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Statistical Monitoring of Complex Multivariate Processes: With Applications in Industrial Process Control

Book Description

The development and application of multivariate statistical techniques in process monitoring has gained substantial interest over the past two decades in academia and industry alike. Initially developed for monitoring and fault diagnosis in complex systems, such techniques have been refined and applied in various engineering areas, for example mechanical and manufacturing, chemical, electrical and electronic, and power engineering. The recipe for the tremendous interest in multivariate statistical techniques lies in its simplicity and adaptability for developing monitoring applications. In contrast, competitive model, signal or knowledge based techniques showed their potential only whenever cost-benefit economics have justified the required effort in developing applications.

Statistical Monitoring of Complex Multivariate Processes presents recent advances in statistics based process monitoring, explaining how these processes can now be used in areas such as mechanical and manufacturing engineering for example, in addition to the traditional chemical industry.

This book:

  • Contains a detailed theoretical background of the component technology.

  • Brings together a large body of work to address the field's drawbacks, and develops methods for their improvement.

  • Details cross-disciplinary utilization, exemplified by examples in chemical, mechanical and manufacturing engineering.

  • Presents real life industrial applications, outlining deficiencies in the methodology and how to address them.

  • Includes numerous examples, tutorial questions and homework assignments in the form of individual and team-based projects, to enhance the learning experience.

  • Features a supplementary website including Matlab algorithms and data sets.

This book provides a timely reference text to the rapidly evolving area of multivariate statistical analysis for academics, advanced level students, and practitioners alike.

Table of Contents

  1. Cover
  2. Series Page
  3. Title Page
  4. Copyright
  5. Dedication
  6. Preface
  7. Acknowledgements
  8. Abbreviations
  9. Symbols
  10. Nomenclature
  11. Introduction
  12. Part I: Fundamentals of Multivariate Statistical Process Control
    1. Chapter 1: Motivation for multivariate statistical process control
      1. 1.1 Summary of statistical process control
      2. 1.2 Why multivariate statistical process control
      3. 1.3 Tutorial session
    2. Chapter 2: Multivariate data modeling methods
      1. 2.1 Principal component analysis
      2. 2.2 Partial least squares
      3. 2.3 Maximum redundancy partial least squares
      4. 2.4 Estimating the number of source signals
      5. 2.5 Tutorial Session
    3. Chapter 3: Process monitoring charts
      1. 3.1 Fault detection
      2. 3.2 Fault isolation and identification
      3. 3.3 Geometry of variable projections
      4. 3.4 Tutorial session
  13. Part II: Application Studies
    1. Chapter 4: Application to a chemical reaction process
      1. 4.1 Process description
      2. 4.2 Identification of a monitoring model
      3. 4.3 Diagnosis of a fault condition
    2. Chapter 5: Application to a distillation process
      1. 5.1 Process description
      2. 5.2 Identification of a monitoring model
      3. 5.3 Diagnosis of a fault condition
  14. Part III: Advances in Multivariate Statistical Process Control
    1. Chapter 6: Further modeling issues
      1. 6.1 Accuracy of estimating PCA models
      2. 6.2 Accuracy of estimating PLS models
      3. 6.3 Robust model estimation
      4. 6.4 Small sample sets
      5. 6.5 Tutorial session
    2. Chapter 7: Monitoring multivariate time-varying processes
      1. 7.1 Problem analysis
      2. 7.2 Recursive principal component analysis
      3. 7.3 Moving window principal component analysis
      4. 7.4 A simulation example
      5. 7.5 Application to a Fluid Catalytic Cracking Unit
      6. 7.6 Application to a furnace process
      7. 7.7 Adaptive partial least squares
      8. 7.8 Tutorial Session
    3. Chapter 8: Monitoring changes in covariance structure
      1. 8.1 Problem analysis
      2. 8.2 Preliminary discussion of related techniques
      3. 8.3 Definition of primary and improved residuals
      4. 8.4 Revisiting the simulation examples of Section 8.1
      5. 8.5 Fault isolation and identification
      6. 8.6 Application study of a gearbox system
      7. 8.7 Analysis of primary and improved residuals
      8. 8.8 Tutorial session
  15. Part IV: Description of Modeling Methods
    1. Chapter 9: Principal component analysis
      1. 9.1 The core algorithm
      2. 9.2 Summary of the PCA algorithm
      3. 9.3 Properties of a PCA model
    2. Chapter 10: Partial least squares
      1. 10.1 Preliminaries
      2. 10.2 The core algorithm
      3. 10.3 Summary of the PLS algorithm
      4. 10.4 Properties of PLS
      5. 10.5 Properties of maximum redundancy PLS
  16. References
  17. Index
  18. Statistics in Practice