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Nuclear Power Plant Accident Diagnosis Algorithm Including Novelty Detection Function Using LSTM

  • Jaemin Yang
  • Subong Lee
  • Jonghyun KimEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 965)

Abstract

Diagnosis of the accident or transient at the nuclear power plants is performed under the judgment of operators based on the procedures. Although procedures given to operators, numerous and rapidly changing parameters are generated by measurements from a variety of indicators and alarms, thus, there can be difficulties or delays to interpret a situation. In order to deal with this problem, many approaches have suggested based on computerized algorithms or networks. Although those studies suggested methods to diagnose accidents, if an unknown (or untrained) accident is given, they cannot respond as they do not know about it. In this light, this study aims at developing an algorithm to diagnose the accidents including “don’t know” response. Long short term memory recurrent neural network and the auto encoder are applied for implementing the algorithm including novelty detection function. The algorithm is validated with various examples regarding untrained cases to demonstrate its feasibility.

Keywords

LSTM Auto encoder Accident diagnosis Novelty detection 

Notes

Acknowledgement

This research was supported by Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Science, ICT & Future Planning (N01190021-06). Also, it was supported by the NRF grant funded by the Korean government (Ministry of Science and ICT) (2018M2B2B1065651).

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of Nuclear EngineeringChosun UniversityGwangjuRepublic of Korea

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