Nuclear Engineering and Technology

Korean Nuclear Society (한국원자력학회)
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Deep-learning-based system-scale diagnosis of a nuclear power plant with multiple infrared cameras

  • Ik Jae Jin (Department of Nuclear Engineering Ulsan National Institute of Science and Technology (UNIST)) ;
  • Do Yeong Lim (Department of Nuclear Engineering Ulsan National Institute of Science and Technology (UNIST)) ;
  • In Cheol Bang (Department of Nuclear Engineering Ulsan National Institute of Science and Technology (UNIST))
  • Received : 2022.04.26
  • Accepted : 2022.10.12
  • Published : 2023.02.25
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Abstract

Comprehensive condition monitoring of large industry systems such as nuclear power plants (NPPs) is essential for safety and maintenance. In this study, we developed novel system-scale diagnostic technology based on deep-learning and IR thermography that can efficiently and cost-effectively classify system conditions using compact Raspberry Pi and IR sensors. This diagnostic technology can identify the presence of an abnormality or accident in whole system, and when an accident occurs, the type of accident and the location of the abnormality can be identified in real-time. For technology development, the experiment for the thermal image measurement and performance validation of major components at each accident condition of NPPs was conducted using a thermal-hydraulic integral effect test facility with compact infrared sensor modules. These thermal images were used for training of deep-learning model, convolutional neural networks (CNN), which is effective for image processing. As a result, a proposed novel diagnostic was developed that can perform diagnosis of components, whole system and accident classification using thermal images. The optimal model was derived based on the modern CNN model and performed prompt and accurate condition monitoring of component and whole system diagnosis, and accident classification. This diagnostic technology is expected to be applied to comprehensive condition monitoring of nuclear power plants for safety.

Keywords

Acknowledgement

This work was supported by the A.I. Incubation Project Fund (1.220043.01) of UNIST(Ulsan national Institute of Science & Technology), and partly by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT) (No.2021M2D2A1A03048950) and KOREA HYDRO & NUCLEAR POWER CO., LTD (No. 2020-Tech-17).

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