&M University, College Station, in 1995, and the M.S. and Ph.D. degrees from Ohio State University, Columbus, in 1997 and 2000, respectively, all in electrical engineering. In 2001, he was a Development Engineer for PDF Solutions, a consulting firm for the semiconductor industry, in San Jose, CA. Then, in 2001, he joined the Department of Electrical Engineering at King Fahd University of Petroleum and Minerals in Dhahran, Saudi Arabia, as an Assistant Professor. In 2002, he moved to the Department of Electrical Engineering, United Arab Emirates University, Al-Ain, UAE. In 2007, he joined the Electrical and Computer Engineering Program at Texas A&M University at Qatar, Doha, Qatar, where he is currently a professor. He published more than 200 refereed journal and conference papers and