# Fault Diagnosis Method of Rolling Bearing Based on CBAM_ResNet and ACON Activation Function
## 3 Experimental Results and Discussion
### Diagnostic Results under Noise Interference  
The actual working operation of the rolling bearing is bound to generate noise because of the relative friction of the parts, which both damage the bearing's health status and interferes with the collected vibration signal. In this subsection, we use the samples in Table 1 and only add Gaussian white noise to the samples in the testing set, and the calculation principle is shown in Equation (12):  
\[SNR_{dB}=10\,lg\,\left(\frac{P_{signal}}{P_{noise}}\right) \tag{12}\]  
where \(SNR_{dB}\) is the signal-to-noise ratio, \(P_{\text{subscript}}\) is the energy of this subscript. Five testing sets with SNR of 2 dB, 4 dB, 6 dB, 8 dB, and 10 dB are constructed for experiments, and the proposed model is compared with three different models MSCNN [41], 1 DCNN [8], and WDCNN [18]. (1) The MSCNN is structured with convolutional layers containing \(3\times 1\), \(5\times 1,7\times 1\), and \(32\times 1\) convolutional kernels of various sizes. The first part is a convolutional layer with a pooling layer. The kernel size is \(32\times 1\) and the pooling reduces the dimensionality by half, ultimately extracting the short-time features in the original vibration signal. The second part is three different branches corresponding to the use of convolutional kernel sizes of 3, 5, and 7, and the short-time features are passed through the three convolution-pooling structures of each branch. The final part is the output of each branch is subjected to concat operation and then diagnosed using softmax classification. (2) The 1 DCNN is structured with 11 sequentially concatenated layers, having an input layer, three convolutional with batch norm (BN) layers, a pooling layer of \(2\times 1\) size, a Dropout layer, a fully connected layer, and an output layer. The size of the kernel is \(64\times 1\) for the first convolution layer and \(3\times 1\) for the remaining two layers, the number of channels after the short-time features pass from the input layer is always 128, and the Dropout layer coefficient is 0.5. (3) The structure of WDCNN has five layers in a series of convolutional-batch normalization-pooling, the size of the first kernel is set to 64 \(\times\) 1, and the remaining kernels are \(3\times 1\) except for the first layer. The hidden layer has 100 neurons and uses Softmax classification results as the total output. To obtain reliable values, the average value of five replicate experiments is used as the results. The comparison with three similar models for five different noise scenarios is presented in Table 6, which shows the average accuracy results. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Reference**} & \multirow{2}{*}{**Domain of Input Data**} & \multirow{2}{*}{**Whether End-to-End**} & \multicolumn{2}{c}{**Average Accuracies**} \\  & & & **Results (\%)1  \\ \hline [PERSON] et al. [41] & Time & Yes & 90.58 \\ [PERSON] et al. [8] & Frequency & No & 91.44 \\ [PERSON] et al. [15] & Time & Yes & 94.90 \\ Proposed model & Time & Yes & 97.68 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of similar approaches. \begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{1}{c}{**Load Condition of Data**} & \multicolumn{1}{c}{**Accuracy (\%)**} \\ \hline
0 HP & 100 \\
1 HP & 100 \\
2 HP & 100 \\
0\(\rightarrow\)2 HP 1 & 100 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Diagnostic accuracy in certain load condition data. More detail about fault diagnosis results of different models under variable noise is shown in Figure 11.  
As can be seen from Figure 11, the average accuracy of this paper's proposed model under noise reaches 97.68%, which is improved by 7.1%, 6.24%, and 2.78%, respectively, compared with the other three literature methods.