中国高速列车健康监测与管理:进展及展望

王军, 丁荣军

中国工程科学 ›› 2023, Vol. 25 ›› Issue (2) : 232-242.

PDF(1461 KB)
PDF(1461 KB)
中国工程科学 ›› 2023, Vol. 25 ›› Issue (2) : 232-242. DOI: 10.15302/J-SSCAE-2023.02.019
工程前沿
Orginal Article

中国高速列车健康监测与管理:进展及展望

作者信息 +

Prognostics and Health Management of High-Speed Trains in China: Progress and Prospect

Author information +
History +

摘要

随着列车运营速度不断提升、配属规模及车辆种类不断扩展,加之受长交路、多物理场耦合等复杂服役环境的影响,高速列车安全保障及经济运维的要求持续提高;高速列车健康监测与管理技术的研究与应用,为中国高速铁路的长距离、大规模、高密度运营提供了关键支撑。本文阐述了健康监测与管理对高速列车的重要价值,回顾了近20年中国高速列车健康监测与管理的发展历程:从安全监控到关键系统健康监测,再到一体化、全寿命周期的运维管理;总结了列车全方位状态监测、精准评估与诊断预测、车辆远程运维服务、智能运维决策支持等方面的重大技术突破。进一步展望了广域全过程适应性、列车数据 / 计算资源一体化管理与应用、基于健康监测与管理的列车设计、车 – 线 – 站一体化智能运维等未来发展方向,以期应对中国高速铁路面临的高效安全运维、深度降本降耗等发展挑战,推动中国高速列车技术持续领先。

Abstract

Higher requirements have been imposed for the safety and economical operation and maintenance (O&M) of high-speed trains as a result of increasing operating speed, number of train sets in service, and vehicle types as well as complex service conditions such as long routing and multi-physics coupling. The research and application of prognostics and health management (PHM) technology in the field of high-speed trains provides important technical support for the steady operation of China's high-speed trains over long distance, on large scale, and in high density. This study presents the significance of PHM for high-speed trains and reviews the development process of high-speed train PHM in China, which has evolved from the initial safety monitoring to breakthroughs in health monitoring technologies of key systems and then the current integrated lifecycle O&M management in the past two decades. It further summarizes the major technical breakthroughs in four aspects, namely, comprehensive train condition monitoring, accurate assessment and diagnosis prediction, vehicle remote O&M services, and intelligent O&M decision support. In the face of the future challenges of efficient and safe O&M, substantial cost reduction, and consumption reduction of China's high-speed trains, suggestions are proposed in the following aspects: wide-area and entire-process adaptability, centralized management and application of train data and computing resources, train design based on PHM, and train–line–station integrated intelligent O&M, so as to promote China's high-speed train technology to maintain a lead.

关键词

高速列车 / 健康监测与管理 / 故障诊断预测 / 智能运维 / 车 – 线 – 站一体化

Keywords

high-speed train / prognostics and health management / fault diagnosis and prediction / intelligent operation and management / train–line–station integration

引用本文

导出引用
王军, 丁荣军. 中国高速列车健康监测与管理:进展及展望. 中国工程科学. 2023, 25(2): 232-242 https://doi.org/10.15302/J-SSCAE-2023.02.019

参考文献

[1]
Ezhilarasu C M, Skaf Z, K‍ Jennions I. The application of reasoning to aerospace integrated vehicle health management(IVHM): Challenges and opportunities [J]‍. Progress in Aerospace Sciences, 2019; 105(2): 60‒73‍.
[2]
裴大茗, 王建峰, 周鹏太, 等‍‍. 船舶PHM技术综述 [J]‍. 电子测量与仪器学报, 2016, 30(9): 1289‒1297‍.
Pei D M, Wang J F, Zhou P T, al e t‍. Survey on PHM technology in marine system [J]‍. Journal of Electronic Measurement and Instrumentation, 2016, 30(9): 1289‒1297‍.
[3]
王军‍. 面向PHM的高速列车谱系化产品技术平台开发和实践 [J]‍. 中国铁道科学, 2021, 42(1): 80‒86‍.
Wang J‍. Development and practice of PHM oriented high-speed train pedigree product technology platform [J]‍. China Railway Science, 2021;42(1): 80‒86‍.
[4]
黄学文, 刘春明, 冯璨, 等‍. CRH3高速动车组故障诊断系统 [J]‍. 计算机集成制造系统, 2010, 16(10): 2311‒2318‍.
Huang X W, Liu C M, Feng C, al e t‍. Failure diagnosis system for CRH3 electrical multiple unit [J]‍. Computer Integrated Manufacturing Systems, 2010, 16(10): 2311‒2318‍.
[5]
王后闯, 曾陆洋, 郝国梁, 等‍. 铁路客车故障预测与健康管理(PHM)系统 [J]‍. 铁道机车车辆, 2022, 42(2): 94‒98‍.
Wang H C, Zeng L Y, Hao G L, al e t‍. Prognostics health management system and malfunction prediction for railway passenger cars [J]‍. Railway Locomotive & Car, 2022, 42(2): 94‒98‍.
[6]
刘彬, 邵军, 陆航, 等‍. 动车组故障预测与健康管理(PHM)体系架构研究思考 [J]‍. 中国铁路, 2022 (3): 1‒9‍.
Liu B, Shao J, Lu H, al e t‍. Research on prognostics and health management(PHM) architecture for EMUs [J]‍. China Railway, 2022 (3): 1‒9‍.
[7]
王军, 马云双‍. 中国高速动车组发展模式探索与实践 [M]‍. 北京: 中国铁道出版社, 2020‍.
Wang J, Ma Y S‍. Exploration and practice of China's high-speed EMU development model [M]‍. Beijing: China Railway Publishing House, 2020‍.
[8]
申瑞源‍. 机车车载安全防护系统(6A系统)总体方案研究 [J]‍. 中国铁路, 2012 (12): 1‒6‍.
Shen R Y‍. Research on overall plan of on-board safety protection system(6A system) for locomotive [J]‍. China Railway, 2012 (12): 1‒6‍.
[9]
刘峰, 申宇燕, 张瑞芳, 等‍. 机车车载安全防护系统应用研究 [J]‍. 铁路技术创新, 2015 (2): 17‒21‍.
Liu F, Shen Y Y, Zhang R F, al e t‍. Application research of locomotive on-board safety protection system [J]‍. Railway Technical Innovation, 2015 ( 2): 17‒21‍.
[10]
张志波, 张振先, 冯永华, 等‍. 高速动车组转向架综合智能检测技术研究 [J]‍. 铁道车辆, 2021, 59(6): 40‒44‍.
Zhang Z B, Zhang Z X, Feng Y H, al e t‍. Research on the comprehensive intelligent detection technology of high speed EMU bogies [J]‍. Rolling Stock, 2021, 59(6): 40‒44‍.
[11]
高速铁路供电安全检测监测系统( 6C系统)总体技术规范 [EB/OL]‍. (2022-04-06)[2023-02-15]‍.
General technical specification for high-speed railway power supply safety detection and monitoring system( 6C system) [EB/OL]‍. (2022-04-06)[2023-02-15]‍.
[12]
彭文静‍. 车载远程数据传输设备在高速动车组上的应用 [J]‍. 铁道车辆, 2013, 51(10): 29‒30‍.
Peng W J‍. Application of on-board remote data transmission equipment on high-speed EMUs [J]‍. Rolling Stock, 2013, 51(10): 29‒30‍.
[13]
朱彦, 尹振坤, 张国芹, 等‍. 复兴号动车组智能技术创新应用及展望 [J]‍. 城市轨道交通研究, 2022, 25(2): 1‒4‍.
Zhu Y, Yin Z K, Zhang G Q, al e t‍. Innovative application and prospect of Fuxing EMU intelligent technology [J]‍. Urban Mass Transit, 2022, 25(2): 1‒4‍.
[14]
王同军‍. 智能铁路总体架构与发展展望 [J]‍. 铁路计算机应用, 2018, 27(7): 1‒8‍.
Wang T J‍. Overall framework and development prospect of intelligent railway [J]‍. Railway Computer Application, 2018, 27(7): 1‒8‍.
[15]
王同军‍. 中国铁路大数据应用顶层设计研究与实践 [J]‍. 中国铁路, 2017 (1): 8‒16‍.
Wang T J‍. On top-level design for China railway's big data application & case study [J]‍. China Railway, 2017 (1): 8‒16‍.
[16]
常振臣, 逯骁, 张海峰‍. 轨道交通车辆大数据管理平台建设与实施 [J]‍. 城市轨道交通研究, 2019, 22(2): 1‒4‍.
Chang Z C, Lu X, Zhang H F‍. Construction and implementation of big data management platform for rail transit vehicle industry [J]‍. Urban Mass Transit, 2019, 22(2): 1‒4‍.
[17]
秦勇, 马慧, 贾利民‍. 先进轨道交通系统发展趋势与主动安全保障技术 [J]‍. 中国铁路, 2015 (12): 77‒81‍.
Qin Y, Ma H, Jia L M‍. Development trend of advanced rail transit system and active safety guarantee technology [J]‍. China Railway, 2015 (12): 77‒81‍.
[18]
Milojevic A, Tomic M, T‍ Pavlović N. Application of FBG sensors in smart railway [C]‍. Niš: XV International Scientific-expert Conference on Railways, 2012‍.
[19]
Ju Z Y, Wang Z Z, Ma L‍. The damage diagnostic techniques and experimental research of high-speed EMU aluminum car-body bolster based on lamb guided waves [EB/OL]‍. (2018-01-15)[2023-02-15]‍.
[20]
Cerrone C, Cerulli C, Golden B‍. Carousel greedy: A generalized greedy algorithm with applications in optimization [J]‍. Computers & Operations Research, 2017, 85(9): 97‒112‍.
[21]
Ren M Y, Triantafillou E, Ravi S, al e t‍. Meta-learning for semi-supervised few-shot classification [EB/OL]‍. (2018-03-02)‍[2023-02-15]‍.
[22]
Zhou Z H‍. A brief introduction to weakly supervised learning [J]‍. National Science Review, 2018, 5(1): 44‒53‍.
[23]
Zhang Z, Saligrama V‍. Zero-shot learning semantic similarity embedding [C]‍. Washington DC: 2015 Proceedings of the IEEE International Conference on Computer Vision, 2015‍.
[24]
Ren S Q, He K M, Girshick R, al e t‍. Faster R-CNN: Towards real-time object detection with region proposal networks [J]‍. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2017, 39(6): 1137‒1149‍.
[25]
彭云聪, 秦小林, 张力戈, 等‍. 面向图像分类的小样本学习算法综述 [J]‍. 计算机科学, 2022, 49(5): 1‒9‍.
Peng Y C, Qin X L, Zhang L G, al e t‍. Survey on few-shot learning algorithms for image classification [J]‍. Computer Science, 2022, 49 (5): 1‒9‍.
[26]
van Dyk D A, Meng X L‍. The art of data augmentation [J]‍. Journal of Computational and Graphical Statistics, 2001, 10(1): 1‒50‍.
[27]
Song C, Zhao J J, Wang K, al e t‍. A survey of few shot learning based on intelligent perception [J]‍. Acta Aeronautica et Astronautica Sinica, 2020, 41 (S1): 723756‍.
[28]
Zhao G, Gao H D, Zhang G F, al e t‍. Active health monitoring of an aircraft wing with embedded piezoelectric sensor/actuator network: I‍. Defect detection, localization and growth monitoring [J]‍. Smart Materials and Structures, 2007, 16(4): 1208‒1217‍.
[29]
郑跃滨, 武湛君, 雷振坤, 等‍. 基于超声导波的航空航天结构损伤诊断成像技术研究进展 [J]‍. 航空制造技术, 2020, 63(18): 24‒43‍.
Zheng Y B, Wu Z J, Lei Z K, al e t‍. Research progress in damage diagnostic imaging of aerospace structures based on ultrasonic guided waves [J]‍. Aeronautical Manufacturing Technology, 2020, 63(18): 24‒43‍.
[30]
Li Z H, Cheng Y Y, Zhang L, al e t‍. Strain field reconstruction of high-speed train crossbeam based on FBG sensing network and load-strain linear superposition algorithm [J]‍. IEEE Sensors Journal, 2022, 22(4): 3228‒3235‍.
[31]
Cheng Y Y, Li Z H, Wang G J, al e t‍. Strain field reconstruction of crossbeam structure based on load-strain linear superposition method [J]‍. Smart Materials & Structures, 2021, 30(7): 1‒12‍.
[32]
Feng Z P, Zuo M J, Hao R J, al e t‍. Ensemble empirical mode decomposition-based teager energy spectrum for bearig fault diagnosis [J]‍. Journal of Vibration and Acoustics, 2013, 135(3): 031013‍.
[33]
于萍, 金炜东, 秦娜‍. 基于EEMD 降噪和流形学习的高速列车走行部故障特征提取 [J]‍. 铁道学报, 2016, 38(4): 16‒21‍.
Yu P, Jin W D, Qin N‍. High-speed train running gear fault feature extraction based on EEMD denoising and manifold learning [J]‍. Journal of the China Railway Society, 2016, 38(4): 16‒21‍.
[34]
贺德强, 陈二恒, 李笑梅, 等‍. 基于RS-LSSVM 的高速列车走行部滚动轴承故障诊断研究 [J]‍. 广西大学学报(自然科学版), 2017, 42(2): 403‒408‍.
He D Q, Chen E H, Li X M, al e t‍. Research on fault diagnosis method of high-speed train running gear rolling bearing based on RS and LSSVM [J]‍. Journal of Guangxi University(Natural Science Edition), 2017, 42(2): 403‒408‍.
[35]
梁建英, 邓学寿, 刘韶庆, 等‍. 基于大数据技术的动车组数字化智能运维平台 [R]‍. 青岛: 中车青岛四方机车车辆股份有限公司, 2020‍.
Liang J Y, Deng X S, Liu S Q, al e t‍. Digital intelligent EMU O&M platform based on big data technology [R]‍. Qingdao: CRRC Qingdao Sifang Co‍., Ltd‍., 2020‍.
基金
国家重点研发计划项目(2021YFB3203205)
PDF(1461 KB)

Accesses

Citation

Detail

段落导航
相关文章

/