Unsupervised Process Monitoring and Fault Diagnosis with Machine Learning Methods (Advances in Computer Vision and Pattern Recognition)

Unsupervised Process Monitoring and Fault Diagnosis with Machine Learning Methods (Advances in Computer Vision and Pattern Recognition)

Lidia Auret


This designated text/reference describes intimately the newest advances in unsupervised technique tracking and fault prognosis with computer studying tools. ample case stories through the textual content show the efficacy of every technique in real-world settings. The huge insurance examines such state-of-the-art subject matters because the use of data idea to augment unsupervised studying in tree-based equipment, the extension of kernel easy methods to a number of kernel studying for function extraction from facts, and the incremental education of multilayer perceptrons to build deep architectures for improved facts projections. issues and lines: discusses laptop studying frameworks according to man made neural networks, statistical studying idea and kernel-based equipment, and tree-based tools; examines the appliance of laptop studying to regular kingdom and dynamic operations, with a spotlight on unsupervised studying; describes using spectral equipment in procedure fault prognosis.

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