## 3 Result Analysis
### Comparison Analysis  
To evaluate the performance of the proposed approach, several experiments are performed with and without TL. In the experiment, WC 1 is considered as the source task and WC 2 as the target and the performance of WC 2 with transfer learning (WTL) and without transfer learning (NTL) is evaluated. The NTL scenario meant training the network from scratch (training:testing = 60:40) without \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Scenario**} & \multicolumn{3}{c}{**Overall Classification Accuracy (\%)**} \\ \cline{2-4}  & **Training Data 80\%** & **Training Data 60\%** & **Training Data 40\%** & **Training Data 20\%** \\ \hline
1 & 99.99 & 96.44 & 86.22 & 79.18 \\
2 & 99.98 & 96.23 & 86.45 & 77.43 \\
3 & 99.90 & 95.82 & 85.32 & 77.92 \\
4 & 99.54 & 95.59 & 83.96 & 67.11 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Classification results for different sizes of training data. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Scenario**} & \multirow{2}{*}{\begin{tabular}{c} **Source** \\ **Dataset** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Target** \\ **Dataset** \\ \end{tabular} } & \multicolumn{5}{c}{**Classification Accuracy (\%)**} & \multirow{2}{*}{\
\begin{tabular}{c} **Average Classification** \\ **Accuracy (\%)** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Overall Classification** \\ **Accuracy (\%)** \\ \end{tabular} } \\ \cline{4-10}  & & WC 2 & 100 & 100 & 100 & 100 & 100 & 100 & 100 & 100 & 99.99 \\  & WC 3 & 100 & 100 & 100 & 100 & 100 & 100 & 100 & 99.99 \\  & WC 4 & 100 & 99.88 & 100 & 100 & 100 & 100 & 99.98 & \\ \hline \multirow{2}{*}{2} & \multirow{2}{*}{WC 2} & WC 1 & 100 & 100 & 100 & 100 & 100 & 100 & 100 & 99.98 \\  & WC 3 & 100 & 100 & 100 & 100 & 100 & 100 & 100 & 99.98 \\  & WC 4 & 100 & 100 & 100 & 99.69 & 100 & 100 & 99.95 & \\ \hline \multirow{3}{*}{3} & \multirow{3}{*}{WC 3} & WC 1 & 100 & 100 & 100 & 100 & 100 & 100 & 100 & 99.90 \\  & WC 4 & 100 & 99.83 & 100 & 100 & 100 & 100 & 99.97 & \\ \hline \multirow{3}{*}{4} & \multirow{3}{*}{WC 4} & WC 1 & 99.94 & 98.68 & 99.47 & 100 & 99.92 & 99.29 & 99.55 & \\  & WC 2 & 99.96 & 99.73 & 100 & 99.63 & 99.68 & 99.57 & 99.76 & 99.54 \\ \cline{1-1}  & WC 3 & 99.98 & 98.22 & 100 & 98.37 & 99.53 & 99.79 & 99.32 & \\ \hline \multicolumn{2}{c}{Average Accuracy (\%)} & 99.99 & 99.95 & 99.96 & 99.68 & 99.93 & 99.89 & 99.86 & 99.85 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Diagnostic performance of the proposed model under different scenarios. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Scenario**} & \multicolumn{3}{c}{**Overall Classification Accuracy with Transfer Learning (TL) (\%)**} \\ \cline{2-4}  & **Transfer Stages 1–3** & **Transfer Stages 1–2** & **Transfer Stage 1** \\ \hline
1 & 99.99 & 93.44 & 79.61 \\
2 & 99.98 & 93.31 & 79.22 \\
3 & 99.90 & 93.49 & 78.13 \\
4 & 99.54 & 92.61 & 77.29 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Impact on overall classification accuracy of different numbers of stages for TL. transferring any knowledge from the source task. As shown in Table 6, the accuracy performance did not improve much for WTL versus NTL. For normal health type (HC), the accuracy is the same, and for the inner fault (IRF), the accuracy without TL is higher (2.2%). For the other health types, WTL performed better than NTL. On average, WTL provided 1.04% additional accuracy over NTL in this experimental scenario. However, training time with TL is shorter than that without TL. As shown in Figure 8, the network provides the highest training accuracy (100%) with TL after almost 80 epochs (in t1 time), whereas NTL requires almost 160 epochs (in t2 time) to provide the same performance. These results demonstrate that transfer learning greatly reduces the training time while maintaining the overall performance.