## 6 Application Cases
### EMU Bogie Wheel Pair System Fault Data Prediction  
The faults of EMU bogie key points are predicted to analyze the fault reasons. If a fault arises from the product design process, the data shall be fed back to the design department for product optimization design. If a fault arises from the manufacturing department, the data shall be fed back to the manufacturing department for the adjustment of the production plan and the optimization of resource allocation. The faults are predicted to find problems in advance during the EMU operation, replace parts at O&M Center and Operating Point in advance and avoid faults in advance. The LSTM neural network prediction method is adopted to predict the fault times of bogie key parts in a certain year. Such a prediction method, due to its high accuracy rate, is extensively applied. To realize the prediction of the number of failures, an LSTM network prediction model is established, which includes an input layer, a hidden layer and an output layer. This experiment uses the fault data from the past 20 years of a CRH series EMU for model training. The training data set is 5400 key component failure data recorded from 2000 to 2020, accounting for 80% of the total data. First, the data is normalized to remove the dimension difference, and the processed data is input into the LSTM model for training. Considering the small amount of data in this paper, the number of hidden layers of the LSTM model is set to 1, the training step is set to 2, and the number of training rounds is set to 200. To test the effect, the failure data of a certain year can be randomly selected, and the prediction effect of the model is shown in Figure 9. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**No** & **Failure Mode** & **Frequency** & **Repair Location** & **Fault Type** \\ \hline
1 & Wheel tread damage & 4 & Maintenance center & Component bump \\
2 & Wheel diameter is & 3 & Operation site & Other failure \\
3 & Axle box damage & 1 & Return to factory & Component bump \\
39 & Disc damage & 2 & Operation site & Wheelset system failure \\
40 & Shaft disc damage & 1 & Operation site & Wheelset system failure \\
41 & Gearbox magnetic & 3 & Maintenance center & Gearbox failure \\  & bolt failure & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Pre-processed maintenance data information. As can be seen in Figure 9, the number of predictions for the different failures largely matches the true values. However, the predicted values for fault 8 and fault 10 deviated significantly from the true values, and they were both predicted to be lower than the true values. Fault 8 is a cracked rim and fault 10 is a burnt bearing. A possible reason for the lower-than-true rim failure prediction is that the replacement rim is of substandard quality due to a defect in the manufacturing process. The reason for the lower predicted than the true value of bearing burn is the poor lubrication of the bearings during this period. According to the results, the predicted values for the remaining faults are generally consistent with the true values. The prediction results of the model can meet the fault prediction requirements and provide a data-driven basis for complex product design, manufacturing and O&M integration. Through the model to predict the number of failures in the next year, the final failure prediction results of the key components of the bogie within one year are shown in Table 2.