Stacked Spatial-Temporal Autoencoder for Quality Prediction in Industrial Processes

Nowadays, data-driven soft sensors have become a mainstream for the key performance indicators prediction, which guarantees the safety and stability of the industrial process. The typical autoencoder (AE) has been widely used to extract potential features through unsupervised pretraining and supervi...

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Veröffentlicht in:	IEEE transactions on industrial informatics Jg. 19; H. 8; S. 1 - 9
Hauptverfasser:	Yan, Feng, Yang, Chunjie, Zhang, Xinmin
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Piscataway IEEE 01.08.2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Correlation Decontamination Feature extraction Key performance indicator Mathematical models Memory modules Representation learning Soft sensor Soft sensors Sorbents spatial-temporal autoencoder temporal autoencoder
ISSN:	1551-3203, 1941-0050
Online-Zugang:	Volltext
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