## 3 Proposed Method  
### Variable-Scale Attention Mechanism-Guided Feature Fusion Framework  
To accurately extract multi-scale fault features and the temporal dependency relationship from the time-frequency representation of roller bearing monitoring signals, a novel neural network framework with Inception and the LSTM model is established to efficiently diagnose the health of the bearings. The network structure is shown in Figure 4.  
First, an enhanced feature fusion module based on Inception V2 is proposed, as shown in Figure 4a. Convolutional kernels with different scales are used to capture inconspicuous differential features from the input data, and the LSTM module is joined with each branch of the Inception module to learn the temporal dependency features. To improve the generalization and fitting ability of feature fusion, batch normalization is used to improve the consistency of the learned features, and the ReLU nonlinear function is used to activate the learned features to prevent the overfitting of the model.  
Figure 3: LSTM network structure.  
Second, based on the variable-scale attention mechanism-guided feature fusion module, a novel intelligent fault diagnosis framework for roller bearing is proposed, as shown in Figure 4b. A convolutional layer with 3 \(\times\) 3 small kernels is used to extract coarse grained features from the time-frequency map to increase the nonlinear expression ability of the network. The optimal activation function and batch normalization are introduced to modify the output. Two stacked feature fusion modules are used as the multi-scale refined feature extractor to further extract differentiation deep representation features, which can increase the overall nonlinear expression ability of the network and reduce network complexity. After each feature fusion module, mean pooling is introduced to reduce the feature dimension. The parameters of the constructed neural network are shown in Table 1.  
Then, after extracting refined features, the LSTM layer is used to receive the features and further learn the temporal dependencies between them. Subsequently, the Softmax  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Layer** & **Parameter Name** & **Parameter Size** & **Output Size** \\ \hline Input layer & / & / & Batch size \(\times\) 128 \(\times\) 128 \\ Conv & Kernels & 3 \(\times\) 3 & Batch size \(\times\) 3 \(\times\) 128 \(\times\) 128 \\ Feature fusion module 1 & Kernel & As in Figure 4a & Batch size \(\times\) 16 \(\times\) 64 \(\times\) 64 \\ Mean Pooling & Mean pooling size & 2 \(\times\) 2 & Batch size \(\times\) 64 \(\times\) 32 \(\times\) 32 \\ Feature fusion module 2 & Kernels & As in Figure 4a & Batch size \(\times\) 128 \(\times\) 16 \(\times\) 16 \\ Mean pooling & Mean pooling size & 2 \(\times\) 2 & Batch size \(\times\) 256 \(\times\) 8 \(\times\) 8 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The parameters of the constructed neural network framework for intelligent fault diagnosis.  
Figure 4: Intelligent fault diagnosis framework for roller bearing based on the variable-scale attention mechanism-guided time-frequency feature fusion module. (**a**) Typical structure of the variable-scale attention mechanism-guided feature fusion module based on Inception and LSTM. (**b**) The intelligent fault diagnosis framework based on the variable-scale attention mechanism-guided feature fusion module.  
function [28] is introduced to map the learned features into the probability space and select the corresponding labels. Meanwhile, the improved cross entropy function is used as the loss function to train the networks and accelerate the convergence speed of the network.