Search Results (17)

This study experimentally investigated the long-term deformation behavior of a ballasted railway turnout affected by adjacent deep excavation through an integrated multi-sensor field-monitoring approach. The monitored facility consisted of two 60 kg, #10 single turnouts located approximately 13 m from an excavation with a maximum depth of approximately 21 m. Field monitoring was conducted for approximately 640 days, from the pre-excavation stage to the completion of the underground structure. Three complementary measurement methods were employed: a track master system for track geometry, tilt-angle-based rail displacement sensors for local rail displacement, and an automatic leveling system for absolute sleeper displacement. The relative measurement methods primarily identified local rail geometry variations within approximately ±1–2 mm, whereas the absolute sleeper measurements revealed substantially larger support system movements. In particular, a maximum sleeper settlement of approximately −9.1 mm and a maximum heave of approximately +4.9 mm were observed at different locations of the T2 turnout, producing a longitudinal differential displacement of approximately 14 mm within the turnout system. The results indicate that excavation-induced turnout deformation consists of both global support system movement and local differential rail deformation, which cannot be completely characterized using a single measurement reference framework. The principal contribution of this study is the experimental clarification of the complementary roles of relative and absolute displacement measurements in distinguishing local track geometry changes from global sleeper support movement. The proposed integrated monitoring framework provides a practical basis for identifying critical deformation zones and improving the structural assessment and maintenance of turnouts affected by adjacent excavation. Full article

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

Railway turnout systems are important components of railway signaling infrastructure, and timely fault diagnosis is essential for ensuring operational safety and maintenance efficiency. In practical applications, turnout fault diagnosis based on power data is often challenged by limited fault samples and severe class imbalance. To address these issues, this paper proposes a DLeNN-Attention-based fault diagnosis method for railway turnout power data, where DLeNN-Attention denotes Dilated-LeNet5-Attention. First, the original power sequences are standardized to a unified length through truncation, zero-padding, and normalization. Then, a hybrid data augmentation strategy combining the Synthetic Minority Over-sampling Technique (SMOTE) and a generative adversarial network (GAN) is adopted to enrich minority fault samples and alleviate class imbalance. Based on the augmented data, a DLeNN-Attention model is designed by integrating dilated convolution with the Convolutional Block Attention Module (CBAM), so as to capture richer temporal characteristics and enhance discriminative fault-related information. In this way, the proposed method can effectively learn representative features from turnout power data and improve fault classification performance. Experimental results on S700K turnout power data demonstrate that the proposed method achieves better diagnosis performance than several baseline models. The results indicate that the proposed method is effective for turnout fault diagnosis under limited-data conditions and shows promising application potential in intelligent health monitoring of railway turnout systems. Full article

To address the challenges of feature extraction and degradation state identification for railway turnout switch machine power signals over the full life cycle, this paper proposes a multi-dimensional feature-fusion-based degradation state identification method for S700K turnout switch machines. Multi-domain features are first extracted from degradation power signals in the time domain, frequency domain, and time-frequency domain. Subsequently, a Uniform Manifold Approximation and Projection (UMAP)-based feature fusion strategy is employed to construct low-dimensional feature representations that effectively characterize the evolution of the equipment’s operating state, and corresponding degradation performance indicators are established. Based on the fused features, the K-means++ clustering algorithm is applied to divide the performance degradation process of the switch machine into different stages. The clustering results are comprehensively evaluated using the silhouette coefficient, Calinski–Harabasz (CH) index, and Davies–Bouldin (DB) index, and are compared with those obtained by the fuzzy C-means algorithm and the conventional K-means algorithm. Experimental results demonstrate that the proposed method achieves superior clustering quality and stability in degradation stage partitioning, enabling refined identification of degradation states and providing reliable theoretical support and technical foundations for condition monitoring and maintenance decision-making in intelligent railway turnout operation and maintenance systems. Full article

The focus of this research is the automation of the synchronization process for track-recording vehicle signals in turnouts. Accurate synchronization of measurement signals is essential for assessing specific track sections—especially complex areas such as turnouts—and for enabling reliable time series for condition monitoring. Currently, the synchronization process is only partially automated, resulting in high levels of manual effort. With over 4000 turnouts on Austria’s main railways, full automation is important for ensuring efficiency and consistency of the synchronization process. Based on an analysis of 109 turnouts in the OeBB railways, the process begins with rough synchronization using mileage and curvature signals to eliminate invalid measurement runs. Subsequently, longitudinal level signals are synchronized within maintenance time blocks. These blocks include measurement runs with consistent signal characteristics between two maintenance interventions. The latest valid run then serves as reference for each block. Methods such as cumulative sum, Euclidean distance and cross-correlation are then employed to achieve fine synchronization. The results demonstrate the feasibility and efficiency of automated synchronization compared to manual methods, enabling more accurate condition assessment. This allows infrastructure managers to track turnout-specific quality indicators, integrate them into asset management systems, and develop predictive maintenance strategies. Full article

This article investigates how Web3 decentralization unfolds in practice and asks two guiding questions: (i) How democratic are decentralized governance systems in practice? (ii) Under what institutional conditions can technological decentralization translate into social inclusion? Based on multi-year ethnographic fieldwork (2022–2025) across Silicon Valley, Washington, D.C., Europe, and the Global South, this study draws on participant observation, semi-structured interviews, and comparative analysis of seven ecosystems—Ethereum, MakerDAO, Uniswap, Mastodon, Celo, Grassroots Economics, and GoodDollar. The findings show that participation asymmetries are structural: token-based governance is dominated by a small group of technically skilled or capital-rich actors, while voter turnout often remains below ten percent. Intermediaries such as foundations, developers, NGOs, and cooperatives are indispensable for coordination, contradicting the idea of hierarchy-free decentralization. In contrast, projects that institutionalize clear membership, monitoring, and accountability—particularly in cooperative and federated settings—display stronger democratic resilience. Comparative evidence also reveals oligarchic consolidation in Global North ecosystems and infrastructural exclusion in the Global South. These results substantiate what Richard R. Nelson termed “the Moon and the Ghetto” paradox: extraordinary technical innovation without corresponding social progress. Interpreted through innovation systems theory, the study concludes that advancing decentralized technologies requires parallel investment in mission-oriented institutions that ensure participation, equity, and accountability in digital infrastructures. Full article

The straddle-type monorail is an electric-powered public vehicle widely known for its versatility and ease of maintenance. The finger-band is a critical connecting structure for the straddle-type monorail, but issues such as loose bolts are inevitable over time. Manual inspection is the primary method for detecting bolt looseness in the finger-band, but this approach could be more efficient and resistant to missed detections. In this study, we conducted a straddle-type monorail finger-band-anomaly-monitoring experiment using Distributed Acoustic Sensing (DAS), a distributed multi-point-monitoring system widely used in railway monitoring. We analyzed track vibration signals’ time-domain and frequency-domain characteristics under different monorail operating conditions. Our findings revealed the following: 1. DAS can effectively identify the monorail’s operating status, including travel direction, starting and braking, and real-time train speed measurement. 2. Time-domain signals can accurately pinpoint special track structures such as turnouts and finger-bands. Passing trains over finger-bands also results in notable energy reflections in the frequency domain. 3. After the finger-band bolts loosen, there is a significant increase in vibration energy at the finger-band position, with the degree of energy increase corresponding to the extent of loosening. Full article

Turnouts are the weak spot in high-speed rail systems, and it is simple for the phenomenon of the wheel–rail force and the carbody lateral acceleration over-limit to arise when the train passes through, which affects the service life of the rail and the running stability of the train. In this paper, the turnout with wheel–rail force over-limit and carbody lateral acceleration over-limit is selected for analysis, and the profiles of the wheel and rail are monitored. Then, the vehicle–turnout coupled multi-body dynamics model is simulated. Additionally, the portable vibration analyzer, the comprehensive inspection train, and the wheel–rail contact dynamic stress tester monitors the data and evaluates the impact of rail grinding on high-speed railway. The results of this study demonstrated that the turnout profiles are in good agreement with the standard wheel profiles following grinding, and the wheel–rail contact point and equivalent conicity both improved. When the train passes the ground turnout at high speed with and without the wheel polygonal wear, the wheel–rail force and the carbody acceleration were clearly improved. Using the wheel–rail contact dynamic stress tester, the comprehensive inspection train, and the portable vibration analyzer monitoring the changes in the carbody acceleration, the wheel–rail force and the carbody acceleration are definitely better after grinding. Similar to the pattern in the simulation, the train’s running steadiness increased by grinding. Full article

Switches and crossings (S&Cs) are also known as turnouts or railway points. They are important assets in railway infrastructures and a defect in such a critical asset might lead to a long delay for the railway network and decrease the quality of service. A squat is a common rail head defect for S&Cs and needs to be detected and monitored as early as possible to avoid costly emergent maintenance activities and enhance both the reliability and availability of the railway system. Squats on the switchblade could even potentially cause the blade to break and cause a derailment. This study presented a method to collect and process vibration data at the point machine with accelerometers on three axes to extract useful features. The two most important features, the number of peaks and the total power, were found. Three different unsupervised machine learning algorithms were applied to cluster the data. The results showed that the presented method could provide promising features. The k-means and the agglomerative hierarchical clustering methods are suitable for this data set. The density-based spatial clustering of applications with noise (DBSCAN) encounters some challenges. Full article

Railway systems are sometimes faced with the necessity of decommissioning railway stations due to issues in the electricity supply system, control system failures, or a decrease in train traffic. In order for fully or partially decommissioned stations to maintain functionality and turnout availability, the researchers propose the use of a computer system utilizing GSM technology. Using AES-encrypted SMS messages, GSM signaling can be applied to a wide range of electrical equipment at decommissioned stations, enabling monitoring and control of these installations remotely, as well as allowing for integration into an existing SCADA system. This study attempts to estimate the reduction in total delays and operating cost that would arise when implementing this control system in a low-resource setting. An impact and cost analysis was performed on a rail section with partially decommissioned stations (Brașov–Codlea, Romania), to ascertain whether this control method would result in significant delay and cost reductions. The analyzed data show that the proposed control system can significantly reduce delays and costs on railway lines with decommissioned stations, thus allowing for a more efficient use of resources. By leveraging technology to monitor and control electrical installations remotely, the need for physical presence at the decommissioned station is eliminated. Overall, the research described represents a significant step towards the more efficient and safe use of railway infrastructure, and could potentially lead to the reactivation of previously decommissioned stations, providing benefits for both passengers and freight transport operators. Full article

The assessment of indoor air quality (IAQ) in museums is a complex issue. In this study, a comprehensive investigation methodology was defined and applied to a museum to be validated. This methodology includes the analysis of exposed objects, the optimal conditions for conservation, the building features and the HVAC systems, and the indoor thermo-hygrometric and air quality conditions. In 2019, a survey in the Uffizi Gallery of Florence, one of the most important museums in the world, was carried out to assess the IAQ conditions in the museum, and the workers and visitors’ well-being, by focusing on some representative rooms (nine) of the museum complex in terms of visitor turnout and HVAC systems, including rooms closed to the public. Since IAQ is related to the possible presence and concentration of chemical and biological pollutants, these indicators, as well as thermo-hygrometric parameters, were monitored. The monitoring results were analyzed, evaluated, and compared with those suggested by the literature, guidelines and legislative documents dealing with IAQ in museums. Monitoring approaches for deepening investigations, as well as guidelines aimed at improving IAQ in the Uffizi Gallery and similar buildings are proposed. Full article

Improving railway safety depends heavily on the reliability of railway turnouts. The realization of effective, reliable and continuous observations for the spatial analysis and evaluation of the technical condition of railway turnouts is one of the factors affecting safety in railway traffic. The mode and scope of monitoring changes in geometric parameters of railway turnouts with associated indicators needs improvement. The application of digital twins to railway turnouts requires the inclusion of fundamental data indicating their condition along with innovative monitoring of weather conditions. This paper presents an innovative solution for monitoring the status of temperature and other atmospheric conditions. A UbiBot WS1 WIFI wireless temperature logger was used, with an external DS18B20 temperature sensor integrated into an S49 (49E1)-type rail as T_szyn WS1 WIFI. Measurements were made between January and May (winter/spring) at fixed time intervals and at the same measurement point. The aim of the research is to present elements of a fundamental approach of applying digital twins to railway turnouts requiring the consideration and demonstration of rail temperature conditions as a component in the data acquisition of railway turnout condition data and other constituent atmospheric conditions through an innovative solution. The research showed that the presented innovative solution is an effective support for the application of digital twins to railway turnouts and ongoing surveying and diagnostic work of other elements of rail transport infrastructure. The applicability of the T_gCWRII second temperature difference indicator in the monitoring of railway turnouts was also confirmed. Full article

Railway infrastructure plays a major role in providing the most cost-effective way to transport freight and passengers. The increase in train speed, traffic growth, heavier axles, and harsh environments make railway assets susceptible to degradation and failure. Railway switches and crossings (S&C) are a key element in any railway network, providing flexible traffic for trains to switch between tracks (through or turnout direction). S&C systems have complex structures, with many components, such as crossing parts, frogs, switchblades, and point machines. Many technologies (e.g., electrical, mechanical, and electronic devices) are used to operate and control S&C. These S&C systems are subject to failures and malfunctions that can cause delays, traffic disruptions, and even deadly accidents. Suitable field-based monitoring techniques to deal with fault detection in railway S&C systems are sought after. Wear is the major cause of S&C system failures. A novel measuring method to monitor excessive wear on the frog, as part of S&C, based on fiber Bragg grating (FBG) optical fiber sensors, is discussed in this paper. The developed solution is based on FBG sensors measuring the strain profile of the frog of S&C to determine wear size. A numerical model of a 3D prototype was developed through the finite element method, to define loading testing conditions, as well as for comparison with experimental tests. The sensors were examined under periodic and controlled loading tests. Results of this pilot study, based on simulation and laboratory tests, have shown a correlation for the static load. It was shown that the results of the experimental and the numerical studies were in good agreement. Full article

Thanks to the support of Inertial Navigation Systems (INS), Global Navigation Satellite Systems (GNSS) provide a navigation positioning solution that, in the absence of satellite signals (in tunnels, forest and urban areas), allows the continuous positioning of a moving object (air, land and sea). Passenger and freight trains must, for safety reasons, comply with several formal navigation requirements, particularly those that concern the minimum acceptable accuracy for determining their position. Depending on the type of task performed by the train (positioning a vehicle on a route, stopping at a turnout, stopping at a platform, monitoring the movement of rolling stock, etc.), the train must have positioning systems that can determine its position with sufficient accuracy (1–10 m, p = 0.95) to perform the tasks in question. A wide range of INS/GNSS equipment is currently available, ranging from very costly to simple solutions based on Micro-Electro-Mechanical Systems (MEMS), which, in addition to an inertial unit, use one or two GNSS receivers. The paper presents an assessment of the accuracy of both types of solutions by testing them simultaneously in dynamic measurements. The research, due to the costs and logistics complexity, was made using a passenger car. The surveys were carried out in a complex way, because the measurement route was travelled three times at four different speeds: 40 km/h, 80 km/h, 100 km/h and 120 km/h on seven representative test sections with diverse land development. In order to determine the positioning accuracy of INS devices, two precise GNSS geodetic receivers (2 cm accuracy, p = 0.95) were used as a reference positioning system. The measurements demonstrated that only INS/GNSS systems based on two receivers can meet the requirements of most railway applications related to rail navigation, and since a solution with a single GNSS receiver has a much lower positioning accuracy, it is not suitable for many railway applications. It is noted that considerable differences between the standards defining the navigation requirements for railway applications. For example, INS/GNSS systems based on two receivers meet the vast majority of the expectations specified in the Report on Rail User Needs and Requirements. However, according to the Federal Radionavigation Plan (FRP), it cannot be used in any railway application. Full article

Railway turnout system is a key infrastructure to railway safety and efficiency. However, it is prone to failure in the field. Therefore, many railway departments have adopted a monitoring system to monitor the operation status of turnouts. With monitoring data collected, many researchers have proposed different fault-diagnosis methods. However, many of the existing methods cannot realize real-time updating or deal with new fault types. This paper—based on imbalanced data—proposes a Bayes-based online turnout fault-diagnosis method, which realizes incremental learning and scalable fault recognition. First, the basic conceptions of the turnout system are introduced. Next, the feature extraction and processing of the imbalanced monitoring data are introduced. Then, an online diagnosis method based on Bayesian incremental learning and scalable fault recognition is proposed, followed by the experiment with filed data from Guangzhou Railway. The results show that the scalable fault-recognition method can reach an accuracy of 99.11%, and the training time of the Bayesian incremental learning model reduces 29.97% without decreasing the accuracy, which demonstrates the high accuracy, adaptability and efficiency of the proposed model, of great significance for labor-saving, timely maintenance and further, safety and efficiency of railway transportation. Full article