Chris Aldrich, Lidia Auret

Unsupervised Process Monitoring and Fault Diagnosis with Machine Learning Methods

• Gebundenes Buch

Produktbeschreibung

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.

Produktdetails

• Advances in Computer Vision and Pattern Recognition
• Verlag: Springer / Springer London / Springer, Berlin
• Artikelnr. des Verlages: 80022472, 978-1-4471-5184-5
• 2013
• Seitenzahl: 396
• Erscheinungstermin: 9. Juli 2013
• Englisch
• Abmessung: 241mm x 160mm x 26mm
• Gewicht: 773g
• ISBN-13: 9781447151845
• ISBN-10: 1447151844
• Artikelnr.: 37626380

Inhaltsangabe

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.

Rezensionen

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)