Research on bearing fault diagnosis under mixed working condition based on Vision Transformer hybrid model

Yonglin Wang; Jian Zhou

doi:10.1117/12.3026521

27 March 2024 Research on bearing fault diagnosis under mixed working condition based on Vision Transformer hybrid model

Yonglin Wang, Jian Zhou

Author Affiliations +

Proceedings Volume 13105, International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023); 131053S (2024) https://doi.org/10.1117/12.3026521
Event: 3rd International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023), 2023, Qingdao, China

Abstract

In order to solve the problem that the bearing vibration signal identification under a single working condition is difficult to be applied to the fault identification and diagnosis of rolling bearing in the actual production, this paper proposes a classification method of bearing fault mode based on mixed working condition. In the actual industrial production, bearings are often in a variety of complex working states. The bearing fault diagnosis research under mixed working conditions can more accurately restore the working conditions in real industrial scenarios, and improve the practicability and reliability of fault diagnosis. First of all, the timing is realized through the gram angle field conversion, and the time dependence and correlation of the original time series of bearing vibration are retained, which realizes the rich representation of bearing vibration signal and efficient fault type identification. Secondly, in order to improve the fault identification rate, the advantages of EfficientNet and Vision Transformer (ViT) models in representation learning and feature extraction are given full play through the cross-domain fusion of EfficientNet and ViT models.Comparing the experiment of various classification methods show that the method proposed in this paper can effectively extract the fault characteristics of vibration signal, identify the correct rate is significantly higher than the typical classification method, in the migration experiment of two data sets, the classification method of mixed working condition has achieved good migration effect, indicating that the fault mode recognition method of mixed working condition is easier to be applied in the actual production.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yonglin Wang and Jian Zhou "Research on bearing fault diagnosis under mixed working condition based on Vision Transformer hybrid model", Proc. SPIE 13105, International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023), 131053S (27 March 2024); https://doi.org/10.1117/12.3026521

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
8 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

RGB color model

Data modeling

Transformers

Visual process modeling

Feature extraction

Vibration

Convolution

Show All Keywords

Keywords/Phrases

Search In:

Publication Years