## 5 Conclusions  
This paper proposes the MDA-LETCN model to predict the RUL of bearings under composite working conditions in response to the problem of composite working conditions and differential degradation of the run-to-failure data of WT generator bearings. The conclusions are as follows:  
1. MDA-LETCN can effectively extract the degradation features of WT generator bearings from the run-to-failure data under composite working conditions and introduce \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**CLSTM**} & \multicolumn{3}{c}{**VLSTM-LWSAN**} & \multicolumn{3}{c}{**TLHAM**} & \multicolumn{3}{c}{**MTSTAN**} & \multicolumn{3}{c}{**MDA-LETCN**} \\ \cline{2-13}  & **RMSE** & **MAE** & **Score** & **RMSE** & **MAE** & **Score** & **RMSE** & **MAE** & **Score** & **RMSE** & **MAE** & **Score** \\ \hline Task1 & 0.247 & 0.229 & 0.105 & 0.202 & 0.184 & 0.208 & 0.181 & 0.165 & 0.234 & 0.231 & 0.217 & 0.138 & 0.063 & 0.048 & 0.494 \\ Task2 & 0.243 & 0.212 & 0.112 & 0.165 & 0.143 & 0.243 & 0.169 & 0.147 & 0.282 & 0.194 & 0.177 & 0.194 & 0.077 & 0.066 & 0.399 \\ Task3 & 0.233 & 0.209 & 0.147 & 0.191 & 0.177 & 0.258 & 0.172 & 0.153 & 0.279 & 0.205 & 0.195 & 0.151 & 0.056 & 0.044 & 0.554 \\ Task4 & 0.227 & 0.201 & 0.162 & 0.142 & 0.136 & 0.267 & 0.155 & 0.140 & 0.301 & 0.166 & 0.152 & 0.216 & 0.084 & 0.068 & 0.403 \\ Task5 & 0.235 & 0.214 & 0.131 & 0.189 & 0.168 & 0.242 & 0.176 & 0.144 & 0.265 & 0.186 & 0.163 & 0.180 & 0.069 & 0.056 & 0.545 \\ Task6 & 0.215 & 0.199 & 0.188 & 0.137 & 0.119 & 0.261 & 0.164 & 0.151 & 0.299 & 0.178 & 0.154 & 0.234 & 0.064 & 0.052 & 0.475 \\ Average & 0.233 & 0.211 & 0.141 & 0.171 & 0.155 & 0.247 & 0.170 & 0.150 & 0.277 & 0.193 & 0.176 & 0.186 & 0.069 & 0.056 & 0.478 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results of comparison methods. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**LETCN + MDSDM**} & \multicolumn{3}{c}{**LETCN + DA**} & \multicolumn{3}{c}{**TCN + MDSDM + DA**} & \multicolumn{3}{c}{**MDA-LETCN**} \\ \cline{2-13}  & **RMSE** & **MAE** & **Score** & **RMSE** & **MAE** & **Score** & **RMSE** & **MAE** & **Score** & **RMSE** & **MAE** & **Score** \\ \hline Task1 & 0.154 & 0.145 & 0.307 & 0.148 & 0.127 & 0.238 & 0.122 & 0.108 & 0.304 & 0.063 & 0.048 & 0.494 \\ Task2 & 0.127 & 0.118 & 0.275 & 0.176 & 0.143 & 0.201 & 0.127 & 0.114 & 0.353 & 0.077 & 0.066 DA to effectively improve the model's prediction performance of target bearing RUL based on the differences in the degradation processes of each bearing. Through comparative experiments on the generator bearing data of WTs, the methods proposed in this paper, RMS, and MAE have the smallest and the highest scores, which are superior to the comparative methods. 2. In MDA-LETCN, unsupervised clustering is carried out on the extracted temporal features to adaptively classify the degradation state of generator bearings under composite working conditions, which can effectively improve the prediction effect of the model. 3. The MDSDM module measures and minimizes the distribution differences in different degradation stages, which can help the model learn the domain-invariant time-dependent features in different degradation stages. Ablation experiments have proved that the MDSDM module can effectively improve the prediction accuracy of the model under composite working conditions. It should be mentioned that, although MDA-LETCN has achieved high accuracy in WT generator bearing RUL estimation, there are still some limitations that need to be studied in our future work: 1. MDA-LETCN is a data-driven black-box model and, in our future work, we plan to introduce the mechanism model into the network to enhance the interpretability of the model. 2. Due to data reasons, this paper only studies the transfer learning prediction between NU1030M bearings. In future work, transfer learning tasks between different types of bearings will be studied to improve the robustness of the model. Conceptualization, M.T. and X.S. ; Data curation, W.A.