Unsupervised Process Monitoring and Fault Diagnosis With Machine Learning Methods
Language: English
Published by Springer-Verlag New York Inc, 2013
Series: Book 31 of 86 - Advances in Computer Vision and Pattern Recognition
- Hardcover
- New
Seller: Revaluation Books, Exeter, United KingdomRevaluation Books
AbeBooks seller since January 6, 2003
Condition: New
£ 214.97
Quantity: 2 available
Add to basketItem description from seller
2013 edition. 359 pages. 9.25x6.50x1.00 inches. In Stock.
Seller Inventory # x-1447151844
- Title
- Unsupervised Process Monitoring and Fault Diagnosis With Machine Learning Methods
- Author
- Aldrich, Chris/ Auret, Lidia
- Publisher
- Springer-Verlag New York Inc
- Publication year
- 2013
- Condition
- Brand New
- Binding
- Hardcover
- Language
- English
- ISBN 10
- 1447151844
- ISBN 13
- 9781447151845
- Item weight
- 0.93 kilograms
- Series
- Book 31 of 86: Advances in Computer Vision and Pattern Recognition
"Synopsis" may belong to another edition of this title.
From the Back Cover
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.
"About the title" may belong to another edition of this title.
Revaluation Books
Exeter, United Kingdom
AbeBooks seller since January 6, 2003
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Edward Bowditch Ltd
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Exeter, United Kingdom EX3 0PP
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