## 4 Case Studies
### Results of Degradation State Divided and Prediction RUL  
Figure 12 shows the division results of degraded states when the generator bearings of three WT generators are, respectively, used as training datasets. It can be seen that there are differences in the degradation trends of the three WTs but, before a significant degradation trend occurs, all three WTs maintain a healthy state for a long time. This part of the data contains less degradation information, so the point that is initially classified as slightly degraded is chosen as the FPT.  
Figure 11: SRMS curve of three WTs after SG filtering.  
Figure 13 shows the state division of MDA-LETCN for the target domain among six transfer tasks. It can be seen that MDA-LETCN can still accurately classify the degradation status of bearings in the target domain. Although there are a few advanced or delayed points between the two states, the degradation process of bearings is a continuous and slowly changing time series process, and there will be no sudden changes or significant differences between the two points and small deviations are also acceptable.  
Figure 14 shows the spectrum of the WTs in the three degradation processes. Three signals are extracted from each state in time order. It can be seen that the spectrum amplitude in the healthy state is low and there is no obvious fault frequency. When the bearing enters a slightly degraded state, the fault features appear in the high-frequency part of the spectrum. When bearings enter the severe degradation state, the high-frequency amplitude of the spectrum is larger and the low-frequency part has fault features.  
Figure 12: The degradation stage division results for three datasets.  
Figure 13: The degradation stage division results.  
The length of the input time series will affect the prediction effect of MDA-LETCN. If the time series is too short, the model cannot learn more temporal features; if they are too long, the model must increase network layers to cover the entire time series, which greatly increases the computational complexity and training time. To observe the relationship between time series length and model prediction effect, the time series length was set as 15, 20, 30, 40, and 50, respectively. For MDA-LETCN to cover the entire time series of each sample, the model is set to three LERES layers when the length is 15, four LERES layers when the length is 20 and 30, and five LERES layers when the length is 40 and 50. The number of channels per layer is 32. Figure 15 shows the evaluation indicators results of different time series lengths. Overall, RMSE and MAE are lower, and the score is higher when the time series length is 30 and 40. However, when the time series is 40, the number of model layers increases and the training time is longer. So, the length of the time series is set to 30.  
Figure 14: Comparison of spectral maps at different degradation stages.  
Figure 15: Results of different time series lengths.  
Figure 16 shows the RUL prediction results of target domain bearing in the six transfer tasks listed in Table 2 after the model recognizes the FPT of target bearings. At this time, the time series length is 30. It can be seen from Figure 16 that the model has the best prediction effect for Task3 (19#-10#), followed by Task1 (10#-19#), indicating that the degradation trend of 10#WT and 19#WT is close and the migration effect is the best. Task4 (19#-21#) has the worst predictive effect, but the overall predicted degradation trend is still close to the true degradation trend. The results show that MDA-LETCN can effectively learn the constant degradation trend between the source domain and the target bearing domain, accurately predict the RUL of WT generator bearings, provide a theoretical basis for predictive maintenance of WT generator bearings, effectively reduce maintenance costs, and improve system operation reliability.