Research on Fault Prediction of Nuclear Safety-Class Signal Conditioning Module Based on Improved GRU

Zhi Chen (), Miaoxin Dai, Jie Liu and Wei Jiang
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Zhi Chen: National Key Laboratory of Nuclear Reactor Technology, Nuclear Power Institute of China, Chengdu 610213, China
Miaoxin Dai: School of Computer Science, University of South China, Hengyang 421200, China
Jie Liu: School of Computer Science, University of South China, Hengyang 421200, China
Wei Jiang: National Key Laboratory of Nuclear Reactor Technology, Nuclear Power Institute of China, Chengdu 610213, China

Energies, 2024, vol. 17, issue 16, 1-16

Abstract: To improve the reliability and maintainability of the nuclear safety-class digital control system (DCS), this paper conducts a study on the fault prediction of critical components in the output circuit of the nuclear safety-class signal conditioning module. To address the issue of insufficient feature extraction for the minor offset fault feature and the low accuracy of fault prediction, a predictive model based on stacked denoising autoencoder (SDAE) feature extraction and an improved gated recurrent unit (GRU) is proposed. Therefore, fault simulation modeling is performed for critical components of the signal output circuit to obtain fault datasets of critical components, and the SDAE model is used to extract fault features. The fault prediction model based on GRU is established, and the number of hidden layers, the number of hidden layer nodes, and the learning rate of the GRU model are optimized using the adaptive gray wolf optimization algorithm (AGWO). The prediction performance evaluation metrics include the root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and absolute error (EA), which are used for evaluating the prediction results of models such as the AGWO-GRU model, recurrent neural network (RNN) model, and long short-term memory network (LSTM). The results show that the GRU model optimized by AGWO has a better prediction accuracy (errors range within 0.01%) for the faults of the circuit critical components, and, moreover, can accurately and stably predict the fault trend of the circuit.

Keywords: nuclear safety-class signal conditioning module; stacked denoising self-coding network; feature extraction; gate recurrent unit; fault prediction (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: 2024
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