Transient parameters prediction and fault diagnosis of nuclear power plant based on Long Short-Term Memory neural network

To enhance the accuracy and real-time performance of transient condition parameter prediction and fault diagnosis in nuclear power plants, this study employs an LSTM neural network model for prediction and diagnosis. By generating and randomizing fault scenarios, the model's dependency on specific patterns is reduced, improving its generalization ability in previously unseen fault conditions. The study incorporates the SHAP method to provide an interpretive analysis of the model's parameter prediction results, assessing the impact of different input features on the model's predictive performance and validating the model's behavior under sensor faults and data transmission errors. Additionally, fault diagnosis was conducted for transient parameters with varying levels of noise, verifying the model’s robustness. The results demonstrate that the LSTM model achieves high accuracy in both prediction and diagnosis, performing well even in the presence of sensor faults, data transmission errors, and noisy data. The proposed LSTM approach enhances the operational safety and stability of nuclear power plants, providing effective technical support for safety under accident conditions.