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Data-Driven Fault Detection for Industrial Processes

Canonical Correlation Analysis and Projection Based Methods

  • Zhiwen Chen

Table of contents

  1. Front Matter
    Pages I-XIX
  2. Zhiwen Chen
    Pages 1-11
  3. Zhiwen Chen
    Pages 13-30
  4. Zhiwen Chen
    Pages 79-97
  5. Zhiwen Chen
    Pages 99-101
  6. Back Matter
    Pages 103-112

About this book

Introduction

Zhiwen Chen aims to develop advanced fault detection (FD) methods for the monitoring of industrial processes. With the ever increasing demands on reliability and safety in industrial processes, fault detection has become an important issue. Although the model-based fault detection theory has been well studied in the past decades, its applications are limited to large-scale industrial processes because it is difficult to build accurate models. Furthermore, motivated by the limitations of existing data-driven FD methods, novel canonical correlation analysis (CCA) and projection-based methods are proposed from the perspectives of process input and output data, less engineering effort and wide application scope. For performance evaluation of FD methods, a new index is also developed.

Contents
  • A New Index for Performance Evaluation of FD Methods
  • CCA-based FD Method for the Monitoring of Stationary Processes
  • Projection-based FD Method for the Monitoring of Dynamic Processes 
  • Benchmark Study and Real-Time Implementation
Target Groups
  • Researchers and students in the field of process control and statistical hypothesis testing
  • Research and development engineers in the process industry
About the Author
Zhiwen Chen’s research interests include multivariate statistical process monitoring, model-based and data-driven fault diagnosis as well as their application to industrial processes. He is currently working at the School of Information Science and Engineering at Central South University, China.

Keywords

Multivariate statistical process monitoring Performance evaluation Data-Driven method Subspace method Kernel representation Deterministic disturbances

Authors and affiliations

  • Zhiwen Chen
    • 1
  1. 1.Faculty of Engineering, Automatic Control and Complex Systems (AKS)University of Duisburg-EssenDuisburgGermany

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-658-16756-1
  • Copyright Information Springer Fachmedien Wiesbaden GmbH 2017
  • Publisher Name Springer Vieweg, Wiesbaden
  • eBook Packages Engineering
  • Print ISBN 978-3-658-16755-4
  • Online ISBN 978-3-658-16756-1
  • Buy this book on publisher's site
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