## 3 Preprocessing of Monitoring Data  
Since the sensors are installed on the operating HSR, the vibration signals captured by sensors contain the information of train vibration, track slab deformation, noise and environmental vibration. In addition, the train grouping, running speed, and load of different trains are all different. All these factors are manifested in the vibration signals, thus making obtaining track deformation status information from a single monitoring data considerably challenging. Hence, the raw data must be preprocessed in order to accurately identify the deformation state of track slab. Figure 3 illustrates the preprocessing process.  
First, data interception is performed on the raw data. When the train passes by a sensor, the information captured by the sensor is the wheel-rail vibration signal containing track slab deformation information; when no train passes by, the information captured by the sensor is environmental noise. Therefore, the effective information for the identification of the track slab deformation is the wheel-rail vibration signal when the train passes. In this regard, we employed a data interception method to derive vibration signals containing  
Figure 3: Preprocessing process.  
Figure 2: Field track-side monitoring system.  
the track slab deformation information. The results of data interception are shown in Figure 3. In the data interception process, the vibration acceleration threshold \(\delta\) is defined. When the vibration acceleration \(x(t)\geq\delta\) at time \(t\), the vibration acceleration \(x(t)\) at time \(t\) is recorded. \(x(t+1)\), \(x(t+2)\), and \(x(t+3)\) are also recorded until the vibration acceleration \(x(t+n-1)\geq\delta\), \(x(t+n)\leq\delta\), and \(x(t+n+1)<\delta\); then recording is stopped. Thus, a new vibration acceleration set X = [\(x(t)\), \(x(t+1)\), \(x(t+2)\),... \(x(t+n-1)\)], in which the environmental vibration information is eliminated, is formed. The threshold value in this paper is set to 0.01 g.  
Second, the denoising process is performed on the new vibration acceleration set. The signal acquired in the field often comprises noise and requires denoising to effectively display the useful information in the original signal. This paper adopts \"sym3\" wavelet function for five-layer wavelet threshold denoising of the new vibration acceleration set. The signals before and after denoising are shown in Figure 3. The red curves represent the denoised signal. It can be seen that the \"purr\" of the denoised signal disappears, indicating that the method can effectively remove the high-frequency noise from the original signal. The correlation coefficient between the original signal and the denoised signal reaches 0.98, so the denoised signal can characterize the original signal and the subsequent analysis can be performed on it.  
Finally, data segments are implemented on the denoised signals. When trains of different groupings and speeds pass by a sensor, the time duration of the vibration signal is different. To eliminate the effect of the time difference on the recognition effect, a fixed window is applied to realize the data segmentation of the denoised signal. Since the sampling frequency of the monitoring system is 1000 Hz, in this paper, the length of the fixed window is set to 1000 and the time duration is 1 s. The time durations of the examples in Figure 3 are 6 s, 4 s, and 4 s, so they can be divided into 6, 4, and 4 data subsets, respectively.  
By using data interception, wavelet threshold denoising, and fixed window segmentation, the raw data are transformed into a subset of data with the same time length. These algorithms are all coded in the MATLAB environment to form an automated data preprocessing system.