RESEARCH PAPER
Fault diagnosis method of automobile rolling bearing based on transfer learning and improved DenseNet
1
School of Aviation and Transportation, Jiansu College of Engineering and Technology, China
2
School of Mechanical Engineering, Xinjiang University, China
Submission date: 2024-08-27
Final revision date: 2024-09-23
Acceptance date: 2024-10-15
Online publication date: 2024-10-20
Publication date: 2024-10-20
Corresponding author
Xinxin Lu
School of Aviation and Transportation, Jiansu College of Engineering and Technology, 87 Qingnian Middle Road, Nantong City, Jiangsu Pro, 226001, Nantong, China
School of Aviation and Transportation, Jiansu College of Engineering and Technology, 87 Qingnian Middle Road, Nantong City, Jiangsu Pro, 226001, Nantong, China
Eksploatacja i Niezawodność – Maintenance and Reliability 2025;27(2):194675
HIGHLIGHTS
- The conversion of vibration data into image data improves diagnostic accuracy.
- Sub domain adaptation improves the diagnostic ability for cross condition data.
- Attention mechanism improves diagnostic efficiency and reduces diagnostic time.
KEYWORDS
TOPICS
ABSTRACT
Aiming at the problems caused by ignoring the time series characteristics, the scarcity of labeled data and the long diagnosis time in the fault diagnosis of one-dimensional vibration signals of automobile bearings, a new method combining improved DenseNet and transfer learning is proposed in this study. This method uses Recurrent Plot (RP) technology to convert one-dimensional vibration data into two-dimensional images to fully tap the potential value of time series. By optimizing the DenseNet network structure, the fault features are extracted effectively. Lightweight network design and MobileViT Attention mechanism are used to reduce the number of parameters and improve computing efficiency. With the help of transfer learning technology, the fault features in the source domain are transferred to the target domain, which solves the problem of cross-condition diagnosis and greatly reduces the diagnosis time. The experimental results show that the proposed method can improve the accuracy of fault identification and diagnosis efficiency, and achieve accurate classification.
ACKNOWLEDGEMENTS
The work is supported by theNational Vocational Education Teacher Teaching Innovation Team Project (ZI2021020406), Higher Education Project of Jiangsu Province (2021JSJG036), Nantong Basic Science Research Youth Innovation Project (JC12022090), Nantong Scientific Research Project (MS2023013),Research Projects of Jiangsu College of Engineering and Technology (GYKY/2022/13, GYKY/2022/14). The authors also wish to thank them for their financial support.
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| eISSN: | 2956-3860 |
| ISSN: | 1507-2711 |
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