# An Evidential Reasoning Assessment Method Based on Multidimensional Fault Conclusion
## 4 Simulation Experiment and Analysis  
This paper validates the proposed method on the problem of lack of multi-source feature indicators and difficulty in obtaining feature indicators in a health status assessment by using a case study on a high-speed train's running gear. Specifically, it examines the health status of the CRH5 model's running gear. Figure 3 shows the actual CRH5 train's running gear. This study utilizes data from a fault log provided by a company on 27 July 2017, focusing on the temperature fault data of small motors in the running gear. By conducting a comparative analysis of the frequency, causes, and impact range of motor temperature faults during this period, the obtained fault diagnosis results, including data on fault  
Figure 3: The running gear of the CRH5 model high-speed train. Figure 2: Diagram of the health status assessment process for running gear. types and fault severity, are used for the health status assessment of the running gear. This analysis further validates the accuracy and effectiveness of the proposed evidence reasoning assessment model based on multidimensional fault conclusions in evaluating the health status of the running gear. The health status classification is grounded in the expertise and experience of technicians and experts who have established a normalization process that maps real-time data, such as temperature, into a health index value between 0 and 1. This normalization process uses predefined thresholds based on historical data and expert knowledge. According to technicians and experts, the health status is categorized into four levels: a value between 0 and 0.3 indicates a healthy status, 0.3 to 0.6 signifies a degraded status, 0.6 to 0.8 represents an abnormal status, and 0.8 to 1 denotes a failed status. The descriptions corresponding to each status of the running gear are shown in Table 1.  
The running gear's health status is evaluated using the established indicator system and collected fault data. The fault data are fed into the ER model, and the model parameters are optimized using the CMA-ES algorithm. The final outcomes are illustrated in Figure 4.  
Figure 4 illustrates that as the number of samples increases over time, the health status of the running gear progressively declines from an initially healthy status to a failure status. This trend aligns with real-world engineering observations, suggesting that the model accurately assesses the running gear's health status. When the health condition assessment value degrades to a certain extent, maintenance personnel can develop appropriate maintenance plans to ensure running gear's safe and reliable operation. To verify the proposed model's effectiveness, it is compared with several other models, including back propagation neural networks (BPNNs), convolutional neural networks (CNNs), and fuzzy C-means (FCMs). The comparative results are shown in Figure 5, where all methods utilize the same data samples. The initial weight parameters for the optimized ER method proposed in this paper are set to \(\omega 1=\omega 2=\omega 3=1\). The BPNN parameters are set with 100 iterations. The CNN parameters are set with four convolutional layers, where the number of neurons is 8, 16, 32, and 32, respectively, with two average pooling layers and one fully connected layer. In the FCM method, \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Level** & **Health Level** & **Interval Value** & **Status Description** \\ \hline
1 & Healthy & [0, 0.3) & The system is in optimal condition. \\
2 & Degraded & [0.3, 0.6) & Some signs of degradation are present, indicating potential need for maintenance. \\
3 & Abnormal & [0.6, 0.8) & The health status has deteriorated, signaling urgent maintenance needs. \\
4 & Failed & [0.8, 1] & The system has failed and requires immediate action. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Health grade of running gear. Figure 4: Assessment value of health status of walking parts.