# Intelligent Prediction of Aeroengine Wear Based on the SVR Optimized by GMPSO
## 5 Conclusions  
PSO is designed to optimize the structure parameters of SVRs and the embedding dimensions of training samples. Based on contrast experiments, the following conclusions can be drawn:  
1. The guided mutation strategy can increase the diversity of particles and improve the global optimal performance. Therefore, GMPSO is basically better than other PSO variants. 2. The structure parameters and the embedding dimension have an obvious impact on the prediction accuracy; it is necessary to optimize them to improve the prediction accuracy of SVR. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**True Values**} & \multirow{2}{*}{\begin{tabular}{c} **Multi-Step GMPSO-SVR** \\ **Prediction** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Single-Step GMPSO-SVR** \\ **RE** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Single-Step GMPSO-SVR** \\ **Fitness Value** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Prediction** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **RE** \\ \end{tabular} } \\ \hline
5.98 & 5.9842 & 0.07\% & (0.1, 13.5887, 0.01, 13) & 0.049\% & 5.9842 & 0.07\% \\ \hline
6.02 & 6.0338 & 0.23\% & (0.1, 11.6259, 0.01, 13) & 0.052\% & 6.0213 & 0.022\% \\ \hline
5.75 & 5.9962 & 4.28\% & (0.1, 4.9941, 0.01, 16) & 0.056\% & 5.7554 & 0.094\% \\ \hline
6.23 & 5.8659 & 5.85\% & (0.1, 54.7909, 0.01, 26) & 0.0489\% & 6.2236 & 0.103\% \\ \hline
5.88 & 5.7425 & 2.34\% & (0.1, 16.4312, 0.01, 3) & 0.0442\% & 5.8819 & 0.032\% \\ \hline Average \(RE\) & \multicolumn{2}{c}{2.55\%} & \multicolumn{2}{c}{0.064\%} & \multicolumn{2}{c}{0.064\%} \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison between multi-step and single-step predictions. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**True Values**} & \multirow{2}{*}{\begin{tabular}{c} **Multi-Step GMPSO-SVR** \\ **Prediction** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Single-Step GMPSO-SVR** \\ **RE** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **BP Network** \\ **Prediction** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **ARMA** \\ **Prediction** \\ \end{tabular} } \\ \hline
5.98 & 5.9842 & 0.07\% & 5.9842 & 0.07\% & 5.6074 & 6.23\% & 5.5091 & 7.87\% \\ \hline
6.02 & 6.0338 & 0.23\% & 6.0213 & 0.022\% & 6.3361 & 5.25\% & 5.5980 & 7.01\% \\ \hline
5.75 & 5.9962 & 4.28\% & 5.7554 & 0.094\% & 5.8454 & 1.66\% & 6.2631 & 8.92\% \\ \hline
6.23 & 5.8659 & 5.85\% & 6.2236 & 0.103\% & 5.9702 & 4.17\% & 4.7603 & 23.59\% \\ \hline
5.88 & 5.7425 & 2.34\% & 5.8819 & 0.032\% & 5.8382 & 0.71\% & 3.8466 & 34.58\% \\ \hline Average \(RE\) & \multicolumn{2}{c}{2.55\%} & \multicolumn{2}{c}{0.064\%} & \multicolumn{2}{c}{3.60\%} & \multicolumn{2}{c}{16.39\%} \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of different prediction results. 3. Multi-step prediction has relatively lower accuracy than that of single-step prediction due to the error cumulative effect, but the time costs of the two forms exhibit the opposite trend. The SVR optimized using GMPSO had better accuracy and higher consistency, and can thus provide reliable predictions of aeroengine wear and ensure the safe running of aeroengines. Conceptualization, B.Z. and F.G.; methodology, B.Z. and X.M. ; software, X.Z. ; validation, B.Z., F.G. and X.Z. ; writing--original draft preparation, F.G.; writing--review and editing, B.Z. ; visualization, X.Z. ; project administration, B.Z. ; funding acquisition, B.Z., X.Z. and X.M. All authors have read and agreed to the published version of the manuscript. This research was funded using funds from the Project of Sichuan Province Science and Technology Program, grant number 2021Y0519; China Civil Aviation Administration Development Foundation Educational Talents Program, grant number 14002600100018J034; General Foundation of Civil Aviation Flight University of China, grant number 2019-53. Not applicable. The authors declare no conflict of interest.