Unsupervised Process Monitoring and Fault Diagnosis with Machine Learning Methods

de Chris Aldrich
État : Neuf
107,63 €
TVA incluse - Livraison GRATUITE
Chris Aldrich Unsupervised Process Monitoring and Fault Diagnosis with Machine Learning Methods
Chris Aldrich - Unsupervised Process Monitoring and Fault Diagnosis with Machine Learning Methods

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La description

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.

Contributeurs

Écrivain:
Chris Aldrich
Lidia Auret

Détails du produit

Commentaire illustrations:
57 SW-Abb., 151 Farbabb., 56 Tabellen
Remarks:

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

Type de média:
Souple
Éditeur:
Springer London
Évaluation:
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)
Langues:
Anglais
Nombre de pages:
374
Sommaire:
This book describes the latest developments in nonlinear methods and their application in fault diagnosis. It details advances in machine learning theory and contains numerous case studies with real-world data from industry.

Données de base

Type d'produit:
Livre de poche
Date de publication:
23 août 2016
Dimensions du colis:
0.235 x 0.155 x 0.024 m;
GTIN:
09781447171607
DUIN:
3T587UJ6TU0
MPN:
26645057
107,63 €
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