Supervised and semi-supervised probabilistic learning with deep neural networks for concurrent process-quality monitoring

Concurrent process-quality monitoring helps discover quality-relevant process anomalies and quality-irrelevant process anomalies. It especially works well in chemical plants with faults that cause quality problems. Traditional monitoring strategies are limitedly applied in chemical plants because qu...

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Bibliographic Details

Published in:	Neural networks Vol. 136; pp. 54 - 62
Main Authors:	Wang, Kai, Yuan, Xiaofeng, Chen, Junghui, Wang, Yalin
Format:	Journal Article
Language:	English
Published:	United States Elsevier Ltd 01.04.2021
Subjects:	Algorithms Models, Statistical Neural Networks, Computer Nonlinear process monitoring Quality-relevant monitoring Semi-supervised variational autoencoders Supervised Machine Learning - standards Supervised variational autoencoders Nonlinear process monitoring Supervised variational autoencoders Quality-relevant monitoring Semi-supervised variational autoencoders
ISSN:	0893-6080, 1879-2782, 1879-2782
Online Access:	Get full text
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