## 3 Proposed Method
### Inception V3 Fault Diagnosis Network  
Inception V3 is an improvement on the original Inception model [41]. It decomposes large convolutions into smaller convolutions, and further decomposes small convolutions into asymmetric convolutions, which is shown in Figure 4. Additionally, each channel has \(1\times 1\) convolutional kernels to reduce dimensionality, which lowers parameter computation and enhances the nonlinear fitting capability. This strategy allows for richer feature extraction and improved adaptability to different scales, realizing a balance between network performance and computational efficiency by reducing the number of parameters while maintaining the receptive field range.  
Figure 4: The structure of Inception V3.  
To further boost the training effectiveness of the neural network, the ECA attention mechanism is employed here to enhance feature extraction capabilities [42; 43; 44]. ECA improves upon the squeeze-and-excitation (SE) attention mechanism by replacing the fully connected layer following global average pooling with a 1 \(\times\) 1 convolution. This avoids dimension reduction and eliminates the need to capture dependencies between all channels, which can achieve excellent results with fewer parameters. As shown in Figure 5, the principle of ECA is as follows:  
1. Pass the input feature map through global average pooling and transform it from a [h, w, c] matrix to a [1, 1, c] vector;
2. Calculate the size of adaptive one-dimensional convolution kernel size \(k\) based on the number of channels in the feature map, \(k=\left|\frac{\log_{2}(c)}{\gamma}+\frac{b}{\gamma}\right|\), and generally set \(\gamma=2\), \(b=1\);
3. Apply the kernel size to the one-dimensional convolution to obtain the weights for each channel of the feature map;
4. Multiply the normalized weights channel-wise with the original input feature map to generate the weighted feature map.  
In terms of the overall network architecture design, all the convolutional layers in the network are uniformly structured as convolution + batch normalization + activation, which accelerates the convergence speed and enhances the generalization ability. Relative experiments demonstrate that the decomposition strategy of Inception V3 performs exceptionally well on feature maps of an intermediate size (m \(\times\) m, where m ranges from 12 to 20) [41]. Therefore, the time-series input spans 20 points, and the input parameters are set to 12 according to the analysis in Section 3.2. Consequently, the input dimension is 20 \(\times\) 12, ensuring the efficiency of the Inception V3 network. As the ECA attention mechanism does not alter the data dimensionality, the result of the channel concatenation of Inception V3 is directly fed into the ECA module. Then, it passes through fully connected layers and a SoftMax classifier to obtain the fault diagnosis result. Table 1 shows the structure of the Inception V3 network adopted in this paper.  
To illustrate the meaning of the structure parameters, take 1 \(\times\) 1 \(\times\) 32 as an example, which means the convolutional kernel size is 1 \(\times\) 1, with 32 channels, and / represents the connection relationship. As for other settings, 64 stands for the batch size and 7 stands for the number of categories.  
Figure 5: The principle of ECA.