Nuclear Engineering and Technology

Korean Nuclear Society (한국원자력학회)
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A reliable intelligent diagnostic assistant for nuclear power plants using explainable artificial intelligence of GRU-AE, LightGBM and SHAP

  • Park, Ji Hun (Department of Nuclear Engineering, Chosun University) ;
  • Jo, Hye Seon (Department of Nuclear Engineering, Chosun University) ;
  • Lee, Sang Hyun (Department of Nuclear Engineering, Chosun University) ;
  • Oh, Sang Won (Department of Nuclear Engineering, Chosun University) ;
  • Na, Man Gyun (Department of Nuclear Engineering, Chosun University)
  • Received : 2021.01.28
  • Accepted : 2021.10.17
  • Published : 2022.04.25
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Abstract

When abnormal operating conditions occur in nuclear power plants, operators must identify the occurrence cause and implement the necessary mitigation measures. Accordingly, the operator must rapidly and accurately analyze the symptom requirements of more than 200 abnormal scenarios from the trends of many variables to perform diagnostic tasks and implement mitigation actions rapidly. However, the probability of human error increases owing to the characteristics of the diagnostic tasks performed by the operator. Researches regarding diagnostic tasks based on Artificial Intelligence (AI) have been conducted recently to reduce the likelihood of human errors; however, reliability issues due to the black box characteristics of AI have been pointed out. Hence, the application of eXplainable Artificial Intelligence (XAI), which can provide AI diagnostic evidence for operators, is considered. In conclusion, the XAI to solve the reliability problem of AI is included in the AI-based diagnostic algorithm. A reliable intelligent diagnostic assistant based on a merged diagnostic algorithm, in the form of an operator support system, is developed, and includes an interface to efficiently inform operators.

Keywords

Acknowledgement

This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT)(No. 2018M2B2B1065651).

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