# Residual Life Prediction of SA-CNN-BILSTM Aero-Engine Based on a Multichannel Hybrid Network
## 4 Experimental Validation
### Experimental Results and Analyses  
The optimal model was employed to conduct tests on the FD001 and FD003 datasets, and its prediction results are presented in the following section. Additionally, for the purpose of attaining a more comprehensive understanding of the predicted engine degradation process, two engines were randomly chosen from each of the two sub-datasets, namely FD001 and FD003, to carry out engine prediction as well as the visualization and analysis of the actual degradation process. The results are displayed in Figures 12-14.  
From the prediction degradation process of the random test samples within the FD001 and FD003 test sets depicted in the above figure, it was observed that during the early stage of the test samples, the prediction value yielded by the proposed method was close to the set remaining useful life (RUL) threshold. Subsequently, the RUL prediction value generally decreased linearly with the operation time. Although there existed relatively large errors between the prediction value and the actual value at certain time points, when the engine operation approached the decommissioning stage, the accuracy of the prediction value was higher. This is because the later stages of the airline's service period are of crucial importance for engine maintenance. A precise assessment of the engine state during this period can enhance the reliability and safety of operation and curtail maintenance costs.  
Furthermore, to more effectively validate the efficacy of the proposed method, the prediction results of the proposed method were compared with those of other methods documented in the literature. The compared results are presented in Table 4.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Model Hyperparameters**} & \multicolumn{2}{c}{**FD001**} & \multicolumn{2}{c}{**FD003**} \\ \cline{2-5}  & **RMSE** & **Score** & **RMSE** & **Score** \\ \hline F = 32, K = 2 & 13.7407 & 135.9350 & 12.9706 & 267.9601 \\ F = 64, K = 2 & 12.0347 & 67.5300 & 12.7825 & 194.5720 \\ F = 32, K = 3 & 14.3873 & 154.2784 & 13.4312 & 278.8967 \\ F = 64, K = 3 & 15.8711 & 170.6143 & 13.5116 & 345.2449 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Effect of different model hyperparameter sizes on the prediction results.  
Figure 12: Prediction results of FD001 test set on the model.  
Figure 14: Predicted results of the engine degradation process. (**a**) Unit #31 in FD001. (**b**) Unit #64n-FD001. (**c**) Unit #57 in FD003. (**d**) Unit #70 in FD003.  
Figure 13: Prediction results of FD003 test set on the model.  
Figures 15-17 display a comparison of the results of the evaluation criteria under different methods. Observing these figures shows that the various network models attained good prediction results for the FD001 and FD003 datasets. Additionally, the remaining useful life (RUL) prediction model proposed in this chapter exhibited a significant improvement in prediction performance in comparison to the other network models. Concerning the prediction results for the FD001 dataset, when compared to the multi-attention + TCN method, which had the lowest mean absolute error (MAE) and root-mean-square error (RMSE), the MAE of the proposed method was reduced by 5.67%, the RMSE and Score of the proposed method were reduced by 7.47% and 17.02%, respectively. When contrasted with the Auto-Encoder method, which had the lowest Score, the MAE was reduced by 8.09%, the RMSE and Score decreased by 9.72% and 11.36%, respectively. As for the prediction results for FD003, in comparison to the multi-attention + TCN method, which exhibited the lowest MAE, RMSE, and Score, the MAE, RMSE, and Score of the proposed method were reduced by 1.67%, 4.84%, and 5.02%, respectively. The results of the two evaluation indexes, namely the MAE, RMSE, and Score, demonstrate that the prediction results of the method proposed in this paper are superior.