## 6 Conclusions  
SRRV deviation is a typical turnout structural defect that significantly affects their functionality and jeopardizes train operation safety. However, current methods for detecting SRRV conditions rely heavily on inefficient manual inspections. To address this issue, the study carried out a comprehensive investigation combining numerical and experimental analyses. The main work and conclusions are summarized as follows:  
(1) A rigid-flexible coupled dynamics model of a vehicle-turnout system was developed to investigate the impact patterns of SRRV deviation on the wheel-rail dynamic interaction forces and contact relationships. The results indicate that while SRRV deviation has a minimal effect on wheel-rail vertical forces, it significantly influences lateral forces. Positive SRRV deviation causes a gradual rearward shift in the wheel load transition range, whereas negative SRRV deviation results in a forward shift and a corresponding reduction in the length of the transition range. The turnout structural irregularities under different SRRV deviation exhibit long-wave patterns with wavelengths ranging from 2 to 6 m, representing a unique form of long-wave irregularities distinct from geometric irregularities. (2) We proposed an efficient and accurate method for identifying SRRV deviation based on the wheel-rail force trend component. Based on DWT, it was derived that the 7 th-order wheel-rail force trend component can effectively analyze the irregular wavelength of the turnout, and a mapping relationship between the 7 th-order trend component and SRRV deviation was established. The results indicate that with an increase in positive SRRV deviation, the trough value of the 7 th-order trend component of wheel-rail vertical force gradually decreases and shifts backward, while the peak value of the 7 th-order trend component of wheel-rail lateral force gradually increases and shifts backward; as negative SRRV deviation increases, the peak value of the 7 th-order trend component of wheel-rail vertical force gradually increases and shifts forward, and the amplitude of the 7 th-order trend component of wheel-rail lateral force oscillations increases with a simultaneous decrease in wavelength. Figure 15: DWT results of measured lateral wheel–rail force: (**a**) turnout with SRRV deviation; (**b**) turnout without SRRV deviation. (3) We validated the identification method using field-measured data from trains passing through turnouts with different SRRV deviations. The analysis of the measured data aligns well with the theoretical results presented in the paper. The findings indicate that the method effectively identifies SRRV deviations while mitigating the impact of disturbances, such as turnout positioning and track geometry irregularities. This study offers significant references and a foundation for research on turnout defect identification and condition monitoring methods. The proposed analysis method can be applied to analyze large amounts of wheel-rail force measurement data from track inspection vehicles, significantly improving the efficiency of detecting turnout service conditions, promptly identifying track defects, and ensuring train operation safety. However, this study has the following limitations: The identification of wheel-rail force trend components still relies on manual processing and has not been automated. Furthermore, this paper mainly focuses on the SRRV deviation identification method for high-speed turnouts, while the structural irregularity wavelength and wheel-rail force sampling distances vary for turnouts of different speed levels, which may result in differences in the characteristics of wheel-rail force trend components. However, the study does not explore SRRV deviation identification methods for turnouts of different speed levels. Finally, in practical service, turnouts may exhibit other track defects, such as insufficient switch rail displacement.