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Toward compound fault diagnosis via EMAGAN and large kernel augmented few-shot learning.

Authors :
Wenchang Xu
Zhexian Zhang
Zhijun Wang
Tianao Wang
Zijian He
Shijie Dong
Source :
Frontiers in Mechanical Engineering; 2024, p1-13, 13p
Publication Year :
2024

Abstract

Bearings are essential in machinery. Damage to them can cause financial losses and safety risks at industrial sites. Therefore, it is necessary to design an accurate diagnostic model. Although many bearing fault diagnosis methods have been proposed recently, they still cannot meet the requirements of high-accurate prediction of bearing faults. There are several challenges in this: 1) In practical settings, gathering sufficient and balanced sample data for training diagnostic network models proves challenging. 2) The damage to bearings in real industrial production sites is not singular, and compound faults are also a huge challenge for diagnostic networks. To address these issues, this study introduces a novel fault diagnosis model called EMALKNet that integrates DCGAN with Efficient Multi-Scale Attention (EMAGAN) and RepLKNet-XL, enhancing the detection and analysis of bearing faults in industrial machinery. This model employs EMAGAN to explore the underlying distribution of raw data, thereby enlarging the fault sample pool and enhancing the model's diagnostic capabilities; The large kernel structure of RepLKNet-XL is different from the current mainstream small kernel and has stronger representation extraction ability. The proposed method has been validated on the Paderborn University dataset and the Huazhong University of Science and Technology dataset. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
22973079
Database :
Complementary Index
Journal :
Frontiers in Mechanical Engineering
Publication Type :
Academic Journal
Accession number :
179008482
Full Text :
https://doi.org/10.3389/fmech.2024.1430542