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

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

Book2020

Authors:

Majdi Mansouri, Mohamed-Faouzi Harkat, ... Mohamed N. Nounou

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

Book2020

 

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

Authors:

Majdi Mansouri, Mohamed-Faouzi Harkat, ... Mohamed N. Nounou

About the book

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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 envi ... read full description

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  2. Book chapterAbstract only

    Chapter 1 - Introduction

    Pages 1-10

  3. Book chapterAbstract only

    Chapter 2 - PCA and PLS-based generalized likelihood ratio for fault detection

    Pages 11-48

  4. Book chapterAbstract only

    Chapter 3 - Kernel PCA- and Kernel PLS-based generalized likelihood ratio tests for fault detection

    Pages 49-77

  5. Book chapterAbstract only

    Chapter 4 - Linear and nonlinear multiscale latent variable-based generalized likelihood ratio for fault detection

    Pages 79-133

  6. Book chapterAbstract only

    Chapter 5 - Linear and nonlinear interval latent variable approaches for fault detection

    Pages 135-219

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    Chapter 6 - Model-based approaches for fault detection

    Pages 221-258

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    Chapter 7 - Conclusions and perspectives

    Pages 259-278

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    Appendix

    Pages 279-287

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    Index

    Pages 289-295

About the book

Description

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 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.

Key Features

  • 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
  • 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

Details

ISBN

978-0-12-819164-4

Language

English

Published

2020

Copyright

Copyright © 2020 Elsevier Inc. All rights reserved.

Imprint

Elsevier

Authors

Majdi Mansouri

Mohamed-Faouzi Harkat

Hazem N. Nounou

Mohamed N. Nounou