# Transfer Learning with Deep Recurrent Neural Networks for Remaining Useful Life Estimation
## 3 Experiments and Results
We also found that the changes of Mean Score and RMSE have similar trends. In Figure 6 we showed the box plot of the Mean Scores to illustrate the distribution of every transfer learning experiment. Figure 6a-d respectively illustrate the performance of four groups of dataset target models (FD001, FD002, FD003, and FD004). The blue boxes are the performance without transfer and the yellow and green boxes are the performance with transfer. In Figure 6a, the transfer experiment E2 is more stable and gets lower mean score than the without-transfer experiment E1. In Figure 6b, both the transfer experiment E5 and E6 are more stable and get lower mean score than the without-transfer experiment E4. In Figure 6c, the transfer experiment E8 is more stable and gets a lower mean score than the without-transfer experiment E7. In Figure 6d, both the transfer experiment E11 and E12 are more stable and get lower mean score than the without-transfer experiment E10. Thus, from the distribution of box plots for experiments, we can easily observe that the performance of models with transfer learning are more stable and make great improvement than those without transfer learning except the transfer experiment E3 and E9, which will be explained in Section 4. Figure 7 illustrates the RUL estimation results in test data for experiments, in which the training data set is generated by randomly selected 50 trajectories. Those figures show and compare the RUL estimation results for four test data sets (FD001, FD002, FD003, FD004). Figure 7a,d,j are the without-transfer experiment and others are the transfer experiments. For every figure, the X-axis is the test unit with increasing RUL, which is sorted by actual RUL values. The Y-axis is the actual and prediction RUL value. The blue points are the actual RUL value of test unit and the red points are the prediction RUL value of test unit. Comparing with Figure 7a,b, we found that the prediction RUL values in the transfer experiment E2 (Figure 7b) are nearer to the actual RUL values than in the without-transfer experiment E1 (Figure 7a). Comparing with Figure 7d-f, we found that both the prediction RUL values in the transfer experiment E5 (Figure 7e) and E6 (Figure 7f) are nearer to the actual RUL values than in the without-transfer experiment E4 (Figure 7d). Comparing with Figure 7g,h, we found that the prediction RUL values in the transfer experiment E8 (Figure 7h) are nearer to the actual RUL values than in the without-transfer experiment E7 (Figure 7g). Comparing with Figure 7j-l, we found that both the prediction RUL values in the transfer experiment E11 (Figure 7k) and E12 (Figure 7l) are nearer to the actual RUL values than in the without-transfer experiment E10 (Figure 7j). For these figures, comparing between transfer and no-transfer learning performance, we found that the effect of transfer experiments performance is obvious except for E3 and E9, which will be explained in Section 4. \begin{table}
\begin{tabular}{l l c c c c c c c c c c c c c c c c c c c} \hline \hline \multicolumn{1}{c}{**Label**} & \multicolumn{1}{c}{**From TarMP**} & \multicolumn{1}{c}{**Mean Score**} & \multicolumn{1}{c}{**RMSE**} & \multicolumn{1}{c}{**RMSE**} & \multicolumn{1}{c}{**RMSE**} \\ \cline{2-13} \multicolumn{1}{c}{} & **10** & **20** & **30** & **40** & **50** & **60** & **70**  
Figure 6: Box plot of the mean score for every experiment. The blue boxes are the without-transfer model and the yellow and green boxes are the performance of transfer model. The X-axis is the size of trajectories which are randomly selected 10, 20,..., 90, 100 trajectories. In the caption, A\(\rightarrow\)B means from the source data A transfer to the target data B.  
Figure 7: _Cont._  
To further examine how different transfer learnings work, we analyzed the IMP performances of a transfer experiment which transfers from different source datasets to the same target datasets. In Figure 8, Figure 8a-d respectively illustrate the improvement performance of transferring to four datasets (FD001, FD002, FD003, and FD004).