## 6 Conclusions  
* A model for predicting faults in the CRH5 gearbox has been developed by establishing a dynamic coupling dynamics model and utilizing vibration acceleration data from key components throughout the gearbox's degradation process. By creating a six  
Figure 14: The results of the velocity–hidden state distribution.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Normal State**} & \multicolumn{2}{c}{**Universal Joint**} & \multirow{2}{*}{**Driving Gear**} & \multicolumn{2}{c}{**Driven Gear**} & \multicolumn{1}{c}{\multirow{2}{*}{
\begin{tabular}{c} **Recognition Times** \\ **(min)** \\ \end{tabular} } \\ \hline GRU-HMM & 0.988 & 0.9 & 0.975 & 0.975 & 7.5 \\ HMM & 0.912 & 0.622 & 0.683 & 0.725 & 2.3 \\ SVM & 0.925 & 0.883 & 0.875 & 0.875 & 3.1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of recognition accuracy of various fault prediction methods.  
-dimensional degradation hybrid feature domain, the issue of extracting fault features with limited CRH5 gearbox fault samples is addressed. The hybrid feature domains obtained through dynamics simulation also have some limitations. For example, the simulation data are not generalized enough to fully simulate the data fluctuations caused by wheel-rail excitation during EMU operation, and the performance of the model in the real environment cannot be fully evaluated. The accelerated life test of key gearbox components will be utilized to compensate for the above shortcomings in the subsequent study.
* The proposed GRU-HMM CRH5 gearbox fault prediction model combines the strengths of HMM in handling time-series data with GRU to overcome limitations on input sequence length, enhancing its ability to handle long-range dependency data. The effectiveness of the GRU-HMM fault prediction model was confirmed using operational monitoring data from a CRH5 prior to advanced repair and actual overhaul component records. Compared to traditional fault prediction models like HMM and SVM, the combined accuracy of the GRU-HMM model was found to be 15% higher.
* Although the GRU-HMM fault prediction model improves the recognition accuracy, it requires a large number of calculations in the preprocessing and training phases of the monitoring data, resulting in time-consuming fault prediction. In the subsequent study, the hyperparameters of the model will be adjusted to find the optimal combination of parameters to reduce the computation time. Cross-scale fault prediction studies of EMU gearboxes at a material scale and structural scale will also be considered, combining the multi-physical field coupling effect of temperature-vibration-load and performing comprehensive analysis to improve the accuracy of fault prediction.  
Conceptualization, Z.S.; Methodology, C.L.; Software, C.L.; Investigation, S.Z., Z.W., F.M. and Z.S.; Writing--original draft, C.L.; Project administration, C.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Liaoning Provincial Education Department grant number IJKZ0506.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflict of interest.