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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