# Intra-Domain Transfer Learning for Fault Diagnosis with Small Samples
## 3 Experiments and Results
### Testing Resultss  
Testing accuracy of the three learning schemes in our target task is shown in Table 3. The proposed intra-domain transfer learning strategy exhibits the best diagnostic accuracy, even with small training samples. With proper model training, TFS can achieve a high prediction accuracy in the testing set when training samples are abundant, but it degenerates as the number of training samples decreases. In general, TFI improves the performance compared to TFS, indicating that inter-domain experience helps classify faults in the gearbox dataset. But as shown in Figure 8B,C, negative transfer does occur occasionally, though this is not always evident.  
As shown in Figure 8 and Table 3, TFC (the proposed learning scheme) consistently outperforms TFI. There is a large gain in accuracy using intra-domain transfer instead of its inter-domain counterpart. This confirms that the feature extractors learned from a different but akin domain can not only avoid negative transfer but also boost prediction accuracy in the target task. Table 3 also demonstrates that TFC has strong generalization capability even when the splitting ratio is set to 1:99, resulting in an accuracy of 84.3% after 100 epochs of training. We note that a nine-class classification problem is not an easy task.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c} \hline \multirow{3}{*}{
\begin{tabular}{c} **Learning** \\ **Schemes** \\ \end{tabular} } & \multicolumn{3}{c}{**Best Accuracy in First**} & \multicolumn{3}{c}{**Best Accuracy in First**} & \multicolumn{3}{c}{**Best Accuracy in First**} \\  & \multicolumn{3}{c}{**10 Epochs (\%)**} & \multicolumn{3}{c}{**50 Epochs (\%)**} & \multicolumn{3}{c}{**100 Epochs (\%)**} \\ \cline{2-13}  & **90:10** & **50:50** & **10:90** & **5:95** & **1:99** & **90:10** & **50:50** & **10:90** & **5:95** & **1:99** & **90:10** & **50:50** & **10:90** & **5:95** & **1:99** \\ \hline TFS & 97.1 & 82.4 & 72.5 & 51.6 & 11.1 &Figure 8: Training history of the three learning schemes in our target task, under various splitting ratios: (**A**) 90:10; (**B**) 50:50; (**C**) 10:90; (**D**) 5:95; (**E**) 1:99.  
The confusion matrices of the three learning schemes with small training samples are shown in Figure 9. As the figure indicates, TFS performs slightly better than random guessing, and the most misclassified classes are \"chip4a\" and \"chip2a\". TFI obtains a higher diagnostic accuracy, and misclassifications distribute more evenly across different classes. TFC significantly improves the classification of \"healthy\", \"missing\", \"crack\", \"spall\", and \"chip5a\", leading to an accuracy of 93.5% in these five states (red rectangle). The remaining four states (blue rectangle) have a relatively higher misclassification rate. An intuitive explanation is that the four faults are of the same type but have different severities. Consequently, their features are comparable, making it hard to distinguish them. Although data size considerably affects the diagnostic accuracy, the proposed method can achieve satisfactory results when training samples are scarce. This proves the efficacy of using the intra-domain transfer learning strategy for fault diagnosis with small samples.