Development and Validation of a Nuclear Power Plant Fault Diagnosis System Based on Deep Learning
Bing Liu, Jichong Lei, Jinsen Xie, Jian‐Liang Zhou
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.