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

Author:

Lin Weiqing,Lu Yanzhen,Miao Xiren,Qiu Xinghua

Publisher

Springer Science and Business Media LLC

Subject

Multidisciplinary

Reference28 articles.

1. SANG Y D, DENG B J, ZHANG Q M, et al. Development and verification of a simulation toolkit for Self-Powered Neutron Detector [J]. Annals of Nuclear Energy, 2021, 150: 107784.

2. PENG X J, LI Q, WANG K. Fault detection and isolation for self powered neutron detectors based on Principal Component Analysis [J]. Annals of Nuclear Energy, 2015, 85: 213–219.

3. XU Y, CAI Y Z, SONG L. Review of research on condition assessment of nuclear power plant equipment based on data-driven [J]. Journal of Shanghai Jiao Tong University, 2022, 56(3): 267–278 (in Chinese).

4. CHEN Y S, YANG Y H, LIN M, et al. Fault diagnosis technology of nuclear power plant based on deep learning neural network [J]. Journal of Shanghai Jiao Tong University, 2018, 52(S1): 58–61 (in Chinese).

5. LI W, PENG M J, WANG Q Z. Improved PCA method for sensor fault detection and isolation in a nuclear power plant [J]. Nuclear Engineering and Technology, 2019, 51(1): 146–154.

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