A frequency channel-attention based vision Transformer method for bearing fault identification across different working conditions

Xiang, Ling; Bing, Hankun; Li, Xianze; Hu, Aijun

doi:10.1016/j.eswa.2024.125686

Expert Systems with Applications

2025

DOI: 10.1016/j.eswa.2024.125686

|View full text |Cite

A frequency channel-attention based vision Transformer method for bearing fault identification across different working conditions

Ling Xiang,

Hankun Bing,

Xianze Li

et al.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Publication Types

Select...

Article1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

A Novel Method for Fault Migration Diagnosis of Rolling Bearings Based on MSCVIT Model

Liu,

He,

Guo

et al. 2024

Electronics

View full text Add to dashboard Cite

The normal operation of rolling bearings is crucial to the performance and reliability of rotating machinery. However, the collected vibration signals are often mixed with complex noise, and the transformer network cannot fully extract the characteristics of the vibration signals. To solve this problem, we propose a data preprocessing method that utilizes singular value decomposition (SVD) and continuous wavelet transform (CWT) along with an improved vision transformer (ViT) model for fault diagnosis. First, the SVD is applied to identify the noise components to improve the data quality. Then, the CWT is used to convert the denoised signal into a two-dimensional (2D) time–frequency representation (TFR) to display the fault features more intuitively. Finally, an improved multi-scale convolutional block attention module (MSCBAM) is embedded into the ViT network to extract fault features. Experimental results on the classical Case Western Reserve University (CWRU) dataset show that the average diagnostic accuracy of the proposed method is 99.3%. Compared with six other fault diagnosis methods, the method proposed in this paper has also achieved good diagnostic results on three other datasets, which can be effectively applied to the timely handling of problematic equipment and reduce downtime.

show abstract

A Novel Method for Fault Migration Diagnosis of Rolling Bearings Based on MSCVIT Model

Liu,

He,

Guo

et al. 2024

Electronics

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

A frequency channel-attention based vision Transformer method for bearing fault identification across different working conditions

Cited by 1 publication

References 30 publications

A Novel Method for Fault Migration Diagnosis of Rolling Bearings Based on MSCVIT Model

A Novel Method for Fault Migration Diagnosis of Rolling Bearings Based on MSCVIT Model

Contact Info

Product

Resources

About