2024
DOI: 10.3390/app14198582
|View full text |Cite
|
Sign up to set email alerts
|

A Bearing Fault Diagnosis Method in Scenarios of Imbalanced Samples and Insufficient Labeled Samples

Xiaohan Cheng,
Yuxin Lu,
Zhihao Liang
et al.

Abstract: In practical working environments, rolling bearings are one of the components that are prone to failure. Their vibration signal samples are faced with challenges, mainly including the imbalance between normal and fault samples as well as an insufficient number of labeled samples. This study proposes a sample-expansion method based on generative adversarial networks (GANs) and a fault diagnosis method based on a transformer to solve the above issues. First, selective kernel networks (SKNets) and a genetic algor… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

0
0
0

Publication Types

Select...

Relationship

0
0

Authors

Journals

citations
Cited by 0 publications
references
References 30 publications
0
0
0
Order By: Relevance

No citations

Set email alert for when this publication receives citations?