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공과대학
화공신소재공학과
Journal papers
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Adversarial Autoencoder Based Feature Learning for Fault Detection in Industrial Processes
Title
Adversarial Autoencoder Based Feature Learning for Fault Detection in Industrial Processes
Authors
Jang K.
;
Hong S.
;
Kim M.
;
Na J.
;
Moon I.
Ewha Authors
나종걸
SCOPUS Author ID
나종걸
Issue Date
2022
Journal Title
IEEE Transactions on Industrial Informatics
ISSN
1551-3203
Citation
IEEE Transactions on Industrial Informatics vol. 18, no. 2, pp. 827 - 834
Keywords
Adversarial autoencoder (AAE)
;
data-driven method
;
dimensionality reduction
;
fault detection
;
process monitoring
;
Tennessee Eastman (TE) process
Publisher
IEEE Computer Society
Indexed
SCIE; SCOPUS
Document Type
Article
Abstract
Deep learning has recently emerged as a promising method for nonlinear process monitoring. However, ensuring that the features from process variables have representative information of the high-dimensional process data remains a challenge. In this study, we propose an adversarial autoencoder (AAE) based process monitoring system. AAE which combines the advantages of a variational autoencoder and a generative adversarial network enables the generation of features that follow the designed prior distribution. By employing the AAE model, features that have informative manifolds of the original data are obtained. These features are used for constructing and monitoring statistics and improve the stability and reliability of fault detection. Extracted features help calculate the degree of abnormalities in process variables more robustly and indicate the type of fault information they imply. Finally, our proposed method is testified using the Tennessee Eastman benchmark process in terms of fault detection rate, false alarm rate, and fault detection delays. © 2021 IEEE
DOI
10.1109/TII.2021.3078414
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