EAN: 9781447151845

UNSUPERVISED PROCESS MONITORING AND FAULT DIAGN...

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.

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