# Gearbox Fault Diagnosis Based on ICEEMDAN-MPE-AWT and SE-ResNeXt50 Transfer Learning Model
## 5 Experiment and Analysis
### Model Training and Comparative Analysis  
All experiments in this paper were implemented using the TensorFlow2.7.0 deep learning framework and ran on a computer with an Intel core i5-12490F processor and a RTX4060 graphics card. In addition, the computer's operating system was Windows11. During model training, the mini-batch size was set to 20. The Adam optimization algorithm and LambdaLR custom learning rate scheduling strategy were employed, and the initial learning rate was set to 0.001. The generated GASF image data were shuffled randomly and fed into the SE-ResNeXt50 model with transfer learning (TSE-ResNeXt50) in batches for training. Figure 5 shows the training accuracy, test accuracy, and loss curves of GASF+TSE-ResNeXt50 method under 0 Nm and 0.72 Nm loads.  
As can be seen from the figure, TSE-ResNeXt50 trained on GASF datasets generated under different loads, and all achieved high diagnosis accuracy from the beginning. When the load is 0 Nm, the accuracy and loss curves do not fluctuate greatly, and the convergence speed is fast. When the load is 7.32 Nm, the accuracy and loss curves show small fluctuations at the beginning of training, but all converge quickly after 20 epochs and tend to stabilize, and the accuracy reaches 100%. The training results show that using GASF to convert one-dimensional vibration signals into two-dimensional feature maps yields more obvious fault features, making it easier to extract fault features. Therefore, the method proposed in this article has some ability to extract abstract features at the beginning of training and has achieved good results.  
For the sake of additionally checking the reliability of the suggested approach, the GASF+TSE-ResNeXt50 method was experimentally compared to other fault diagnosis methods by using the GASF gear dataset under loads of 7.32 Nm and 0 Nm, respectively, as training samples, and all experimental results were the average of 10 test results. In this paper, a comparison and analysis were conducted between five models: TICNN [12], GASF+SE-ResNeXt18, GASF+MobileNetV3, MTF+TSE-ResNeXt50, and GASF+TSE-ResNeXt50. Among the above methods, the MTF (Markov transition field) in MTF+TSE-ResNeXt50 is a method of transforming one-dimensional time series data into two-dimensional feature images; it adopts a quantile partitioning strategy, which refers to the division of each  
Figure 5: Training accuracy, testing accuracy and loss curves of GASF+TSE-ResNeXt50.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Fault Table** & **Normal** & **Tooth Defect** & **Tooth Broken** & **Tooth Root Crack** & **Tooth Surface Wear** & **Load** \\ \hline Train & 120 & 120 & 120 & 120 & 120 & 0 Nm \\ Test & 480 & 480 & 480 & 480 & 480 & 0 Nm \\ Train & 120 & 120 & 120 & 120 & 120 & 120 \\ Test & 480 & 480 & 480 & 480 & 480 & 7.32 Nm \\ \hline \hline \end{tabular}
\end{table}
Table 3: Fault samples distribution of gearbox.  
sample into different regions that contain the same number of sampling points. Table 4 demonstrates the experimental results of the five methods written above.  
It can be found from Table 4 that in the gearbox fault diagnosis, the TSE-ResNeXt50 model based on MTF and GASF dataset has good generalization performance. When using the GASF dataset, the accuracy of the TSE-ResNeXt50 reached 100% under two different loads. When using the MTF dataset, the accuracy of the TSE-ResNeXt50 reached 99.8% and 100% under two different loads, respectively. The accuracy of GASF+SE-ResNeXt18 and GASF+TSE-ResNeXt50 is very close, which confirms the advantages of the SE-ResNeXt module. Although the accuracy of GASF+MobileNetV3 and TICNN has also reached over 99%, overall, GASF+TSE-ResNeXt50 performs better in gear fault classification, with higher accuracy and stronger robustness.