Arching Detection Method of Slab Track in High-Speed Railway Based on Track Geometry Data

Abstract

During the long-term service of slab track, various external factors (such as complicated temperature) can result in a series of slab damages. Among them, slab arching changes the structural mechanical properties, deteriorates the track geometry conditions, and even threatens the operation of trains. Therefore, it is necessary to detect slab arching accurately to achieve effective maintenance. However, the current damage detection methods cannot satisfy high accuracy and low cost simultaneously, making it difficult to achieve large-scale and efficient arching detection. To this end, this paper proposed a vision-based arching detection method using track geometry data. The main works include: (1) data nonlinear deviation correction and arching characteristics analysis; (2) data conversion and augmentation; (3) design and experiments of convolutional neural network- based detection model. The results show that the proposed method can detect arching damages effectively, and the F1-score reaches 98.4%. By balancing the sample size of each pattern, the performance can be further improved. Moreover, the method outperforms the plain deep learning network. In practice, the proposed method can be employed to detect slab arching and help to make maintenance plans. The method can also be applied to the data-based detection of other structural damages and has broad prospects.

Study of Patterns of Movement of Groups of Seed Potatoes in Conical Seed Box Based on the Dem-Model of the Process

“…To solve the arching problem for granular materials, in terms of taking the arching phenomenon as the research object, Jenike used the flow factor to study the arching of granular material particles in the hopper in the early stage and found that the flow factor is related to the friction angle of material particles and the friction angle in the hopper [8]. In recent years, there have been an extensive number of studies on soil-particle-arch formation [9,10] and track-slab-arch detection [11]. Nikiitenko proposed measures of staticarch stabilization that affected the quality of drainage construction by mathematically simulating the arching of loose materials in the drainage hangar [12].…”

Random-Forest Machine Learning Approach for High-Speed Railway Track Slab Deformation Identification Using Track-Side Vibration Monitoring

“…A ballastless track slab is an important part of various types of track structures, as it plays a "top-down" role (i.e., bearing the train load and transferring it down to the foundation) [3]. However, prolonged operation could cause various defects in track slabs, such as warping deformation of track slab [4,5], wide and narrow joint damages [6,7], interlayer debonding [3,8,9], and arching deformation of track slab [10][11][12]. Under temperature load [13,14] and high-frequency train load [14], these damages may cause track slab deformation and abnormal vibrations in wheel-rail system.…”

“…Therefore, the real-time monitoring of track slab performance and the identification of the damage condition of track slabs are essential for long-term safe operation of the HSR. Existing track slab defect detection methods can be divided into three categories: visual inspection [15,16], nondestructive testing (NDT) [4,11,[17][18][19][20][21][22], and structural health monitoring (SHM) [23][24][25][26][27]. Most of the visual inspection methods rely on a variety of track inspection equipment, such as road rulers, gauges, and various customized equipment.…”

“…By contrast, NDT methods mainly use noncontact measurement to detect track defects. Some scholars use the differences between rail inspection vehicles' static and dynamic inspection data [17] and the geometric data [4,11] to detect track slab deformation. The track inspection data are derived from various sensing devices installed on the track inspection car.…”

“…The track slab cracks were detected on the basis of the region proposal network and fully convolutional networks [47] and the different severity levels of track slab cracks were studied on the basis of the Inception-ResNet-v2 network [48]. The arching deformation of track slabs was detected on the basis of CNN [11] using track geometry data. The damage of CA mortar layer was identified on the basis of SVM by using the acceleration of wheelset [9], the back propagation neural network by using vibration data obtained from modal analysis [49], and CNN by using Hilbert spectrum [34].…”