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Process Systems Engineering
Semisupervised Bayesian method for soft sensor modeling with unlabeled data samples
Article first published online: 20 OCT 2010
DOI: 10.1002/aic.12422
Copyright © 2010 American Institute of Chemical Engineers (AIChE)
Additional Information
How to Cite
Ge, Z. and Song, Z. (2011), Semisupervised Bayesian method for soft sensor modeling with unlabeled data samples. AIChE J., 57: 2109–2119. doi: 10.1002/aic.12422
Publication History
- Issue published online: 12 JUL 2011
- Article first published online: 20 OCT 2010
- Accepted manuscript online: 3 SEP 2010 11:25AM EST
- Manuscript Revised: 27 AUG 2010
- Manuscript Received: 6 FEB 2010
Funded by
- National Natural Science Foundation of China. Grant Numbers: 61004134, 60974056
- National 863 High Technology Research and Development Program of China. Grant Number: 2009AA04Z154
- China Postdoctoral Science Foundation. Grant Number: 20090461370
- Abstract
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- Cited By
Keywords:
- semisupervised learning;
- Bayesian regularization;
- soft sensor modeling;
- probabilistic
Abstract
Most traditional soft sensors are built upon the labeled dataset that contains equal numbers of input and output data samples. However, the output variables that correspond to quality variables and other important controlled variables are always difficult to obtain in chemical processes. Therefore, we may only obtain the output data for a small portion of the whole dataset and have much more input data samples. In this article, a semisupervised method is proposed for soft sensor modeling, which can successfully incorporate the unlabeled data information. To determine the effective dimensionality of the latent space, the Bayesian regularization method is introduced into the semisupervised model structure. Two industrial application case studies are provided to evaluate the feasibility and efficiency of the newly developed probabilistic soft sensor. © 2010 American Institute of Chemical Engineers AIChE J, 2011