Data-feature-driven nonlinear process monitoring based on joint deep learning models with dual-scale

The interactions among the gauged data in most exiting real-life cases are correlative inevitably given the complicated behavior of process systems, that is the observed input data should better be interpreted as generating from joint interaction of static and dynamic feature sources. Therefore, the...

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Bibliographic Details

Published in:	Information sciences Vol. 591; pp. 381 - 399
Main Authors:	Yu, Jianbo, Yan, Xuefeng
Format:	Journal Article
Language:	English
Published:	Elsevier Inc 01.04.2022
Subjects:	Kernel slow feature analysis Nonlinear process monitoring RNN-LSTM autoencoder Stacked autoencoder Static/dynamic features sources Nonlinear process monitoring Kernel slow feature analysis Static/dynamic features sources Stacked autoencoder RNN-LSTM autoencoder
ISSN:	0020-0255, 1872-6291
Online Access:	Get full text
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