Bearing Fault Diagnosis Method Based on Improved DenseNet-BiGRU Network
[1]SHEN Peng,WANG Shuhao,SUO Hongguang,et al.Bearing Fault Diagnosis Method Based on Improved DenseNet-BiGRU Network[J].Journal of Zhengzhou University (Engineering Science),2027,48(XX):1-9.
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Journal of Zhengzhou University (Engineering Science)[ISSN
1671-6833/CN
41-1339/T] Volume:
48
Number of periods:
2027 XX
Page number:
1-9
Column:
Public date:
2027-12-10
- Title:
- Bearing Fault Diagnosis Method Based on Improved DenseNet-BiGRU Network
- Keywords:
- rolling bearing; densely connected convolution network; bidirectional gated recurrent unit; attention mechanism
- CLC:
- TH133.33TP18
- DOI:
- -
- Abstract:
- To address the shortcomings of existing rolling bearing fault prediction models, such as their insufficient adaptability to noise and variable operating conditions, and their reliance on manual feature extraction which leads to strong subjectivity, a joint fault prediction model based on an improved DenseNet-BiGRU is proposed. This model combines the advantages of feature reuse in dense connection networks and the time series modeling capabilities of bidirectional gated recurrent units, and adds a channel attention mechanism between the models to construct an end-to-end fault prediction framework. The experiments used the Case Western Reserve University rolling bearing dataset. Under ideal conditions, the highest prediction accuracy reached 99.55%. When various noise interferences were added (signal-to-noise ratio range -9dB to 9dB), the average prediction accuracy remained at 91.71%, showing a significant improvement compared to common fault prediction models. Additionally, the model’s average prediction accuracy of 99.18% under cross-operating condition experiments (rotational speeds of 1772/1750/1730 r/min) was superior to the prediction results of other models. The experimental results demonstrate that this joint model can accurately predict the fault types of rolling bearings and exhibits superior prediction performance under different noise intensities and load conditions, verifying its robustness and adaptability to cross-operating conditions, providing a new solution for the field of fault diagnosis.
- References:
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Last Update:
2026-06-12
