Novel Semi-Supervised Deep Probabilistic Slow Feature Extraction for Online Chemical Process Soft Sensing Application

Nonlinear and slowness characteristics commonly appear with increasing complexity and dynamics in chemical processes, which have brought challenges for soft sensor modeling. To handle these characteristics, a feature extractor Siamese variational autoencoder (SVAE) model is designed for obtaining pr...

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Bibliographic Details
Published in:	IEEE transactions on instrumentation and measurement Vol. 73; pp. 1 - 11
Main Authors:	Wang, Jiayu, Yao, Le, Xiong, Weili
Format:	Journal Article
Language:	English
Published:	New York IEEE 2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Chemical processes Chemical reactions Data models Decoding Feature extraction Performance prediction Predictive models Probabilistic logic Probability theory Resampling Semi-supervised machine learning Siamese network slow feature extraction soft sensor Soft sensors Statistical analysis variational autoencoder (VAE)
ISSN:	0018-9456, 1557-9662
Online Access:	Get full text
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