Search Results (16)

The gap dimension of a railway switch machine is a critical physical quantity for determining the locking status of railway turnouts. Under operating conditions characterized by heavy oil contamination, complex illumination, and equipment vibration, existing visual measurement methods often struggle to maintain stability and achieve sub-pixel precision. To address this issue, this paper proposes a gap measurement method based on the fusion of vision and geometric features (G-VFM). The method first utilizes a confidence-aware optimized YOLOv8 model to achieve robust localization of the gap region. Subsequently, an improved multi-channel U-Net is employed to extract soft-edge probability maps, based on which a 20-dimensional structured geometric descriptor is constructed. Finally, visual semantic features and geometric priors are fused for regression through an R34-Fusion two-stream residual network, and systematic errors are corrected using a weighted Huber loss combined with a piecewise linear calibration strategy. Test results on a constructed field dataset show that the proposed method achieves a Mean Absolute Error (MAE) of 0.0076 mm and a maximum error of 0.0193 mm. It achieves a 100% pass rate under an industrial tolerance of 0.02 mm, with an end-to-end inference time of 52.23 ms (~19.15 FPS), balancing both precision and efficiency. Further tests on illumination degradation, noise interference, and cross-batch evaluations indicate that the method maintains relatively stable performance across various complex scenarios. However, performance decreases significantly under extremely low-light conditions, suggesting that actual deployment may require integration with active lighting or multi-sensor fusion to ensure system reliability across all working conditions. Overall, this method achieves high-precision gap measurement under current experimental conditions and provides a feasible solution for vision-based switch machine status monitoring. Full article

Station Signal Layout Plans (SSLPs) are pivotal engineering drawings used in the design of railway signaling systems. Accurate recognition of such drawings is essential for enabling intelligent railway operations and supporting digital management. However, the inherent complexity of engineering drawings—characterized by diverse object categories and significant scale variations—substantially increases the difficulty of detection tasks. To address these challenges, this paper proposes an improved YOLOv8-based algorithm for rapid and accurate object detection. First, to enhance the detection of small objects in engineering drawings, a cross-scale attention mechanism is introduced into the mid-scale detection head. During prediction, this mechanism leverages fine-grained details from lower-level features to improve small-object detection. In addition, to suppress noise and blurred edges in drawings, the YOLOv8 neck network is enhanced with a DWRSeg-based design. This structure enlarges the receptive field while preserving local details, thereby effectively reducing the impact of noise on localization. To evaluate the proposed method, a complex dataset was constructed from station signal layout plans provided by a railway bureau, featuring substantial variations in target scale, diverse categories, and densely distributed objects. Experimental results demonstrate that, compared with YOLOv8n, the proposed DCS-YOLO model improves precision, recall, and mAP@0.5 by 3.1%, 0.8%, and 2.1%, respectively, while maintaining a comparable mAP@0.5:0.95. Comparative experiments with representative object detection methods demonstrate that the proposed algorithm achieves competitive detection accuracy and real-time performance for SSLP symbol recognition, providing a practical technical solution for the intelligent analysis of engineering drawings in the railway industry. Full article

Ensuring the structural integrity and service reliability of railway wheels has become a key challenge in modern manufacturing and maintenance strategies within the railway sector. In this context, Eddy Current (EC)-based Non-Destructive Testing (NDT) provides an automated and efficient approach for detecting surface and near-surface defects, while reducing inspection time and operator dependency compared to conventional manual methods. This study presents the integration of an EC inspection system into a precision lathe, enabling in-machining evaluation during wheel turning. Experimental validation was conducted on wheels with artificial defects, yielding high signal-to-noise ratios and enabling reliable defect characterization. Furthermore, computationally efficient and easily deployable machine learning algorithms were developed to enable automatic defect detection, localization, and size estimation. The results confirm the feasibility of in-machine EC inspection during machining operations, enabling early defect detection and contributing to safer, more efficient, and higher-quality manufacturing processes in the railway sector. Full article

This study presents the first integrated magnetic and electromagnetic (EMI) survey of the Pianabella Basilica (Ostia, Italy), combining high-resolution magnetic gradient measurements with EMI mapping. The site, characterized by late-antique Christian architecture and funerary structures, provides a complex environment for testing non-invasive geophysical techniques. Magnetic data were acquired using the MagEx system (v.1.2.2558), a new prototype based on Micro-Fabricated Atomic Magnetometer (MFAM) technology, marking its first field deployment in archaeological prospection. Simultaneously, EMI measurements using the CMD-Mini Explorer provided data on apparent conductivity and in-phase components across three depth levels (0.5–1.8 m). The magnetic gradient map successfully delineated the Basilica’s planimetric outline, revealing anomalies (~20 nT/m) corresponding to masonry and internal enclosures. A significant anomaly (50–60 nT/m) north of the Basilica suggests a basalt-paved Roman road leading toward Porta Laurentina. EMI results corroborated these findings, with low-conductivity zones outlining walls and in-phase responses highlighting reused Roman building materials. Despite significant urban noise from a nearby railway and fences, this integrated approach enhanced interpretability and reduced ambiguity. These findings demonstrate the efficacy of next-generation magnetic gradiometry and EMI for high-resolution archaeological investigations, providing a new methodological benchmark for cultural heritage prospection. Full article

Ballast compaction is a critical factor influencing ballast bed condition and the operational safety of heavy-haul railways. However, existing quantitative evaluation methods often suffer from overly idealized simulation models and limitations in signal processing and assessment frameworks. To address these issues, this study proposes a quantitative analysis approach for ballast compaction by integrating non-uniform medium simulation modeling, wavelet–Singular Value Decomposition (SVD) joint denoising, frequency–wavenumber (F-K) migration imaging, and wavelet packet decomposition (WPD)-based feature extraction. Forward simulations were conducted based on the constructed model, and the proposed methodology was validated using 1.5 GHz (gigahertz, 1 GHz = ${10}^9$ Hz) ground penetrating radar (GPR) data acquired from compaction experiments. The results demonstrate that wavelet–SVD joint denoising effectively suppresses deep coherent noise caused by strong reflections from sleepers, significantly enhancing the identification of deep effective signals and ensuring accurate localization and feature extraction of compaction zones. The Geometric Mean of WPD High/Low-Frequency Energy Ratio (GMHLFER) exhibits a strong positive correlation with the degree of compaction. In simulations, as the proportion of compacted material increased from $9\%$ to $21\%$, the GMHLFER rose from 21.555 to 26.581. In field tests, the value increased from 22.012 to 26.012 as compaction severity progressed from slight to severe, demonstrating stable responses across full-gradient compaction conditions and indicating high robustness and sensitivity. The proposed method provides an effective approach for quantitative characterization of ballast compaction in heavy-haul railways, and offers a promising technical pathway for intelligent inspection and condition assessment of railway ballast beds. Full article

Structural health monitoring (SHM) increasingly contributes to the safety and durability of key infrastructure, especially bridges. This research introduces a rotation-based approach for damage detection and quantification using a damage candidate grid search technique (DCGS) on simply supported girder bridges under quasi-static or slowly moving loading conditions. Applying the principle of virtual work, the healthy and candidate-damaged rotation responses are analytically obtained and compared with the rotation observed directly at the moving load location. Damage is defined in terms of three key parameters: the start and the end of the damage, $L_1$ and $L_2$, respectively, and the damage severity $\beta$. The DCGS method is validated using finite element model simulations of 12 damage scenarios subjected to different noise levels. A statistical analysis and confidence interval characterize the accuracy and consistency of the top ten estimations produced by the DCGS method. A damage length ratio (DLR), defined from the span of the beam, $L$, and the damage location, $L_1$ and $L_2$, improves the robustness of the methodology against measurement noise by reducing possible false positive estimations. Additionally, the experimental results on two beam structures further validate the method. Absolute relative errors (AREs) of about 6% and absolute errors (AEs) of around 0.16 between the estimated and real damage parameters characterize the performance of the technique, considering damage location and damage severity, respectively. The results show that the DCGS methodology can effectively locate damage and estimate its severity in the presence of noise. The developed framework provides a sensitive and practical SHM tool that is suitable for early damage detection in railway and road bridges. Full article

Magnetic Barkhausen noise (MBN) represents a powerful non-destructive testing and material characterization methodology enabling quantitative assessment of microstructural features, mechanical properties, and stress states in ferromagnetic materials. This comprehensive review synthesizes recent advances spanning theoretical foundations, sensor design, signal processing methodologies, and industrial applications. The physical basis rooted in domain wall dynamics and statistical mechanics provides rigorous frameworks for interpreting MBN signals in terms of grain structure, dislocation density, phase composition, and residual stress. Contemporary instrumentation innovations including miniaturized sensors, multi-parameter systems, and high-entropy alloy cores enable measurements in challenging environments. Advanced signal processing techniques—encompassing time-domain analysis, frequency-domain spectral methods, time–frequency transforms, and machine learning algorithms—extract comprehensive material information from raw Barkhausen signals. Deep learning approaches demonstrate superior performance for automated material classification and property prediction compared to traditional statistical methods. Industrial applications span manufacturing quality control, structural health monitoring, railway infrastructure assessment, and predictive maintenance strategies. Key achievements include establishing quantitative correlations between material properties and stress states, with measurement uncertainties of ±15–20 MPa for stress and ±20 HV for hardness. Emerging challenges include standardization imperatives, characterization of advanced materials, machine learning robustness, and autonomous system integration. Future developments prioritizing international standards, physics-informed neural networks, multimodal sensor fusion, and wireless monitoring networks will accelerate industrial adoption supporting safe, efficient engineering practice across diverse sectors. Full article

Railway transportation systems have high accuracy and high integrity demands for safe localization. In the future, railway signaling is expected to rely on onboard sensors like Global Navigation Satellite Systems (GNSSs) in order to reduce installation and maintenance costs. GNSS position determination can, however, be highly degraded because of the presence of multipath on the train and railway environment. This paper tackles the characterization of multipath in code measurements caused exclusively by the antenna installation and derives a conservative error model of the antenna-installation-induced multipath and noise. First, we isolate multipath and noise from other GNSS errors using the Code-Minus-Carrier method. Second, an overbounding error model is derived. The limitation of modeling with restricted set of real data typically found in practice is discussed and we review methods that ensure the independence of samples. A new approach that creates separate data sets is ultimately proposed to derive an overbounding sigma. The presented methodology is supported by real measurements collected in an open-sky railway scenario. The derived models can be used as a reference nominal error models to build the null hypothesis of fault detection algorithms that detects the presence of excessive multipath in dynamic scenarios or as a part of a total error budget consideration. Full article

The China Loess Plateau (CLP) is the world’s most extensive and thickest region of loess deposits. The inherently loose structure of loess makes the CLP particularly vulnerable to geohazards such as landslides, collapses, and subsidence, resulting in substantial geological and environmental challenges. Xining City, situated at the northwest edge of the CLP, is especially prone to frequent geological hazards due to intensified human activities and natural forces. Synthetic Aperture Radar Interferometry (InSAR) has become a widely used tool for identifying landslide hazards and displacement monitoring because of its high accuracy, low cost, and wide coverage. In this study, we utilized the small baseline subset (SBAS) InSAR technique to derive the line of sight (LOS) displacements of Xining City using Sentinel-1 datasets from ascending and descending orbits between October 2014 and September 2022. By integrating LOS displacements from the two datasets, we retrieved the eastward and vertical displacements to characterize the kinematics of active slopes. To identify the active areas semi-automatically, we applied the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm to cluster InSAR measurement points (IMPs). Forty-eight active slopes with areas ranging from 0.0049 to 0.5496 km² and twenty-five subsidence-dominant areas ranging from 0.023 to 3.123 km² were identified across Xining City. Kinematics analysis of the Jiujiawan landslide indicated that acceleration started in August 2016, likely triggered by rainfall, and continued until the landslide. The extreme rainfall in August 2022 may have pushed the Jiujiawan landslide beyond its critical threshold, leading to instability. Additionally, the study identified nine active slopes that threaten the normal operation of the Lanzhou–Xinjiang High-Speed Railway, with kinematic analysis suggesting rainfall-related accelerations. The influence of anthropogenic activities on ground displacements in loess areas was also confirmed through time series displacement analysis. Our results can be leveraged for geohazard prevention and management in Xining City. As SAR image data continue to accumulate, InSAR can serve as a regular tool for maintaining up-to-date landslide inventories, thereby contributing to more sustainable geohazard management. Full article

The growth in the utilization and development of rail transport within urban networks is crucial for transitioning towards a more sustainable form of mobility. However, challenges related to discomfort and noise pollution arising from rail traffic must be addressed and mitigated to foster a harmonious coexistence between residents and trains. This study focuses on analyzing an experimental campaign conducted on the surface metropolitan network of Porto to study and identify the frequency content and pressure levels associated with light rail traffic. The presented experimental campaign holds significant relevance as it comprises various and distinct circulation conditions within the railway network, enabling a comprehensive characterization of railway noise. The collected data indicates a noticeable increase in sound pressure levels as the speed of circulation rises, particularly emphasizing the 1/3 octave band centered around 1000 Hz. The choice of tracks with components having a limited capacity for absorbing acoustic energy leads to a significant rise in noise levels compared to track solutions with elements exhibiting excellent acoustic energy absorption. Furthermore, the study highlights a substantial increase in noise levels (10 dBA) associated with small radius curves, even at low speeds. These findings underscore the importance of considering the track characteristics and geometric features in noise assessment within rail networks. Therefore, the insights gained from this experimental campaign contribute significantly to the understanding and comprehensive characterization of railway noise under diverse circulation conditions within the railway network. Full article

Power line communication (PLC) is a technology that exploits existing electrical transmission and distribution networks as guiding structures for electromagnetic signal propagation. This facilitates low-rate data transmission for signaling and control operations. As the demand in terms of data rate has greatly increased in the last years, the attention paid to broadband PLC (BPLC) has also greatly increased. This concept also extended to railways as broadband traction power line communication (BTPLC), aiming to offer railway operators an alternative data network in areas where other technologies are lacking. However, BTPLC implementation faces challenges due to varying operating scenarios like urban, rural, and galleries. Hence, ensuring coverage and service continuity demands the suitable characterization of the communication channel. In this regard, the scientific literature, which is an indicator of the body of knowledge related to BTPLC systems, is definitely poor if compared to that addressed to BPLC systems installed on the electrical transmission and distribution network. The relative papers dealing with BTPLC systems and focusing on the characterization of the communication channel show some theoretical approaches and, rarely, measurements guidelines and experimental results. In addition, to the best of the author’s knowledge, there are no surveys that comprehensively address these aspects. To compensate for this lack of information, a survey of the state of the art concerning BTPLC systems and the measurement methods that assist their installation, assessment, and maintenance is presented. The primary goal is to provide the interested readers with a thorough understanding of the matter and identify the current research gaps, in order to drive future research towards the most significant issues. Full article

The negative effect of traffic noise on human health is indisputable. The article illustrates the magnitude of this problem in selected European capitals using data made available by the European Environment Agency, collected as a part of strategic noise mapping under Directive 2002/49/EC. The purpose of the described studies is to determine the number of people suffering from noise-induced health problems, and to compare the effect of the road noise on human health with the effect of noise from rail vehicles. Studies concern noise annoyance, induced sleep disturbance, and Disability-Adjusted Life Years, or DALY index. Analyses clearly show that environmental pollution from road noise often exceeds environmental pollution from railway noise by a factor of 10. In addition, studies show that in many cities, more than half of the population is exposed to road noise exceeding the acceptable values. The worst of all selected agglomerations with regard to people exposed to road and rail noise is Paris, the capital of France. It should be noted, however, that it has the highest population density of all the analyzed cities and a very well developed transport network. Further studies are needed to characterize the effect of traffic noise on inhabitant health more accurately, using specific indicators allowing to do so. Full article

Aluminum-based cellular solids are promising lightweight structural materials considering their high specific strength and vibration damping, being potential candidates for future railway vehicles with enhanced riding comfort and low fuel consumption. The filling of these lattices with polymer-based (i.e., polyurethane) foams may further improve the overall vibration/noise-damping without significantly increasing their density. This study explores the dynamic (i.e., frequency response) and acoustic properties of unfilled and polyurethane-filled aluminum cellular solids to characterize their behavior and explore their benefits in terms of vibration and noise-damping. It is shown that polyurethane filling can increase the vibration damping and transmission loss, especially if the infiltration process uses flexible foams. Considering sound reflection, however, it is shown that polyurethane filled samples (0.27–0.30 at 300 Hz) tend to display lower values of sound absorption coefficient relatively to unfilled samples (0.75 at 600 Hz), is this attributed to a reduction in overall porosity, tortuosity and flow resistivity. Foam-filled samples (43–44 dB at 700–1200 Hz) were shown to be more suitable to reduce sound transmission rather than reflection than unfilled samples (21 dB at 700 Hz). It was shown that the morphology of these cellular solids might be optimized depending on the desired application: (i) unfilled aluminum cellular solids are appropriate to mitigate internal noises due to their high sound absorption coefficient; and (ii) PU filled cellular solids are appropriate to prevent exterior noises and vibration damping due to their high transmission loss in a wide range of frequencies and vibration damping. Full article

Maritime transportation is recognized to have advantages in terms of environmental impact compared to other forms of transportation. However, an increment in traffic volumes will also produce an increase in noise emissions in the surroundings for a greener source, as ports are frequently surrounded by urban areas. When more sources or higher noise emissions are introduced, the noise exposure of citizens increases, and the likelihood of official complaints rises. As a consequence, among the most demanding aspects of port management is effective noise management aimed at a reduction in the exposure of citizens while ensuring the growth of maritime traffic. At the same time, the topic has not been thoroughly studied by the scientific community, mostly because port areas are challenging from a noise management point of view; they are often characterized by a high degree of complexity, both in terms of the number of different noise sources and their interaction with the other main transportation infrastructure. Therefore, an effective methodology of noise modeling of the port area is currently missing. With regard to the INTERREG Maritime Program, the present paper reports a first attempt to define noise mapping guidelines. On the basis of the current state-of-the-art and the authors’ experiences, noise sources inside port areas can be divided into several different categories: road sources, railway sources, ship sources, port sources, and industrial sources. A further subdivision can be achieved according to the working operation mode and position of the sources. This classification simplifies actions of identification of the responsible source from control bodies, in the case that noise limits are exceeded or citizen complaints arise. It also represents a necessary tool to identify the best placing of medium/long-term noise monitoring stations. The results also act as a base for a future definition of specific and targeted procedures for the acoustic characterization of port noise sources. Full article

After the European Environmental Noise Directive prescribed noise maps and action plans, wide scientific literature and a consistent number of mitigation strategies emerged for road, railway, airport, and industrial noise. Unfortunately, very little attention has been paid to the noise produced by ports in their surroundings, even though there could be many areas affected by it. At present, more attention seems to be paid to noise produced underwater, mostly for military and security reasons and for its interference with wildlife, rather than airborne noise and its influence on human health. In the framework of a project aiming to shed more light on a topic so far under-investigated, this paper presents an acoustical characterization of different small vessels at various speeds that move around on a daily basis in every type of port, produced by means of short- and long-term measurements. The new information acquired was used to produce a map of noise generated by vessels moving in Livorno’s canals, which branch off in a densely inhabited area. The simulations were validated using long-term measurement. The number of citizens exposed was also estimated and used to calculate the number of highly annoyed people according to the recent curve for road traffic noise proposed by Guski et al. In order to prevent citizen exposure to noise and possible complaints about small boats, different scenarios and possible future situations such as various vessel speeds, limited flow, restricted areas for some categories, or new residential areas were studied. Full article