High-speed Railway最新文献

Nowadays, high mobility scenarios have become increasingly common. The widespread adoption of High-speed Rail (HSR) in China exemplifies this trend, while more promising use cases, such as vehicle-to-everything, continue to emerge. However, the Internet access provided in high mobility environments still struggles to achieve seamless connectivity. The next generation of wireless cellular technology 5 G further poses more requirements on the end-to-end evolution to fully utilize its ultra-high band-width, while existing network diagnostic tools focus on above-IP layers or below-IP layers only. We then propose HiMoDiag, which enables flexible online analysis of the network performance in a cross-layer manner, i.e., from the top (application layer) to the bottom (physical layer). We believe HiMoDiag could greatly simplify the process of pinpointing the deficiencies of the Internet access delivery on HSR, lead to more timely optimization and ultimately help to improve the network performance.

The complex bridge-track interaction between kilometer-span bridges and continuous Welded Rail (CWR) brings great challenges to CWR designing. Taking a suspension bridge with laying CWR as a case, the mechanical properties of CWR on the bridge are analyzed to reveal the sensitive areas of the track, and the design method of CWR and track structures on the beam ends are proposed. The results show that the unidirectional Rail Expansion Joints(REJ) need to be installed on the beam end of the kilometer-span bridge to reduce rail longitudinal force. Due to the bridge characteristics, there is no CWR fixed area on the kilometer-span bridge, and rail longitudinal force on the main span caused by bending loads needs to be concerned. The deformation of track on the beam end is complex, which is the weak area on the kilometer bridge, the large relative displacement between the stock rail of REJ and the main beam can cause poor stability of ballast bed on beam end, small resistance fasteners need to be laid on the sides of stock rail on the main beam to increase the stability of ballast and fasteners on the beam end. To improve the driving safety and comfort of beam end, the Sleeper-Supporting Apparatus (SSA) should be specially designed to ensure the uniform transition of track on beam ends. Temperature and wind loads have a significant impact on track regularity on the kilometer span bridge, the dynamic response of trains and bridges under those loads needs to be attended to.

Railway Point System (RPS) is an important infrastructure in railway industry and its faults may have significant impacts on the safety and efficiency of train operations. For the fault diagnosis of RPS, most existing methods assume that sufficient samples of each failure mode are available, which may be unrealistic, especially for those modes of low occurrence frequency but with high risk. To address this issue, this work proposes a novel fault diagnosis method that only requires the power signals generated under normal RPS operations in the training stage. Specifically, the failure modes of RPS are distinguished through constructing a reasoning diagram, whose nodes are either binary logic problems or those that can be decomposed into the problems of the binary logic. Then, an unsupervised method for the signal segmentation and a fault detection method are combined to make decisions for each binary logic problem. Based on the results of decisions, the diagnostic rules are established to identify the failure modes. Finally, the data collected from multiple real-world RPSs are used for validation and the results demonstrate that the proposed method outperforms the benchmark in identifying the faults of RPSs.

Image fusion refers to extracting meaningful information from images of different sources or modalities, and then fusing them to generate more informative images that are beneficial for subsequent applications. In recent years, the growing data and computing resources have promoted the development of deep learning, and image fusion technology has continued to spawn new deep learning fusion methods based on traditional fusion methods. However, high-speed railroads, as an important part of life, have their unique industry characteristics of image data, which leads to different image fusion techniques with different fusion effects in high-speed railway scenes. This research work first introduces the mainstream technology classification of image fusion, further describes the downstream tasks that image fusion techniques may combine within high-speed railway scenes, and introduces the evaluation metrics of image fusion, followed by a series of subjective and objective experiments to completely evaluate the performance level of each image fusion method in different traffic scenes, and finally provides some possible future image fusion in the field of rail transportation of research.

Further improving the railway innovation capacity and technological strength is the important goal of the 14th Five-Year Plan for railway scientific and technological innovation. It includes promoting the deep integration of cutting-edge technologies with the railway systems, strengthening the research and application of intelligent railway technologies, applying green computing technologies and advancing the collaborative sharing of transportation big data. The high-speed rail system tasks need to process huge amounts of data and heavy workload with the requirement of ultra-fast response. Therefore, it is of great necessity to promote computation efficiency by applying High Performance Computing (HPC) to high-speed rail systems. The HPC technique is a great solution for improving the performance, efficiency, and safety of high-speed rail systems. In this review, we introduce and analyze the application research of high performance computing technology in the field of high-speed railways. These HPC applications are cataloged into four broad categories, namely: fault diagnosis, network and communication, management system, and simulations. Moreover, challenges and issues to be addressed are discussed and further directions are suggested.

With the development of High-Speed Rail (HSR), countries and individual passengers alike have enjoyed far ranging benefits as a result – economic, social, environment and in added convenience. One of the important parts of HSR construction is the signaling system, where wireless communications play a key role in the transmission of train control data. Channel estimation has a significant impact on the quality of the wireless communication, however, whose performance is degraded due to the fast mobility of HSR. This paper focuses on the channel estimation technology in HSR. We first summarize the key challenges for HSR channel estimation, especially the Inter-Carrier Interference (ICI) faced by Orthogonal Frequency Division Multiplexing (OFDM) systems. Then we provide a comprehensive review of existing pilot-aided channel estimation schemes from three points: channel model, estimation algorithm, joint channel estimation and ICI mitigation schemes. Lastly, we present the challenges of channel estimation for the Orthogonal Time Frequency Space (OTFS) system and Reconfigurable Intelligent Surface (RIS), which are promising techniques for HSR systems in the future sixth Generation (6G) wireless communication.

The application of Global Navigation Satellite Systems (GNSSs) in the intelligent railway systems is rapidly developing all over the world. With the GNSS-based train positioning and moving state perception, the autonomy and flexibility of a novel train control system can be greatly enhanced over the existing solutions relying on the track-side facilities. Considering the safety critical features of the railway signaling applications, the GNSS stand-alone mode may not be sufficient to satisfy the practical requirements. In this paper, the key technologies for applying GNSS in novel train-centric railway signaling systems are investigated, including the multi-sensor data fusion, Virtual Balise (VB) capturing and messaging, train integrity monitoring and system performance evaluation. According to the practical characteristics of the novel train control system under the moving block mode, the details of the key technologies are introduced. Field demonstration results of a novel train control system using the presented technologies under the practical railway operation conditions are presented to illustrate the achievable performance feature of autonomous train state perception using BeiDou Navigation Satellite System (BDS) and related solutions. It reveals the great potentials of these key technologies in the next generation train control system and other GNSS-based railway implementations.

High speed railway technologies are rapidly development in the world. The total distance of the high speed railway is more than 40,000 kilometers in China, and many types of high speed Electrical Multi-Units (EMUs) are operated. The top operation velocity of the train reaches 350 km/h. New science and technologies are developing rapidly. New generation technologies such as the information technology, intelligent manufacturing, new material and processing, innovating design philosophy have revolutionary influence on the high speed train. It promotes the high speed train performance such as intelligence, reliable operation and environment-friendly. Based on many years investigation of the trend of the technology development and requirement of general public, the trend of technology development in five aspects are presented in this paper. The five aspects include economic applicability, high speed and high efficiency, green and low carbon, intelligent safety, comfortable and high quality.

Image detection based on machine learning and deep learning currently has a good application prospect for railway fault diagnosis, with good performance in feature extraction and the accuracy of image localization and good classification results. To improve the speed of locating small target objects of fasteners, the YOLOv5 framework model with faster algorithm speed is selected. To improve the classification accuracy of fasteners, YOLOv5-based heavy-duty railway rail fastener detection is proposed. The anchor size is modified on the original basis to improve the attention to small targets of fasteners. The CBAM (Convolutional Block Attention Module) module and TPH (Transformer Prediction Head) module are introduced to improve the speed and accuracy issues. The rail fasteners are divided into 6 categories. Experiment comparisons show that before the improvement, the MAP@ 0.5 value of all categories are close to the peak of 0.989 after the epoch of 150, and the F1 score approaches 1 with confidence in the interval (0.2, 0.95). The improved mAP@ 0.5 value approached the highest value of 0.991 after the epoch of 75, and the F1 score approached 1 with confidence in the interval (0.01, 0.95). The experiment results indicate that the improved YOLOv5 model proposed in this paper is more suitable for the task of detecting rail fasteners.

With the rapid development of railway transportation, higher requirements for capacity are increasing and amount of communication, computer and control technologies are applied in train control systems which is the popular method for train control. The application of 3 C brings the enhancement of railway transportation performance. However, the number of internal and external cyber security threats is rising, which could lead to some railway accidents. On the other hand, safety is the core of the design process for railway but security is rarely considered due to the closure of traditional railway systems. As cyber security is threatening the normal operation of industrial control systems and the critical infrastructure, security issues of train control systems should be paid more attention as railway transportation play an important role for society and economy. However, due to the safety-critical characteristics, safety assessment and analysis works have been performed for a long time, but cyber security related works are rarely considered. In the paper, we demonstrate the security situation of train control systems based on the inherent features and the experience of other industrial control systems, where the technical defects and potential threats are summarized. According to the practical engineering experience, the current security protection strategies are illustrated, which shows the popular security protection methods are limited and cannot realize the defense-in-depth. Finally, some research challenges of security issues of train control systems are identified.