# Fault Detection Method for Wind Turbine Generators Based on Attention-Based Modeling
## 3 Experimental Analysis
### Experimental Results  
Comparison between AW-LSTM prediction results and actual values: After training the fault signal feature analysis indicators, the network model could be input for testing the AW-LSTM fault prediction model. The SCADA database recorded some training sample data information. Under consistent experimental conditions, and with the help of the AW-LSTM network principle, the effect of AW-LSTM on gearbox faults was analyzed. At the same time, an AW-LSTM prediction model combining LSTM with the attention mechanism was constructed. Figure 6 details the prediction error of the network. It can be seen that the proposed AW-LSTM model had a very small deviation between the predicted accuracy and the actual values that was almost indistinguishable by the naked eye. This result fully demonstrates the performance of the AW-LSTM model. Detailed Results Analysis: To fully validate the effectiveness of the proposed AW-LSTM algorithm, this paper selected FFT, LSTM, CNN, and EMD-GRU as the comparative methods. Additionally, the root mean square error (RMSE) was calculated for different prediction time steps (60, 100, 160, 200, 220) on two different datasets (SCADA and OWT). The prediction results on the SCADA dataset are shown in Table 3, and the prediction results on the OWT dataset are shown in Table 4. The bold results indicate the accuracy achieved by the proposed AW-LSTM model in this paper. Overall, AW-LSTM had higher prediction accuracy for different time steps, and the error between the predicted and actual values was very small. In particular, compared to the comparative methods, the long-term prediction results of AW-LSTM were significantly better, indicating that the proposed method has a significant advantage in fault prediction performance. This is mainly due to the fact that the LSTM network can model the time correlation between different data frames in the bearing signal well. Additionally, compared to the comparative methods, the attention  
Figure 6: Empirical mode decomposition (EMD). We show that six subsequences \(\{IMF_{i}\}_{i=1}^{6}\) and one residual component \(\mathbf{r}\) are obtained by the EMD decomposition. mechanism used in AW-LSTM can extract the weights of different frames fully, as well as the different contributions to the model performance. At the same time, the attention mechanism can assign small weights to irrelevant or interfering data, while assigning more attention to data that can contribute significantly to prediction accuracy. AW-LSTM identification results for different gear operating modes: In addition, to better validate the effectiveness of the AW-LSTM network in diagnosing gearbox faults, this paper trained and tested the network using test data samples under different gear operating modes of the wind turbine signal in the same experimental environment. The EMD-GRU method, which performed well in the previous experiments, was selected as the comparative baseline, and the identification results are shown in Table 5. It can be seen that AW-LSTM has good prediction accuracy for different gear modes. Average Results Analysis: In the process of identifying gearbox faults, this paper concludes--through sufficient experimental verification and analysis--that, compared to the FFT, LSTM, CNN, and EMD-GRU networks, the advantages of the AW-LSTM network lie in its accurate diagnostic function, convergence speed, and strong diagnostic adaptability. For example, when identifying gearbox fracture, the RMSE error of the AW-LSTM network was reduced by more than 10%, compared to the best-performing comparative method. In addition to the distributed RMSE error, the average RMSE, MAE, and MAPE for all prediction time steps on the two datasets were also calculated for this paper. The experimental results are shown in Table 6. It can be seen from the results that the RMSE, MAE, and MAPE obtained by the four comparative methods were larger than those of the proposed AW-LSTM.