| 引用本文: | 赵学义1
,张晓光2,3,4
,陆凡凡5
,周 立5
,徐清晨.基于多分支稀疏残差网络的轴承故障诊断方法(J/M/D/N,J:杂志，M：书，D：论文，N：报纸).期刊名称,2025，42（6）：48-54 |
| CHEN X. Adap tive slidingmode contr ol for discrete2ti me multi2inputmulti2 out put systems[ J ]. Aut omatica, 2006, 42(6): 4272-435 |
|
| 本文已被：浏览 211次 下载 228次 |
码上扫一扫！ |
|
|
| 基于多分支稀疏残差网络的轴承故障诊断方法 |
|
赵学义1
,张晓光2,3,4
,陆凡凡5
,周 立5
,徐清晨
|
|
1. 安徽海螺水泥股份有限公司,安徽 芜湖 241000
2. 上海智质科技有限公司,上海 201801
3. 中国科学技术大学 计算机科学与技术学院,合肥 230026
4. 长三角信息智能创新研究院,安徽 芜湖 241000
5. 安徽智质工程技术有限公司,安徽 芜湖 241000
|
|
| 摘要: |
| 目的 工业生产中,滚动轴承运行环境复杂,环境噪声降低了常见网络模型对轴承故障识别的准确率,抗噪
声干扰能力低,为了提高模型识别准确率与抗噪声干扰能力,设计了多分支稀疏残差网络对轴承故障进行诊断。
方法 多分支稀疏残差网络采用多分支结构,分别采用不同卷积核对不同窗口大小的特征进行多尺度特征融合以
提高网络模型的识别准确率与抗噪声干扰能力;此外,将注意力机制融入多分支网络中,对各个分支卷积层的权重
进行调整;最后,为了提高所提方法的特征学习能力,引入稀疏残差连接,避免数据冗余。 结果 通过对比实验,验证
了所提方法的有效性,处于无噪声情况下,识别准确率达 99. 2%,处于强噪声情况下,模型识别准确率达 95. 85%,
在实际生产场景中模型的识别率为 98. 90%。 结论 实验结果表明:该模型有着很强的特征学习能力,轴承故障识别
精度较高,有着较强的抗噪声干扰能力,有着较好的实用价值。 |
| 关键词: 多分支 稀疏残差 故障诊断 多尺度特征融合 |
| DOI： |
| 分类号: |
| 基金项目: |
|
| Bearing Fault Diagnosis Method Based on Multi-branch Sparse Residual Network |
|
ZHAO Xueyi
1
ZHANG Xiaoguang
2 3 4
LU Fanfan
5
ZHOU Li
5
XU Qingchen
5
|
|
1. Anhui Conch Cement Co. Ltd. Wuhu 241000 Anhui China
2. Shanghai Zhizhi Technology Co. Ltd. Shanghai 201801 China
3. School of Computer Science and Technology University of Science and Technology of China Hefei 230026 China
4. Yangtze River Delta Information Intelligence Innovation Research Institute Wuhu 241000 Anhui China
5. Anhui Zhizhi Engineering Technology Co. Ltd. Wuhu 241000 Anhui China
|
| Abstract: |
| Objective In industrial production the operating environment of rolling bearings is complex and
environmental noise reduces the accuracy of common network models for bearing fault identification leading to low noise
interference resistance. To improve model identification accuracy and noise interference resistance a multi-branch sparse
residual network was designed for bearing fault diagnosis. Methods The multi-branch sparse residual network utilized a
multi-branch structure employing different convolution kernels to perform multi-scale feature fusion on features of varyingwindow sizes thereby enhancing the model?? s identification accuracy and resistance to noise interference. Additionally an
attention mechanism was integrated into the multi-branch network to adjust the weights of the convolutional layers in each
branch. Finally to improve the feature learning capability of the proposed method sparse residual connections were
introduced to avoid data redundancy. Results Through comparative experiments the effectiveness of the proposed method
was verified achieving an identification accuracy of 99. 2% under noise-free conditions and an identification accuracy of
95. 85% under strong noise conditions. In actual production scenarios the model?? s recognition rate was 98. 90%.
Conclusion The experimental results indicate that the model has strong feature learning capability. It achieves high
accuracy in bearing fault identification demonstrates strong resistance to noise interference and possesses significant
practical value. |
| Key words: multi-branch sparse residual fault diagnosis multi-scale feature fusion |
