Data-driven fault detection for chemical processes using autoencoder with data augmentation
Process monitoring plays an essential role in safe and profitable operations. Various data-driven fault detection models have been suggested, but they cannot perform properly when the training data are insufficient or the information to construct the manifold is confined to a specific region. In thi...
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Published in | The Korean journal of chemical engineering Vol. 38; no. 12; pp. 2406 - 2422 |
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Main Authors | , , , |
Format | Journal Article |
Language | English |
Published |
New York
Springer US
01.12.2021
Springer Nature B.V 한국화학공학회 |
Subjects | |
Online Access | Get full text |
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Summary: | Process monitoring plays an essential role in safe and profitable operations. Various data-driven fault detection models have been suggested, but they cannot perform properly when the training data are insufficient or the information to construct the manifold is confined to a specific region. In this study, a process monitoring framework integrated with data augmentation is proposed to supplement rare but informative samples for the boundary regions of the normal state. To generate data for augmentation, a variational autoencoder was employed to exploit its advantage of stable convergence. For the construction of the process monitoring system, an autoencoder that can extract useful features in an unsupervised manner was used. To illustrate the efficacy of the proposed method, a case study for the Tennessee Eastman process was applied. The results show that the proposed method can improve the monitoring performance compared to the autoencoder without data augmentation in terms of fault detection accuracy and delay, particularly within the feature space. |
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ISSN: | 0256-1115 1975-7220 |
DOI: | 10.1007/s11814-021-0894-1 |