Multi-scale deep residual shrinkage networks with a hybrid attention mechanism for rolling bearing fault diagnosis

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.