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Fault Detection and Diagnosis in Industrial Systems

  • Leo H. Chiang
  • Evan L. Russell
  • Richard D. Braatz

Table of contents

  1. Front Matter
    Pages i-xiv
  2. Introduction

    1. Front Matter
      Pages 1-1
    2. Leo H. Chiang, Evan L. Russell, Richard D. Braatz
      Pages 3-11
  3. Background

    1. Front Matter
      Pages 13-13
    2. Leo H. Chiang, Evan L. Russell, Richard D. Braatz
      Pages 15-25
    3. Leo H. Chiang, Evan L. Russell, Richard D. Braatz
      Pages 27-31
  4. Data-driven Methods

    1. Front Matter
      Pages 33-33
    2. Leo H. Chiang, Evan L. Russell, Richard D. Braatz
      Pages 35-55
    3. Leo H. Chiang, Evan L. Russell, Richard D. Braatz
      Pages 57-70
    4. Leo H. Chiang, Evan L. Russell, Richard D. Braatz
      Pages 71-84
    5. Leo H. Chiang, Evan L. Russell, Richard D. Braatz
      Pages 85-99
  5. Application

    1. Front Matter
      Pages 101-101
    2. Leo H. Chiang, Evan L. Russell, Richard D. Braatz
      Pages 103-112
    3. Leo H. Chiang, Evan L. Russell, Richard D. Braatz
      Pages 113-120
    4. Leo H. Chiang, Evan L. Russell, Richard D. Braatz
      Pages 121-169
  6. Analytical and Knowledge-based Methods

    1. Front Matter
      Pages 171-171
    2. Leo H. Chiang, Evan L. Russell, Richard D. Braatz
      Pages 173-222
    3. Leo H. Chiang, Evan L. Russell, Richard D. Braatz
      Pages 223-254
  7. Back Matter
    Pages 255-279

About this book

Introduction

Early and accurate fault detection and diagnosis for modern chemical plants can minimise downtime, increase the safety of plant operations, and reduce manufacturing costs. The process monitoring techniques that have been most effective in practice are based on models constructed almost entirely from process data.
The goal of the book is to present the theoretical background and practical techniques for data-driven process monitoring. Process monitoring techniques presented include: Data-driven methods - principal component analysis, Fisher discriminant analysis, partial least squares and canonical variate analysis; Analytical Methods - parameter estimation, observer-based methods and parity relations; Knowledge-based methods - causal analysis, expert systems and pattern recognition.
The text demonstrates the application of all of the data-driven process monitoring techniques to the Tennessee Eastman plant simulator - demonstrating the strengths and weaknesses of each approach in detail. This aids the reader in selecting the right method for his process application. Plant simulator and homework problems in which students apply the process monitoring techniques to a non-trivial simulated process, and can compare their performance with that obtained in the case studies in the text are included. A number of additional homework problems encourage the reader to implement and obtain a deeper understanding of the techniques. The reader will obtain a background in data-driven techniques for fault detection and diagnosis, including the ability to implement the techniques and to know how to select the right technique for a particular application.

Keywords

chemometrics classification fault detection fault diagnosis multivariate statistics neural network neural networks process monitoring safety

Authors and affiliations

  • Leo H. Chiang
    • 1
  • Evan L. Russell
    • 2
  • Richard D. Braatz
    • 1
  1. 1.Department of Chemical EngineeringUniversity of Illinois at Urbana-ChampaignUrbanaUSA
  2. 2.ExxonMobil Upstream Reasearch CompanyHoustonUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-4471-0347-9
  • Copyright Information Springer-Verlag London Limited 2001
  • Publisher Name Springer, London
  • eBook Packages Springer Book Archive
  • Print ISBN 978-1-85233-327-0
  • Online ISBN 978-1-4471-0347-9
  • Series Print ISSN 1439-2232
  • Series Online ISSN 2510-3814
  • Buy this book on publisher's site