Hierarchical Convolution-Transformer Framework for Gear Fault Diagnosis Under Severe Noise

Hierarchical Convolution-Transformer Framework for Gear Fault Diagnosis Under Severe Noise

To address the limitations of convolutional neural networks in capturing global fault features, the high computational cost and overfitting risk of Transformer models in gear fault diagnosis, and the feature degradation under strong noise, this study proposes a novel convolution-Transformer&#x20...

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Bibliographic Details
Main Authors: Qiushi He, Bo Kang, Shanshan Fan, Xueyi Li
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Access
Subjects:
Attention mechanisms
convolutional neural networks
diagnostic accuracy
feature extraction
fault diagnosis
gears
Online Access:https://ieeexplore.ieee.org/document/11037744/
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https://ieeexplore.ieee.org/document/11037744/

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