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Data-driven fault detection for chemical processes using autoencoder with data augmentation

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Abstract
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.
Author(s)
김창수이종민이호동정동휘
Issued Date
2021
Type
Article
Keyword
Process MonitoringFault Detection and Isolation (FDI)AutoencoderVariational AutoencoderData Augmentation
DOI
10.1007/s11814-021-0894-1
URI
https://oak.ulsan.ac.kr/handle/2021.oak/8840
https://ulsan-primo.hosted.exlibrisgroup.com/primo-explore/fulldisplay?docid=TN_cdi_nrf_kci_oai_kci_go_kr_ARTI_9890675&context=PC&vid=ULSAN&lang=ko_KR&search_scope=default_scope&adaptor=primo_central_multiple_fe&tab=default_tab&query=any,contains,Data-driven%20fault%20detection%20for%20chemical%20processes%20using%20autoencoder%20with%20data%20augmentation&offset=0&pcAvailability=true
Publisher
KOREAN JOURNAL OF CHEMICAL ENGINEERING
Location
대한민국
Language
영어
ISSN
0256-1115
Citation Volume
38
Citation Number
12
Citation Start Page
2406
Citation End Page
2422
Appears in Collections:
Engineering > Aerospace Engineering
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