Development of deep autoencoder-based anomaly detection system for HANARO

被引:8
作者
Ryu, Seunghyoung [1 ]
Jeon, Byoungil [1 ]
Seo, Hogeon [1 ]
Lee, Minwoo [2 ]
Shin, Jin-Won [2 ]
Yu, Yonggyun [1 ,3 ]
机构
[1] Korea Atom Energy Res Inst, Artificial Intelligence Applicat & Strategy Team, 111, Daedeok daero 989 beon gil, Daejeon, South Korea
[2] Korea Atom Energy Res Inst, HANARO Management Div, 111, Daedeok daero 989 beon gil, Daejeon, South Korea
[3] Univ Sci & Technol, Nucl & Radiat Safety, 217, Gajeong ro, Daejeon, South Korea
关键词
Deep learning; Autoencoder; Anomaly detection; Nuclear reactor; Research reactor; NUCLEAR-POWER-PLANTS; ALGORITHM;
D O I
10.1016/j.net.2022.10.009
中图分类号
TL [原子能技术]; O571 [原子核物理学];
学科分类号
0827 ; 082701 ;
摘要
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.(c) 2023 Korean Nuclear Society, Published by Elsevier Korea LLC. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
引用
收藏
页码:475 / 483
页数:9
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