Development and Validation of a Nuclear Power Plant Fault Diagnosis System Based on Deep Learning

Bing Liu, Jichong Lei, Jinsen Xie () and Jianliang Zhou ()
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Bing Liu: School of Nuclear Science and Technology, University of South China, Hengyang 421000, China
Jichong Lei: School of Nuclear Science and Technology, University of South China, Hengyang 421000, China
Jinsen Xie: School of Nuclear Science and Technology, University of South China, Hengyang 421000, China
Jianliang Zhou: School of Nuclear Science and Technology, University of South China, Hengyang 421000, China

Energies, 2022, vol. 15, issue 22, 1-15

Abstract: As artificial intelligence technology has progressed, numerous businesses have used intelligent diagnostic technology. This study developed a deep LSTM neural network for a nuclear power plant to defect diagnostics. PCTRAN is used to accomplish data extraction for distinct faults and varied fault degrees of the PCTRAN code, and some essential nuclear parameters are chosen as feature quantities. The training, validation, and test sets are collected using random sampling at a ratio of 7:1:2, and the proper hyperparameters are selected to construct the deep LSTM neural network. The test findings indicate that the fault identification rate of the nuclear power plant fault diagnostic model based on a deep LSTM neural network is more than 99 percent, first validating the applicability of a deep LSTM neural network for a nuclear power plant fault-diagnosis model.

Keywords: nuclear power plant; PCTRAN; deep learning; fault diagnosis; deep LSTM (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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