###### Abstract
\begin{table}
\begin{tabular}{c c c c} \hline
**Reference** & **Year** & **Type of Data Analyzed** & **Type of Turnout Defect** \\ \hline [PERSON] [11] & 2014 & ABA & Rail worn and ballast degradation \\ [PERSON] et al. [12] & 2015 & Bogie acceleration & The poor fit defect of the switch point and \\ [PERSON] et al. [24] & 2016 & Turnout vibration signals & Rail crack \\ [PERSON] et al. [13] & 2017 & ABA & Rail degradation \\ [PERSON] et al. [14] & 2018 & Rail acceleration and displacement of rail & Rail worn and damaged and ballast \\  & & and sleeper & settlement \\ [PERSON] et al. [15] & 2019 & Wheel acceleration, wheel impact position & Crossing deterioration \\ [PERSON] et al. [16] & 2020 & Track vertical acceleration & Ballast degradation \\ [PERSON] et al. [17] & 2021 & Wheelset lateral displacement & Railhead top surface wear \\ [PERSON] et al. [18] & 2022 & Track geometry & Rail worn \\ [PERSON] et al. [20] & 2022 & The position and condition of the switch & Toe gap \\ [PERSON] [25] & 2022 & Acoustic emission & Rail crack \\ [PERSON], [PERSON] et al. [21; 22] & 2022, 2023 & Sleeper acceleration & Ballast vertical stiffness change and sleeper \\ [PERSON] et al. [19] & 2023 & ABA and Crossing nose displacement & void \\ [PERSON] et al. [23] & 2023 & Acceleration of the point machine rod & Support deterioration \\ [PERSON], [PERSON] et al. [26; 27; 28; 29; 30] & 2020–2024 & Ultrasonic guided wave & Squat on the switchblade \\ \hline \end{tabular}
\end{table} 
Table 1: Existing Research on Methods for Identifying Turnout Structural Defects. In summary, the existing research faces the following issues: (1) multi-rigid-body dynamics models lack accuracy in studying the impact of SRRV deviation on wheel-rail interaction; (2) efficient and accurate identification methods for SRRV deviation are insufficiently explored. To better understand the effects of SRRV deviation on wheel-rail dynamic interaction and develop an efficient and accurate identification method, this study established a rigid-flexible coupled dynamics model of the vehicle-turnout system based on multi-body system dynamics theory. The model was used to calculate the wheel-rail dynamic interaction forces and contact relationships under different SRRV deviation conditions. The results showed that SRRV deviation significantly affects both wheel-rail forces and the turnout structural irregularity wavelength. Based on this, the 7 th-order wheel-rail force trend component was derived using discrete wavelet transform (DWT), which effectively analyzes the turnout structural irregular wavelength. A mapping relationship between SRRV deviations and the 7 th-order wheel-rail force trend component was then established. Accordingly, an efficient method based on the 7 th-order wheel-rail force trend component was proposed for identifying SRRV deviation. This method was validated using field-measured data, demonstrating its accuracy. The paper is organized as follows: Section 2 introduces the research methodology, including the establishment of a vehicle-turnout rigid-flexible coupled dynamics model based on multi-body dynamics and the principles of data trend component extraction using DWT. Section 3 utilizes the established vehicle-turnout dynamics model to analyze the changes in wheel-rail dynamic interaction forces and contact relationships under different SRRV deviation conditions. Section 4 derives the wheel-rail force trend components for SRRV deviation identification based on the trend extraction method and the results from Section 3 and analyzes the mapping relationship between different SRRV deviations and the 7 th-order trend components of wheel-rail forces. Section 5 validates the proposed SRRV deviation identification method based on wheel-rail force trend components using measured data. Section 6 concludes the study, discusses its limitations, and outlines several directions for future research.