Unsupervised Process Monitoring and Fault Diagnosis with Machine Learning Methods
(Sprache: Englisch)
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...
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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.
Klappentext zu „Unsupervised Process Monitoring and Fault Diagnosis with Machine Learning Methods “
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.
Inhaltsverzeichnis zu „Unsupervised Process Monitoring and Fault Diagnosis with Machine Learning Methods “
IntroductionOverview of Process Fault Diagnosis
Artificial Neural Networks
Statistical Learning Theory and Kernel-Based Methods
Tree-Based Methods
Fault Diagnosis in Steady State Process Systems
Dynamic Process Monitoring
Process Monitoring Using Multiscale Methods
Bibliographische Angaben
- Autoren: Chris Aldrich , Lidia Auret
- 2013, 2013, XIX, 374 Seiten, 151 farbige Abbildungen, Masse: 16 x 24,1 cm, Gebunden, Englisch
- Verlag: Springer, Berlin
- ISBN-10: 1447151844
- ISBN-13: 9781447151845
- Erscheinungsdatum: 31.07.2013
Sprache:
Englisch
Pressezitat
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)
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