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,...

Full description

Bibliographic Details

Published in:	Knowledge-based systems Vol. 296; p. 111922
Main Authors:	Chen, Zhuohang, Liu, Shen, Li, Chao, Chang, Yuanhong, Chen, Jinglong, Feng, Gaoshan, He, Shuilong
Format:	Journal Article
Language:	English
Published:	Elsevier B.V 19.07.2024
Subjects:	Domain shift Fault diagnosis Graph autoencoder Self-supervised learning Fault diagnosis Domain shift Graph autoencoder Self-supervised learning
ISSN:	0950-7051, 1872-7409
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!