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.

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