A bearing fault diagnosis method based on VGG SENet-BiGRU global attention
Rolling bearings are the core devices in the industrial field, widely used in machinery production and automation industry. Therefore, it is necessary to detect the fault of rolling bearing. In this paper, an improved VGG model based on Channel Attention mechanism (SENet) is proposed to extract the spatial features of multi-scale features after fault signal preprocessing. The BiGRU network model based on Global Attention optimization is proposed to extract the time domain features of multi-scale features after fault signal pretreatment. Excellent results are obtained by parallel fusion for fault detection.
Authors
- Guangbin Wang (ORCID: https://orcid.org/0000-0001-7410-5540)
- Xiaoming Xu (ORCID: https://orcid.org/0000-0003-1149-7123)
- Mingxin Hou (ORCID: https://orcid.org/0000-0001-7751-4120)
- Youlin Liang (ORCID: https://orcid.org/0000-0002-2261-2724)
- Qiang Liu (ORCID: https://orcid.org/0000-0002-5451-4862)
- Peirong Chen
- Minghao Chen (ORCID: https://orcid.org/0000-0002-2871-5812)
- Hongxi Lai
- Zhengwei Dai (ORCID: https://orcid.org/0009-0001-0310-6746)
Institutions
- Lingnan Normal University (CN)
- Guangdong Ocean University (CN)
Publication Details
- Journal
- IET conference proceedings.
- Published
- 2025-01-01
- DOI
- https://doi.org/10.1049/icp.2024.3618
- Citations
- 1
- Primary Topic
- Advanced Decision-Making Techniques
- Type
- article
- Field-Weighted Citation Impact
- 3.25