###### Abstract
studied a method for evaluating the types and severity of turnout rail degradation based on the spatial distribution and energy concentration of the characteristic frequencies  
Figure 1: (**a**) Turnout; (**b**) train derailment due to poor condition of turnout. Figure 2: (**a**) Switch rail reduction value (SRRV); (**b**) manual measurement of SRRV. of ABA [13]. [PERSON] et al. proposed two methods for turnout condition monitoring: one for identifying turnout rail damage based on rail vibration acceleration, and another for assessing ballast condition by capturing rail and sleeper displacement using machine vision [14]. [PERSON] et al. proposed a method for detecting turnout faults based on machine learning, utilizing information such as wheelset acceleration and wheel-rail impact locations [15]. [PERSON] et al. proposed a method for monitoring turnout ballast degradation using statistical low-complexity behavioral models [16]. [PERSON] et al. developed a wheelset lateral displacement test system using laser displacement sensors and proposed a method for recognizing turnout conditions based on wheelset lateral displacement [17]. [PERSON] et al. proposed a machine learning-based method for detecting turnout defects using track geometry [18], as well as a method for detecting and predicting turnout support deterioration through machine learning by utilizing ABA and crossing nose displacement [19]. [PERSON] et al. proposed a condition monitoring and inspection solution for turnouts based on machine vision to identify the position and condition of the switch rail [20]. [PERSON] and [PERSON] et al. explored a method for monitoring the geometric condition of turnouts based on sleeper acceleration [21; 22]. [PERSON] et al. studied a method for identifying squat on the switchblade by analyzing the vibration acceleration of the rod of the point machine using unsupervised machine learning algorithms [23]. In the research on turnout rail crack identification, [PERSON] et al. proposed a method for identifying rail cracks by analyzing turnout vibration signals using EEMD and LSSVM [24]. However, more studies on turnout rail crack identification methods have focused on using acoustic emission [25] and ultrasonic guided waves [26; 27; 28; 29; 30]. In the research on turnout condition monitoring equipment, [PERSON] et al. developed wireless sensors that can be installed on the rail web and sleeper to collect vibration acceleration signals, enabling real-time monitoring of turnout conditions [31]. [PERSON] et al. studied the method of inspecting railway turnouts using cameras [32]. In addition, as the turnout system also includes complex electromechanical devices such as switch machines, many studies on turnout condition monitoring and fault diagnosis focus on the electromechanical equipment [33; 34; 35; 36; 37; 38; 39], most of these studies are based on analyzing the operating current curve data for turnouts. The data types analyzed and the types of turnout defects focused on in the above-mentioned studies on turnout structural defect identification have been summarized, as shown in Table 1. It can be seen from the table that there is currently no research on efficient methods for identifying SRRV deviations.