This study deals with the fatigue behavior of on-site-installation-type track slabs subject to cycling train load developed by applying glass-fiber-reinforced polymer (GFRP) reinforcing bars. Concrete track slabs have the most severe deterioration in track circuit characteristic values due to the conduction influence of existing steel bars. Therefore, a track slab applying an insulator and lightweight GFRP reinforcement by replacing the existing steel bar was proposed from a design perspective. In order to present the validity of the proposed method, a full-size specimen was manufactured and a fatigue performance test was performed, and the results were compared with the test specimen applied with steel bars. From the results of various fatigue behaviors, it was found that displacement variations during cyclic loading remained within 1 mm, and load variations were within 10 kN, indicating excellent stability under accumulated fatigue cycles. This study analyzed the macro-level structural behavior of GFRP-reinforced concrete track slabs under fatigue loading. Future research will further investigate micro-level bond interactions between the reinforcement and concrete to validate long-term performance.

K-means clustering and Deep Q-Network enhanced fault diagnosis for ZPW-2000A jointless track circuits

By monitoring the alternating interference voltage at the intersections and parallels of gas pipelines with high-speed railways, the alternating voltage between the high-speed railway track supports and the ground, the alternating ground voltage gradient along parallel and perpendicular high-speed railway tracks, and the timing of train passages, the interference patterns caused by high-speed railways on pipelines are analyzed. A numerical model was developed to elucidate interference mechanisms. The conclusions indicate that the interference caused by the parallel and intersecting presence of high-speed railways and pipelines is far greater than that caused solely by the intersection of railways and pipelines. The peak alternating voltage interference on pipelines occurs at the insulation joints of the pipelines, the positions of the pipelines corresponding to the high-speed railway track circuits (AT), and the positions of the pipelines corresponding to the passage of trains. The alternating interference caused by high-speed railway lines on pipelines involves both resistive coupling interference and electromagnetic induction coupling interference, with the latter dominating.

Abstract The ZPW-2000A track circuit’s limited low-frequency codes (18 types) constrain the train control system capacity. This study proposes code expansion via modulation coefficient optimization, adding four frequencies (8.1/9.2/30.1/31.2 Hz). Theoretical and simulation analyses confirm that the new codes maintain spectral consistency with existing standards, showing over 96% energy concentration for stable transmission. Hardware-software co-designed transceivers achieve secure detection of extended codes. Integrated with CTCS-3 systems, the technology reduces synchronization delay by 32%, shortens temporary speed restriction response time by 19%, and minimizes braking redundancy during communication failures. This advancement supports precision control in complex environments (e.g., Sichuan-Tibet Railway) and future ultra-high-speed systems.

<p>The research presented the design of safety electronic load suppression (SELS) for mitigating transient overvoltage in the track circuits of railway signaling systems while changing the track occupancy in the track circuits of the signaling system that caused damage to the BR966F2 relay. The analysis of the average failure of the electronic devices, the failure modes and effect analysis (FMEA), and the performance test of electronic devices were conducted. and the performance test of electronic devices were conducted. which can control the operation with 2oo3 processing mode (two out of three voting) under the series circuits pattern to resolve the damage caused by the application. Results illustrated that the mean operating time of the SELS between failures was 9,399 hours. In addition, regarding the performance of the electronic load for mitigating transient overvoltage of 1 kV at 31.4 V and overvoltage 50 VDC at 178.6 °C within 83 seconds at 35.4 V. Additionally, the SELS could function adequately without failure or causing any damage. Therefore, the SELS was more reliable.</p>

The track circuit is a device used to determine whether a vehicle is on the track. This device is installed on the track and needs to be monitored to ensure it functions properly. All objects detected in practice are considered railway vehicles, even though the detected objects may not be railway vehicles. Accurate and reliable detection technology is crucial for preventing accidents and keeping train traffic safe. This detection system uses metal sensors to identify objects as railway or non-railway assets. This is done based on two variables: the frequency of the object's metal and the object's speed. The test results show that this tool can classify objects according to their class. The class with no objects has an accuracy level of 100%, while the class of non-railway has an accuracy level of 86.67%. The railway class trials using the inspection train axle show an accuracy level of 93.33%.

One of the main challenges in the operation of electric traction vehicles is ensuring safety and operational reliability. To ensure the safety of railway traffic, vehicles must undergo a series of tests related to the investigation of disturbances generated, among others, in the return current to the mains. This problem is further complicated by the inability to perform such measurements under laboratory conditions. The implementation of tests under real conditions determines the appearance of additional potential interference sources, from power sources to improper interactions between current collectors and the overhead contact system, and it requires strict compliance with regulatory standards and the implementation of standardized testing procedures. This article presents issues related to the investigation and analysis of the electromagnetic compatibility of rolling stock with train detection systems using track circuits. The aim of these tests is to determine the harmonic components in the traction current in relation to the permissible levels specified in the latest editions of the European Railway Agency—ERA/ERTMS/033281 version 5.0 documents and Annex S-02 to the List of the President of the Office of Rail Transport. The measurement methodology and test procedures are presented in detail with respect to current legal requirements.

The safe management of traffic on rail networks requires that trains can be reliably located at all times. In many countries, this is achieved by electrically identifying their presence using ‘track circuits’ at regular intervals along each track: these devices detect when the wheels and axles of a train short-circuit the two rails (they are said to ‘shunt’ them) and the information is passed on to the signaling system. The quality of the electrical contacts between the rails and the wheels obviously plays a decisive role in the correct operation of this detection principle. The occurrence of high wheel–rail contact resistances can lead to malfunctions, known as ‘deshunting’, in which the system is unable for a certain period of time to conclude whether a train is present or absent on a section of track. This type of potentially risky event must obviously be avoided at all costs. In this article, we present the second part of a study devoted to the degradation of the wheel–rail contact resulting from steel oxidation, using a scaled-down home-made test bench that reproduces the real contact in the laboratory under controlled conditions. Following on from the first part, which dealt with the static characterization of the contact studied in the direct current, we are now tackling its dynamic characterization (rolling contact) under AC electrical conditions close to those of a track circuit. These new investigations again involve different degrees of rail oxidation, and analyze the influence of parameters such as contact force, speed, and current intensity.

To enhance the efficiency of reusing massive unstructured operation and maintenance (O&M) data generated during routine railway maintenance inspections, this paper proposes a Named Entity Recognition (NER) method that integrates multi-granularity semantics and a Multi-Scale Retention (MSR) mechanism. The proposed approach effectively transforms expert knowledge extracted from manually processed fault data into structured triplet information, enabling the in-depth mining of track circuit O&M text data. Given the specific characteristics of railway domain texts, which include a high prevalence of technical terms, ambiguous entity boundaries, and complex semantics, we first construct a domain-specific lexicon stored in a Trie tree structure. A lexicon adapter is then introduced to incorporate these terms as external knowledge into the base encoding process of RoBERTa-wwm-ext, forming the lexicon-enhanced LE-RoBERTa-wwm model. Subsequently, a hidden feature extractor captures semantic representations from all 12 output layers of LE-RoBERTa-wwm, performing weighted fusion to fully leverage multi-granularity semantic information across encoding layers. Furthermore, in the downstream processing stage, two computational paradigms are designed based on the MSR mechanism and the Regularized Dropout (R-Drop) mechanism, enabling low-cost inference and efficient parallel training. Comparative experiments conducted on the public Resume and Weibo datasets demonstrate that the model achieves F1 scores of 96.75% and 72.06%, respectively. Additional experiments on a track circuit dataset further validate the model’s superior recognition performance and generalization capability.

To manage and ensure the safety of traffic on rail networks, trains need to be reliably located at all times. This is achieved in many countries by electrically detecting their presence using so-called “track circuits” installed at regular intervals on each track, designed to detect when the wheels and axles of a train are shunting the two rails and to act accordingly on the signaling system. Such a detection principle is highly dependent on the quality of the electrical contacts between rails and wheels; the occurrence of high wheel–rail contact resistances can induce malfunctions known as “deshunting”, when the system is unable to judge the presence or absence of a train on a section of track. This type of potentially risky event must obviously be avoided at all costs. In this article, we focus on wheel–rail contact degradation resulting from steel oxidation, using a home-made, scaled-down test bench that reproduces real contact in the laboratory under controlled conditions. Given the complexity of the topic, the investigations are focused on static contact characterizations involving different degrees of rail oxidation and slow, stepwise variations in DC intensity.

Design of locomotive insulation joint detection device based on track circuit

Modern railway signalling systems are critical for ensuring the safe and efficient operation of railway networks. Educational institutions play a vital role in training students to design and implement these systems, replicating practical scenarios to bridge the gap between theory and application. Advanced electronic interlocking systems, such as SIMIS W, are widely used to manage complex signalling requirements in real-world railway environments. Existing model railway systems lack the capability to simulate advanced interlocking systems with real-time performance. This paper presents a model railway signalling system based on the SIMIS W interlocking system. The setup integrates ILTIS-N dispatcher workstations with a SIMATIC S7-1200 PLC, controlling signals, switches, and track circuits. A REST API ensures seamless data exchange between ILTIS-N and the PLC, achieving efficient system responsiveness. The system effectively replicates real-world signalling conditions with compatibility with existing protocols. Performance testing demonstrated a response time of approximately 1.2 s, achieving the design objective of a maximum 1.5 s. This ensures rapid and accurate signalling under real-time constraints. The developed system provides a practical educational platform for students to gain experience with advanced interlocking systems and serves as a basis for further research into scalable railway signalling solutions.

The article considers the history of construction of the salt railway line, as well as the current transportation of salt from the deposit (Lake Baskunchak) from the Nizhny Baskunchak station to the Vladimirskaya pier station for further transshipment to river barges. An analysis of the deterioration of the track superstructure during the transportation of salt (halite) in an open way in gondola cars (in bulk) is carried out. The main causes of failures that can be caused by contamination of the ballast with bulk cargo, especially salts, are studied.It is proposed to apply a paint and varnish coating in order to protect against corrosion, as well as to use water-jet track machines for the complete removal of salts and insulating joints made of fiberglass. To improve the reliability of the track circuits and eliminate deviations from the normal operation of signaling and interlocking devices due to metal corrosion, it is proposed to coat the iron with a protective layer of zinc.When developing a salt deposit, a technical solution is recommended that consists of using modern technology. When mining salt in an open way, the proposed loader will make it possible to collect salt in such a way that a layer of new salt will form more quickly in the future. Such innovative approaches will ensure environmental safety and preserve natural resources.

Fault localization using sliding adaptive data segmentation and improved k-nearest neighbour with application to railway track circuits

A heterogeneous transfer learning method for fault prediction of railway track circuit

The modular railway track and signaling simulation trainer research project was developed to provide an enjoyable and educational tool to train railway students and professionals upon recognizing the need for effective training in traffic and signaling safety-critical areas. It provides a miniature model that accurately replicates railway switch points and signaling systems with modules depicting track layouts and signaling conditions created using Programmable Logic Controller (PLC) and Human-Machine Interface (HMI). The prototype's accurate and efficient miniature railway model showed that the simulation trainer was very effective. The survey demonstrated that railway students and professionals were satisfied with the trainer's performance and various use and educational potential for students. This simulation trainer design successfully changes railway training programs because it offers a relatively safe and exciting environment that allows learners to get practical experience and understanding of railway operations. The modular design of the trainer will facilitate future expansion and customization, making it adaptable to railway training needs and technology. The simulation trainer should include level crossings, track circuits, and train detection systems for further investigation. This would provide an improvement in the comprehensiveness and flexibility of the trainer, therefore helping with providing skilled railway workers.

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.

ABSTRACTTrain localization is an essential technology for effective train control. Currently, train localization primarily relies on using track circuits and balises, which are placed along the track to provide precise location information. However, balises have to be placed at intervals of a few kilometers. This increases maintenance costs and makes them vulnerable to being damaged by ice blocks falling from moving trains. Therefore, in this study, we propose a method for absolute train localization based on structure detection and identification using a 1D light detection and ranging (LiDAR) sensor to reduce the number of balises. Structure identification is achieved using scan matching. In the experiments using a car, the proposed method achieved an identification success rate of over 90%. We also considered the effect of raindrops by filtering the measurement data. By testing and analyzing the identification results, we successfully reduced all cases of misidentification.

The accuracy of ZPW-2000 frequency shift signal demodulation is related to the safety and efficiency of high-speed trains. This paper proposed a kind of ZPW-2000 frequency-shift signal detection algorithm based on improved Relaxation algorithm (ZFSD-IR) to address the issue of low accuracy in detecting ZPW-2000 frequency shift signal under strong noise interference, as the traditional fast Fourier transform (FFT) spectrum analysis methods are susceptible to fence effects and spectrum leakage effects. Firstly, the principle of ZPW-2000 jointless track circuit was analyzed, and then the ideal and simplified models of ZPW-2000 frequency shift signal were established, respectively. Finally, the simulation experiments were conducted on ZPW-2000 frequency shift signal detection under different sampling durations and signal-to-noise ratios (SNR). The effectiveness of the proposed algorithm was verified through comparative analysis with the traditional algorithms. The research results indicated that the ZFSD-IR algorithm has better accuracy in carrier-frequency and low-frequency estimation than traditional algorithms, and can achieve accurate detection of ZPW-2000 frequency shift signal under the lower SNR conditions. It has high detection accuracy and good anti-interference ability, ensuring the safe operation of high-speed trains.

Abstract Predicting the remaining useful life (RUL) of track circuits is essential to ensure the safe and reliable operation of high-speed railways. In response to the challenges faced by current machine-learning-based RUL prediction methods, which struggle to represent the uncertainty in the probability distribution of RUL predictions, this paper suggests a hybrid-driven method for estimating remaining life. Firstly, the track circuit Health Index is constructed by feature dimensionality reduction and fusion of the original multivariate monitoring data through kernel principal component analysis and Autoencoder; Secondly, the degraded state of the rail circuit is modelled using a nonlinear Wiener degradation model. Finally, the principle of first hitting time is used to derive the probability density function of the anticipated RUL. The efficacy and superiority of the approach presented in this paper are validated by experimental research on the track circuit monitoring dataset. The method enhances forecast accuracy and reduces prediction uncertainty, offering robust technical support for track circuit maintenance decision-making.