# Small-Sample Bearings Fault Diagnosis Based on ResNet18 with Pre-Trained and Fine-Tuned Method
## 4 Experimental Verification
### Case 1: CWRU Bearing Dataset
#### 4.1.3 Comparison with Other Networks  
To demonstrate the advancement and generalization capabilities of the VSDP-TLResNet18 model for small-sample FD, utilizing the pre-trained and fine-tuned method, four datasets (A, B, C and D) were introduced for diagnostic analysis, each containing a different number of training data samples. Among them, the combined number of training samples in datasets A, B, C and D is 400, 200, 100 and 60, respectively, and each dataset contains 800 test data samples. The number of training and testing samples is uniform across all fault categories in each dataset. Under the same experimental conditions in the training set, we compared the model with the TLVgg16, TLEfficientNet and TLSqueezeNet models, popular in deep learning. The above three networks all adopted the pre-trained and fine-tuned method. We also compared it with the re-trained ResNet18.  
Figure 13 presents the experimental findings. The pre-trained and fine-tuned method greatly enhances the performance of the basic model, especially for the VSDP-TLResNet18 model. This is because, through the pre-trained and fine-tuned method, the model can take advantage of the rich features learned from large-scale datasets, so as to achieve better generalization on new and relatively small-sample datasets. Among all the experimental methods, the average accuracy of VSDP-TLResNet18 based on the pre-trained and fine-tuned method is the highest, with an accuracy rate of 99.81%. It reached 100% accuracy on datasets A, B and C, which is superior to other network models. The average accuracy of the re-trained VSDP-TLResNet18 is 97.96%, which shows that the pre-trained and fine-tuned method has a strong generalization ability compared with the model re-training. The  
Figure 12: Experimental results of dataset D under Case 1: (**a**) training accuracy and training loss; (**b**) confusion matrix.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Accuracy** & **Recall** & **Precision** & **F1** \\ \hline VSDP (ours) & 100.00\% & 100.00\% & 100.00\% & 100.00\% \\ SDP & 83.63\% & 83.63\% & 82.01\% & 81.67\% \\ CWT & 98.38\% & 98.38\% & 98.54\% & 98.40\% \\ WVD & 97.38\% & 97.38\% & 97.56\% & 97.36\% \\ Gray-scale image & 92.00\% & 92.00\% & 92.49\% & 91.85\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Test results of different two-dimensional image methods under Case 1.  
average accuracy rates of VSDP-TLVgg16, VSDP-TLEfficientNet and VSDP-TLSqueezeNet, which also adopt the pre-trained and fine-tuned method, were 99.15%, 98.97% and 98.56%, respectively. This also proves that for the residual connection in the new task with less data, the deep network's training will not face obstacles due to gradient issues, so it can effectively migrate and use the complex features learned by the pre-trained model.  
Figure 14 displays the confusion matrix outcomes for the accuracy of five distinct models tested on validation sets within dataset D under Case 1. Figure 14 illustrates that when limited to only six training samples for each type of fault, the VSDP-TLEfficientNet model exhibits the lowest accuracy at 97.25%. VSDP-TLEfficientNet and VSDP-TLVgg16 have a little more misclassification in categories 1, 2, 3, 4 and 7, because VSDP-TLVgg16 and VSDP-TLSqueezeNet have no residual connection, so it is easy to overfit when processing small-sample data. The VSDP-TLEfficientNet model may be too complex to capture effective key information when the training data are insufficient. Compared with VSDP-ResNet18, VSDP-TLResNet18 misjudged six faults and predicted all the remaining categories correctly, showing a strong feature extraction ability on small-sample datasets.  
Figure 14: _Cont._  
Figure 13: Verification accuracy of different datasets under Case 1.