The reactor pressure vessel(RPV)is susceptible to brittle fracture due to the influence of ion irradiation and high temperature,which presents a significant risk to the safe operation of nuclear reactors.It has been d...The reactor pressure vessel(RPV)is susceptible to brittle fracture due to the influence of ion irradiation and high temperature,which presents a significant risk to the safe operation of nuclear reactors.It has been demonstrated that pulsed electric current can effectively address the issue of embrittlement in RPV steel.However,the relationship between pulse parameters(duty ratio,frequency,current,and time)and the effectiveness of pulse current processing has not been systematically studied.The application of machine learning methods enables autonomous exploration and learning of the relationship between data.Consequently,this study proposes a machine learning method based on the random forest model to establish the relationship between the parameters of electrical pulses and the repair effect of RPV steel.A generative adversarial network is employed to enhance data diversity and scalability,while a particle swarm optimization algorithm is utilized to optimize the initialization weights and biases of the random forest model,aiming to improve the model’s fitting ability and training performance.The results indicate that the coefficient of determination R-square(R^(2)),root mean squared error and mean absolute error values are 0.934,0.045,and 0.036,respectively,suggesting that the model has the potential to predict the performance recovery of RPV steel after pulsed electric field treatment.The prediction of the impact of pulse current parameters on the repair effect will help to enhance and optimize the repair process,thereby providing a scientific basis for pulse current repair processing.展开更多
Intelligent fault diagnosis is an important method in rotating machinery fault diagnosis and equipment health management.To deal with co-frequency vibration faults,a type of typical fault in rotating machinery,this pa...Intelligent fault diagnosis is an important method in rotating machinery fault diagnosis and equipment health management.To deal with co-frequency vibration faults,a type of typical fault in rotating machinery,this paper proposes a fault diagnosis method based on the stacked autoencoder(SAE)and ensembled ResNet-SVM.Furthermore,the time-and frequency-domain features of several co-frequency vibration faults are summarized based on the mechanism analysis and calculated using actual vibration data.To realize and validate the high-precision diagnosis method of rotating equipment with co-frequency faults proposed in this study,the following three criteria are required:First,to improve the effectiveness and robustness of the ensembled model and the sliding window using data augmentation,adding noise,autoencoder(AE)and SAE methods are analyzed in terms of principle and practical effects.Second,ResNet is used as the feature extractor for the ensembled ResNet-SVM model.Feature extraction is carried out twice,and the extracted co-frequency fault features are more comprehensive.Finally,the data augmentation method and ensemble ResNet-SVM are combined for fault diagnosis and compared with other methods.The experimental results show that the accuracy of the proposed method can exceed 99.9%.展开更多
基金financially supported by the National Natural Science Foundation of China(U21B2082,52474410)the National Key R&D Program of China(2023YFB3709903,2020 YFA0714900)+5 种基金the Key R&D Program of Shandong Province,China(2023CXGC010406)the Scientific Research Special Project for First-Class Disciplines in Inner Mongolia Autonomous Region(YLXKZXNKD-001)the Natural Science Foundation of Inner Mongolia Autonomous Region of China(2024ZD06)the Technology Support Project for the Construction of Major Innovation Platforms in Inner Mongolia Autonomous Region(XM2024XTGXQ16)the Beijing Municipal Natural Science Foundation(2222065)the Fundamental Research Funds for the Central Universities(FRF-TP-22-02C2).
文摘The reactor pressure vessel(RPV)is susceptible to brittle fracture due to the influence of ion irradiation and high temperature,which presents a significant risk to the safe operation of nuclear reactors.It has been demonstrated that pulsed electric current can effectively address the issue of embrittlement in RPV steel.However,the relationship between pulse parameters(duty ratio,frequency,current,and time)and the effectiveness of pulse current processing has not been systematically studied.The application of machine learning methods enables autonomous exploration and learning of the relationship between data.Consequently,this study proposes a machine learning method based on the random forest model to establish the relationship between the parameters of electrical pulses and the repair effect of RPV steel.A generative adversarial network is employed to enhance data diversity and scalability,while a particle swarm optimization algorithm is utilized to optimize the initialization weights and biases of the random forest model,aiming to improve the model’s fitting ability and training performance.The results indicate that the coefficient of determination R-square(R^(2)),root mean squared error and mean absolute error values are 0.934,0.045,and 0.036,respectively,suggesting that the model has the potential to predict the performance recovery of RPV steel after pulsed electric field treatment.The prediction of the impact of pulse current parameters on the repair effect will help to enhance and optimize the repair process,thereby providing a scientific basis for pulse current repair processing.
基金Supported by National Natural Science Foundation of China (Grant No.51875031)Beijing Municipal Natural Science Foundation (Grant No.3212010)。
文摘Intelligent fault diagnosis is an important method in rotating machinery fault diagnosis and equipment health management.To deal with co-frequency vibration faults,a type of typical fault in rotating machinery,this paper proposes a fault diagnosis method based on the stacked autoencoder(SAE)and ensembled ResNet-SVM.Furthermore,the time-and frequency-domain features of several co-frequency vibration faults are summarized based on the mechanism analysis and calculated using actual vibration data.To realize and validate the high-precision diagnosis method of rotating equipment with co-frequency faults proposed in this study,the following three criteria are required:First,to improve the effectiveness and robustness of the ensembled model and the sliding window using data augmentation,adding noise,autoencoder(AE)and SAE methods are analyzed in terms of principle and practical effects.Second,ResNet is used as the feature extractor for the ensembled ResNet-SVM model.Feature extraction is carried out twice,and the extracted co-frequency fault features are more comprehensive.Finally,the data augmentation method and ensemble ResNet-SVM are combined for fault diagnosis and compared with other methods.The experimental results show that the accuracy of the proposed method can exceed 99.9%.
