Search Results (16)

The Building Information Modeling (BIM) concept has been recently implemented in railway infrastructure, assisting mainly in the project elaboration, and further, the facility management aspect. The present study addresses the inspection activity of the railway geometry, in a BIM context, using a rigorous modeling process of the railway track components, and the development of a Dynamo script for the evaluation of the degree of geometric irregularity detected during inspection works. The monitoring phase of the rail tracks involves a planned railway inspection schedule, normally supported by human analyses of data collected in a railway geometric inspection. The created script allows for evaluating the inspection data and categorizes the data by alert levels that are associated with a color code, visualized over the railway components of the BIM model. The Dynamo script uses new BIM parameters considering the maintenance activity, allowing for analyzing inspection data and visualizing the colored alerts. This capacity alerts the maintenance engineer about the urgency of planning a retrofitting action, according to the severity level of the detected geometric anomaly. An illustrative real railway track segment is considered supporting the modeling process, the inspection data collection and the efficiency analyses of the script application. This research intends to contribute to increment knowledge of BIM adoption in railway infrastructures, emphasizing the potential of using Dynamo programming on BIM model database management. Full article

The automation of rail track inspection addresses key issues in railway transportation, notably reducing maintenance costs and improving safety. However, it presents numerous technical challenges, including sensor selection, calibration, data acquisition, defect detection, and storage. This paper introduces a compression method tailored for laser triangulation scanners, which are crucial for scanning the entire rail track, including the rails, rail fasteners, sleepers, and ballast, and capturing rail profiles for geometry measurement. The compression technique capitalizes on the regularity of rail track data and the sensors’ limited measurement range and resolution. By transforming scans, they can be stored using widely available image compression formats, such as PNG. This method achieved a compression ratio of 7.5 for rail scans used in the rail geometry computation and maintained rail gauge reproducibility. For the scans employed in defect detection, a compression ratio of 5.6 was attained without visibly compromising the scan quality. Lossless compression resulted in compression ratios of 5.1 for the rail geometry computation scans and 3.8 for the rail track inspection scans. Full article

Adequate vertical track support is essential for safe and efficient railway operations. Insufficient support leads to distorted track geometry, increased dynamic loads, component stress, poor ride quality, rolling stock damage, and derailment risks. Current inspection practices focus on assessing the condition of the track components and geometry, rather than the root causes of degradation. To improve this condition, this study presents the use of a methodology that utilizes an autonomous vertical track deflection measurement system mounted on a loaded rail car (36 tonnes/axle) to support track maintenance decisions in a heavy haul railroad located in southeast Brazil. The system continuously measured substructure stiffness along the railway line. Over one year, data were collected from over 8000 km of track. The study highlighted seasonal effects on track degradation over time, identifying areas with significant deflections and high deflection rates, which contribute to issues such as differential settlement and reduced lifespan of track components. Additionally, the study revealed seasonal effects, with deflections peaking during wet weather and decreasing during dry cycles. A method to classify weak track areas was developed, facilitating monitoring and enabling more effective maintenance planning, contributing to the reduction of overall track maintenance costs and enhancing safety and operational efficiency. Full article

Nowadays, railway track monitoring strategies are based on the use of railway inspection vehicles and wayside dynamic monitoring systems. The latter sometimes requires traffic disruption, as well as higher time and cost-consumption activities, and the use of dedicated inspection vehicles is less economical and efficient as the use of in-service vehicles. Furthermore, the use of non-automated algorithms faces challenges when it comes to early damage detection in railway infrastructure, considering operational, environmental, and big data aspects, and may lead to false alarms. To overcome these challenges, the application of artificial intelligence (AI) algorithms for early detection of track defects using accelerations, measured by dynamic monitoring systems in in-service railway vehicles is attracting the attention of railway managers. In this paper, an AI-based methodology based on axle box acceleration signals is applied for the early detection of distributed damage to track in terms of the longitudinal level and lateral alignment. The methodology relies on feature extraction using an autoregressive model, data normalization using principal component analysis, data fusion and feature discrimination using Mahalanobis distance and outlier analysis, considering eight onboard accelerometers. For the numerical simulations, 75 undamaged and 45 damaged track scenarios are considered. The alert limit state defined in the European Standard for assessing track geometry quality is also assumed as a threshold. It was found that the detection accuracy of the AI-based methodology for different sensor layouts and types of damage is greater than 94%, which is acceptable. Full article

With the continuous development of precise measurement and precise tamping (PMPT) technology on Chinese railway conventional speed lines, the efficiency of machinery tamping operation and the quality of the track have been effectively improved. A variety of PMPT modes have been tried in the field operation, however there are some differences in the operation effect. The quality of the tamping operation is affected by multiple factors. In order to identify the key factors affecting the operation quality and to further improve the tamping operation effect, this paper establishes both the database of PMPT operation modes and the selection index system for evaluating the operation effect. Based on mega multi-source heterogeneous data and track geometry inspection data, this paper adopts the Back Propagation Neural Network (BPNN) prognosis model to quantify and sort the main factors affecting the effect of PMPT. The research results show that the initial quality of the track before tamping, whether the stabilizing operation or the tamping modes have great influence weights. It can scientifically guide the field operation to control the key factors and put forward some practical suggestions for promoting the field application of PMPT and the optimization of operation modes on the conventional speed lines. Full article

The inspection and maintenance of track ballast are fundamental tasks for the preservation of the condition of railway networks. This work presents an application based on a low-cost solid-state LiDAR system, which allows the user to accurately measure the ballast geometry from a mobile inspection trolley or draisine. The solid-state LiDAR system, the LiVOX Avia, was validated on a test track through comparison with a traditional static LiDAR system, the Faro Focus 3D. The results show a standard deviation of around 6 mm for the solid-state LiDAR system. The LiVOX system also provides the capability to measure the ballast digital elevation model and profiles. The LiVOX results are in agreement with those obtained from the Faro Focus. The results demonstrate that the LiVOX system can sufficiently measure even the displacement of a single layer of ballast stones typically between 2.5 cm and 5 cm. The data provided can be easily digitalized using image processing tools and integrated into geographic information systems for infrastructure management. Full article

Railway tracks must be managed appropriately because their conditions significantly affect railway safety. Safety is ensured through inspections by track maintenance staff and maintenance based on measurements using dedicated track geometry cars. However, maintaining regional railway tracks using conventional methods is becoming difficult because of their poor financial condition and lack of manpower. Therefore, a track condition diagnostic system is developed, wherein onboard sensing devices are installed on in-service vehicles, and the vibration acceleration of the car body is measured to monitor the condition of the track. In this study, we conduct long-term measurements using the system and evaluate changes in the track conditions over time using car-body vibration data. Filed test results showed that sections with degraded tracks were identified using car-body vibration data. The track degradation trend can be constructed using the results obtained. Furthermore, this study demonstrated that the track maintenance effect could be confirmed. A method for improving train position using the yaw angular velocity is proposed. The track irregularity position can be shown more clearly by monitoring the track condition using position-corrected data using the proposed method. It is also shown that the time-frequency analysis of measured car-body vertical acceleration is effective for evaluating the track condition more clearly. Full article

This paper deals with the time-frequency characteristic analysis for track geometry irregularities using field data recorded by a comprehensive track inspection train. The parameters of the track gauge and the left and right rail alignment are considered to identify their characteristic wavelengths and the locations of their waveforms. In addition to the conventional time and frequency domain analysis, auto-adaptive signal decomposition techniques are used on four pre-selected track sections. During the time series analysis of the track gauge, the cumulative difference from the mean value is calculated, which makes it possible to distinguish the track section constructed with non-standard initial track gauges. The sensitive wavelengths of the track irregularities are obtained from the proper allocation of wavelength ranges in the Fourier Amplitude Spectrum of the original signal and the Fourier transform of the components detected by the Variational Mode Decomposition. This analysis can elucidate the wavelengths and positions of track irregularities that affect vehicle responses. Full article

This study aims to identify parametric models to estimate track irregularities in high-speed railways with simple acceleration measurements. The primary contribution of current research is the development of effective parametric models with smaller parameters. These parameters are derived from the measured data via a specialized track geometry inspection system. An adaptive Kalman filter algorithm, using the displacement estimated from the acceleration signals as the input and measured track irregularities as the output, is applied to obtain the model’s unknown parameters. These models are applied to acceleration measured from high-speed rail vehicles in operation, and track irregularities are estimated in spatial and wavelength domains. The estimated irregularities are compared to the track geometry inspection system’s results. Full article

The effect of track geometry on vehicle vibration is a major concern in high-speed rail (HSR) operation from the perspectives of ride comfort and safety. However, how to quantitatively characterize the relation between them remains a problem to be solved in track quality assessment. By using fractal analysis, this paper studies the detailed correlation between track surface and alignment irregularities and car body vertical and lateral acceleration in various wavelength ranges. The time-frequency features of the track irregularity and car-body acceleration are first analyzed based on empirical mode decomposition (EMD). Then, the fractal features of the inspection data are determined by calculating the Hurst exponent of their intrinsic mode functions (IMFs). Finally, the fractal dimensions of the track irregularity and car-body acceleration are obtained, and the correlation between their fractal dimensions with respect to different IMFs is revealed using regression analysis. The results show that the fractal dimension is only related to the roughness of the IMF waveforms of the track irregularity and car-body vibration and is irrelevant to the amplitude of the time series of the data; the correlation coefficient of the fractal dimension of the track irregularity and car-body acceleration is greater than 0.7 for wavelengths greater than 30 m, indicating that the relationship between track irregularity and car-body vibration acceleration is more obvious for long wavelengths. The findings of this research could be used for optimizing HSR track maintenance work from the viewpoint of the ride quality of high-speed trains. Full article

The precision of a linear object measurement using satellite techniques is determined by the number and the relative position of the visible satellites by the receiver. The status of the visible constellation is described by the Dilution Of Precision (DOP). The obtained geometric coefficient values are dependent on many variables. When determining these values, field obstacles at the receiver location and satellite positions changing with time must be taken into account. Carrying out a series of surveys as part of a linear object Global Navigation Satellite System (GNSS) measurement campaign requires the optimisation problem to be solved. The manner of the inspection vehicle’s movement should be determined in such a way that the surveys are taken only within the pre-defined time frames and that the geometric coefficient values obtained at subsequent points of the route are as low as possible. The purpose of this article is to develop a software for the planning of a linear object GNSS measurement campaign to implemented in motion and taking into account the terrain model and its coverage. Additionally, it was determined how much the developed program improves DOP values on the planned route under simulated conditions. This software has no equivalent elsewhere in the world, as the current solutions for the planning of a GNSS measurement campaign, e.g., Trimble GNSS Planning, GNSS Mission Planning, or GPS Navigation Toolbox, allow the satellite constellation geometry to be analysed exclusively for specific coordinates and at a specific time. Analysis of the obtained simulation test results indicates that the campaign implementation in accordance with the pre-determined schedule significantly improves the quality of the recorded GNSS data. This is particularly noticeable when determining the position using the Global Positioning System (GPS) and GLObal NAvigation Satellite System (GLONASS) satellite constellations at the same time. During the tests conducted on the road along a three-kilometre-long route (tram loop) in Gdańsk Brzeźno, the average value of the obtained Position Dilution Of Precision (PDOP) decreased by 22.17% thanks to using the software to plan a linear object GNSS measurement campaign. The largest drop in the geometric coefficient values was noted for an area characterised by a very large number of field obstacles (trees with crowns and high buildings). Under these conditions, the PDOP value decreased by approx. 25%. In areas characterised by a small number of field obstacles (single trees in the vicinity of the track, clusters of trees and buildings located along the track), the changes in the PDOP were slightly smaller and amounted to several percent. Full article

We present a method to train a deep-network-based feature descriptor to calculate discriminative local descriptions from renders and corresponding real images with similar geometry. We are interested in using such descriptors for automatic industrial visual inspection whereby the inspection camera has been coarsely localized with respect to a relatively large mechanical assembly and presence of certain components needs to be checked compared to the reference computer-aided design model (CAD). We aim to perform the task by comparing the real inspection image with the render of textureless 3D CAD using the learned descriptors. The descriptor was trained to capture geometric features while staying invariant to image domain. Patch pairs for training the descriptor were extracted in a semisupervised manner from a small data set of 100 pairs of real images and corresponding renders that were manually finely registered starting from a relatively coarse localization of the inspection camera. Due to the small size of the training data set, the descriptor network was initialized with weights from classification training on ImageNet. A two-step training is proposed for addressing the problem of domain adaptation. The first, “bootstrapping”, is a classification training to obtain good initial weights for second training step, triplet-loss training, that provides weights for extracting the discriminative features comparable using $l_2$ distance. The descriptor was tested for comparing renders and real images through two approaches: finding local correspondences between the images through nearest neighbor matching and transforming the images into Bag of Visual Words (BoVW) histograms. We observed that learning a robust cross-domain descriptor is feasible, even with a small data set, and such features might be of interest for CAD-based inspection of mechanical assemblies, and related applications such as tracking or finely registered augmented reality. To the best of our knowledge, this is the first work that reports learning local descriptors for comparing renders with real inspection images. Full article

Manual assembly operations are sensitive to human errors that can diminish the quality of final products. The paper shows an application of human reliability analysis in a realistic manufacturing context to identify where and why manual assembly errors occur. The techniques SHERPA and HEART were used to perform the analysis of human reliability. Three critical tasks were selected for analysis based on quality records: (1) installation of three types of brackets using fasteners, (2) fixation of a data cable to the assembly structure using cushioned loop clamps and (3) installation of cap covers to protect inlets. The identified error modes with SHERPA were: 36 action errors, nine selection errors, eight information retrieval errors and six checking errors. According to HEART, the highest human error probabilities were associated with assembly parts sensitive to geometry-related errors (brackets and cushioned loop clamps). The study showed that perceptually engaging assembly instructions seem to offer the highest potential for error reduction and performance improvement. Other identified areas of action were the improvement of the inspection process and workers’ provision with better tracking and better feedback. Implementation of assembly guidance systems could potentially benefit worker’s performance and decrease assembly errors. Full article

Slab track is widely used in many newly built high-speed rail (HSR) lines as it offers many advantages over ballasted tracks. However, in actual operation, slab tracks are subjected to operational and environmental factors, and structural damages are frequently reported. One of the most critical problems is temperature-induced slab-warping deformation (SWD) which can jeopardize the safety of train operation. This paper proposes an automatic slab deformation detection method in light of the track geometry measurement data, which are collected by high-speed track geometry car (HSTGC). The characteristic of track vertical irregularity is first analyzed in both time and frequency domain, and the feature of slab-warping phenomenon is observed. To quantify the severity of SWD, a slab-warping index (SWI) is established based on warping-sensitive feature extraction using discrete wavelet transform (DWT). The performance of the proposed algorithm is verified against visual inspection recorded on four sections of China HSR line, which are constructed with the China Railway Track System II (CRTSII) slab track. The results show that among the 24,806 slabs being assessed, over 94% of the slabs with warping deformation can be successfully identified by the proposed detection method. This study is expected to provide guidance for efficiently detecting and locating slab track defects, taking advantage of the massive track inspection data. Full article

Recent developments in composite materials have resulted in their pilot adoption in railway industry, such as ‘fibre-reinforced foamed urethane (FFU)’, ‘geopolymer concrete’, ‘recycled polymer’, and ‘CarbonLoc composite’. Railway track support systems are critical for safe and reliable operations of railway tracks. There are two types of support structures, which can be designed to be either a slab or a cluster of discrete bearers or sleepers. The choice of turnout support system depends on asset management strategy of the rail operators or maintainers. The aim of this paper is to present the criteria, fundamental and multi-disciplinary issues for the design and practical selection of composite materials in railway turnout systems. As a case study, a full-scale trial to investigate in-situ behaviours of a turnout grillage system using an alternative material, ‘fibre-reinforced foamed urethane (FFU)’ bearers, is presented. Influences of the composite bearers on track geometry (recorded by track inspection vehicle ‘AK Car’ and based on survey data), track settlement, track dynamics, and acoustic characteristics are highlighted in this paper. Comparative studies of composite materials for railway track applications are reviewed and presented in order to improve material design process. This state-of-the-art review paper will also focus on practicality and environmental risks of composite components in railway built environments. It embraces the requirement considerations of new materials for use as safety-critical track elements. Full article