# Prediction of Key Parameters of Wheelset Based on LSTM Neural Network
## 6 Conclusions  
In this study, an analysis of the wheelset wear on a certain line in the Shanghai Metro was conducted to identify key parameters: wheel diameter, flange thickness, and flange height. A prediction model was then established to forecast these three key parameters. The model is based on the LSTM neural network, and through optimization of the network structure, number of layers, and various parameters, the optimal prediction model was obtained. Utilizing the established prediction model, the values for a specific wheelset over one year were predicted, and the predicted values were compared and validated against the actual values. The results showed that the mean absolute MAPE was below 0.5%, indicating the reliability and accuracy of the results. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Parameter**} & \multicolumn{2}{c}{**Left Wheel Evaluation Metrics**} & \multicolumn{2}{c}{**Right Wheel Evaluation Metrics**} \\ \cline{3-8}  & & _RMSE_ & _MAE_ & _MAPE_ & _RMSE_ & _MAE_ & _MAPE_ \\ \hline \multirow{3}{*}{**BP**} & Wheel diameter \(D\) & 0.145 & 0.124 & 0.11\% & 0.125 & 0.107 & 0.10\% \\  & Flange thickness _Sw_ & 0.103 & 0.090 & 0.45\% & 0.111 & 0.090 & 0.51\% \\  & Flange height _Sh_ & 0.168 & 0.120 & 0.49\% & 0.131 & 0.117 & 0.50\% \\ \hline \multirow{3}{*}{**GRU**} & Wheel diameter \(D\) & 0.262 & 0.195 & 0.21\% & 0.223 & 0.175 & 0.16\% \\  & Flange thickness _Sw_ & 0.201 & 0.154 & 0.62\% & 0.191 & 0.132 & 0.55\% \\  & Flange height _Sh_ & 0.361 & 0.268 & 0.86\% & 0.141 & 0.114 & 0.51\% \\ \hline \multirow{3}{*}{**LSTM**} & Wheel diameter \(D\) & 0.098 & 0.078 & 0.08\% & 0.091 & 0.074 & 0.07\% \\  & Flange thickness _Sw_ & 0.077 & 0.061 & 0.42\% & 0.058 & 0.044 & 0.35\% \\ \cline{1-1}  & Flange height _Sh_ & 0.133 & 0.114 & 0.47\% & 0.105 & 0.085 & 0.44\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Evaluation metrics of prediction results (unit: mm). The main conclusions of this study are as follows:  
1. The wheelset data will undergo significant changes after reprofiling. The predictive performance of the current methods is greatly affected by reprofiling. However, the proposed prediction model remains reliable in predicting after reprofiling. 2. Considering the temporal nature of the wheelset data, the introduction of a time series network for wheelset parameter prediction results in higher accuracy compared to traditional neural network models. 3. This study provides a method for maintenance engineers in the railway vehicle industry to understand the trend in wheelset changes, facilitating the prediction of wheel operating conditions and assisting engineers in planning reprofiling work. It has practical value in the rail transit industry. Since the operation of rail vehicles is also influenced by the running route, such as in the case of circular lines, there may be significant wear on one side of the wheel due to more frequent turns [32]. Therefore, this study predicts both the left and right wheels to facilitate monitoring of wheelset changes on special routes. Subsequent research will incorporate parameters related to the changes in the left and right wheels, as well as the average differences in coaxial wheel diameters, average thicknesses of coaxial flanges, and relevant parameters of the same bogie, to further investigate wheelset wear. This will be followed by optimization of reprofiling strategies and management strategies for wheels in the next step. Conceptualization, D.Y. and J.W. ; methodology, D.Y. ; soft-ware, D.Y. ; validation, D.Y. and J.W. ; formal analysis, D.Y. ; investigation, D.Y. ; resources, J.W. ; data curation, J.W. ; writing--original draft preparation, D.Y. ; writing--review and editing, D.Y. and J.W. ; visualization, D.Y. ; supervision, J.W., S.Z., Q.Z., W.P., H.J., C.Z. and Y.G. ; project administration, J.W. and Q.Z. All authors have read and agreed to the published version of the manuscript. Supported by National Natural Science Foundation of China (Grant No. 51975347). Not applicable.