Hazem N. Nounou – författare

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

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

Advances in Data-Driven Modeling, Fault Detection, and Fault Identification: Applications to Chemical Processes presents a comprehensive collection of research focused on data-driven modeling techniques for robust modeling, fault detection, and fault identification in chemical processes.This accessible guide caters to both academic and industrial researchers seeking to enhance their work with data-driven methodologies. The book begins with an overview of key methods, emphasizing their significance in research and industry applications. Chapters delve into various chemical processes, such as the Tennessee Eastman Process and a Fischer-Tropsch bench scale setup, to validate and compare the discussed techniques. The content is organized into three main categories:Basic and advanced robust empirical techniquesProminent empirical statistical charts for detecting faults in multivariate systemsConventional and novel, multiclass classification, machine-learning techniques for accurately distinguishing between different fault types in batch or real-time scenariosWhether a researcher or practitioner, this book is an essential resource for leveraging data-driven approaches in chemical engineering fields.Seamlessly bridges the gap between experts and beginners by offering in-depth mathematical formulations for advanced users and simplified explanations for newcomers, ensuring clarity and comprehension for allOffers step-by-step instructions for optimizing and tuning empirical methods toward specific goals, enabling users to replicate and validate results effectivelyDelivers targeted advice on the optimal use of each technique, empowering users to quickly harness the full potential of data-driven methods without the need for trial and error

Advances in Data-Driven Modeling, Fault Detection, and Fault Identification: Applications to Chemical Processes presents a comprehensive collection of research focused on data-driven modeling techniques for robust modeling, fault detection, and fault identification in chemical processes.This accessible guide caters to both academic and industrial researchers seeking to enhance their work with data-driven methodologies. The book begins with an overview of key methods, emphasizing their significance in research and industry applications. Chapters delve into various chemical processes, such as the Tennessee Eastman Process and a Fischer-Tropsch bench scale setup, to validate and compare the discussed techniques. The content is organized into three main categories:- Basic and advanced robust empirical techniques- Prominent empirical statistical charts for detecting faults in multivariate systems- Conventional and novel, multiclass classification, machine-learning techniques for accurately distinguishing between different fault types in batch or real-time scenariosWhether a researcher or practitioner, this book is an essential resource for leveraging data-driven approaches in chemical engineering fields. - Seamlessly bridges the gap between experts and beginners by offering in-depth mathematical formulations for advanced users and simplified explanations for newcomers, ensuring clarity and comprehension for all- Offers step-by-step instructions for optimizing and tuning empirical methods toward specific goals, enabling users to replicate and validate results effectively- Delivers targeted advice on the optimal use of each technique, empowering users to quickly harness the full potential of data-driven methods without the need for trial and error

Intelligent Fault Detection and Diagnosis Techniques for Monitoring Wind and Solar Energy Systems provides innovative solutions for fault detection and diagnosis in renewable energy systems. By leveraging advanced AI-based techniques such as deep learning, multiscale representation, and statistical analysis, this book aims to enhance system reliability, performance, and cost-efficiency. Readers will gain insights into the fundamentals of FDD processes tailored for photovoltaic and wind turbine operations. The book delves into data preprocessing techniques, feature extraction and selection methods, and optimization of deep learning models.It also includes case studies and explores future directions for AI and machine learning in renewable energy, making it valuable for researchers, engineers, and policy makers.Provides comprehensive methodologies for fault detection and diagnosis (FDD) that integrate AI with multiscale representation and statistical analysisIncludes advanced feature extraction and selection techniques, helping readers to identify the most relevant features for accurate fault diagnosis while reducing model complexityPresents guidelines for data pre-processing, model optimization, and enhanced decision-making frameworks that leverage adaptive control strategies, enabling improved accuracy and efficiency