Research on the new generation framework of PHM systems for railway trains

QIN Yong; DING Ao; WANG Biao; LIU Han; XU Lei; CAI Changjun; CHANG Zhenchen

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

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Research on the new generation framework of PHM systems for railway trains

General | 更新时间：2024-08-02

- Research on the new generation framework of PHM systems for railway trains
- Electric Drive for Locomotives Issue 1, Pages: 1-10(2024)
- 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：
- 基金信息：
- DOI：10.13890/j.issn.1000-128X.2024.01.001
  CLC： U279
- Published：10 January 2024，
  
  Received：09 December 2023，
  
  Revised：26 December 2023，
- 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

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