Unsupervised Process Monitoring and Fault Diagnosis with Machine Learning Methods

Softcover Nachdruck of the original 1. Auflage 2013
ISBN: 978-1-4471-7160-7
Verlag: Springer

Buch, Englisch, 374 Seiten, Previously published in hardcover, Format (B × H): 155 mm x 235 mm, Gewicht: 6692 g

Reihe: Advances in Computer Vision and Pattern Recognition

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.

Zielgruppe

Research

Autoren/Hrsg.

Auret, Lidia

Aldrich, Chris

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen
• Technische Wissenschaften Verfahrenstechnik | Chemieingenieurwesen | Biotechnologie Verfahrenstechnik, Chemieingenieurwesen
• Technische Wissenschaften Elektronik | Nachrichtentechnik Nachrichten- und Kommunikationstechnik Regelungstechnik
• Technische Wissenschaften Technik Allgemein Mess- und Automatisierungstechnik

Inhaltsverzeichnis

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.