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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Bibliographic Details

Published in:	Annals of nuclear energy Vol. 220; p. 111460
Main Authors:	Yin, Wenzhe, Xia, Hong, Huang, Xueying, Shan, Longfei, Ran, Wenhao, Jia, Zhujun
Format:	Journal Article
Language:	English
Published:	Elsevier Ltd 15.09.2025
Subjects:	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 Access:	Get full text
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