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ORIGINAL RESEARCH article

Front. Mech. Eng.
Sec. Vibration Systems
Volume 10 - 2024 | doi: 10.3389/fmech.2024.1430542
This article is part of the Research Topic Applications of Artificial Intelligence in Vibration Engineering and Big Data Analytics. View all articles

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

Provisionally accepted
Wenchang Xu Wenchang Xu 1*Zhexian Zhang Zhexian Zhang 1Zhijun Wang Zhijun Wang 1Tianao Wang Tianao Wang 1Zijian He Zijian He 1Shijie Dong Shijie Dong 2
  • 1 School of Electrical and Information Engineering, Anhui University of Technology, Ma'anshan, China
  • 2 School of Mechanical Engineering, Anhui University of Technology, Ma'anshan, China

The final, formatted version of the article will be published soon.

    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

    Keywords: Fault diagnosis, Compound fault, few-shot, Feature engineering, artificial intelligence

    Received: 10 May 2024; Accepted: 08 Jul 2024.

    Copyright: © 2024 Xu, Zhang, Wang, Wang, He and Dong. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Wenchang Xu, School of Electrical and Information Engineering, Anhui University of Technology, Ma'anshan, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.