Advances in Computer Vision and Pattern Recognition

Unsupervised Process Monitoring and Fault Diagnosis with Machine Learning Methods

Authors: Aldrich, Chris, Auret, Lidia

  • Describes the latest developments in nonlinear methods and their application in fault diagnosis
  • Discusses in detail several advances in machine learning theory
  • Contains numerous case studies with real-world data from industry
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eBook $99.00
price for USA (gross)
  • ISBN 978-1-4471-5185-2
  • Digitally watermarked, DRM-free
  • Included format: PDF, EPUB
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover $129.00
price for USA
  • ISBN 978-1-4471-5184-5
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
Softcover n/a
  • ISBN 978-1-4471-7160-7
  • Free shipping for individuals worldwide
About this book

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: 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.

Reviews

From the reviews:

“The text elaborates a range of classifiers used for supervised and unsupervised machine learning methods, for different types of processes. … The rich examples of various industrial processes and the illustration of subsequent simulation results qualify the work as a reference textbook for graduate studies in machine learning.” (C. K. Raju, Computing Reviews, October, 2013)


Table of contents (8 chapters)

  • Introduction

    Aldrich, Chris (et al.)

    Pages 1-15

  • Overview of Process Fault Diagnosis

    Aldrich, Chris (et al.)

    Pages 17-70

  • Artificial Neural Networks

    Aldrich, Chris (et al.)

    Pages 71-115

  • Statistical Learning Theory and Kernel-Based Methods

    Aldrich, Chris (et al.)

    Pages 117-181

  • Tree-Based Methods

    Aldrich, Chris (et al.)

    Pages 183-220

Buy this book

eBook $99.00
price for USA (gross)
  • ISBN 978-1-4471-5185-2
  • Digitally watermarked, DRM-free
  • Included format: PDF, EPUB
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover $129.00
price for USA
  • ISBN 978-1-4471-5184-5
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
Softcover n/a
  • ISBN 978-1-4471-7160-7
  • Free shipping for individuals worldwide
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Bibliographic Information

Bibliographic Information
Book Title
Unsupervised Process Monitoring and Fault Diagnosis with Machine Learning Methods
Authors
Series Title
Advances in Computer Vision and Pattern Recognition
Copyright
2013
Publisher
Springer-Verlag London
Copyright Holder
Springer-Verlag London
eBook ISBN
978-1-4471-5185-2
DOI
10.1007/978-1-4471-5185-2
Hardcover ISBN
978-1-4471-5184-5
Softcover ISBN
978-1-4471-7160-7
Series ISSN
2191-6586
Edition Number
1
Number of Pages
XIX, 374
Number of Illustrations and Tables
57 b/w illustrations, 151 illustrations in colour
Topics