## 4 Validation Analysis of TCNMABG Based on FEMTO-ST Dataset  
In order to further verify the generalization performance of TCNMABG, the bearing dataset released by the French FEMTO-ST Institute is used for experimental verification. The FEMTO-ST dataset [39] collects the full life cycle data of the test bearings through the PRONOSTIA experimental platform, which is capable of performing accelerated bearing degradation tests under different working conditions. The platform is mainly composed of asynchronous motors, drive shafts, couplings, test bearings, pneumatic jack loading systems, and digital regulators, which are shown in Figure 14. The adjustable working condition parameters are the radial force applied to the test bearings and the rotational speed. The vibration signals were collected by two Dytran 3035 B micro-accelerometers at 90 degrees to each other with a sampling frequency of 25.6 kHz. The specific parameters of the test bearing are shown in Table 7. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Prediction Model** & **MSE** & **RMSE** & **MAE** & **R\({}^{2}\)** \\ \hline TCN & 0.035751 & 0.189080 & 0.137930 & 0.609812 \\ TCN-MA & 0.030283 & 0.174019 & 0.123644 & 0.669498 \\ TCN-BiGRU & 0.008670 & 0.093111 & 0.064587 & 0.905379 \\ TCNMABG & **0.006753** & **0.082175** & **0.056021** & **0.926302** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of prediction results of ablation experiment test set. Figure 14: PRONOSTIA experimental platform. In this paper, the bearingsings under the load of 4000 N and speed of 1800 rpm working conditions are selected to test the general performance of TCNMABG. Bearing1_1, Bearing1_2, and Bearing1_3 are set as training sets, and Bearing1_4 is set as a test set. According to the proposed framework of TCNMABG, the extraction of multi-domain mixed features can be obtained by the comprehensive index, and the extracted features of Bearing1_1 are shown in Figure 15.  
The FPT of the early degradation point is determined by KFCM, and the results of the FPT of the four bearings in working condition one are shown in Table 8.  
Based on the determined FPT, the segmented linear degradation labels are used to evaluate the RUL of bearings, which is shown in Figure 16. To better analyze the error indexes of TCNMABG, RNN, GRU, LSTM, and BiLSTM are used to analyze the RMSE, MSE, MAE, and R\({}^{2}\) based on FEMTO-ST dataset, and the results are listed in Table 9. \begin{table}
\begin{tabular}{c c c c} \hline
**Parameter** & **Value** & **Parameter** & **Value** \\ \hline Outer ring raceway diameter/mm & 29.1 & Outer race diameter/mm & 32 \\ Inner ring raceway diameter/mm & 22.1 & Inner race diameter/mm & 20 \\ Static load rating/N & 2470 & Thickness/mm & 7 \\ Dynamic load rating/N & 4000 & Middle diameter/mm & 25.6 \\ \hline \end{tabular}
\end{table} 
Table 7: Specific parameters of bearings tested on the FEMTO-ST dataset. Figure 15: 10 features of Significant Degradation Trends in Vibration Signals of Bearing1_1. \begin{table}
\begin{tabular}{c c c} \hline
**Number** & **Total Experiment Time/(\(\times\)10 s)** & **FPT Position /(\(\times\)10 s)** \\ \hline Bearing1\_1 & 2803 & 1640 \\ Bearing1\_2 & 871 & 827 \\ Bearing1\_3 & 2375 & 1648 \\ Bearing1\_4 & 1428 & 1112 \\ \hline \end{tabular}
\end{table} 
Table 8: Bearing FPT Identification Results from the FEMTO-ST Dataset. According to the results of Bearing1_4 in Table 9, compared with RNN, GRU, LSTM, and BiLSTM, the mean square error of TCNMABG is reduced by 71.02%, 68.11%, 66.57%, and 57.52%, respectively, with an average reduction of 65.96%. The root mean square error of TCNMABG is reduced by 46.17%, 43.53%, 42.18%, and 34.83%, respectively, with an average reduction of 41.68%. The mean absolute error of TCNMABG is reduced by 37.33%, 39.71%, 30.70%, and 34.82%, respectively, with an average reduction of 35.64%.