Research On Intelligent Fault Diagnosis Technology For Nuclear Power Plant Based On Deep Learning

As a highly complex nonlinear system,nuclear power plants have very strict safety requirements,and therefore require more advanced system-level fault diagnosis technologies to ensure their safe operation.With the rapid development of AI technology,traditional diagnostic technologies have gradually shifted towards intelligent approaches.This paper focuses on the challenges of small fault samples,unlabeled data and inconsistent data distributions in nuclear power plant fault diagnosis.Specifically,we propose two methods based on deep learning: a single power level fault diagnosis method and a cross-power level fault diagnosis method based on deep transfer learning.These methods aim to improve the accuracy and reliability of nuclear power plant fault diagnosis and ensure the safety and stability of nuclear reactors.To address the problem of label-free data processing in traditional nuclear power plant fault diagnosis,this paper proposes a deep neural network model for nuclear power plant fault diagnosis based on deep belief networks’ powerful feature learning abilities combined with sparse noise-reducing auto-encoders’ excellent robustness.The model uses deep belief network and stacked auto-encoders to extract deep features from data and trains the model layer by layer using iterative learning.Fine-tuning is also performed through parameter tuning to improve the model’s feature extraction ability,accuracy,and efficiency of diagnosis.To further overcome the limitations of data dependency in traditional fault diagnosis,this paper presents a deep transfer learning model for cross-power level fault diagnosis of nuclear power plants.The model leverages the powerful knowledge migration capability of transfer learning and deep learning methods.A pre-trained network model is used to extract migratable features between the source and target power levels.A fine-grained deep domain adaptive method is then employed to reduce the differences in feature distributions between different power levels,enabling effective diagnosis of nuclear power plant faults at various power levels and improving the generalization ability of the model.This paper provides a comprehensive overview of the current research status of intelligent fault diagnosis methods for nuclear power plants.It proposes two different fault diagnosis methods: an improved deep learning-based method and a deep migration learning-based method.The experiments are conducted using the simulation software PCTRAN to provide data support and validate the effectiveness and reliability of the two fault diagnosis systems.The fault diagnosis accuracy is compared at the same power level using both deep confidence networks and noise-reducing self-encoders.Comparative tests of fault diagnosis accuracy across power levels are conducted based on deep convolutional neural networks and migration component analysis methods,and the resulting fault classification is visualized using confusion matrices.The experimental results demonstrate that the deep learning fault diagnosis method proposed in this paper achieves better diagnosis results through feature extraction and layer-bylayer pre-training.The proposed deep migration learning fault diagnosis method achieves effective migration learning of data,improving the accuracy of the system.In general,the two intelligent methods proposed in this paper can effectively improve the accuracy and reliability of fault diagnosis,and provide a guarantee for the safe and stable operation of nuclear power plants,which has practical value and good application prospects in practical applications.Overall,the two intelligent methods proposed in this paper can enhance the accuracy and reliability of fault diagnosis,ensuring the safe and stable operation of nuclear power plants.These methods have practical value and promising application prospects.