RESEARCH PAPER
Fault diagnosis of gear based on URP-CVAE-MGAN under imbalanced and small sample conditions
1
Shenyang University of Technology, China
2
Northeastern University, China
Submission date: 2024-08-29
Final revision date: 2024-10-21
Acceptance date: 2024-12-20
Online publication date: 2024-12-22
Publication date: 2024-12-22
Eksploatacja i Niezawodność – Maintenance and Reliability 2025;27(3):199417
HIGHLIGHTS
- The Un-threshold Recurrence Plots (URP) and Vision Transformer (ViT) improved by Dropkey are used for diagnosing gear fault types and severities.
- A new generated model, the Variational Autoencoder added Conditional variable (CVAE) combined with Generative Adversarial Network improved Mean feature difference function (MGAN), is used for data augmentation of un-threshold recurrence plots from gear under imbalanced and small sample conditions.
- Dropkey-ViT has more advantages in comprehensively capturing fault information compared with the comparison method.
KEYWORDS
Gear fault diagnosisGenerative Adversarial NetworkVision TransformerConditional Variational AutoencoderDropkeymean feature matching
TOPICS
ABSTRACT
To improve diagnosis accuracy for gear fault diagnosis under imbalanced and small sample conditions, a method combining the Un-threshold Recurrence Plots - Conditional Variational Autoencoder-Mean Generative Adversarial Network (URP-CVAE-MGAN) combined with Dropkey-Vision Transformer (DViT) is proposed. First, gear vibrational signals are transformed into Recurrence Plots (RP) images to extract more fault features without threshold effect. Then, a conditional variable and mean feature difference function are incorporated into VAE-GAN to improve the quality and diversity of generated samples, balancing the imbalanced and small sample sets. Dropkey is applied to the diagnosis model Vision Transformer to capture more fault information, improving diagnosis accuracy across various fault types and severities for gear. Finally, the proposed method is verified based on two datasets, demonstrating a significant accuracy improvement of up to 7.84% under the imbalanced and small samples, and confirming its feasibility and superiority.
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| eISSN: | 2956-3860 |
| ISSN: | 1507-2711 |
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