Unsupervised anomaly detection of nuclear power plants under noise background based on convolutional adversarial autoencoder combining self-attention mechanism

•This study involves an anomaly detection problem of NPPs under a noisy background, which is rarely studied in previous research.•A novel unsupervised anomaly detection method (CAAE-SA) is designed for solving UAD-NPPs.•The self-attention mechanism is proposed to improve the robustness of CAAE-SA.•E...

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Bibliographic Details

Published in:	Nuclear engineering and design Vol. 428; p. 113493
Main Authors:	Sun, Xiang, Guo, Shunsheng, Liu, Shiqiao, Guo, Jun, Du, Baigang
Format:	Journal Article
Language:	English
Published:	Elsevier B.V 01.11.2024
Subjects:	Adversarial convolutional autoencoder Noise background Nuclear power plants Self-attention mechanism Unsupervised anomaly detection Noise background Self-attention mechanism Nuclear power plants Adversarial convolutional autoencoder Unsupervised anomaly detection
ISSN:	0029-5493
Online Access:	Get full text
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