## 3 Experimental Results and Discussion
### Evaluation Metrics
This proves that the RSA mechanism predicts better than the traditional SA mechanism. Meanwhile, both RSA and SA improve the model prediction ability. In order to verify the stability of the model presented in this paper, the model is trained independently for several cycles, and the fluctuation range of its MAE and RMSE on the test set is very small, thus proving that the model has stability. Moreover, the IEEE PHM 2012 challenge dataset is used to validate the generalization ability of the presented method. The dataset comes from the data collected by the PRONOSTIA experimental platform for bearings running under different operating conditions through to the state of complete failure [38]. The sampling frequency of this experimental platform is 25.6 kHz, which is collected every 10 s, and the adoption time is 0.1 s each time, so the length of each sample is 2560. The details of this dataset are demonstrated in Table 7.  
Consistent with the experimental procedure described above, data from six other bearings under the same operating conditions were used for testing a particular bearing in order to make the division of the training set and test set consistent for each experiment. The network parameters and average metrics are kept consistent with the above, while the percentage of continued service life is used as the network output. In order to verify the generalization ability, the prediction RUL method of this paper is compared with other traditional bearing prediction methods, and the results are shown in Table 8. The presented model shows better prediction accuracy than those of traditional bearing prediction methods in most cases. It is also verified that the model presented in this paper has a certain generalization ability and achieves the life prediction of bearings. \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Model** & **Evaluation Metrics** & \multicolumn{2}{c}{**Injury Type**} \\ \hline  & & Wave abrasion & fish scale injury & stripping off the blocks \\ CNNBiLSTM & MAE & 0.0897 & 0.0957 & 0.0929 \\  & RMSE & 0.1140 & 0.1218 & 0.1172 \\ CNNBiLSTM-SA & MAE & 0.0689 & 0.0739 & 0.0723 \\  & RMSE & 0.0960 & 0.0947 & 0.1043 \\ CNNBiLSTM-RSA & MAE & 0.0472 & 0.0571 & 0.0535 \\  & RMSE & 0.0647 & 0.0765 & 0.0698 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Evaluation metrics for ablation experiments under operating condition 4. \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Operating** & **Load (N)** & **Speed (rpm)** & **Bearings** & **Sample Length** \\ \hline conditions 1 & 4000 & 1800 & Bearing 1–1–1–7 & 2560 \\ conditions 2 & 4200 & 1650 & Bearing 2–1–2–7 & 2560 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: The original vibration signal information of the bearing in the PHM2012 is under different working conditions. \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Model** & **Evaluation Metrics** & **B1–5** & **B2–5** \\ \hline CNN & RMSE & 0.126 & 0.139 \\ LSTM & RMSE & 0.114 & 0.121 \\ CNN-LSTM & RMSE & 0.911 & 0.925 \\ CNNBiLSTM-RSA & RMSE & 0.078 & 0.069 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Comparison of RMSE results using different methods for some bearings. The above experiments show that the MAE and RMSE of the method proposed in this paper are lower than the existing state-of-the-art lifetime prediction methods. This shows that the model proposed in this paper is higher than the existing state-of-the-art methods in terms of prediction accuracy. Meanwhile, the experiments on the IEEE PHM 2012 challenge dataset [38] show that the model is highly adaptable to different datasets and has good generalization ability.