Nuclear Engineering and Technology

Korean Nuclear Society (한국원자력학회)
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Approach to diagnosing multiple abnormal events with single-event training data

  • Ji Hyeon Shin (Department of Nuclear Engineering, Ulsan National Institute of Science and Technology) ;
  • Seung Gyu Cho (Department of Nuclear Engineering, Ulsan National Institute of Science and Technology) ;
  • Seo Ryong Koo (Korea Atomic Energy Research Institute) ;
  • Seung Jun Lee (Department of Nuclear Engineering, Ulsan National Institute of Science and Technology)
  • Received : 2023.05.26
  • Accepted : 2023.10.22
  • Published : 2024.02.25
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Abstract

Diagnostic support systems are being researched to assist operators in identifying and responding to abnormal events in a nuclear power plant. Most studies to date have considered single abnormal events only, for which it is relatively straightforward to obtain data to train the deep learning model of the diagnostic support system. However, cases in which multiple abnormal events occur must also be considered, for which obtaining training data becomes difficult due to the large number of combinations of possible abnormal events. This study proposes an approach to maintain diagnostic performance for multiple abnormal events by training a deep learning model with data on single abnormal events only. The proposed approach is applied to an existing algorithm that can perform feature selection and multi-label classification. We choose an extremely randomized trees classifier to select dedicated monitoring parameters for target abnormal events. In diagnosing each event occurrence independently, two-channel convolutional neural networks are employed as sub-models. The algorithm was tested in a case study with various scenarios, including single and multiple abnormal events. Results demonstrated that the proposed approach maintained diagnostic performance for 15 single abnormal events and significantly improved performance for 105 multiple abnormal events compared to the base model.

Keywords

Acknowledgement

This work was supported by National Research Foundation of Korea (NRF) grants funded by the Korean government (Ministry of Science and ICT) (RS-2022-00144042 and RS-2022-00144150).

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