# Improved SE-ResNet Acoustic-Vibration Fusion for Rolling Bearing Composite Fault Diagnosis
## 3 Test Verification
### Experimental Method  
The detailed experimental procedure is outlined below:  
1. We gathered sound and vibration signals from rolling bearings under different failure forms.
2. The acquired signals were subjected to GS-SVR-EEMD noise reduction, respectively, and the effective eigenmode components were outputted.
3. The obtained intrinsic modal components were summed. The processed sound and data signals were obtained. The sound and vibration signals were then subjected to low-rank multimodal fusion.
4. The abnormal situations of the sound vibration fusion dataset were checked. Z-score standardization was used to identify abnormal situations. Samples containing anomalies were deleted. The fused signal was converted to a 2D color image by Markov transition fields (MTFs).
5. The acquired 2D color images were partitioned into a validation set and a test set in a 3:7 ratio.  
Figure 5: Sensor arrangement.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Pitch Diameter** & **Ball Diameter** & **Ball Number** & **Contact Angle** \\ \hline
39 mm & 7.5 mm & 13 & 0\({}^{\circ}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Bearing configuration.  
Figure 6: Bearing fault conditions: (**a**) outer race fault and (**b**) inner race fault.  
(6) The segmented dataset was subjected to feature classification and recognition using the improved SE-ResNet network.  
A flowchart of the experimental method is shown in Figure 7.  
Prior to utilizing the enhanced SE-ResNet network, the dataset was transformed into an image-based format in order to facilitate feature extraction. Deep learning models such as ResNet excel in image processing, where key features can be automatically learned. By converting samples into images, the powerful feature extraction capabilities of these models can be better utilized. Moreover, employing images as input data facilitates seamless expansion and modification of the deep learning model's structure and parameters. This flexibility enables it to adapt to different fault diagnosis tasks.  
In the multimodal fusion feature process, the vibration and sound unimodal feature extraction channels \(f_{v}\) and \(f_{s}\) are firstly created using feed-forward neural networks. They  
Figure 7: Diagnosis flowchart.  
are used to extract modal features \(Z_{v}\) and \(Z_{s}\) of the original vibration and sound signal data that have been preprocessed after noise reduction. The obtained modal features are then fused by the LMF method to obtain the final fused features. For specific details, please refer to Reference [25]. The processed vibration and sound signals, respectively, are transformed into 2D images by MTFs.  
Figure 8 shows the signal plots of the composite fault (inner ring and outer ring), inner ring fault, outer ring fault, and normal state after acoustic-vibration fusion. It is clear from the 2D image that the signals of the composite fault are more complex than other signals. The 2D image of the composite fault is darker in color. Whether it is the sound signal or the vibration signal, the dark block tends toward the center, which indicates the existence of clustering characteristics of the sound signal and vibration signal. After multimodal fusion, the fault data information is not lost. The sound and vibration signals form a complementary posture. The features of the data are more obvious and it is easier for the convolutional neural network to recognize the fault features.  
The images obtained from MTFs were scaled and cropped to pixel 224 \(\times\) 224 size. In performing model training, the number of training times was set to 100, the data batch size was set to 16, and the learning rate was 0.0001.  
Figure 8: Diagram of acoustic-vibration fusion: (**a**) composite fault; (**b**) inner ring fault; (**c**) outer ring fault; and (**d**) normal state.