High-speed Railway最新文献_第3页

High-speed railway track components inspection framework based on YOLOv8 with high-performance model deployment 基于 YOLOv8 的高速铁路轨道部件检测框架与高性能模型部署

High-speed Railway Pub Date : 2024-03-01 DOI: 10.1016/j.hspr.2024.02.001

Youzhi Tang, Yu Qian

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引用次数: 0

A study on temperature monitoring method for inverter IGBT based on memory recurrent neural network 基于记忆递归神经网络的逆变器 IGBT 温度监控方法研究

High-speed Railway Pub Date : 2024-03-01 DOI: 10.1016/j.hspr.2024.02.003

Yunhe Liu, Tengfei Guo, Jinda Li, Chunxing Pei, Jianqiang Liu

{"title":"A study on temperature monitoring method for inverter IGBT based on memory recurrent neural network","authors":"Yunhe Liu, Tengfei Guo, Jinda Li, Chunxing Pei, Jianqiang Liu","doi":"10.1016/j.hspr.2024.02.003","DOIUrl":"10.1016/j.hspr.2024.02.003","url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":100607,"journal":{"name":"High-speed Railway","volume":"2 1","pages":"Pages 64-70"},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949867824000138/pdfft?md5=087cca3eb0d18193c47f24bb07cf80af&pid=1-s2.0-S2949867824000138-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139892851","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Disturbance rejection tube model predictive levitation control of maglev trains 磁悬浮列车的干扰抑制管模型预测悬浮控制

High-speed Railway Pub Date : 2024-03-01 DOI: 10.1016/j.hspr.2024.01.001

Yirui Han, Xiuming Yao, Yu Yang

{"title":"Disturbance rejection tube model predictive levitation control of maglev trains","authors":"Yirui Han, Xiuming Yao, Yu Yang","doi":"10.1016/j.hspr.2024.01.001","DOIUrl":"https://doi.org/10.1016/j.hspr.2024.01.001","url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":100607,"journal":{"name":"High-speed Railway","volume":"2 1","pages":"Pages 57-63"},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949867824000011/pdfft?md5=0e6dbc9b12b2460cef9f8a1d1ede1187&pid=1-s2.0-S2949867824000011-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140187274","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Research and application of key technologies for data delivery in railway engineering design based on metadata 研究和应用基于元数据的铁路工程设计数据传输关键技术

High-speed Railway Pub Date : 2024-03-01 DOI: 10.1016/j.hspr.2024.01.003

Xiangru Lyu, Xunxiao Yin, Kun Wang, Yongwen Wei

{"title":"Research and application of key technologies for data delivery in railway engineering design based on metadata","authors":"Xiangru Lyu, Xunxiao Yin, Kun Wang, Yongwen Wei","doi":"10.1016/j.hspr.2024.01.003","DOIUrl":"https://doi.org/10.1016/j.hspr.2024.01.003","url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":100607,"journal":{"name":"High-speed Railway","volume":"2 1","pages":"Pages 51-56"},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949867824000035/pdfft?md5=25842f68b6e509ff5ae4984c5e3fcc61&pid=1-s2.0-S2949867824000035-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140187273","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Key technologies for wireless network digital twin towards smart railways 面向智能铁路的无线网络数字孪生关键技术

High-speed Railway Pub Date : 2024-03-01 DOI: 10.1016/j.hspr.2024.01.004

Ke Guan , Xinghai Guo , Danping He , Philipp Svoboda , Marion Berbineau , Stephen Wang , Bo Ai , Zhangdui Zhong , Markus Rupp

{"title":"Key technologies for wireless network digital twin towards smart railways","authors":"Ke Guan , Xinghai Guo , Danping He , Philipp Svoboda , Marion Berbineau , Stephen Wang , Bo Ai , Zhangdui Zhong , Markus Rupp","doi":"10.1016/j.hspr.2024.01.004","DOIUrl":"10.1016/j.hspr.2024.01.004","url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":100607,"journal":{"name":"High-speed Railway","volume":"2 1","pages":"Pages 1-10"},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949867824000096/pdfft?md5=aebf8963273edd2adbf7f1b5fcb2353d&pid=1-s2.0-S2949867824000096-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139872146","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Key Technologies for Wireless Network Digital Twin Towards Smart Railways 面向智能铁路的无线网络数字孪生关键技术

High-speed Railway Pub Date : 2024-02-01 DOI: 10.1016/j.hspr.2024.01.004

K. Guan, Xinghai Guo, Danping He, P. Svoboda, Marion Berbineau, Bo Ai, Z. Zhong, Markus Rupp