# Research on the Remaining Life Prediction Method of Rolling Bearings Based on Multi-Feature Fusion
## 4 Experimental Verification
### Case Study 2: Predicting the RUL of Bearings Using the XJTU-SY Dataset
#### 4.3.2 Analysis of Projected Results  
To assess the prediction network's generalization capacity to the fullest extent possible, the bearing vibration data under Cases 1 and 2 were selected from the working conditions. When RUL prediction is performed using the XJTU-SY dataset, the network parameters, _MAE_, _RMSE_, and scoring function of multi-feature fusion are identical to those of Case 1. In addition, the proposed method's superiority is shown by comparing the multi-feature fusion prediction results with those of the four approaches in Case 1, and Table 6 displays the outcomes of their predictions.  
In this experiment, a total of 10 bearings were subjected to testing. Table 6 demonstrates that the proposed method exhibits reduced MAE and RMSE values compared to the comparison method for the majority of the tested bearings. Figure 14 clearly demonstrates that the proposed method achieves the lowest average MAE and average RMSE while also attaining the greatest score. This suggests that the proposed approach exhibits superior performance in predicting the RUL of bearings. The scoring values of the proposed method in this paper surpass those of the comparison methods, except that the score of tests bearing B1-1 is lower than that of TCN-SA, B2-5 is lower than that of TCN-RSCB, and the score of tests bearing B1-5 is the same as that of TCN-SA. Furthermore, due to the diverse range of failures that bearings often experience as they degrade, it is very challenging for prediction  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Test Set**} & \multicolumn{3}{c}{**TCN-SA [21]**} & \multicolumn{3}{c}{**TCN-RSA [28]**} & \multicolumn{3}{c}{**Standard-CNN**} & \multicolumn{3}{c}{**TCN-RSCB [29]**} & \multicolumn{3}{c}{**Proposed Method**} \\ \cline{2-19}  & _MAE_ & _RMSE_ & **Score** & _MAE_ & _RMSE_ & **Score** & _MAE_ & _RMSE_ & **Score** & _MAE_ & _RMSE_ & **Score** \\ \hline A1-1 & 5.2 & 7.1 & 0.90 & 21.5 & 25.9 & 0.75 & 14.3 & 16.6 & 0.70 & 12.3 & 14.8 & 0.82 & 5.7 & 7.3 & 0.88 \\ A1-2 & 19.0 & 24.0 & 0.80 & 18.8 & 23.0 & 0.61 & 8.2 & 11.8 & 0.81 & 7.7 & 9.3 & 0.83 & 7.2 & 9.3 & 0.84 \\ A1-3 & 17.0 & 21.7 & 0.65 & 13.6 & 16.0 & 0.65 & 16.1 & 18.9 & 0.61 & 8.4 & 10.0 & 0.69 & 8.2 & 9.7 & 0.71 \\ A1-4 & 26.8 & 30.4 & 0.80 & 11.9 & 14.0 & 0.66 & 18.5 & 22.2 & 0.46 & 7.9 & 10.1 & 0.84 & 7.1 & 8.8 & 0.85 \\ A1-5 & 20.3 & 25.3 & 0.51 & 19.7 & 22.3 & 0.45 & 28.2 & 33.6 & 0.31 & 27.8 & 32.7 & 0.40 & 19.2 & 21.4 & 0.51 \\ A2-1 & 13.6 & 16.4 & 0.80 & 23.7 & 29.5 & 0.65 & 23.0 & 28.3 & 0.64 & 20.8 & 24.9 & 0.76 & 12.4 & 15.1 & 0.81 \\ A2-2 & 14.5 & 18.6 & 0.85 & 11.3 & 13.8 & 0.68 & 14.3 & 16.7 & 0.75 & 8.2 & 10.2 & 0.86 & 8.6 & 10.6 & 0.86 \\ A2-3 & 13.6 & 17.1 & 0.85 & 21.8 & 24.4 & 0.76 & 11.0 & 13.4 & 0.66 & 10.8 & 15.4 & 0.86 & 10.7 & 12.8 & 0.87 \\ A2-4 & 16.6 & 19.4 & 0.74 & 16.0 & 19.0 & 0.64 & 16.5 & 20.8 & 0.60 & 13.5 & 17.5 & 0.79 & 14.4 & 16.6 & 0.80 \\ A2-5 & 11.1 & 14.3 & 0.80 & 18.3 & 22.8 & 0.71 & 14.7 & 18.1 & 0.62 & 7.2 & 9.2 & 0.81 & 11.5 & 14.9 & 0.78 \\ Average & 15.8 & 19.4 & 0.77 & 17.7 & 21.1 & 0.66 & 16.5 & 20.0 & 0.62 & 12.5 & 15.4 & 0.77 & 10.5 & 12.6 & 0.79 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Prediction results of different methods in the XJTU-SY dataset.  
Figure 13: Failure photos of bearings.  
methods to accurately determine the optimal RUL forecast for any individual bearing. Figure 15 displays the RUL forecast outcomes for the B1-1, B1-2, B2-2, and B2-5 bearings. Despite the presence of local oscillations in the curves of the proposed method, accurate predictions may be made on the state of the bearings throughout the latter stages of their service life. Ultimately, the proposed method demonstrates a commendable capacity to anticipate the RUL of rolling bearings.