A fast nonlinear equivalent magnetic network model for magnetic jack type control rod drive mechanism in reactor

Yang, Yun; Xu, Qiwei; Chen, Yangming; Yu, Tianda; Fu, Guozhong; Huang, Siyu

doi:10.1016/j.pnucene.2024.105058

Progress in Nuclear Energy

2024

DOI: 10.1016/j.pnucene.2024.105058

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A fast nonlinear equivalent magnetic network model for magnetic jack type control rod drive mechanism in reactor

Yun Yang,

Qiwei Xu,

Yangming Chen

et al.

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2024

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Cited by 3 publications

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A random effects Wiener process model with acceleration factors for remaining useful life prediction

Yang,

Zou,

Guo

et al. 2024

J Braz. Soc. Mech. Sci. Eng.

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A random effects Wiener process model with acceleration factors for remaining useful life prediction

Yang,

Zou,

Guo

et al. 2024

J Braz. Soc. Mech. Sci. Eng.

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Data-Driven Feature Extraction-Transformer: A Hybrid Fault Diagnosis Scheme Utilizing Acoustic Emission Signals

Ma,

Gao,

Wang

et al. 2024

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This paper introduces a novel network, DDFE-Transformer (Data-Driven Feature Extraction-Transformer), for fault diagnosis using acoustic emission signals. The DDFE-Transformer network integrates two primary modules: the DDFE module, focusing on noise reduction and feature enhancement, and the Transformer module. The DDFE module employs two techniques: the Wavelet Kernel Network (WKN) for noise reduction and the Convolutional Block Attention Module (CBAM) for feature enhancement. The wavelet function in the WKN reduces noise, while the attention mechanism in the CBAM enhances features. The Transformer module then processes the feature vectors and sends the results to the softmax layer for classification. To validate the proposed method’s efficacy, experiments were conducted using acoustic emission datasets from NASA Ames Research Center and the University of California, Berkeley. The results were compared using the four key metrics obtained through confusion matrix analysis. Experimental results show that the proposed method performs excellently in fault diagnosis using acoustic emission signals, achieving a high average accuracy of 99.84% and outperforming several baseline models, such as CNN, CNN-LSTM, CNN-GRU, VGG19, and ZFNet. The best-performing model, VGG19, only achieved an accuracy of 88.61%. Additionally, the findings suggest that integrating noise reduction and feature enhancement in a single framework significantly improves the network’s classification accuracy and robustness when analyzing acoustic emission signals.

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Deep Learning-Based Fatigue Strength Prediction for Ferrous Alloy

Huang,

Yan,

Zhang

et al. 2024

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As industrial development drives the increasing demand for steel, accurate estimation of the material’s fatigue strength has become crucial. Fatigue strength, a critical mechanical property of steel, is a primary factor in component failure within engineering applications. Traditional fatigue testing is both costly and time-consuming, and fatigue failure can lead to severe consequences. Therefore, the need to develop faster and more efficient methods for predicting fatigue strength is evident. In this paper, a fatigue strength dataset was established, incorporating data on material element composition, physical properties, and mechanical performance parameters that influence fatigue strength. A machine learning regression model was then applied to facilitate rapid and efficient fatigue strength prediction of ferrous alloys. Twenty characteristic parameters, selected for their practical relevance in engineering applications, were used as input variables, with fatigue strength as the output. Multiple algorithms were trained on the dataset, and a deep learning regression model was employed for the prediction of fatigue strength. The performance of the models was evaluated using metrics such as MAE, RMSE, R2, and MAPE. The results demonstrated the superiority of the proposed models and the effectiveness of the applied methodologies.

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A fast nonlinear equivalent magnetic network model for magnetic jack type control rod drive mechanism in reactor

Cited by 3 publications

References 18 publications

A random effects Wiener process model with acceleration factors for remaining useful life prediction

A random effects Wiener process model with acceleration factors for remaining useful life prediction

Data-Driven Feature Extraction-Transformer: A Hybrid Fault Diagnosis Scheme Utilizing Acoustic Emission Signals

Deep Learning-Based Fatigue Strength Prediction for Ferrous Alloy

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