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

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.
- Outlines latent variable based hypothesis testing fault detection techniques to enhance monitoring processes represented by linear or nonlinear input-space models (such as PCA) or input-output models (such as PLS)- Explains multiscale latent variable based hypothesis testing fault detection techniques using multiscale representation to help deal with uncertainty in the data and minimize its effect on fault detection- Includes interval PCA (IPCA) and interval PLS (IPLS) fault detection methods to enhance the quality of fault detection- Provides model-based detection techniques for the improvement of monitoring processes using state estimation-based fault detection approaches- Demonstrates the effectiveness of the proposed strategies by conducting simulation and experimental studies on synthetic data

Dr. Majdi Mansouri is an Associate Professor, at the Department of Electrical and Computer Engineering, Sultan Qaboos University, in the Sultanate of Oman. A Senior Member of the IEEE, he received this Ph.D. degree in electrical engineering from the University of Technology of Troyes (UTT), France, in 2011, and the H.D.R. degree (accreditation to supervise research) in electrical engineering from the University of Orleans, France, in 2019. From 2011 to 2024, he held different research positions at Texas A&M University at Qatar, in Doha. Since September 2024, he has been with Sultan Qaboos University as an Associate Professor. Dr. Mansouri has authored more than 250 publications, as well as the book 'Data-Driven and Model-Based Methods for Fault Detection and Diagnosis' (Elsevier, 2020). His research interests include the development of model-based, data-driven, and AI-based techniques for fault detection and diagnosis.is a member of IEEE.Dr. Mohamed Faouzi Harkat is a Professor in the Department of Electronics, at Badji Mokhtar - Annaba University, Algeria, which he joined in 2004. He received his Ph.D. degree from the Institut National Polytechnique de Lorraine (INPL), France, in 2003. From 2002 to 2004, he was an Assistant Professor at the School of Engineering Sciences and Technologies of Nancy (ESSTIN), France. Prof. Harkat has over twenty years of research and practical experience in systems engineering and process monitoring. He is the author of more than 100 refereed journal and conference publications, as well as book chapters, and has served as an Associate Editor and in technical committees of several international journals and conferences.Dr. Hazem N. Nounou is a Professor and Program Chair of the Electrical Engineering Program in the College of Science and Engineering at Hamad Bin Khalifa University (HBKU). Prior to joining HBKU, he was the Senior Associate Dean for Undergraduate Studies and Student Success and Professor of Electrical and Computer Engineering at Texas A&M University at Qatar. 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. He was the holder of Itochu Professorship from 2015-2017. He published more than 200 refereed journal and conference papers and book chapters. He served as an Associate Editor and in technical committees of several international journals and conferences. His research interests include data-based control, intelligent and adaptive control, control of time-delay systems, and system monitoring, identification and estimation. Dr. Nounou is a senior member of IEEE.

1. Introduction2. Linear latent variable approaches for fault detection3. Nonlinear latent variable approaches for fault detection4. Multiscale latent variable (MSLV) approaches for fault detection5. Interval latent variable (ILV) approaches for fault detection6. Model based approaches for fault detection7. Conclusions and Perspectives