Development of deep autoencoder-based anomaly detection system for HANARO

• Published in: Nuclear engineering and technology Vol. 55; no. 2; pp. 475 - 483
• Main Authors: Ryu, Seunghyoung, Jeon, Byoungil, Seo, Hogeon, Lee, Minwoo, Shin, Jin-Won, Yu, Yonggyun
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.02.2023; Elsevier; 한국원자력학회
• Subjects: Anomaly detection; Autoencoder; Deep learning; Nuclear reactor; Research reactor; 원자력공학; Deep learning; Autoencoder; Anomaly detection; Research reactor; Nuclear reactor
• ISSN: 1738-5733; 2234-358X
• DOI: 10.1016/j.net.2022.10.009

The high-flux advanced neutron application reactor (HANARO) is a multi-purpose research reactor at the Korea Atomic Energy Research Institute (KAERI). HANARO has been used in scientific and industrial research and developments. Therefore, stable operation is necessary for national science and industrial prospects. This study proposed an anomaly detection system based on deep learning, that supports the stable operation of HANARO. The proposed system collects multiple sensor data, displays system information, analyzes status, and performs anomaly detection using deep autoencoder. The system comprises communication, visualization, and anomaly-detection modules, and the prototype system is implemented on site in 2021. Finally, an analysis of the historical data and synthetic anomalies was conducted to verify the overall system; simulation results based on the historical data show that 12 cases out of 19 abnormal events can be detected in advance or on time by the deep learning AD model.