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2025 , Vol. 30 >Issue 5: 1018 - 1027

DOI: https://doi.org/10.1007/s12204-023-2684-x

Computing & Computer Technologies

Fault Identification Method for In-Core Self-Powered Neutron Detectors Combining Graph Convolutional Network and Stacking Ensemble Learning

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  • College of Electrical Engineering and Automation, Fuzhou University, Fuzhou 350108, China

Received date: 2023-07-10

  Accepted date: 2023-07-31

  Online published: 2023-12-12

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Abstract

Self-powered neutron detectors (SPNDs) play a critical role in monitoring the safety margins and overall health of reactors, directly affecting safe operation within the reactor. In this work, a novel fault identification method based on graph convolutional networks (GCN) and Stacking ensemble learning is proposed for SPNDs. The GCN is employed to extract the spatial neighborhood information of SPNDs at different positions, and residuals are obtained by nonlinear fitting of SPND signals. In order to completely extract the time-varying features from residual sequences, the Stacking fusion model, integrated with various algorithms, is developed and enables the identification of five conditions for SPNDs: normal, drift, bias, precision degradation, and complete failure. The results demonstrate that the integration of diverse base-learners in the GCN-Stacking model exhibits advantages over a single model as well as enhances the stability and reliability in fault identification. Additionally, the GCN-Stacking model maintains higher accuracy in identifying faults at different reactor power levels.

Cite this article

LIN Weiqing, LU Yanzhen, MIAO Xiren, QIU Xinghua . Fault Identification Method for In-Core Self-Powered Neutron Detectors Combining Graph Convolutional Network and Stacking Ensemble Learning[J]. Journal of Shanghai Jiaotong University(Science), 2025 , 30(5) : 1018 -1027 . DOI: 10.1007/s12204-023-2684-x

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