Study on Transient Parameter Prediction and Fault Diagnosis of Nuclear Power Plant Based on LSTM Neural Network

Liu Tao; Xie Jinsen

doi:10.13832/j.jnpe.2024.080036

Volume 46 Issue 2

Apr. 2025

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Liu Tao, Xie Jinsen. Study on Transient Parameter Prediction and Fault Diagnosis of Nuclear Power Plant Based on LSTM Neural Network[J]. Nuclear Power Engineering, 2025, 46(2): 230-238. doi: 10.13832/j.jnpe.2024.080036

Citation:

Liu Tao, Xie Jinsen. Study on Transient Parameter Prediction and Fault Diagnosis of Nuclear Power Plant Based on LSTM Neural Network[J]. Nuclear Power Engineering, 2025, 46(2): 230-238. doi: 10.13832/j.jnpe.2024.080036

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Study on Transient Parameter Prediction and Fault Diagnosis of Nuclear Power Plant Based on LSTM Neural Network

doi: 10.13832/j.jnpe.2024.080036

Liu Tao^{1, 2
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Xie Jinsen^{2, 3
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1.
School of Resources, Environment and Safety Engineering, University of South China, Hengyang, Hunan, 421001, China
2.
Nuclear Energy and Nuclear Technology Engineering Virtual Simulation Experiment Teaching Center, University of South China, Hengyang, Hunan, 421001, China
3.
School of Nuclear Science and Technology, University of South China, Hengyang, Hunan, 421001, China

Received Date: 2024-07-14
Rev Recd Date: 2024-10-08

Available Online: 2025-01-15

Publish Date: 2025-04-15

Abstract

Abstract

To improve the accuracy and real-time performance of parameter prediction and fault diagnosis under transient conditions in nuclear power plants, this study employs a Long Short-Term Memory (LSTM) neural network model for prediction and diagnosis. By generating and randomizing fault scenarios, the model’s dependence on specific patterns is reduced, and its generalization capability in unknown fault situations is enhanced. The study integrates SHAP (SHapley Additive exPlanations) to conduct interpretability analysis on the parameter prediction results, evaluates the impact of different input features on the model’s predictive performance, and verifies its effectiveness under sensor failures and data transmission errors. Furthermore, fault diagnosis is performed on transient parameters with different noise levels to validate the model's robustness. The results demonstrate that the LSTM model achieves high accuracy in both prediction and diagnosis, and it maintains excellent performance even under sensor failures, data transmission errors, and noisy data. The method proposed in this study can improve the safety and stability of nuclear power plant operation and provide effective technical support for safety under accident conditions.
- Nuclear power plant,
- Transient parameter prediction,
- Fault diagnosis,
- Long short-term memory neural network,
- SHapley Additive exPlanations (SHAP) method

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References(14)

References

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