High-speed Railway最新文献

Railway inspection poses significant challenges due to the extensive use of various components in vast railway networks, especially in the case of high-speed railways. These networks demand high maintenance but offer only limited inspection windows. In response, this study focuses on developing a high-performance rail inspection system tailored for high-speed railways and railroads with constrained inspection timeframes. This system leverages the latest artificial intelligence advancements, incorporating YOLOv8 for detection. Our research introduces an efficient model inference pipeline based on a producer-consumer model, effectively utilizing parallel processing and concurrent computing to enhance performance. The deployment of this pipeline, implemented using C++, TensorRT, float16 quantization, and oneTBB, represents a significant departure from traditional sequential processing methods. The results are remarkable, showcasing a substantial increase in processing speed: from 38.93 Frames Per Second (FPS) to 281.06 FPS on a desktop system equipped with an Nvidia RTX A6000 GPU and from 19.50 FPS to 200.26 FPS on the Nvidia Jetson AGX Orin edge computing platform. This proposed framework has the potential to meet the real-time inspection requirements of high-speed railways.

The power module of the Insulated Gate Bipolar Transistor (IGBT) is the core component of the traction transmission system of high-speed trains. The module's junction temperature is a critical factor in determining device reliability. Existing temperature monitoring methods based on the electro-thermal coupling model have limitations, such as ignoring device interactions and high computational complexity. To address these issues, an analysis of the parameters influencing IGBT failure is conducted, and a temperature monitoring method based on the Macro-Micro Attention Long Short-Term Memory (MMALSTM) recursive neural network is proposed, which takes the forward voltage drop and collector current as features. Compared with the traditional electrical-thermal coupling model method, it requires fewer monitoring parameters and eliminates the complex loss calculation and equivalent thermal resistance network establishment process. The simulation model of a high-speed train traction system has been established to explore the accuracy and efficiency of MMALSTM-based prediction methods for IGBT power module junction temperature. The simulation outcomes, which deviate only 3.2% from the theoretical calculation results of the electric-thermal coupling model, confirm the reliability of this approach for predicting the temperature of IGBT power modules.

Magnetic levitation control technology plays a significant role in maglev trains. Designing a controller for the levitation system is challenging due to the strong nonlinearity, open-loop instability, and the need for fast response and security. In this paper, we propose a Disturbance-Observe-based Tube Model Predictive Levitation Control (DO-TMPLC) scheme combined with a feedback linearization strategy for the levitation system. The proposed strategy incorporates state constraints and control input constraints, i.e., the air gap, the vertical velocity, and the current applied to the coil. A feedback linearization strategy is used to cancel the nonlinearity of the tracking error system. Then, a disturbance observer is implemented to actively compensate for disturbances while a TMPLC controller is employed to alleviate the remaining disturbances. Furthermore, we analyze the recursive feasibility and input-to-state stability of the closed-loop system. The simulation results indicate the efficacy of the proposed control strategy.

In view of the problems of inconsistent data semantics, inconsistent data formats, and difficult data quality assurance between the railway engineering design phase and the construction and operation phase, as well as the difficulty in fully realizing the value of design results, this paper proposes a design and implementation scheme for a railway engineering collaborative design platform. The railway engineering collaborative design platform mainly includes functional modules such as metadata management, design collaboration, design delivery management, model component library, model rendering services, and Building Information Modeling (BIM) application services. Based on this, research is conducted on multi-disciplinary parameterized collaborative design technology for railway engineering, infrastructure data management and delivery technology, and design multi-source data fusion and application technology. The railway engineering collaborative design platform is compared with other railway design software to further validate its advantages and advanced features. The platform has been widely applied in multiple railway construction projects, greatly improving the design and project management efficiency.

An emerging railway technology called smart railway promises to deliver higher transportation efficiency, enhanced comfort in services, and greater eco-friendliness. The smart railway is expected to integrate fifth-generation mobile communication (5G), Artificial Intelligence (AI), and other technologies, which poses new problems in the construction, operation and maintenance of railway wireless networks. Wireless Digital Twins (DTs), which have recently emerged as a new paradigm for the design of wireless networks, can address these problems and enable the whole lifecycle management of railway wireless networks. However, there are still many scientific issues and challenges for railway-oriented wireless DT. Relevant key technologies to solve these problems are introduced and described, including characterization of materials' physical-EM properties, autonomous reconstruction of Three-dimensional (3D) environment model, AI-empowered environmental cognition, Ray-Tracing (RT), model-based and AI-based RT acceleration, and generation of multi-spectra sensing data. Moreover, this paper presents our research results for each key technology and describes the wireless network planning and optimization system based on high-performance RT developed by our laboratory. This paper outlines the framework for realizing the wireless DT of smart railways, providing the direction for future research.