Unsupervised Process Monitoring and Fault Diagnosis with Machine Learning Methods / by Chris Aldrich, Lidia Auret.

Format:

Book

Author/Creator:

Aldrich, C. (Chris), author.

Auret, Lidia, author.

Contributor:

SpringerLink (Online service)

Series:

Computer Science (Springer-11645)

Advances in computer vision and pattern recognition 2191-6586

Advances in Computer Vision and Pattern Recognition, 2191-6586

Language:

English

Subjects (All):

Artificial intelligence.

Artificial Intelligence.

Local Subjects:

Artificial Intelligence.

Physical Description:

1 online resource (XIX, 374 pages) : 208 illustrations, 151 illustrations in color.

Edition:

First edition 2013.

Contained In:

Springer eBooks

Place of Publication:

London : Springer London : Imprint: Springer, 2013.

System Details:

text file PDF

Summary:

Algorithms for intelligent fault diagnosis of automated operations offer significant benefits to the manufacturing and process industries. Furthermore, machine learning methods enable such monitoring systems to handle nonlinearities and large volumes of data. 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: Reviews the application of machine learning to process monitoring and fault diagnosis 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 This highly practical and clearly-structured work is an invaluable resource for all researchers and practitioners involved in process control, multivariate statistics and machine learning. Dr. Chris Aldrich is a Professor in the Department of Metallurgical and Minerals Engineering at Curtin University, Perth, Australia. Dr. Lidia Auret is a Lecturer in the Department of Process Engineering at Stellenbosch University, South Africa.

Contents:

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.

Other Format:

Printed edition:

ISBN:

978-1-4471-5185-2

9781447151852

Access Restriction:

Restricted for use by site license.