## 1 Introduction
Another approach used distributed fiber optics to monitor and locate cracks in the track slab [25] as well as to monitor the load deflection behavior of the track [26] and the arching deformation of the track slab [27]. This paper proposes a method for monitoring track slab deformation using fiber optic sensing technology and the results show that track-side monitoring can effectively capture the vibration signals caused by train vibration, track slab deformation, noise, and environmental vibration. The complex and weak information of track slab deformation hidden in the track-side monitoring data makes it difficult to identify the track defects from the measurement data that contain a lot of redundant information. As the track monitoring data in the railway field include information of train vibration, track deformation, noise, and environmental vibration, it is challenging to extract the desired track slab defect information from the complex signals. Feature extraction of track slab deformation is crucial to accurate identification. Wavelet analysis is considered to be an effective tool with strong analysis capability for signal localization, but the basis function needs to be defined in advance, and the choice of the basis function directly affects the analysis performance. Thus, the analysis method lacks adaptability [29]. To address the shortcomings of the traditional time-frequency analysis method, [PERSON] et al. proposed empirical mode decomposition (EMD) [30], which is widely used in the field of rail damage feature extraction [31], track irregularity feature extraction [32; 33], cement asphalt (CA) mortar layer damage feature extraction [34], and other track defect feature extraction. Although this method has the ability to decompose the signal adaptively, its algorithm itself suffers from modal mixing, endpoint effect, and negative frequency problem. Therefore, to avoid these problems, the local mean decomposition (LMD) method [35] was proposed. This method has extensive application in the field of bearing fault identification [36; 37; 38]. However, LMD method for feature extraction in the field of railway track defect identification is rarely reported. Since damage identification methods based on machine learning do not require a priori knowledge of system dynamics, they not only are applicable to linear mapping systems but also can represent arbitrarily complex nonlinear mapping systems. They also have the advantages of nonparametric methods for damage identification, thus avoiding some of the complications associated with traditional damage identification methods, which must establish a functional relationship between structural response and physical parameters. More importantly, high noise and large uncertainty of the monitoring data in the railway track field make identifying the track damage difficult by constructing a simple indicator; machine learning algorithms have, therefore, been introduced. In recent years, machine learning methods have had many applications in track defect identification and prediction. Machine learning methods were used to detect defects of important components of the track structure that are directly related to safe train operation, including the defects of rails [39; 40], fasteners [41; 42; 43; 44], rail pads [45], and turnout [46]. The classification of three types of rail defects--surface defect, cross level defect and depression in track profiles--by track geometry data on the basis of logistic regression and decision tree [39] and the classification of rail crack by acoustic emission waves on the basis of a multibranch convolutional neural network (CNN) [40] were explored. The rail fastener defects were detected from images on the basis of a CNN [41], generative adversarial network, residual network [42], point cloud deep learning [43], and Faster region-CNN [44].