Search Results (402)

Predictive maintenance can help infrastructure managers to reduce costs and improve railway availability while ensuring safety. However, its accuracy depends on reliable data from various sources, especially track measurement data. When analysing track data over time, historical maintenance actions must be considered, as otherwise the interpretation of the data would be misleading. This research aims to address inconsistencies in recorded maintenance data by detecting unrecorded track works through track geometry evaluations. The main goal is to provide the foundations for accurate descriptions of track behaviour, supporting the implementation of effective predictive maintenance regimes. As part of the research, three different approaches are analysed and evaluated, whereby two of them are based on cross-sectional analyses and the third one detects track works in longitudinal track dimension. The results show that the CRAB algorithm produces the most statistically significant results. Conversely, the cumulative track geometry-based algorithm provides a homogeneous representation of past maintenance work and a result that is statistically only marginally inferior. Consequently, these two methods are best suited to build the foundation for making accurate cross-sectional conclusions about track geometry behaviour. This allows for the verification and enhancement of existing maintenance databases. Full article

Due to increasing demands on the available railway infrastructure, accurate estimates of safety-critical track condition as well as breakdowns of individual track components are crucial. This task can be supported by analyzing track measurement data. Ballast breakdown can be determined by analyzing the longitudinal level using fractal analysis: Commonly, a window with a width of 150 m is dragged over the signal computing an approximation of a fractal dimension of the signal for each position of the window. However, while a large window size can be used to describe the condition of ballast and substructure simultaneously, it fails to precisely localize short-section ballast breakdowns in the track. With the objective of describing and detecting these local effects in the ballast bed, this work analyzes a set of 114 known weak ballast spots. By reducing the width of the sliding window, the position of short-section ballast breakdowns can be reliably depicted. The application of a modified version of fractal analysis allows for a more accurate targeted maintenance on a component-specific basis. Full article

Railway transportation plays a vital role in modern society, enabling the safe and efficient movement of people and goods over long distances. To ensure the longevity and safety of a railway infrastructure, the regular maintenance of tracks is crucial. Traditional track inspections, conducted manually to monitor geometric parameters and to identify defects, are time-consuming, labor-intensive, and prone to human error. Current Scan-to-BIM frameworks for railway maintenance also lack standardized methods for extracting geometric parameters that can be easily integrated into Building Information Modeling (BIM). Additionally, the Industry Foundation Classes (IFC) standard, used for BIM data exchange, does not support storing parameter values at specific chainage points along the track, limiting defect localization. A framework is proposed to address these challenges by standardizing the extraction of geometric parameters from point cloud data and ensuring seamless integration with BIM. The framework calculates parameters at station chainage points and generates additional chainage points along the track, associating the data with the corresponding chainage. A case study demonstrates the framework’s ability to enhance defect localization, using the EN 13848-5 European Standard to identify defects at specific chainages. Ultimately, this approach contributes to the more effective lifecycle management of railway tracks. Full article

To address the issue of efficiently reusing the massive amount of unstructured knowledge generated during the handling of track circuit equipment faults and to automate the construction of knowledge graphs in the railway maintenance domain, it is crucial to leverage knowledge extraction techniques to efficiently extract relational triplets from fault maintenance text data. Given the current lag in joint extraction technology within the railway domain and the inefficiency in resource utilization, this paper proposes a joint extraction model for track circuit entities and relations, integrating Global Pointer and tensor learning. Taking into account the associative characteristics of semantic relations, the nesting of domain-specific terms in the railway sector, and semantic diversity, this research views the relation extraction task as a tensor learning process and the entity recognition task as a span-based Global Pointer search process. First, a multi-layer dilate gated convolutional neural network with residual connections is used to extract key features and fuse the weighted information from the 12 different semantic layers of the RoBERTa-wwm-ext model, fully exploiting the performance of each encoding layer. Next, the Tucker decomposition method is utilized to capture the semantic correlations between relations, and an Efficient Global Pointer is employed to globally predict the start and end positions of subject and object entities, incorporating relative position information through rotary position embedding (RoPE). Finally, comparative experiments with existing mainstream joint extraction models were conducted, and the proposed model’s excellent performance was validated on the English public datasets NYT and WebNLG, the Chinese public dataset DuIE, and a private track circuit dataset. The F1 scores on the NYT, WebNLG, and DuIE public datasets reached 92.1%, 92.7%, and 78.2%, respectively. Full article

The high-speed railway (HSR) system imposes stringent requirements for track smoothness. However, conventional seismic isolation bearings frequently fail to meet these demands. To address this challenge, a novel seismic isolation bearing was developed based on the principle of functional separation design. This innovative bearing effectively achieves the multistage control objectives, including amplitude limitation to ensure track smoothness during frequent earthquakes, energy dissipation to guarantee train running safety during design earthquakes, and structural integrity maintenance to prevent beam collapse during rare earthquakes. Firstly, an overview of the novel isolation bearing’s structural design and operational principle is provided. Subsequently, a corresponding mechanical model is formulated, with the parameters of the new bearing determined through finite element analysis. The study then compares the seismic performance of the general rubber bearing and the new bearing, using an HSR simply supported bridge as an engineering background. The dynamic response of the bridge under varying seismic waves, pier heights, and bridge spans is meticulously analyzed. The results indicate that the new bearing can achieve multistage control. Compared to general bearings, it reduces bridge displacement vibration by over 46.4% under frequent, design, and rare earthquakes. The bridge deformation under frequent earthquakes remains below 3 mm, thus meeting the track smoothness requirements for normal HSR operations. Additionally, the study reveals that higher pier heights increase the seismic response, peaking at 15 m. The vibration reduction provided by the new bearing varies but remains effective in most earthquake scenarios, with maximum reductions of 92.9% for displacement and 74.17% for bending moment. Furthermore, larger bridge spans also increase the seismic response, with the 24 m span bridge outperforming the 32 m span bridge. In conclusion, the novel seismic isolation bearing significantly enhances the seismic performance of HSR bridges, ensuring train running safety and operational reliability. Full article

Twin-disc testing is crucial for understanding wheel–rail interactions in railway systems, but the vast array of testing parameters and conditions makes data interpretation challenging. This review presents a comprehensive analysis of the twin-disc literature experimental data, focusing on how various parameters influence friction and wear characteristics under stationary contaminant conditions. We systematically collected and analyzed data from numerous studies, considering factors such as contact pressure, speed, material hardness, sliding speeds, adhesion, and a range of contaminants. This research showed inconsistent data reporting across different studies and statistical analyses revealed significant correlations between testing parameters and wear rates. For sand-contaminated tests, a correlation between particle size and flow rate was also highlighted. Based on these findings, we developed a simple predictive model for forecasting wear rates under varying conditions. This model achieved an adjusted R² of 0.650, demonstrating its potential for optimizing railway component design and maintenance strategies. Our study provides a valuable resource for researchers and practitioners in railway engineering, offering insights into the complex tribological interactions in wheel–rail systems and a tool for predicting wear behavior. Full article

Prestressed concrete box girders are commonly employed in the development of high-speed railway bridge constructions. The prestressed strands in the girder may corrode due to long-term chloride erosion, leading to the degradation of its flexural performance. To examine the flexural performance of corrosion-affected simply supported prestressed concrete box girders, eight T-shaped mock-up beams related to the girders used in the construction of high-speed railway bridges were manufactured utilizing similarity theory. Seven of the beams underwent electrochemical accelerated corrosion, and then each beam was subjected to failure under the four-point load test method. Measurements recorded and analyzed in detail during the loading process included the following: crack propagation, crack width at various loads, crack load, ultimate load, deflection, and concrete strain of the mid-span section. The results demonstrate that a corrosion rate of just 8.31% has a considerable impact on the structural integrity of the beams, as evidenced by a pronounced reduction in flexural cracks and a tendency towards reduced reinforcement failure. Furthermore, the corrosive process has a detrimental effect on mid-span deflection, ductility, and ultimate flexural bearing capacity, which could have significant implications for bridge safety. This study provides valuable insights for the assessment of flexural performance and the development of appropriate maintenance strategies for corroded simply supported box girders in high-speed railways. 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

Track and preventive maintenance are necessary for the safe and comfortable operation of railways. Track displacement measured by track inspection vehicles or trolleys has been primarily used for track management. Thus, vibration data measured in in-service vehicles have not been extensively used for track management. In this study, we propose a new technique for estimating track irregularities from measured car body vibration for track management. The correlation between track irregularity and car body vibration was analysed using a multibody dynamics simulation of travelling rail vehicles. Gaussian process regression (GPR) was applied to the track irregularity and car body vibration data obtained from the simulation, and a method was proposed to estimate the track irregularities from the constructed regression model. The longitudinal-level, alignment, and cross-level irregularities were estimated from the measured car body vibrations and travelling speeds on a regional railway, and the results were compared with the actual track irregularity data. The results showed that the proposed method is applicable for track irregularity management in regional railways. Full article

In an ever expanding railway network all around the world, the need for track maintenance grows steadily. Traditionally, one major part of track maintenance is ramming large vibrating steel picks into the gravel between and under railway sleepers to compress the gravel and generate a safe substructure. Even today, maintenance personnel still have to manually locate the sleepers if they cannot be detected by computer vision systems or visually by the operator. Here we developed a first of its kind magnetic sleeper detector, even able to find sleepers, buried in gravel, undetectable by vision based systems. Our approach of magnetic detection is based on a DC magnetic field excitation and a detector moving with respect to the rail system, including the sleepers and fasteners for mounting the rails. Due to railway application constraints a large air gap between the sensor and the sleeper structure is required, which significantly complicates the magnetic sensing task for robust sleeper detection. The design and optimization of the magnetic circuit was based on extensive 3D simulation studies to ensure highest possible variation in magnetic flux density at the sensor locations for absence and presence of a sleeper. Furthermore, a low noise and high sensitivity electronic circuit has been realized to cope with sensor signal offsets from unknown or changing sensor orientations with respect to the earth’s magnetic field, or magnetic interferences from other trains potentially passing by during active measurements. Since we only want to detect sleepers in close vicinity of the moving sensor system, digital signal processing of the acquired signals can easily compensate for disturbing slowly changing or static field components within real world application scenarios. We demonstrate that magnetic detection of even buried sleepers on railway tracks is possible for distances of up to 172 mm between the sensor and the sleeper. This enables an even higher level of railway maintenance automation previously impossible in certain scenarios. Full article

The incompleteness of the existing knowledge graphs in the railway domain creates information gaps, impacting their quality and effectiveness in the operation and maintenance of high-speed railway turnout switch machines. To address this, we propose a multi-layer model (KBGC) that combines KG-BERT, graph attention network (GAT), and Convolutional Embedding Network (ConvE) for knowledge graph completion in railway maintenance. KG-BERT fine-tunes a pre-trained BERT model to extract deep semantic features from entities and relationships, converting them into graph structures. GAT captures key structural relationships between nodes using an attention mechanism, producing enriched semantic and structural embeddings. Finally, ConvE reshapes and convolves these embeddings to learn complex entity interactions, enabling accurate link prediction. Extensive experiments on the HRTOM dataset, containing triplet data from high-speed railway turnout switch machines, demonstrate the model’s effectiveness, achieving an MRR of 50.8% and a Hits@10 of 60.7%. These findings show that the KBGC model significantly improves knowledge graph completion, aiding railway maintenance personnel in decision making and preventive maintenance, and providing new tools for railway maintenance applications. Full article

This article aims to provide an accurate mathematical model with the minimum number of degrees of freedom for describing the floating sleeper phenomenon. This was accomplished using mathematical modeling supported by extensive field measurements of the railway track. Although the observed phenomenon is very complex, the simplified single degree of freedom (SDOF) mathematical model proved accurate enough for its characterization. The progression of the deterioration of the railway track was successfully correlated to changes in the maximal dynamic factor for different types of pulse loading. The results of the presented study might enable the enhanced construction and maintenance of railroads, particularly in karst areas. Full article

Detecting track geometry and rail surface defects using on-board vehicle monitoring systems is a key issue for rail infrastructure managers to increase availability and reliability while reducing the costs associated with monitoring and maintenance. Rail corrugation is one of the most common rail surface defects which grows in almost all metro, conventional and high-speed lines. This paper focuses on the development of a methodology to detect rail corrugation using axle box acceleration measurements acquired on an in-service high-speed vehicle. The main purpose of the proposed methodology is to distinguish the effect of rail corrugation on the accelerations from the other excitations that can be observed in the same wavelength range. For this purpose, the accelerations are analysed by calculating the fast Fourier transform and the spectrogram. Based on the characteristics of each excitation, the effects of modes of vibration, resonances, bridges, switches, and wheel defects are identified. From the remaining effects, which have congruent characteristics, a hypothesis of rail corrugation is formulated. The hypothesis is consolidated with multibody dynamics simulations and by comparing the corrugation indicators provided by the railway infrastructure company. Full article

Rail infrastructure plays an important role in fulfilling the demand for freight and passenger transportation. Increases in traffic volume, heavier axles and vehicles, higher speeds, and increasing climate extremes all contribute to the constant strain on the infrastructure. Due to their major importance in the transportation of people and freight, they are subject to continuous condition monitoring. This is an essential requirement for the selective planning of maintenance tasks and ultimately for safe and reliable operation. Various measuring systems have been developed for this purpose. These must measure precisely, quickly, and robustly under difficult conditions. Whether installed from mobile or stationary platforms, they have to cope with a wide range of ambient temperatures and lighting conditions, harsh environmental influences, and varying degrees of reflection. Despite these circumstances, railway operators require precise measurement data, high data densities even at high traveling speeds, and a user-friendly presentation of the results. Photogrammetry, laser scanning, and fiber optics are light-based measurement methods that are used in this sector. They are able to record with high precision rail infrastructure such as overhead contact systems, clearance profiles, rail tracks, and much more. This article provides an overview of the established and modern optical sensing methods, as well as the use of artificial intelligence as an evaluation method, and highlights their advantages and disadvantages. Full article

To quantitatively compare the carbon emissions between the filled embankment scheme and the excavated graben scheme of railway subgrade engineering, first, according to the life cycle assessment theory, the two schemes were separated into four stages: building materials production, building materials transportation, construction, and operation and maintenance. The carbon emission factor method was then used to compute the carbon emissions of the filled embankment scheme and the excavated graben scheme. The results indicate that the carbon emissions of the filled embankment scheme are 8783.76 t, 801.71 t, 627.78 t, and 1021.33 t at each stage, and 11,234.58 t over its total life cycle. The carbon emissions at each stage of the excavated graben scheme are 954.96 t, 52.62 t, 772.69 t, and 178.03 t, respectively, and 1958.30 t over its total life cycle. Finally, the carbon abatement potential of the excavated graben scheme with less carbon emissions was investigated by changing the soil nail wall slope to an ecological slope. The results show that after changing the soil nail wall slope of the excavated graben scheme to an ecological slope, the excavated graben scheme’s carbon sequestration of the total life cycle is 3274.38 t. Full article