Majdi Mansouri, Mohamed Faouzi Harkat, Hazem N. Nounou, Mohamed N. Nounou

Data-Driven and Model-Based Methods for Fault Detection and Diagnosis (eBook, ePUB)

• Format: ePub

Produktbeschreibung

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

---

Dieser Download kann aus rechtlichen Gründen nur mit Rechnungsadresse in A, B, BG, CY, CZ, D, DK, EW, E, FIN, F, GR, HR, H, IRL, I, LT, L, LR, M, NL, PL, P, R, S, SLO, SK ausgeliefert werden.

Produktdetails

• Verlag: Elsevier Science & Techn.
• Seitenzahl: 322
• Erscheinungstermin: 5. Februar 2020
• Englisch
• ISBN-13: 9780128191651
• Artikelnr.: 61687837

Autorenporträt

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 Nounou is currently a Professor of electrical and computer engineering with Texas A&M University at Qatar. A Senior Member of the IEEE, he has over 19 years of academic and industrial experience, particularly in research on control systems, database control, system identification and estimation, fault detection, and system biology. He has been awarded several NPRP research projects in these areas, and has successfully served as the Lead PI or a PI on 5 QNRF projects. Prof. Nounou has published more than 200 refereed journal and conference papers, as well as book chapters. He has served as an Associate Editor on technical committees for several international journals and conferences.Dr. Mohamed Nounou is currently a Professor of chemical engineering with TAMU-Texas A&M University at Qatar. A Senior Member of the IEEE, he has more than 19 years of combined academic and industrial experience. He has published more than 200 refereed journal and conference publications and book chapters. He has successfully served as the Lead PI or a PI on several QNRF projects (6 NPRP projects and 3 UREP projects). Prof. Nounou's research interests include systems engineering and control, with emphasis on process modelling, monitoring, and estimation. He is also a Senior Member of the American Institute of Chemical Engineers (AIChE).