Improved one-dimensional convolutional neural network model.

<p>Features dual convolutional pathways with different kernel sizes for multi-scale feature extraction. Each pathway includes CBAM attention and GRU layers to enhance feature and temporal learning. Outputs are combined after global max pooling to generate refined representations.</p>

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Bibliographic Details
Main Author:	Hongwei Bai (1447999) (author)
Other Authors:	Weiyan Tong (22032834) (author), Zhenkun Geng (22032837) (author), Cheng Gao (463618) (author)
Published:	2025
Subjects:	Cell Biology Molecular Biology Biotechnology Science Policy Space Science Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified noise ratio environments improving feature selection improving computational efficiency gated recurrent unit experimental results demonstrate existing methods achieve decreases parameter redundancy channel convolutional kernel channel configuration captures pooling layer replaces pooling layer followed fully connected layer improving equipment reliability improved parallel one vanishing gradient issue reducing unplanned downtime reduces overfitting risk softmax layer industrial equipment xlink "> term dependencies rolling bearings performed using parallel dual paper proposes models long low signal local features gru addresses fault diagnosis critical component attention mechanism
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Improved one-dimensional convolutional neural network model.

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