Abstract
Abstract
The fault diagnosis of rolling bearings based on deep
networks is hindered by the unexpected noise involved with
accessible vibration signals and global information abatement in
deepened networks. To combat the degradation, a multi-scale deep
residual shrinkage network with a hybrid attention mechanism
(MH-DRSN) is proposed in this paper. First, a spatial domain
attention mechanism is introduced into the residual shrinkage module
to represent the distance dependence of the feature maps. Then, a
hybrid attention mechanism considering both the inner-channeled and
cross-channeled characteristics is constructed. Through the
comprehensive evaluation of the feature map, it provides a soft
threshold for the activation function and realizes the feature-map
selection adaptively. Second, the dilated convolution with different
dilation rates is implemented for multi-scale context information
extraction. Through the feature combination of the DRSN and the
dilated convolution, the global information of the rolling bearing
fault is strengthened and preserved as the fault diagnosis network
is deepened. Finally, the performance of the proposed
fault-diagnosis model is validated on the dataset from Case Western
Reserve University (CWRU). The experimental results show that,
compared with common convolution neural networks, the proposed
neural diagnosis model provides a higher identification accuracy and
better robustness under noise interference.
Cited by
2 articles.
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