Probabilistic slow feature analysis-based representation learning from massive process data for soft sensor modeling

Latent variable (LV) models provide explicit representations of underlying driving forces of process variations and retain the dominant information of process data. In this study, slow features (SFs) as temporally correlated LVs are derived using probabilistic SF analysis. SFs evolving in a state‐sp...

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Bibliographic Details
Published in:AIChE journal Vol. 61; no. 12; pp. 4126 - 4139
Main Authors: Shang, Chao, Huang, Biao, Yang, Fan, Huang, Dexian
Format: Journal Article
Language:English
Published: New York Blackwell Publishing Ltd 01.12.2015
American Institute of Chemical Engineers
Subjects:
Algorithms
Chemical engineering
Chemical engineers
Correlation analysis
Data analysis
Evolution
latent variable model
Learning
Mathematical models
Probabilistic methods
Probability
Probability theory
process data analysis
Representations
Sensors
slow feature analysis
soft sensor
ISSN:0001-1541, 1547-5905
Online Access:Get full text
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Summary:Latent variable (LV) models provide explicit representations of underlying driving forces of process variations and retain the dominant information of process data. In this study, slow features (SFs) as temporally correlated LVs are derived using probabilistic SF analysis. SFs evolving in a state‐space form effectively represent nominal variations of processes, some of which are potentially correlated to quality variables and hence help improving the prediction performance of soft sensors. An efficient expectation maximum algorithm is proposed to estimate parameters of the probabilistic model, which turns out to be suitable for analyzing massive process data. Two criteria are also proposed to select quality‐relevant SFs. The validity and advantages of the proposed method are demonstrated via two case studies. © 2015 American Institute of Chemical Engineers AIChE J, 61: 4126–4139, 2015
Bibliography:National Natural Science Foundation of China - No. 21276137
National Science Engineering Research Council of Canada (NSERC)
istex:628A48723AC59D55B4B77DDBADAF7DD39B169EB0
National Basic Research Program of China - No. 2012CB720505
Alberta Innovates Technology Futures (AITF)
ark:/67375/WNG-Q4HS35WR-Z
ArticleID:AIC14937
China Scholarship Council (CSC)
SourceType-Scholarly Journals-1
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ISSN:0001-1541
1547-5905
DOI:10.1002/aic.14937