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Lee, Seung Jun
Risk Assessment and Risk-Informed Application
Research Interests
  • Probabilistic Safety Assessment, Digital I&C System Reliability, Human Reliability Analysis

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Abnormal state diagnosis model tolerant to noise in plant data

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Title
Abnormal state diagnosis model tolerant to noise in plant data
Author
Shin, Ji HyeonKim, Jae MinLee, Seung Jun
Issue Date
2021-04
Publisher
KOREAN NUCLEAR SOC
Citation
NUCLEAR ENGINEERING AND TECHNOLOGY, v.53, no.4, pp.1181 - 1188
Abstract
When abnormal events occur in a nuclear power plant, operators must conduct appropriate abnormal operating procedures. It is burdensome though for operators to choose the appropriate procedure considering the numerous main plant parameters and hundreds of alarms that should be judged in a short time. Recently, various research has applied deep-learning algorithms to support this problem by classifying each abnormal condition with high accuracy. Most of these models are trained with simulator data because of a lack of plant data for abnormal states, and as such, developed models may not have tolerance for plant data in actual situations. In this study, two approaches are investigated for a deep learning model trained with simulator data to overcome the performance degradation caused by noise in actual plant data. First, a preprocessing method using several filters was employed to smooth the test data noise, and second, a data augmentation method was applied to increase the acceptability of the untrained data. Results of this study confirm that the combination of these two approaches can enable high model performance even in the presence of noisy data as in real plants. ? 2020 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/).
URI
https://scholarworks.unist.ac.kr/handle/201301/48686
URL
https://www.sciencedirect.com/science/article/pii/S1738573320308871?via%3Dihub
DOI
10.1016/j.net.2020.09.025
ISSN
1738-5733
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