High-speed Railway最新文献

To investigate the influences of Cr₂AlC mass fraction and supersonic plasma spraying process on the microstructure and mechanical properties of Cr₂AlC reinforced 410 stainless steel composite coatings, the coatings containing different mass fractions of Cr₂AlC were prepared and investigated. The composite coating exhibited low porosity and high adhesion strength. The addition of Cr₂AlC significantly enhanced the hardness of the composite coatings through particle strengthening. However, when the mass fraction of Cr₂AlC was 20%, the aggregation of Cr₂AlC resulted in a strong decrease in the coating preparation efficiency, as well as a decline in adhesion strength. In the supersonic plasma spraying process, the Ar flow rate mainly influenced the flight velocity of the particles, while the H₂ flow rate and the current mainly affected the temperature of the plasma torch. Consequently, all of them influenced the melting degree of particles and the quality of the coating. The lowest porosity and the highest hardness and adhesion strength could be obtained when the Ar flow rate is 125 L/min, the H₂ flow rate is 25 L/min, and the current is 385 A.

Aerospace optical cables and fiber-optic connectors have numerous advantages (e.g., low loss, wide transmission frequency band, large capacity, light weight, and excellent resistance to electromagnetic interference). They can achieve optical communication interconnections and high-speed bidirectional data transmission between optical terminals and photodetectors in space, ensuring the stability and reliability of data transmission during spacecraft operations in orbit. They have become essential components in high-speed networking and optically interconnected communications for spacecrafts. Thermal stress simulation analysis is important for evaluating the temperature stress concentration phenomenon resulting from temperature fluctuations, temperature gradients, and other factors in aerospace optical cables and connectors under the combined effects of extreme temperatures and vacuum environments. Considering this, advanced optical communication technology has been widely used in high-speed railway communication networks to transmit safe, stable and reliable signals, as high-speed railway optical communication in special areas with extreme climates, such as cold and high-temperature regions, requires high-reliability optical cables and connectors. Therefore, based on the finite element method, comprehensive comparisons were made between the thermal distributions of aerospace optical cables and J599III fiber optic connectors under different conditions, providing a theoretical basis for evaluating the performance of aerospace optical cables and connectors in space environments and meanwhile building a technical foundation for potential optical communication applications in the field of high-speed railways.

The forging stage of rail flash welding has a decisive influence on joint strength, and the study of the temperature distribution in the process has an important role in further improving joint strength. In this paper, three calculation methods for the temperature field are given. First, the finite element model of the temperature field before forging rail flash welding is established by using the transient heat module of Ansys software and verified by infrared temperature measurement. Second, the temperature distribution of different parts of the rail before flash welding is obtained by using infrared thermal imaging equipment. Third, Matlab software is used to calculate the temperature of the non-measured part. Finally, the temperature distribution function along the rail axis is fitted through the temperature measurement data. The temperature distribution before the top forging of the rail flash welding can be used to analyze the joint and heat-affected zone organization and properties effectively and to guide the parameter setting and industrial production.

It is of great significance to improve the efficiency of railway production and operation by realizing the fault knowledge association through the efficient data mining algorithm. However, high utility quantitative frequent pattern mining algorithms in the field of data mining still suffer from the problems of low time-memory performance and are not easy to scale up. In the context of such needs, we propose a related degree-based frequent pattern mining algorithm, named Related High Utility Quantitative Item set Mining (RHUQI-Miner), to enable the effective mining of railway fault data. The algorithm constructs the item-related degree structure of fault data and gives a pruning optimization strategy to find frequent patterns with higher related degrees, reducing redundancy and invalid frequent patterns. Subsequently, it uses the fixed pattern length strategy to modify the utility information of the item in the mining process so that the algorithm can control the length of the output frequent pattern according to the actual data situation and further improve the performance and practicability of the algorithm. The experimental results on the real fault dataset show that RHUQI-Miner can effectively reduce the time and memory consumption in the mining process, thus providing data support for differentiated and precise maintenance strategies.

The sliding chairs are important components that support the switch rail conversion in the railway turnout. Due to the harsh environmental erosion and the attack from the wheel vibration, the failure rate of the sliding chairs accounts for up to 10% of the total failure number in turnout. However, there is little research carried out in the existing literature to diagnose the deterioration states of the sliding chairs. To fill out this gap, by utilizing the images containing the sliding chairs, we propose an improved You Only Look Once version 7 (YOLOv7) to identify the state of the sliding chairs. Specifically, to meet the challenge brought by the small inter-class differences among the sliding chair states, we first integrate the Convolutional Block Attention Module (CBAM) into the YOLOv7 backbone to screen the information conducive to state identification. Then, an extra detector for a small object is customized into the YOLOv7 network in order to detect the small-scale sliding chairs in images. Meanwhile, we revise the localization loss in the objective function as the Efficient Intersection over Union (EIoU) to optimize the design of the aspect ratio, which helps the localization of the sliding chairs. Next, to address the issue caused by the varying scales of the sliding chairs, we employ K-means++ to optimize the priori selection of the initial anchor boxes. Finally, based on the images collected from real-world turnouts, the proposed method is verified and the results show that our method outperforms the basic YOLOv7 in the state identification of the sliding chairs with 4% improvements in terms of both mean Average [email protected] ([email protected]) and F1-score.

Multisensor data fusion method can improve the accuracy of bearing fault diagnosis, in order to address the problems of single-sensor data types and the insufficient exploration of redundancy and complementarity between different modal data in most existing multisensor data fusion methods for bearing fault diagnosis, a bearing fault diagnosis method based on a Multiple-Constraint Modal-Invariant Graph Convolutional Fusion Network (MCMI-GCFN) is proposed in this paper. Firstly, a Convolutional Autoencoder (CAE) and Squeeze-and-Excitation Block (SE block) are used to extract features of raw current and vibration signals. Secondly, the model introduces source domain classifiers and domain discriminators to capture modal invariance between different modal data based on domain adversarial training, making use of the redundancy and complementarity between multimodal data. Then, the spatial aggregation property of Graph Convolutional Neural Networks (GCN) is utilized to capture the dependency relationship between current and vibration modes with similar time step features for accurately fusing contextual semantic information. Finally, the validation is conducted on the public bearing damage current and vibration dataset from Paderborn University. The experimental results showed that the delivered fusion method achieved a bearing fault diagnosis accuracy of 99.6 %, which was about 9 %–11.4 % better than that with nonfusion methods.

Accurate wheel-rail force data serves as the cornerstone for analyzing the wheel-rail relationship. However, achieving continuous and precise measurement of this force remains a significant challenge in the field. This article introduces a calibration algorithm for the wheel-rail force that leverages graph neural networks and long short-term memory networks. Initially, a comprehensive wheel-rail force detection system for trains was constructed, encompassing two key components: an instrumented wheelset and a ground wheel-rail force measuring system. Subsequently, utilizing this system, two distinct datasets were acquired from the track inspection vehicle: instrumented wheelset data and ground wheel-rail force data, a feedforward neural network was employed to calibrate the instrumented wheelset data, referencing the ground wheel-rail force data. Furthermore, ground wheel-rail force data for the locomotive was obtained for the corresponding road section. This data was then integrated with the calibrated instrumented wheelset data from the track inspection vehicle. Leveraging the GNN-LSTM network, the article establishes a mapping relationship model between the wheel-rail force of the track inspection vehicle and the locomotive wheel-rail force. This model facilitates continuous measurement of locomotive wheel-rail forces across three typical scenarios: straight sections, long and steep downhill sections, and small curve radius sections.

As High Speed Rail (HSR) has proliferated globally, so has a related research field dedicated to exploring and addressing its unique issues. Yet, studies to understand and classify the HSR research domain are limited. This paper addresses the gap, using bibliometric analysis to identify future research areas and 20 candidate topics for literature review based on keyword analysis through VOSviewer. Article and review papers related to HSR published in the last 20 years (2003–2022) were retrieved from Scopus, and then analyzed to determine the split in knowledge between languages, the collaboration between countries and institutions, highly productive and cited journals, and research topics which have and have not been reviewed. Approximately 30% of the search results were published exclusively in Chinese, highlighting the importance of extending the evaluation to cover both languages. This is a novel aspect of the work, which has enabled the recognition of potential knowledge gaps. It is recommended that future reviews incorporate works in both languages, possibly through international collaboration. Institutions in China and other countries that are strong collaborators have been identified, as well as relevant, highly cited journals.

In recent years, the global surge of High-speed Railway (HSR) revolutionized ground transportation, providing secure, comfortable, and punctual services. The next-gen HSR, fueled by emerging services like video surveillance, emergency communication, and real-time scheduling, demands advanced capabilities in real-time perception, automated driving, and digitized services, which accelerate the integration and application of Artificial Intelligence (AI) in the HSR system. This paper first provides a brief overview of AI, covering its origin, evolution, and breakthrough applications. A comprehensive review is then given regarding the most advanced AI technologies and applications in three macro application domains of the HSR system: mechanical manufacturing and electrical control, communication and signal control, and transportation management. The literature is categorized and compared across nine application directions labeled as intelligent manufacturing of trains and key components, forecast of railroad maintenance, optimization of energy consumption in railroads and trains, communication security, communication dependability, channel modeling and estimation, passenger scheduling, traffic flow forecasting, high-speed railway smart platform. Finally, challenges associated with the application of AI are discussed, offering insights for future research directions.

For high-speed railways, the smoothness of the railway line significantly affects the operational speed of trains. When the train passes through the turnout on a long-span bridge, the wheel-rail impacts caused by the turnout structure irregularities, and the instability arising from the bridge's flexural deformation lead to a strong coupling effect in the vehicle-turnout-bridge system. This significantly affects both ride comfort and operational safety. For addressing this issue, the present study considered a long-span continuous rigid-frame bridge as an example and established a train-turnout-bridge coupled dynamic model of high-speed railway. Utilizing a self-developed dynamic simulation program, the study analysed the dynamic response characteristics when the train passes through the turnouts on the bridge. It also investigated the influence of different span-to-depth ratios of the bridge on the vehicle dynamic response when the train passes through the main line and branch line of turnouts and then proposed a span-to-depth ratio limit value for a long-span continuous rigid-frame bridge. The research findings suggest that the changes in the span-to-depth ratio have a relatively minor impact on the train’s operational performance but significantly affect the dynamic characteristics of the bridge structure. Based on the findings and a comprehensive assessment of safety indicators, it is advisable to establish a span-to-depth ratio limit of 1/4500 for a long-span continuous rigid-frame bridge.