Presentation Details
Bearing Fault Diagnosis Based on Dual-channel DenseNet-GRU Model
ID：17
View Protection：ATTENDEE
Updated Time：2025-06-12 15:18:45 Hits：356
Oral Presentation
Start Time：Pending（Asia/Shanghai）
Duration：Pending
Session：No Session »
No files
Abstract
In practical engineering applications, noise often contaminates the fault signals of rolling bearings, making the accurate diagnosis of compound faults challenging. To address this issue, this paper introduces an enhanced dual-channel DenseNet-GRU model for the diagnosis of compound faults in rolling bearings. The model constructs a DenseNet channel for initial feature extraction, while integrating a gated recurrent unit (GRU) with convolutional and pooling layers to form a GRU channel, aiming to extract linear features. By employing a dual-channel connection approach, the model minimizes potential information loss or error accumulation that may occur in single-model structures. In the identification module, a multi-label classification framework is established to recognize compound faults. The proposed model underwent evaluation using the Case Western Reserve University (CWRU) dataset, with findings indicating that the DC-DenseNet-GRU architecture consistently delivers robust performance across varying load and noise scenarios.
Keywords
DenseNet, gated recurrent unit, fault diagnosis, rolling bearing
Speaker
锐 姚
讲师 长安大学Submission Author
All Comments
Important Date
-
Conference Date
Aug 01
2025
toAug 04
2025
-
Aug 20 2025
Draft paper submission deadline
Sponsored By
中国机械工程学会设备智能运维分会
Organized By
新疆大学
Contact Information
- Mr. Yanruqiang
- ya******@xjtu.edu.cn
Previous Conferences
-
2025-08-01 China wulumuqi
2025 International Conference on Equipment Intelligent Operation and Maintenance -
2023-09-21 China Hefei
2023 International Conference on Equipment Intelligent Operation and Maintenance
Submit Comment