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Unsupervised Process Monitoring and Fault Diagnosis with Machine Learning Methods

  • Chris Aldrich
  • Lidia Auret

Part of the Advances in Computer Vision and Pattern Recognition book series (ACVPR)

Table of contents

  1. Front Matter
    Pages i-xix
  2. Chris Aldrich, Lidia Auret
    Pages 1-15
  3. Chris Aldrich, Lidia Auret
    Pages 17-70
  4. Chris Aldrich, Lidia Auret
    Pages 71-115
  5. Chris Aldrich, Lidia Auret
    Pages 117-181
  6. Chris Aldrich, Lidia Auret
    Pages 183-220
  7. Chris Aldrich, Lidia Auret
    Pages 221-279
  8. Chris Aldrich, Lidia Auret
    Pages 281-339
  9. Chris Aldrich, Lidia Auret
    Pages 341-369
  10. Back Matter
    Pages 371-374

About this book

Introduction

Algorithms for intelligent fault diagnosis of automated operations offer significant benefits to the manufacturing and process industries. Furthermore, machine learning methods enable such monitoring systems to handle nonlinearities and large volumes of data.

This unique text/reference describes in detail the latest advances in Unsupervised Process Monitoring and Fault Diagnosis with Machine Learning Methods. Abundant case studies throughout the text demonstrate the efficacy of each method in real-world settings. The broad coverage examines such cutting-edge topics as the use of information theory to enhance unsupervised learning in tree-based methods, the extension of kernel methods to multiple kernel learning for feature extraction from data, and the incremental training of multilayer perceptrons to construct deep architectures for enhanced data projections.

Topics and features:

  • Reviews the application of machine learning to process monitoring and fault diagnosis
  • Discusses machine learning frameworks based on artificial neural networks, statistical learning theory and kernel-based methods, and tree-based methods
  • Examines the application of machine learning to steady state and dynamic operations, with a focus on unsupervised learning
  • Describes the use of spectral methods in process fault diagnosis

This highly practical and clearly-structured work is an invaluable resource for all researchers and practitioners involved in process control, multivariate statistics and machine learning.

Dr. Chris Aldrich is a Professor in the Department of Metallurgical and Minerals Engineering at Curtin University, Perth, Australia. Dr. Lidia Auret is a Lecturer in the Department of Process Engineering at Stellenbosch University, South Africa.

Keywords

Classification Trees Fault Detection Fault Identification Kernel-based Methods Neural Networks Regression Trees

Authors and affiliations

  • Chris Aldrich
    • 1
  • Lidia Auret
    • 2
  1. 1.Western Australian School of MinesCurtin UniversityPerthAustralia
  2. 2.Department of Process EngineeringUniversity of StellenboschStellenboschSouth Africa

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-4471-5185-2
  • Copyright Information Springer-Verlag London 2013
  • Publisher Name Springer, London
  • eBook Packages Computer Science
  • Print ISBN 978-1-4471-5184-5
  • Online ISBN 978-1-4471-5185-2
  • Series Print ISSN 2191-6586
  • Series Online ISSN 2191-6594
  • Buy this book on publisher's site