Lee, Hodong Kim, Changsoo Jeong, Dong Hwi Lee, Jong Min 2024-01-19T13:03:51Z 2024-01-19T13:03:51Z 2022-01-25 2021-12 0256-1115 https://pubs.kist.re.kr/handle/201004/115978 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. English 한국화학공학회 Data-driven fault detection for chemical processes using autoencoder with data augmentation Article 10.1007/s11814-021-0894-1 1 Korean Journal of Chemical Engineering, v.38, no.12, pp.2406 - 2422 Korean Journal of Chemical Engineering 38 12 2406 2422 N scie scopus kci ART002779247 000698034200002 2-s2.0-85115146159 Chemistry, Multidisciplinary Engineering, Chemical Chemistry Engineering Article COMPONENT ANALYSIS NEURAL-NETWORKS DIAGNOSIS Process Monitoring Fault Detection and Isolation (FDI) Autoencoder Variational Autoencoder Data Augmentation