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 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.