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Data-driven Methods for Fault Detection and Diagnosis in Chemical Processes

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

Part of the Advances in Industrial Control book series (AIC)

Table of contents

  1. Front Matter
    Pages I-XIII
  2. Introduction

    1. Front Matter
      Pages 1-1
    2. Evan L. Russell, Leo H. Chiang, Richard D. Braatz
      Pages 3-10
  3. Background

    1. Front Matter
      Pages 11-11
    2. Evan L. Russell, Leo H. Chiang, Richard D. Braatz
      Pages 13-23
    3. Evan L. Russell, Leo H. Chiang, Richard D. Braatz
      Pages 25-29
  4. Methods

    1. Front Matter
      Pages 31-31
    2. Evan L. Russell, Leo H. Chiang, Richard D. Braatz
      Pages 33-52
    3. Evan L. Russell, Leo H. Chiang, Richard D. Braatz
      Pages 53-65
    4. Evan L. Russell, Leo H. Chiang, Richard D. Braatz
      Pages 67-80
    5. Evan L. Russell, Leo H. Chiang, Richard D. Braatz
      Pages 81-95
  5. Application

    1. Front Matter
      Pages 97-97
    2. Evan L. Russell, Leo H. Chiang, Richard D. Braatz
      Pages 99-108
    3. Evan L. Russell, Leo H. Chiang, Richard D. Braatz
      Pages 109-116
    4. Evan L. Russell, Leo H. Chiang, Richard D. Braatz
      Pages 117-165
  6. Other Approaches

    1. Front Matter
      Pages 167-167
    2. Evan L. Russell, Leo H. Chiang, Richard D. Braatz
      Pages 169-174
  7. Back Matter
    Pages 175-192

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: Principal component analysis; Fisher discriminant analysis; Partial least squares; Canonical variate analysis.
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 nontrivial 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

Control Applications Control Engineering Fault Identification Monitoring Multivariate Analysis classification control

Authors and affiliations

  • Evan L. Russell
    • 1
  • Leo H. Chiang
    • 2
  • Richard D. Braatz
    • 2
  1. 1.Exxon Production Research CompanyHoustonUSA
  2. 2.Department of Chemical EngineeringUniversity of Illinois at Urbana-ChampaignUrbanaUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-4471-0409-4
  • Copyright Information Springer-Verlag London Limited 2000
  • Publisher Name Springer, London
  • eBook Packages Springer Book Archive
  • Print ISBN 978-1-4471-1133-7
  • Online ISBN 978-1-4471-0409-4
  • Series Print ISSN 1430-9491
  • Series Online ISSN 2193-1577
  • Buy this book on publisher's site
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