Developing semi-supervised variational autoencoder-generative adversarial network models to enhance quality prediction performance

One common serious issue of training a prediction model is that the process data significantly outnumber the quality data. Such discrepancy exists because of the time lag for obtaining quality data. This paper proposes semi-supervised variational autoencoder-generative adversarial network (S2-VAE/GA...

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Bibliographic Details
Published in:Chemometrics and intelligent laboratory systems Vol. 217; p. 104385
Main Authors: Ooi, Sai Kit, Tanny, Dave, Chen, Junghui, Wang, Kai
Format: Journal Article
Language:English
Published: Elsevier B.V 15.10.2021
Subjects:
Generative adversarial network
Latent variable model
Semi-supervised variational autoencoder
Soft sensors
Latent variable model
Soft sensors
Semi-supervised variational autoencoder
Generative adversarial network
ISSN:0169-7439, 1873-3239
Online Access:Get full text
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Summary:One common serious issue of training a prediction model is that the process data significantly outnumber the quality data. Such discrepancy exists because of the time lag for obtaining quality data. This paper proposes semi-supervised variational autoencoder-generative adversarial network (S2-VAE/GAN), that is able to make use of all the data even with some missing quality data. The key idea in S2-VAE/GAN is the capability of enhancing the performance of the decoder/generator in learning the true distribution of both process and quality data in a competition between the decoder/generator and the discriminator in S2-VAE/GAN through Nash Equilibrium, allowing the model to improve the qualities of reconstruction and prediction data. The S2-VAE/GAN model is also flexible enough to automatically adjust itself according to the input data. If the quality data are missing, the model can fill up the data through the trained prediction model and the same network structure defined in the supervised case can still be re-used. With the probabilistic distribution format, the proposed method is also capable of capturing the nonlinear feature of the process and representing the stochastic nature of operating plants. The results of the numerical case and the industrial case in this paper show that S2-VAE/GAN outperforms conventional methods in terms of predictabilities of the missing quality data. •Variational autoencoders & generative adversarial networks (VAE/GAN) are combined.•Semi-supervised learning of the VAE/GAN (S2-VAE/GAN) model is proposed.•The proposed method and various regression models are compared in performance.•Numerical & industrial case studies of the proposed S2-VAE/GAN model are presented.
ISSN:0169-7439
1873-3239
DOI:10.1016/j.chemolab.2021.104385