Adversarial Autoencoder Based Feature Learning for Fault Detection in Industrial Processes
- 주제(키워드) Feature extraction , Process monitoring , Fault detection , Data models , Informatics , Generative adversarial networks , Data mining , Adversarial autoencoder (AAE) , data-driven method , dimensionality reduction , fault detection , process monitoring , Tennessee Eastman (TE) process
- 주제(기타) Automation & Control Systems; Computer Science, Interdisciplinary Applications; Engineering, Industrial
- 설명문(일반) [Jang, Kyojin; Hong, Seokyoung; Kim, Minsu; Moon, Il] Yonsei Univ, Sch Chem & Biomol Engn, Seoul 03722, South Korea; [Na, Jonggeol] Ewha Womans Univ, Dept Chem Engn & Mat Sci, Grad Program Syst Hlth Sci & Engn, Seoul 03760, South Korea
- 관리정보기술 faculty
- 등재 SCIE, SCOPUS
- 발행기관 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
- 발행년도 2022
- URI http://www.dcollection.net/handler/ewha/000000183803
- 본문언어 영어
- Published As http://dx.doi.org/10.1109/TII.2021.3078414
초록/요약
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.
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