Development of a contactless sensor system to support rail track geometry on-board monitoring

This paper is focused on the ongoing research, within a work package of the Shift2Rail project Assets4Rail, related to the development of an on-board contactless sensor system able to measure the wheel's transversal position in relation to the rail in order to support track geometry measurements. In particular, this research work focuses on developing a sensor system to support track geometry monitoring performed by the master system under development in other Shift2Rail projects. The aim is to develop a sensor system to detect the relative transversal position between the wheelset and the rail, suitable for the use on commercial (in-service) vehicles. In fact, a possible track geometry monitoring system alternative to the sophisticated and expensive optical/inertial systems and suitable for use on commercial vehicles, could be based on the measurement of accelerations. However, some parameters of the track geometry, such as lateral alignment, are extremely difficult to determine through the measurement of accelerations. In this case, it is necessary to find an innovative sensor system able to determine the wheel's transversal position in relation to the rail. For this reason, this project intends to focus on innovative systems that allow the detection of the wheel-track position by avoiding the optical/inertial systems already used on diagnostic trains. After a state-of-the-art overview on the potentially applicable technologies for the sensor system to be developed, a corresponding analytical tool for comparison of contactless sensors to choose the most suitable technology has been developed and two candidate technologies (stereo and thermal cameras) have been selected and assessed by means of a test platform in the facilities laboratory of VGTU (Vilnius Tech). This work will be the basis for developing a concept design of the sensor system together with a montage solution, which will be finally tested on a vehicle in real operation conditions

Track geometry monitoring by an on-board computer-vision-based sensor system

This article illustrates some outcomes of the EU project Assets4Rail, founded within the Shift2Rail Joint Undertaking. Nowadays, Track recording vehicles (TRV) are equipped with laser/optical systems with inertial units to monitor track geometry (TG). Dedicated trains and sophisticated measurement equipment are difficult, costly to acquire and maintain. So the time interval between two TRV recordings of the TG on the same line section cannot be too close (twice per month to twice per year). Recently, infrastructure managers have been more interested in using commercial trains to monitor track condition in a cost-effective manner. TRVs' expensive and constantly maintained optical systems make them unsuitable for commercial fleets. On-board sensor systems based on indirect measurements such as accelerations have been developed in various studies. While detecting the vertical irregularity is a straightforward method by doubling the recorded acceleration, it is yet an unsolved issue for lateral irregularities due to the complicated relative wheel-rail motion. The proposed system combines wheel-rail transversal relative position data with on-board lateral acceleration sensors to detect lateral alignment issues. It includes a functional prototype of an on-board computer vision sensor capable of monitoring Lateral displacement for TG measurements. This eliminates measurement errors due to wheelset transverse displacements relative to the track, which is essential for calculating lateral alignment. The sensor system prototype was tested in Italy at 100 km/h on the Aldebaran 2.0 TRV of RFI, the main Italian Infrastructure Manager. It was found that the estimated lateral displacement well corresponds to the lateral alignment acquired by the Aldebaran 2.0 commercial TG inspection equipment. Moreover, due to the lack of measurement of the acceleration on board the Aldebaran 2.0 TRV, a Simpack® simulation provide with axle box acceleration values, to evaluate the correlation between them, LDWR and track alignment issues

Deep Learning based Virtual Point Tracking for Real-Time Target-less Dynamic Displacement Measurement in Railway Applications

In the application of computer-vision based displacement measurement, an optical target is usually required to prove the reference. In the case that the optical target cannot be attached to the measuring objective, edge detection, feature matching and template matching are the most common approaches in target-less photogrammetry. However, their performance significantly relies on parameter settings. This becomes problematic in dynamic scenes where complicated background texture exists and varies over time. To tackle this issue, we propose virtual point tracking for real-time target-less dynamic displacement measurement, incorporating deep learning techniques and domain knowledge. Our approach consists of three steps: 1) automatic calibration for detection of region of interest; 2) virtual point detection for each video frame using deep convolutional neural network; 3) domain-knowledge based rule engine for point tracking in adjacent frames. The proposed approach can be executed on an edge computer in a real-time manner (i.e. over 30 frames per second). We demonstrate our approach for a railway application, where the lateral displacement of the wheel on the rail is measured during operation. We also implement an algorithm using template matching and line detection as the baseline for comparison. The numerical experiments have been performed to evaluate the performance and the latency of our approach in the harsh railway environment with noisy and varying backgrounds