Semi-supervised gearbox fault diagnosis under variable working conditions based on masked contrastive learning

To address the problem that it is difficult to label variable working condition gearbox fault samples and the significant data distribution discrepancies in practical engineering, which result in reduced accuracy of fault diagnosis models, a semi-supervised gearbox fault diagnosis method based on ma...

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Auteurs principaux:	ZHANG Huiyun, ZUO Fangjun, LI Hang, YU Xi
Format:	Artigo
Langue:	Chinês
Publié:	Editorial Office of Journal of Mechanical Strength 2025-06-01
Collection:	Jixie qiangdu
Sujets:	Gearbox Variable working condition Fault diagnosis Contrastive learning Semi-supervised
Accès en ligne:	http://www.jxqd.net.cn/thesisDetails#DOI：10.16579/j.issn.1001.9669.2025.06.009
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