(EBook PDF) Data Driven and Model Based Methods for Fault Detection and Diagnosis 1st edition by Majdi Mansouri, Mohamed Faouzi Harkat, HazemNounou, Mohamed Nounou 0128191651 9780128191651 full chapters

Data-Driven and Model-Based Methods for Fault Detection and Diagnosis 1st edition by Majdi Mansouri, Mohamed Faouzi Harkat, Hazem N. Nounou, Mohamed N. Nounou – Ebook PDF Instant Download/DeliveryISBN:  0128191651, 9780128191651 

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Product details:

ISBN-10 :  0128191651

ISBN-13 :  9780128191651

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

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.

Data-Driven and Model-Based Methods for Fault Detection and Diagnosis 1st Table of contents:

Chapter 1: Introduction
Abstract
References
Chapter 2: PCA and PLS-based generalized likelihood ratio for fault detection
Abstract
2.1. PCA and PLS-based generalized likelihood ratio for fault detection
2.2. PLS-based generalized likelihood ratio for fault detection
References
Chapter 3: Kernel PCA- and Kernel PLS-based generalized likelihood ratio tests for fault detection
Abstract
3.1. Kernel PCA-based generalized likelihood ratio test for fault detection
3.2. Kernel PLS-based generalized likelihood ratio test for fault detection
References
Chapter 4: Linear and nonlinear multiscale latent variable-based generalized likelihood ratio for fault detection
Abstract
4.1. Linear multiscale latent variable-based generalized likelihood ratio for fault detection
4.2. Multiscale nonlinear latent variable-based generalized likelihood ratio test for fault detection
References
Chapter 5: Linear and nonlinear interval latent variable approaches for fault detection
Abstract
5.1. Interval latent variable approaches for fault detection
5.2. Interval nonlinear latent variable approaches for fault detection
References
Chapter 6: Model-based approaches for fault detection
Abstract
6.1. Introduction
6.2. State estimation
6.3. Fault detection-based state estimation approaches
6.4. Fault detection-based state estimation approach
6.5. Conclusions
References
Chapter 7: Conclusions and perspectives
Abstract
7.1. Conclusions
7.2. Perspectives and research proposals

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