报告详情
Double-Path Residual Network with Attention Mechanism and Bidirectional Gated Recurrent Unit and its Application in Planetary Gear Fault Classification
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更新：2025-06-13 18:18:17 浏览：20次
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摘要
The convolution neural network structure in double-path convolution with attention mechanism (AM) and bidirectional gated recurrent unit (BGRU), termed DCAM-BGRU, suffers from the problem of gradient vanishing or exploding with the network increases, affecting the recognition accuracy and stability in the fault classification task. To alleviate this problem, a double-path residual network with an attention mechanism and bidirectional gated recurrent unit (DRAM-BGRU) is developed. First, two parallel one-dimensional residual networks (1D-ResNet) with the optimized structure are designed to extract spatial features and eliminate gradient vanishing or exploding. Second, an AM is designed to catch outstanding impact features for fault samples. Next, a bidirectional gated recurrent unit is set back of 1D-ResNet and AM to mine the temporal features of the sample signal. Finally, the information processed by the hybrid module is summarized through global average pooling, and the fully connected layer is used to output the final classification result. The developed technique is tested using one set of planetary gear data and achieves a satisfied classification accuracy of 100%. Compared with the other five advanced methods, the proposed technique can detect planetary gear faults much better.
The convolution neural network structure in double-path convolution with attention mechanism (AM) and bidirectional gated recurrent unit (BGRU), termed DCAM-BGRU, suffers from the problem of gradient vanishing or exploding with the network increases, affecting the recognition accuracy and stability in the fault classification task. To alleviate this problem, a double-path residual network with an attention mechanism and bidirectional gated recurrent unit (DRAM-BGRU) is developed. First, two parallel one-dimensional residual networks (1D-ResNet) with the optimized structure are designed to extract spatial features and eliminate gradient vanishing or exploding. Second, an AM is designed to catch outstanding impact features for fault samples. Next, a bidirectional gated recurrent unit is set back of 1D-ResNet and AM to mine the temporal features of the sample signal. Finally, the information processed by the hybrid module is summarized through global average pooling, and the fully connected layer is used to output the final classification result. The developed technique is tested using one set of planetary gear data and achieves a satisfied classification accuracy of 100%. Compared with the other five advanced methods, the proposed technique can detect planetary gear faults much better.
The convolution neural network structure in double-path convolution with attention mechanism (AM) and bidirectional gated recurrent unit (BGRU), termed DCAM-BGRU, suffers from the problem of gradient vanishing or exploding with the network increases, affecting the recognition accuracy and stability in the fault classification task. To alleviate this problem, a double-path residual network with an attention mechanism and bidirectional gated recurrent unit (DRAM-BGRU) is developed. First, two parallel one-dimensional residual networks (1D-ResNet) with the optimized structure are designed to extract spatial features and eliminate gradient vanishing or exploding. Second, an AM is designed to catch outstanding impact features for fault samples. Next, a bidirectional gated recurrent unit is set back of 1D-ResNet and AM to mine the temporal features of the sample signal. Finally, the information processed by the hybrid module is summarized through global average pooling, and the fully connected layer is used to output the final classification result. The developed technique is tested using one set of planetary gear data and achieves a satisfied classification accuracy of 100%. Compared with the other five advanced methods, the proposed technique can detect planetary gear faults much better.
关键词
residual neural network,attention mechanism,bidirectional gated recurrent unit,fault diagnosis
报告人
Zhilin Dong
Ph.D Beijing unversity of technology稿件作者
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重要日期
-
会议日期
08月01日
2025
至08月04日
2025
-
06月23日 2025
初稿截稿日期
主办单位
中国机械工程学会设备智能运维分会
承办单位
新疆大学
联系方式
- Mrs. Yanruqiang
- ya******@xjtu.edu.cn
历届会议
-
2025年08月01日 中国 wulumuqi
2025 International Conference on Equipment Intelligent Operation and Maintenance -
2023年09月21日 中国 Hefei
第一届（国际）设备智能运维大会
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