Research on small sample bearing fault classification based on transfer learning and data augmentation

Ruizhi Li; Fanghua Yang; Qing Sun; Tao Zhang

doi:10.1117/12.2640892

2 December 2022 Research on small sample bearing fault classification based on transfer learning and data augmentation

Ruizhi Li, Fanghua Yang, Qing Sun, Tao Zhang

Proceedings Volume 12288, International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2022); 1228827 (2022) https://doi.org/10.1117/12.2640892
Event: International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2022), 2022, Zhuhai, China

Abstract

In the conventional mechanical bearing fault classification problem, deep learning has been widely used and has achieved very good results. However, in practical engineering, the lack of training data makes it difficult for the model to achieve the desired effect, and the model trained with the same data will also have the problem of insufficient generalization ability. Under the premise of small samples, this paper starts with transfer learning and data enhancement technology, uses quadratic interpolation, Mixup, sliding time window methods, and optimizes the model structure and hyperparameters to achieve the fault diagnosis of different bearings and working conditions a better classification effect.

Citation Download Citation

Ruizhi Li, Fanghua Yang, Qing Sun, and Tao Zhang "Research on small sample bearing fault classification based on transfer learning and data augmentation", Proc. SPIE 12288, International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2022), 1228827 (2 December 2022); https://doi.org/10.1117/12.2640892

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