## 4 Analysis and Discussion
### Quantitative Assessment Using the Similarity-Based Indicator  
In order to study the influence of feature selection on recognition results, different features in time domain, frequency domain, and time-frequency domain were selected for fusion, and a total of seven features fusion methods were established, as shown in Table 8. Scheme 1 is the baseline. Scheme 2 verifies the recognition effect of active CAC features, Schemes 3-4 verify the recognition effect of the time-domain feature and frequency-domain feature, respectively, and Schemes 5-7 verify the recognition effect of time-frequency-domain features, respectively. The values of the proposed indicator for Segment 1 and Segment 2, which serve as mutual instantaneous baselines, \(F_{seg1}\) and \(F_{seg2}\), were calculated with the use of Equations (11) and (12) in Section 3.4. The time histories of \(F_{seg1}\) and \(F_{seg2}\), together with their Euclidean distance of Scheme 1, are plotted in Figure 21. The red and black curves represent the indicator values of Segment 1 and Segment 2, and the blue curve is the Euclidean distance quantifying the feature difference between the two segments. It can be found that the two curves for \(F_{seg1}\) and \(F_{seg2}\) were coincident with each other in the early stage of monitoring and later \(F_{seg1}\) fluctuated stably while \(F_{seg2}\) had a continuous increase. The Euclidean distance between them gradually enlarged, which implies that Segment 2 suffered from an anomaly (i.e., debonding in this case), and the severity level of the anomaly had been continuously aggravated. This is coincident with the occurrence and deterioration of interlayer debonding of the monitored track system. Seven features fusion methods differed in the effect of identifying interlayer debonding, and the identification results are shown in Figure 22. In the figure, it can be seen that seven features fusion methods were able to characterize the increase in interlayer debonding with time, indicating that the proposed method in this manuscript does not depend on feature selection. However, different feature fusion methods differed in the identification effect. Compared with Scheme 1 (black curve), Scheme 2 (red curve) was  
Figure 21: Indicators of Segment 1 and Segment 2 and their Euclidean distance. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Scheme**} & \multicolumn{4}{c}{**Fused Features**} \\ \cline{2-5}  & **Time-Domain** & **Frequency-Domain** & & **Time-Frequency-Domain Features** \\  & **Feature** & **Feature** & & **Time-Frequency-Domain Features** \\ \hline
1 & Peak acceleration & Peak frequency & Local peak & Local energy & CAC values \\
2 & Peak acceleration & Peak frequency & Local peak & Local energy & Active CAC values \\
3 & - & Peak frequency & Local peak & Local energy & Active CAC values \\
4 & Peak acceleration & - & Local peak & Local energy & Active CAC values \\
5 & Peak acceleration & Peak frequency & - & Local energy & Active CAC values \\
6 & Peak acceleration & Peak frequency & Local peak & - & Active CAC values \\
7 & Peak acceleration & Peak frequency & Local peak & Local energy & - \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Fusion schemes. flatter, less fluctuating, and had the most significant increase, so Scheme 2 has the best recognition. There was little difference in the recognition effect of Scheme 3-Scheme 7. The contribution of different features which differ in the recognition effect is characterized by the weight index. The weights of different features in seven features fusion methods are shown in Table 9. The contribution to the identification of interlayer debonding increased with increasing weight. In Table 9, it can be seen that time-frequency-domain features contributed the most to the identification effect, accounting for more than 50%, and the time-domain features contributed more than the frequency-domain features.