Enhancing equipment safeguarding in IIoT: A self-supervised fault diagnosis paradigm based on asymmetric graph autoencoder

Thanks to the sufficient monitoring data provided by Industrial Internet of Things (IIoT), intelligent fault diagnosis technology has demonstrated remarkable performance in safeguarding equipment. However, the effectiveness of existing methods heavily relies on manually labeled data. Unfortunately,...

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Vydané v:	Knowledge-based systems Ročník 296; s. 111922
Hlavní autori:	Chen, Zhuohang, Liu, Shen, Li, Chao, Chang, Yuanhong, Chen, Jinglong, Feng, Gaoshan, He, Shuilong
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Elsevier B.V 19.07.2024
Predmet:	Domain shift Fault diagnosis Graph autoencoder Self-supervised learning Fault diagnosis Domain shift Graph autoencoder Self-supervised learning
ISSN:	0950-7051, 1872-7409
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