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Computer Science - Artificial Intelligence | Unsupervised Process Monitoring and Fault Diagnosis with Machine Learning Methods

Unsupervised Process Monitoring and Fault Diagnosis with Machine Learning Methods

Aldrich, Chris, Auret, Lidia

2013, XIX, 374 p. 208 illus., 151 illus. in color.

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  • 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

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.

Content Level » Research

Keywords » Classification Trees - Fault Detection - Fault Identification - Kernel-based Methods - Neural Networks - Regression Trees

Related subjects » Artificial Intelligence

Table of 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

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