# Prediction of Key Parameters of Wheelset Based on LSTM Neural Network
## 5 Model Prediction Results and Comparative Analysis  
To assess the predictive performance of the model, actual measured data from a wheelset of a certain line in the Shanghai Metro are selected. After preprocessing the data with the aforementioned procedure, the data were input into the established prediction model. The wheel diameter, flange thickness, and flange height values were predicted for approximately one year. These predictions are then analyzed in comparison with predictions from the BP (back propagation) neural network prediction model and the GRU (gate recurrent unit) neural network prediction model [26, 27, 28, 29]. The comparison between the predicted results and the actual values is shown in Figures 13 and 14. The prediction time for the left wheel diameter and flange height spans from September 2020 to October 2021, approximately 13 months. The prediction time for the left wheel flange thickness spans from June 2021 to September 2022, approximately 15 months. The prediction time for the right wheel diameter and flange height spans from August 2020 to August 2021, approximately 12 months. The prediction time for the right wheel flange thickness spans from March 2020 to March 2021, approximately 12 months. Figure 13: Comparison of left wheel predictions: (**a**) flange thickness value; (**b**) flange height value; (**c**) wheel diameter value. Figure 13 shows the comparison of left wheel prediction parameters. After the train traveled 500,000 km in April 2021, the prediction errors of both the BP neural network and GRU neural network increase significantly. In addition, the LSTM neural network maintained good prediction accuracy until October 2021, even after the train had traveled 620,000 km. The LSTM neural network is more advantageous in wheel-to-left wheel parameter prediction. Figure 14 shows the comparison of right wheel prediction parameters. Among the three models compared by the BP neural network, GRU neural network, and LSTM neural network, the predicted value of the LSTM neural network is more consistent with the actual value. In June 2021, after the train had run 560,000 km, the accuracy of the GRU model's prediction of the left wheel decreased significantly. In May 2021, after the train had traveled 550,000 km, the BP model's prediction error for the right wheel increases significantly. In addition, the LSTM neural network model maintained good predictive performance until October 2021, even after the train traveled 620,000 km. It can be seen from these pictures that, compared with other neural networks, the prediction accuracy of the LSTM neural network has been significantly improved, and it is obviously closer to the true value no matter it is the left wheel or right wheel. The accuracy of the wheel diameter value is significantly higher than that of the flange height value and the flange thickness value, which means that the prediction of these two values requires  
Figure 14: Comparison of right wheel predictions:(**a**) flange thickness value; (**b**) flange height value; (**c**) wheel diameter value. higher requirements, and there are many reasons for affecting the flange height value and the thickness value, and it is easy to be affected. In the wheelset maintenance base, the left and right wheel diameter difference to limit, rim height to limit, and rim thickness to limit are the standards that need to be determined [30]. Table 1 shows the prediction result indexes of three different neural network models based on the train wheelset data of a certain line. RMSE, MAE, and MAPE indexes of the LSTM neural network prediction model based on time series in the table are superior to that of the BP neural network and GRU neural network prediction model, especially for the wheelset diameter value. The MAPE value of the left wheel was 0.08%, and that of the right wheel was 0.07%, which were 0.13% and 0.09% lower than that of the GRU prediction model, respectively.