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

  • ID: 4829357
  • Book
  • 412 Pages
  • Elsevier Science and Technology
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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
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1. Introduction
2. Linear latent variable approaches for fault detection
3. Nonlinear latent variable approaches for fault detection
4. Multiscale latent variable (MSLV) approaches for fault detection
5. Interval latent variable (ILV) approaches for fault detection
6. Model based approaches for fault detection
7. Conclusions and Perspectives
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Mansouri, Majdi
Dr. Majdi Mansouri 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 and monitoring, which aim to improve process operations and enhance the data validation.
Dr. Majdi Mansouri is the author of more than 145 refereed journal and conference publications and book
chapters, and has worked on several projects as a lead principal investigator (LPI), principal investigator (PI)
as well as a researcher. In December, 2019, he received the degree of HDR (Accreditation To Supervise
Research) of Electrical Engineering from University of Orleans in France. Dr. Mansouri is a member of IEEE.
Harkat, Mohamed-Faouzi
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.
Nounou, Hazem N.
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 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, monitoring and fault detection, intelligent and adaptive control, control of time-delay systems, system biology, and system identification and estimation. Dr. Nounou is a senior member of IEEE.
Nounou, Mohamed N.
Dr. Mohamed Nounou is a professor of Chemical Engineering at Texas A&M University-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 the Ohio State University, Columbus, in 1997 and 2000, respectively, all in chemical engineering. From 2000 to 2002, he was with PDF Solutions, a consulting company for the semiconductor industry, in San Jose, CA. In 2002, he joined the Department of Chemical and Petroleum Engineering at the United Arab Emirates University. In 2006, he joined the Chemical Engineering Program at Texas A&M University at Qatar, where he is currently a professor. He has received research funding over $5M and published more than 190 refereed journal and conference papers and book chapters. He also served as an associate editor and in technical committees of several international journals and conferences. His research interests include process modeling, monitoring, estimation, system biology, and intelligent control. He is a senior member of the American Institute of Chemical Engineers (AIChE) and a senior member of the Institute of Electrical and Electronics Engineers (IEEE).
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