# Improved SE-ResNet Acoustic-Vibration Fusion for Rolling Bearing Composite Fault Diagnosis
## 4 Results and Discussion  
The ResNet34, ResNet50, SE-ResNet50, VGG-16, and AlexNet networks were used for the classification and identification of features, respectively. The input samples included two forms. One was the sample after the fusion processing of acoustic and vibration data. The sound and vibration signals were fused using the feature fusion method described above. The other was the sample that only contained vibration data. The vibration signal was processed for noise reduction and then the same fault feature classification was conducted. Table 3 shows the fault feature classification accuracy.  
The feature recognition accuracy of SE-ResNet50, based on vibration signals, still outperformed the other networks in fault classification accuracy. For the classification accuracy of outer ring faults, SE-ResNet50 improved by 1.13% over the ResNet50 network. After MTFs were employed, each network model classified fault features more accurately. Under normal conditions, the combined acoustic and vibrational signals improved the accuracy by 0.22% over the vibrational signals. The accuracy increased by 0.7% in the case of inner ring failure. The accuracy increased by 0.17% in the case of outer ring failure. The accuracy increased by 0.47% in the case of the composite fault. This demonstrates that acoustic and vibration signals are complementary in terms of information. By utilizing fused acoustic-vibration input signals, the accuracy of fault diagnosis can be enhanced.  
Under identical input conditions, a comparison was made between the execution times of five distinct networks. The findings indicate that the running time of VGG-16 was roughly double that of the other four networks. The running times of ResNet34, ResNet50, SE-ResNet50, and AlexNet were very close to each other. This indicates that the improved SE-ResNet network ensures computational efficiency while improving accuracy.  
The MTFs sample set and LMF sample set were individually employed as inputs. Figures 9 and 10 present the performance outcomes of the ResNet34, ResNet50, and SE-ResNet50 models across 200 training sessions. As the number of iterations approaches 10, the loss value steadily converges, indicating the model's stability and the absence of overfitting. It fully reflects the excellent feature representation ability of MTFs and LMF in signal classification. Different models are compared, highlighting the superiority of sound-vibration fusion. It complements and superimposes the features of sound and vibration signals, making them easier to classify and recognize. Compared to traditional networks, the SE-ResNet50 network demonstrates superior classification capabilities. In the test of acoustic-vibration fusion, SE-ResNet50 converged faster with an accuracy of 99.02%.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Input Signal Type** & **Bearing Status** & **ResNet34** & **ResNet50** & **SE-ResNet50** & **VGG-16** & **AlexNet** \\ \hline \multirow{3}{*}{Feature Classification for Vibration Signal} & Normal State & 98.03\% & 98.61\% & 99.20\% & 94.22\% & 92.34\% \\  & Inner Ring Fault & 97.41\% & 98.42\% & 98.52\% & 93.82\% & 91.72\% \\  & Outer Ring Fault & 97.52\% & 98.3\% & 99.43\% & 94.03\% & 91.63\% \\  & Composite Fault & 97.86\% & 98.47\% & 99.31\% & 93.16\% & 91.02\% \\ \hline \multirow{3}{*}{Runtime/s} & Runtime/s & 12 & 13 & 13 & 25 & 15 \\ \cline{2-6}  & Normal State & 98.64\% & 98.79\% & 99.73\% & 95.12\% & 92.77\% \\ \cline{1-1}  & Inner Ring Fault & 98.66\% & 99.42\% & 99.62\% & 95.76\% & 92.51\% \\ \cline{1-1}  & Outer Ring Fault & 98.31\% & 98.74\% & 99.81\% & 95.54\% & 91.84\% \\ \cline{1-1}  & Composite Fault & 98.29\% & 99.15\% & 99.50\% & 94.79\% & 91.43\% \\ \hline \hline \multicolumn{2}{c}{Runtime/s} & 11 & 13 & 13 & 25 & 15 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Accuracy of the feature classification.