# An Evidential Reasoning Assessment Method Based on Multidimensional Fault Conclusion
## 4 Simulation Experiment and Analysis
the fuzziness index is set to 2, the maximum number of iterations to 100, and the minimum improvement value to \(1\times 10^{-6}\). From Figure 5, it is evident that the health status assessment value for the high-speed train running gear gradually increases from 0 to 1, indicating a deterioration in the health condition, which aligns well with the fault diagnosis results at the corresponding time points. Additionally, the experimental results obtained from our model are more consistent with the fault diagnosis results. The proposed method's curve shows a steady upward trend, while the FCM model exhibits significant fluctuations despite being closer in some aspects, making it less reliable. The BPNN and CNN models show greater deviations and minor fluctuations. This paper uses the monotonicity and correlation coefficients as performance indicators for the assessment model. The detailed results are presented in Table 2. Note that a higher monotonicity coefficient indicates a more significant rising or falling trend in the degradation curve. In contrast, a higher correlation factor signifies a stronger correspondence between the degradation curve and the actual data. Therefore, larger values for these indicators reflect a more effective model. The proposed method demonstrates significant advantages when analyzing the specific values of the monotonicity and correlation coefficients in Table 2. Among the four methods compared, the proposed method achieves the highest correlation coefficient, indicating a strong alignment between the predicted health status and the actual degradation trend. Additionally, the monotonicity coefficient of the proposed method is notably higher than those of the other methods, reflecting its ability to produce a stable and consistent health assessment curve. In contrast, the other three methods, while achieving relatively high correlation coefficient, suffer from significantly lower monotonicity coefficients. Specifically, the BPNN method has a monotonicity coefficient of 0.373, the CNN method has 0.434, and the FCM method has 0.419. These lower monotonicity values suggest that the assessment curves generated by these methods exhibit intermittent rises and falls, which do not accurately reflect the continuous degradation typically observed in electromechanical equipment. As a result, these methods may not provide reliable assessments of the health status, as they fail to consistently represent the degradation process. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Number** & **Methods** & **Correlation Coefficient** & **Monotonicity Coefficient** \\ \hline
1 & The method & 0.955 & 0.620 \\
2 & BP & 0.928 & 0.373 \\
3 & CNN & 0.842 & 0.434 \\
4 & FCM & 0.868 & 0.419 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Experimental results of different methods. Figure 5: Comparison of different methods. As shown in Table 3, the proposed method demonstrates superior accuracy in the health assessment of high-speed train running gear, with a mean squared error (MSE) of 0.0219, significantly outperforming the other three methods: BPNN, CNN, and FCM. This indicates that the proposed method provides more accurate and reliable predictions of the running gear's health status. In contrast, the FCM method exhibits the highest MSE, indicating poor performance and insufficient predictive accuracy in this application. Although the BPNN and CNN methods perform relatively well, their accuracy is still inferior to that of the proposed method. Therefore, the proposed method offers a clear advantage in this field, making it a more precise and effective tool for the health assessment of running gear systems.