Early fault detection method for nuclear power plants based on sparse denoising autoencoder and kernel principal component analysis

•A data-driven fault detection method is proposed, which can accurately detect early faults in nuclear power plants.•The proposed grouping strategy effectively overcomes the insensitivity of single models to early faults.•The combination of SDAE and KPCA demonstrates good feature extraction and nonl...

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Veröffentlicht in:	Annals of nuclear energy Jg. 220; S. 111460
Hauptverfasser:	Yin, Wenzhe, Xia, Hong, Huang, Xueying, Shan, Longfei, Ran, Wenhao, Jia, Zhujun
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier Ltd 15.09.2025
Schlagworte:	Early fault Fault detection Kernel principal component analysis Nuclear power plant Sparse denoising autoencoder Fault detection Nuclear power plant Kernel principal component analysis Early fault Sparse denoising autoencoder
ISSN:	0306-4549
Online-Zugang:	Volltext
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