轨道交通列车新一代健康管理系统架构研究

秦勇; 丁奥; 王彪; 刘汉; 徐磊; 蔡昌俊; 常振臣

doi:10.13890/j.issn.1000-128X.2024.01.001

您当前的位置：

首页 >

文章列表页 >

轨道交通列车新一代健康管理系统架构研究

综述 | 更新时间：2024-08-02

- 轨道交通列车新一代健康管理系统架构研究
- Research on the new generation framework of PHM systems for railway trains
- 机车电传动 2024年第1期页码：1-10
- 作者机构：
  
  1.北京交通大学先进轨道交通自主运行全国重点实验室，北京 100044
  2.中车青岛四方机车车辆股份有限公司，山东青岛 266111
  3.广州地铁集团有限公司，广东广州 510000
  4.中车长春轨道客车股份有限公司，吉林长春 130062
- 作者简介：
  
  秦　勇（1971—），男，博士，教授，博士生导师，主要从事轨道交通主动安全保障与运输组织、智能交通系统方面的研究； E-mail: yqin@bjtu.edu.cn
- 基金信息：
  
  国家重点研发计划(2022YFB4300601)
- DOI：10.13890/j.issn.1000-128X.2024.01.001
  中图分类号： U279
- 纸质出版日期：2024-01-10，
  
  收稿日期：2023-12-09，
  
  修回日期：2023-12-26，
- 稿件说明：
扫描看全文
秦勇, 丁奥, 王彪, 等. 轨道交通列车新一代健康管理系统架构研究[J]. 机车电传动, 2024(1): 1-10.

QIN Yong, DING Ao, WANG Biao, et al. Research on the new generation framework of PHM systems for railway trains[J]. Electric drive for locomotives,2024(1): 1-10.
秦勇, 丁奥, 王彪, 等. 轨道交通列车新一代健康管理系统架构研究[J]. 机车电传动, 2024(1): 1-10. DOI：10.13890/j.issn.1000-128X.2024.01.001.

QIN Yong, DING Ao, WANG Biao, et al. Research on the new generation framework of PHM systems for railway trains[J]. Electric drive for locomotives,2024(1): 1-10. DOI：10.13890/j.issn.1000-128X.2024.01.001.

摘要

科学维护运营车辆、保障列车运行安全一直以来都是轨道交通领域的核心问题。近年来，随着列车预测性维修和无人驾驶等重大需求的提出，迫切需要实现全息状态感知、精细诊断预测、及时反馈处置的列车健康管理功能。现有系统架构存在着感知低效、融合深度不足、模型优化动态差、计算协同弱、自主化决策水平低等问题，先进的物联网、大数据、人工智能、数字孪生等技术的发展，推动了列车健康管理系统架构向更高水平智能化演进。文章对列车健康管理系统技术发展阶段进行了梳理划分，在此基础上提出了基于泛在感知与协同计算的轨道交通列车健康管理系统4.0架构，详细阐述了其内涵概念、系统架构和关键技术，归纳出泛在感知、协同计算与健康管理深度融合的解决途径、技术手段和预期效果，明确了现阶段技术攻关的主要方向，进而支撑列车安全保障和运维品质的提升。

Abstract

Scientific maintenance of operating vehicles and ensuring the safety of train operations have always been the core issues in the field of rail transit. In recent years

with the major demands of predictive maintenance and unmanned driving

there is an urgent need to realize the PHM (prognostics and health management) functions of holographic state perception

elaborate diagnosis and prediction

and timely feedback and disposal for railway trains. The existing system frameworks suffer from perception inefficiency

insufficient fusion

poor model optimization dynamics

weak computational synergy

and low levels of autonomous decision-making

etc. The development of advanced IoT

big data

artificial intelligence

digital twins

and other technologies has pushed the PHM system frameworks of railway trains to evolve to a higher level of intelligence. In this paper

the technical development stage of PHM systems of railway trains is reviewed and categorized. Furthermore

the PHM 4.0 framework for railway systems

which leverages ubiquitous sensing and collaborative computing

has been introduced. This framework is described in detail

elucidating its foundational principles

system structure

and key technologies. The paper also outlines the approaches

techniques

and anticipated outcomes associated with the deep integration of ubiquitous sensing

collaborative computing

and health management. Additionally

it clarifies the primary focus of current technological research

supporting enhanced safety and maintenance quality in railway operations.

关键词

轨道交通列车健康管理4.0系统泛在感知协同计算智能运维无人驾驶

Keywords

rail transitprognostics and health management 4.0 systems for railway trainsubiquitous sensingcollaborative computingintelligent operation and maintenanceunmanned driving

references

国家统计局. 铁路行业年度数据[EB/OL]. (2022-12-31) [2023-11-18]. https://data.stats.gov.cn/search.htm?s=https://data.stats.gov.cn/search.htm?s=铁路.

National Bureau of Statistics. Annual data for the railway industry[EB/OL]. (2022-12-31) [2023-11-18]. https://data.stats.gov.cn/search.htm?s=https://data.stats.gov.cn/search.htm?s=

铁路.

顾晓辉, 杨绍普, 刘文朋, 等. 高速列车轴箱轴承健康监测与故障诊断研究综述[J]. 力学学报, 2022, 54(7): 1780-1796.

GU Xiaohui, YANG Shaopu, LIU Wenpeng, et al. Review of health monitoring and fault diagnosis of axle-box bearing of high-speed train[J]. Chinese journal of theoretical and applied mechanics, 2022, 54(7): 1780-1796.

刘国桐. 基于PHM技术的和谐型机车主变压器故障诊断研究[D]. 北京: 中国铁道科学研究院, 2022.

LIU Guotong. Research on fault diagnosis of main transformer of HX locomotive based on PHM[D]. Beijing: China Academy of Railway Sciences, 2022.

王军, 丁荣军. 中国高速列车健康监测与管理: 进展及展望[J]. 中国工程科学, 2023, 25(2): 232-242.

WANG Jun, DING Rongjun. Prognostics and health management of high-speed trains in China: progress and prospect[J]. Strategic study of CAE, 2023, 25(2): 232-242.

彭瑜. 我国发展无线技术工业应用的机遇和对策[J]. 中国仪器仪表, 2008(增刊1): 59-63.

PENG Yu. Development opportunity and countermove for Chinese industrial application of wireless technology[J]. China instrumentation, 2008(Suppl 1): 59-63.

ESSA I A. Ubiquitous sensing for smart and aware environments[J]. IEEE personal communications, 2000, 7(5): 47-49.

郭斌, 刘思聪, 刘琰, 等. 智能物联网: 概念、体系架构与关键技术[J]. 计算机学报, 2023, 46(11): 2259-2278.

GUO Bin, LIU Sicong, LIU Yan, et al. AIoT: the concept, architecture and key techniques[J]. Chinese journal of computers, 2023, 46(11): 2259-2278.

高晗, 田育龙, 许封元, 等. 深度学习模型压缩与加速综述[J]. 软件学报, 2021, 32(1): 68-92.

GAO Han, TIAN Yulong, XU Fengyuan, et al. Survey of deep learning model compression and acceleration[J]. Journal of software, 2021, 32(1): 68-92.

CHEN Hualong, WEN Yuanqiao, ZHU Man, et al. From automation system to autonomous system: an architecture perspective[J]. Journal of marine science and engineering, 2021, 9(6): 645.

李文斌, 熊亚锟, 范祉辰, 等. 持续学习的研究进展与趋势[J/OL]. 计算机研究与发展: 1-19. (2023-10-13) [2023-11-26]. https://link.cnki.net/urlid/11.1777.TP.20231012.1035.002https://link.cnki.net/urlid/11.1777.TP.20231012.1035.002

LI Wenbin, XIONG Yakun, FAN Zhichen, et al. Advances and trends of continual learning[J/OL]. Journal of computer research and development: 1-19. (2023-10-13) [2023-11-26]. https://link.cnki.net/urlid/11.1777.TP.20231012.1035.002https://link.cnki.net/urlid/11.1777.TP.20231012.1035.002.

YANG Qiang, LIU Yang, CHEN Tianjian, et al. Federated machine learning: concept and applications[J]. ACM transactions on intelligent systems and technology, 2019, 10(2): 12.

ZHANG Ying, XIANG Tao, HOSPEDALES T M, et al. Deep mutual learning[C]//IEEE. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City, UT：IEEE, 2018: 4320-4328.

VINYALS O, BABUSCHKIN I, CZARNECKI W M, et al. Grandmaster level in StarCraft II using multi-agent reinforcement learning[J]. Nature, 2019, 575(7782): 350-354.

肖玲. 动车组实时监测图像中的异常目标检测方法研究[D]. 武汉: 华中科技大学, 2020.

XIAO Ling. Research on abnormal target detection method in real-time surveillance images of electric multiple unit[D]. Wuhan: Huazhong University of Science and Technology, 2020.

ZHAO Liang, ZHAO Weiliang, HAWBANI A, et al. Novel online sequential learning-based adaptive routing for edge software-defined vehicular networks[J]. IEEE transactions on wireless communications, 2021, 20(5): 2991-3004.

CHIO C, FREEMAN D. Machine learning and security: protecting systems with data and algorithms[M]. Sebastopol, CA: Oreilly & Associates Inc, 2018.

XIAO Liang, WAN Xiaoyue, LU Xiaozhen, et al. IoT security techniques based on machine learning: how do IoT devices use AI to enhance security?[J]. IEEE signal processing magazine, 2018, 35(5): 41-49.

WANG Zhixia, WANG Wei, TANG Lihua, et al. A piezoelectric energy harvester for freight train condition monitoring system with the hybrid nonlinear mechanism[J]. Mechanical systems and signal processing, 2022, 180: 109403.

FANG Zheng, TAN Xing, LIU Genshuo, et al. A novel vibration energy harvesting system integrated with an inertial pendulum for zero-energy sensor applications in freight trains[J]. Applied energy, 2022, 318: 119197.

LIU Mengzhou, ZHANG Yuan, FU Hailing, et al. A seesaw-inspired bistable energy harvester with adjustable potential wells for self-powered internet of train monitoring[J]. Applied energy, 2023, 337: 120908.

DING Ao, QIN Yong, WANG Biao, et al. Lightweight multiscale convolutional networks with adaptive pruning for intelligent fault diagnosis of train bogie bearings in edge computing scenarios[J]. IEEE transactions on instrumentation and measurement, 2023, 72: 1-13.

CHEN Bojian, SHEN Changqing, WANG Dong, et al. A lifelong learning method for gearbox diagnosis with incremental fault types[J]. IEEE transactions on instrumentation and measurement, 2022, 71: 1-10.

LIU Siyuan, HUANG Jinying, MA Jiancheng, et al. Class-incremental continual learning model for plunger pump faults based on weight space meta-representation[J]. Mechanical systems and signal processing, 2023, 196: 110369.

DING Ao, QIN Yong, WANG Biao, et al. An elastic expandable fault diagnosis method of three-phase motors using continual learning for class-added sample accumulations[J/OL]. IEEE transactions on industrial electronics. (2023-08-15) [2023-11-26]. https://ieeexplore.ieee.org/abstract/document/10219046https://ieeexplore.ieee.org/abstract/document/10219046.

浏览量

下载量

CSCD

CNKI被引量

文章被引用时，请邮件提醒。

提交

工具集

关联资源

轨道交通车载智能化应用技术发展展望

轨道交通车载端到端语音合成

轨道交通与氢能融合技术发展现状与对策建议

氢能轨道交通的研究现状与发展趋势

轨道交通多堆燃料电池系统架构及控制技术研究