Unsupervised Process Monitoring and Fault Diagnosis with Machine Learning Methods

Chris Aldrich, Lidia Auret (Autoren)

Buch | Softcover

374 Seiten

2016 | Softcover reprint of the original 1st ed. 2013
Springer London Ltd (Verlag)
978-1-4471-7160-7 (ISBN)

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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.

Introduction.- Overview 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.

Erscheinungsdatum	02.09.2016
Reihe/Serie	Advances in Pattern Recognition
Zusatzinfo	151 Illustrations, color; 57 Illustrations, black and white; XIX, 374 p. 208 illus., 151 illus. in color.
Verlagsort	England
Sprache	englisch
Maße	155 x 235 mm
Themenwelt	Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik
Schlagworte	Classification Trees • fault detection • Fault Identification • Kernel-based Methods • Neural networks • regression trees
ISBN-10	1-4471-7160-8 / 1447171608
ISBN-13	978-1-4471-7160-7 / 9781447171607
Zustand	Neuware