Nuclear Engineering and Technology

Korean Nuclear Society (한국원자력학회)
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Abnormal state diagnosis model tolerant to noise in plant data

  • Shin, Ji Hyeon (Department of Nuclear Engineering, Ulsan National Institute of Science and Technology) ;
  • Kim, Jae Min (Department of Nuclear Engineering, Ulsan National Institute of Science and Technology) ;
  • Lee, Seung Jun (Department of Nuclear Engineering, Ulsan National Institute of Science and Technology)
  • Received : 2020.03.03
  • Accepted : 2020.09.20
  • Published : 2021.04.25
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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.

Keywords

Acknowledgement

This work was supported by a National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (No.NRF-2018M2B2B1065653), and also by the Korea Institute of Energy Technology Evaluation and Planning (KETEP) and the Ministry of Trade, Industry & Energy (MOTIE) of the Republic of Korea (No. 20171510102040).

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