CFNet: cross-domain bearing fault diagnosis under different operating conditions
ID：109
Submission ID：5 View Protection：ATTENDEE
Updated Time：2024-10-23 09:58:44 Hits：80
Poster Presentation
Start Time：Pending （Asia/Shanghai）
Duration：Pending
Session：[No Session] » [No Session Block]
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Abstract
The methods based on unsupervised domain adaptation are now widely used in the diagnosis of bearing faults. However, the global dependence of features in the feature extraction process is often overlooked. This global dependence is crucial for accurately diagnosing bearing faults, as it can reveal the distribution and variation patterns of fault signals in the overall structure. Therefore, we propose a Centralized Features Network (CFNet) for bearing fault diagnosis. The core of CFNet lies in its Transformer-based feature extractor, which not only captures the local details of fault signals but also preserves the global dependence, thereby achieving comprehensive analysis of fault signals. Furthermore, an Explicit Visual Center Module is proposed to further improve the fusion of long-distance features and local angular regions. Finally, a subdomain adaptation module is also proposed to transform the features of each domain to achieve subclass alignment of the feature space distribution. We have performed experiments on the CWRU and a self-built dataset using the mentioned model. The results demonstrate the effectiveness of our model.
Keywords
bearing fault diagnosis;,unsupervised domain adaptation (UDA),global dependency relationship (GDR),subclass alignment,CFNet
Speaker
SunTong
Hefei university of technologySubmission Author
All comments
15th August 2024 31st August 2024- Manuscript Submission
15th September 2024 - Acceptance Notification
1st October 2024 - Camera Ready Submission
1st October 2024 – Early Bird Registration
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