Search Results (3)

The amount of freight transported by rail and the number of passengers are increasing year by year. Any disruption to the passenger or freight transport stream can generate both financial and human losses. Such a disruption can be caused by the rail infrastructure being in poor condition. For this reason, the state of the infrastructure should be monitored periodically. One of the important elements of railroad infrastructure is the ballast. Its condition has a significant impact on the safety of rail traffic. The unevenness of the ballast surface is one of the indicators of its condition. For this reason, a regulation was introduced by Polish railway lines specifying the maximum threshold of ballast unevenness. This article presents an algorithm that allows for the detection of irregularities in the ballast. These irregularities are determined relative to the surface of the sleepers. The images used by the algorithm were captured by a laser triangulation system placed on a rail inspection vehicle managed by the Polish railway lines. The proposed solution has the following elements of novelty: (a) it presents a simple criterion for evaluating the condition of the ballast based on the measurement of its unevenness in relation to the level of the sleeper; (b) it treats ballast irregularity detection as an instance segmentation process and it compares two segmentation algorithms, Mask R-CNN and YOLACT, in terms of their application to ballast irregularity detection; and (c) it uses segmentation-related metrics—mAP (Mean Average Precision), IoU (Intersection over Union) and Pixel Accuracy—to evaluate the quality of the detection of ballast irregularity. Full article

An ultrasonic sonar-based ranging technique is introduced for measuring full-field railroad crosstie (sleeper) deflections. Tie deflection measurements have numerous applications, such as detecting degrading ballast support conditions and evaluating sleeper or track stiffness. The proposed technique utilizes an array of air-coupled ultrasonic transducers oriented parallel to the tie, capable of “in-motion” contactless inspections. The transducers are used in pulse-echo mode, and the distance between the transducer and the tie surface is computed by tracking the time-of-flight of the reflected waveforms from the tie surface. An adaptive, reference-based cross-correlation operation is used to compute the relative tie deflections. Multiple measurements along the width of the tie allow the measurement of twisting deformations and longitudinal deflections (3D deflections). Computer vision-based image classification techniques are also utilized for demarcating tie boundaries and tracking the spatial location of measurements along the direction of train movement. Results from field tests, conducted at walking speed at a BNSF train yard in San Diego, CA, with a loaded train car are presented. The tie deflection accuracy and repeatability analyses indicate the potential of the technique to extract full-field tie deflections in a non-contact manner. Further developments are needed to enable measurements at higher speeds. Full article

Dynamic deflection of a railroad sleeper works as an indicator of ballast stiffness, reflecting the health conditions of a ballast track. However, difficulty exists in measuring dynamic deflection of a railroad sleeper by conventional deflection transducers such as a linear variable differential transformer (LVDT) or a potentiometer. This is because a fixed reference point is unattainable due to ground vibrations during train passage. In this paper, a patented signal processing technique for evaluation of pseudo-deflection is presented to recover dynamic deflection of a railroad sleeper using a coupled measurement of acceleration and strain at the concrete sleeper. The presented technique combines high-frequency deflections calculated from double integration of acceleration and low-frequency deflections determined from strains. Validity of the combined deflections was shown by the deflections measured with a camera target on a concrete sleeper, captured by a high-resolution DSLR camera with superb video capturing features and processed by computer vision techniques, such as Canny edge detection and Blob analysis. Full article