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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Veröffentlicht in:	Knowledge-based systems Jg. 296; S. 111922
Hauptverfasser:	Chen, Zhuohang, Liu, Shen, Li, Chao, Chang, Yuanhong, Chen, Jinglong, Feng, Gaoshan, He, Shuilong
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier B.V 19.07.2024
Schlagworte:	Domain shift Fault diagnosis Graph autoencoder Self-supervised learning Fault diagnosis Domain shift Graph autoencoder Self-supervised learning
ISSN:	0950-7051, 1872-7409
Online-Zugang:	Volltext
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