|Table of Contents|

Review on frontier technical issues of intelligent railways under Industry 4.0(PDF)


Research Field:
Publishing date:


Review on frontier technical issues of intelligent railways under Industry 4.0
MIAO Bing-rong ZHANG Wei-hua LIU Jian-xin ZHOU Ning MEI Gui-ming ZHANG Ying
(State Key Laboratory of Traction Power, Southwest Jiaotong University, Chengdu 610031, Sichuan, China)
intelligent railway smart train Industry 4.0 artificial intelligence internet of things big data
The importance and necessity of the rational use of the connotative elements of Industry 4.0 for the digital construction, transformation, and upgrading of the next generation intelligent railways of China were explained. To this end, railway infrastructures and vehicles were considered as research objects, and frontier technologies and methods pertaining to intelligent manufacturing were combined. Based on the impacts of basic concept, technical connotation, system model, and technical framework of Industry 4.0, the implementation processes and existing problems of intelligent infrastructure, smart train, intelligent operation and maintenance, and related technologies were compared and analyzed. In addition, the key technologies for the digital platform construction of intelligent railways focusing on smart trains were analyzed. The specific technical requirements for the digital construction corresponding to traditional manufacturing to intelligent manufacturing were summarized. Problems pertaining to the integration of frontier technologies, such as artificial intelligence, big data, cloud computing, and digital twins, with the traditional railway manufacturing, were compiled and solved using a six-dimensional model of Industry 4.0. These problems included the data transmission and sharing, exploration of the potential of information communication and security technology, and intelligent management, technology application, information security, and intelligent state awareness. Research result demonstrates that the integration of digital information technology and intelligent technology with the traditional manufacturing process is insufficient. The core know-how of intelligent manufacturing is inadequate. A lack of autonomy of software and hardware technologies, such as intelligent state awareness, online data analysis, and industrial control systems, is observed. The data transmission and standard system for the construction of big data for the railway system is not perfect. The digital design, upgrade, and transformation of the standardized management system and data information security system of railway traditional manufacturing in Industry 4.0 should be strengthened in future intelligent railways. Deep thinking and analysis of the integration and implementation of frontier technologies including artificial intelligence and big data drive in railways are required. Various key technologies covered in Industry 4.0 should be implemented and accurately evaluated to truly and effectively promote the construction and development of an advanced digital platform for intelligent railways of China. 1 tab, 13 figs, 69 refs.


[1] GAO Liang, SHEN Wei-ming, LI Xin-yu. New trends in
intelligent manufacturing[J]. Engineering, 2019, 5(4): 619-620.
[2] QU Y J, MING X G, LIU ZW, et al. Smart manufacturing systems: state of the art and future trends[J]. The International Journal of Advanced Manufacturing Technology, 2019, 103(9/10/11/12): 3751-3768.
[3] 李培根.浅说智能制造[J].科技导报,2019,37(08):1.
LI Pei-gen. Talking about intelligent manufacturing[J]. Science and Technology Review, 2019, 37(8): 1.(in Chinese).
[4] ROBLEK V, MESKO M, KRAPEZ A. A complex view of Industry 4.0[J]. SAGE Open, 2016, 6(2): 2158244016653987.
[5] ZHONG R Y, XU Xun, KLOTZ E, et al. Intelligent
manufacturing in the context of Industry 4.0: a review[J]. Engineering, 2017, 3(5): 616-630.
[6] QI Qing-lin, TAO Fei. Digital twin and big data towards smart manufacturing and Industry 4.0: 360 degree comparison[J]. IEEE Access, 2018, 6: 3585-3593.
[7] XU Li-da, XUE L, Li Ling. Industry 4.0: state of the art and future trends[J]. International Journal of Production Research, 2018, 56(8): 2941-2962.
[8] ROJKO A. Industry 4.0 concept: background and overview[J]. International Journal of Interactive Mobile Technologies, 2017, 11(5): 77-90.
[9] DELGADO TELLO E G. Industry 4.0 application of
advanced services in logistics[R]. Barcelona:Polytechnic University of Catalonia, 2018.
[10] SCHUMACHER A, EROL S, SIHN W. A maturity model for assessing Industry 4.0 readiness and maturity of manufacturing enterprises[J]. Procedia Cirp, 2016, 52(1): 161-166.
[11] FARSI M A, ZIO E. Industry 4.0: some challenges and
opportunities for reliability engineering[J]. International Journal of Reliability Risk Safety: Theory and Application, 2019, 2(1): 23-34.
[12] TAKAKUWA S, VEZA I, CELAR S. “Industry 4.0” in
Europe and East Asia[C]∥KATALINIC B. Proceedings of the 29th DAAAM International Symposium. Vienna: DAAAM International, 2018: 61-69.
[13] HOZDIC E. Smart factory for Industry 4.0: a review[J]. International Journal of Modern Manufacturing Technologies, 2015, 7(1): 28-35.
[14] PRAUSE M. Challenges of Industry 4.0 technology adoption for SMEs: the case of Japan[J]. Sustainability, 2019, 11(20): 5807.
[15] 德勤洞察.数字孪生:连结现实与数字世界[J].软件和集成电路,2020,5:78-85.
DELOITTE Insight. Digital twin: linking reality and the digital world[J]. Software and Integrated Circuit, 2020, 5: 78-85.(in Chinese)
[16] THOBEN K D, WIESNER S, WUEST T. “Industrie 4.0” and smart manufacturing—a review of research issues and application examples[J]. International Journal of Automation Technology, 2017, 11(1): 4-16.
[17] WAGIRE A A, JOSHI R, RATHORE A P S, et al. Development of maturity model for assessing the implementation of Industry 4.0: learning from theory and practice[J]. Production Planning and Control, 2020: 1-20.
[18] 《铁道技术监督》编辑部.新时代交通强国铁路先行规划纲要[J].铁道技术监督,2020,48(9):1-6,24.
Editorial Office of Railway Quality Control. Outline of powerful nation railway advance planning in the new era[J]. Railway Quality Control, 2020, 48(9): 1-6, 24.(in Chinese)
[19] 胡鞍钢.中国进入后工业化时代[J].北京交通大学学报(社会科学版),2017,16(1):1-16..
HU An-gang. China entering post-industrial era[J]. Journal of Beijing Jiaotong University(Social Sciences Edition), 2017, 16(1): 1-16.(in Chinese).
[20] 苗 圩.中国制造2025与德国工业4.0异曲同工[J].装备制造,2015(6):22.
MIAO Wei. Made in China 2025 is similar to German Industry 4.0[J].Equipment Manufacturing, 2015(6):22.(in Chinese).
[21] PICCAROZZI M, AQUILANI B, GATTI C. Industry 4.0 in management studies: a systematic literature review[J]. Sustainability, 2018, 10(10): 3821.
[22] JOSEY J. Intelligent infrastructure for next- generation rail system[J]. Cognizant 2020 insights, 2013: 1-8.
[23] FRAGA-LAMAS P, FERNANDEZ-CARAMES T M, CASTEDO L. Towards the internet of smart trains: a review on industrial IoT connected railways[J]. Sensors, 2017, 17(6): 1457.
[24] 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 Tong-jun. Overall framework and development prospect of intelligent railway[J]. Railway Computer Application, 2018, 27(7): 1-8.(in Chinese)
[25] LIN Shao-fu, JIA Ya-fang, XIA Si-bin. Research and
analysis on the top design of smart railway[J]. Journal of Physics: Conference Series, 2019, 1187(5): 052053.
[26] GRGUREVIC I, ROZIC T. Next generation transport
industry innovations[R]. Opatija: Transport and Traffic Sciences University of Zagreb, 2019.
[27] BIN Sheng, SUN Geng-xin. Optimal energy resources allocation method of wireless sensor networks for intelligent railway systems[J]. Sensors, 2020, 20(2): 482.
[28] ALAWAD H, KAEWUNRUEN S. Wireless sensor networks: toward smarter railway stations[J]. Infrastructures, 2018, 3(3): 24.
[29] 张卫华,缪炳荣,王婷婷,等.下一代高速列车发展战略研究[R].成都:西南交通大学,2017.
ZHANG Wei-hua, MIAO Bing-rong, WANG Ting-ting, et al. Research on development strategy of next generation high speed train[R]. Chengdu: Southwest Jiaotong University, 2017.(in Chinese)
[30] 张 锦,徐君翔,郭静妮,等.智能川藏铁路系统总体架构设计与研究[J].综合运输,2020,42(1):100-107.
ZHANG Jin, XU Jun-xiang, GUO Jing-ni, et al. Design and research on overall architecture of intelligent Sichuan-Tibet Railway System[J]. China Transportation Review, 2020, 42(1): 100-107.(in Chinese)
[31] LU Chun-fang, CAI Chao-xun. Challenges and countermeasures
for construction safety during the Sichuan-Tibet Railway Project[J]. Engineering. 2019, 5(5): 833-838.
[32] 王 峰.我国高速铁路智能建造技术发展实践与展望[J]. 中国铁路,2019(4):1-8.
WANG Feng. Development of China's intelligent HSR building technology and its future[J]. China Railway, 2019(4): 1-8.(in Chinese)
[33] 康学东.我国铁路智能建设与运营管理初探[J].铁道工程学报,2019,36(4):84-89.
KANG Xue-dong. Preliminary exploration on the intelligent construction and operation of china's high-speed railway[J]. Journal of Railway Engineering Society, 2019, 36(4): 84-89.(in Chinese).
[34] 史天运.中国高速铁路信息化现状及智能化发展[J].科技导报,2019,37(6):53-59.
SHI Tian-yun. Present situation of wide applications of information and intelligence in the field of high-speed railway in China[J]. Science and Technology Review, 2019, 37(6): 53-59.(in Chinese)
[35] 王可飞,郝 蕊,卢文龙,等.智能建造技术在铁路工程建设中的研究与应用[J].中国铁路,2019(11):45-50.
WANG Ke-fei, HAO Rui, LU Wen-long, et al. Intelligent construction technology and its application in railway engineering construction[J]. China Railway, 2019(11): 45-50.(in Chinese)
[36] 朱 庆,朱 军,黄华平,等.实景三维空间信息平台与数字孪生川藏铁路[J].高速铁路技术,2020,11(2):46-53.
ZHU Qing, ZHU Jun,HUANG Hua-ping, et al. Real 3D spatial information platform and digital twin Sichuan-Tibet Railway[J]. High speed Railway Technology, 2020, 11(2):46-53.
[37] 王洪雨.智能京张高速铁路总体创新设计[J].铁道标准设计,
WANG Hong-yu. The overall innovative design of the intelligent high-speed railway from Beijing to Zhangjiakou[J]. Railway Standard Design, 2020, 64(1): 7-11.(in Chinese)
[38] STAJANO F, HOULT N, WASSELL I, et al. Smart bridges, smart tunnels: transforming wireless sensor networks from research prototypes into robust engineering infrastructure[J]. Ad Hoc Networks, 2010, 8(8): 872-888.
Home-end and activity-end preferences for access to and egress from train stations in the Copenhagen Region[J]. International Journal of Sustainable Transportation, 2017, 11(10): 776-786.
[40] 缪炳荣,张卫华,邓永权,等.新一代中国高速铁路动车组面临的技术挑战与策略研究[J].中国工程科学,2015,17(4):98-112.
MIAO Bing-rong, ZHANG Wei-hua, DENG Yong-quan, et al. Technology challenges and strategies of the new generation Chinese high-speed railway EMU[J]. Engineering Science, 2015, 17(4): 98-112.(in Chinese).
[41] 张卫华,缪炳荣.下一代高速列车关键技术的发展趋势与展望[J].机车电传动,2018(1):1-5,12.
ZHANG Wei-hua, MIAO Bing-rong. Development trend and prospect of key technologies for next generation high speed trains[J]. Electric Drive for Locomotives, 2018(1): 1-5, 12.(in Chinese)
[42] 缪炳荣,张卫华,池茂儒,等.下一代高速列车关键技术特征分析及展望[J].铁道学报,2019,41(3):58-70.
MIAO Bing-rong, ZHANG Wei-hua, CHI Mao-ru, et al. Analysis and prospects key technical features of next generation high speed trains[J]. Journal of the China Railway Society, 2019, 41(3): 58-70.(in Chinese)
[43] 梁建英.开启智能化轨道交通装备新时代[J].科学,2020,73(2):17-22.
LIANG Jian-ying. Start a new era of intelligent rail transit equipment[J]. Science, 2020, 73(2): 17-22.(in Chinese)
[44] ZHAO Hong-wei, LIANG Jian-ying, LIU Chang-qing. High-
speed EMUs: characteristics of technological development and trend[J]. Engineering, 2020, 6(3): 234-244.
[45] HODGE V J, O'KEEFE S, WEEKS M, et al. Wireless
sensor networks for condition monitoring in the railway industry: a survey[J]. IEEE Transactions on Intelligent Transportation Systems, 2015, 16(3): 1088-1106.
[46] TAKIKAWA M. Innovation in railway maintenance utilizing information and communication technology(smart maintenance initiative)[J]. Japan Railway and Transport Review,2016(67): 22-35.
[47] 梁建英.高速列车智能诊断与故障预测技术研究[J].北京交通大学学报,2019,43(1):63-70.
LIANG Jian-ying. Research on intelligent diagnosis and fault prediction technology for high speed trains[J]. Journal of Beijing Jiaotong University, 2019, 43(1): 63-70.(in Chinese)
[48] GHOFRANI F, HE Q, GOVERDE R M P, et al. Recent
applications of big data analytics in railway transportation systems: a survey[J]. Transportation Research Part C—Emerging Technologies, 2018, 90: 226-246.
[49] GALAR D, KARIM R, KUMAR U. Big data in railway
operations and maintenance[EB/OL].(2020-07-07)[2020-09-07]. https:∥www. globalrailwayreview.com/article/61515/big-data-railway-operations-maintenance-2/.
[50] ZHU Li, YU F R, WANG Yi-ge, et al. Big data analytics in intelligent transportation systems: a survey[J]. IEEE Transactions on Intelligent Transportation Systems, 2018, 20(1): 383-398.
[51] HSU H H, CHANG Chuan-yu, HSU C H. Big Data Analytics for Sensor-network Collected Intelligence[M]. London: Academic Press, 2017.
[52] GUYON I, AMINE R, TAMAYO S, et al. Analysis of the opportunities of industry 4.0 in the aeronautical sector[C]∥IMCIC. 10th International Multi-Conference on Complexity, Informatics and Cybernetics. Orlando: IMCIC 2019: 02063948.
[53] JANUSOVA L, CICMANCOVA S. Improving safety of
transportation by using intelligent transport systems[J]. Procedia Engineering, 2016, 134: 14-22.
[54] FUMEO E, ONETO L, ANGUITA D. Condition based
maintenance in railway transportation systems based on big data streaming analysis[J]. Procedia Computer Science, 2015, 53: 437-446.
support for maintenance tasks by the use of augmented Reality: the ManuVAR project[C]∥VTT. CARVI 2011: IX Congress on Virtual Reality Applications. Alava: VTT, 2011: 1-6.
[56] HALL N, LOWE C, HIRSCH R. Human factors considerations for the application of augmented reality in an operational railway environment[J]. Procedia Manufacturing, 2015, 3: 799-806.
[57] POTTER K. Augmented reality becoming a focus in maintenance technology[EB/OL].(2020-08-08)[2020-09-07]. https:∥www.geospatialworld.net/blogs/augmented-reality-becoming-a-focus-in-maintenance-technology/.
[58] DIDIER J Y, ROUSSEL D, MALLEM M, et al. AMRA: augmented reality assistance for train maintenance tasks[C]∥ISMAR. 4th ACM/IEEE International Symposium on Mixed and Augmented Reality. Vienna: ISMAR, 2005: 00339457
[59] MARR B. 5 important augmented and virtual reality trends for 2019 everyone should read[DB/OL].(2020-08-08)[2020-09-07]. https:∥www.forbes.com/sites/bernardmarr/2019/01/14/5-important-augmented-and-virtual-reality-trends-for-2019-everyone-should-read/#682a027222e7.
[60] GHOBAKHLOO M. Determinants of information and digital technology implementation for smart manufacturing[J]. International Journal of Production Research, 2020, 58(8): 2384-2405.
[61] NIKOLAKIS N, ALEXOPOULOS K, XANTHAKIS E, et al. The digital twin implementation for linking the virtual representation of human-based production tasks to their physical counterpart in the factory-floor[J]. International Journal of Computer Integrated Manufacturing, 2019, 32(1): 1-12.
[62] NICHOLSON G. Digital twins and the railway: one framework many implementations[EB/OL].(2020-08-11)[2020-09-07]. https:∥www.rssb.co.uk/Insights-and-News/Blogs/Digital-twin-and-the-railway-one-framework-many-implementations.
[63] MIKELL M. Immersive analytics: the reality of IoT and
digital twin[EB/OL].(2020-08-11)[2020-09-07]. https:∥www.ibm.com/blogs/internet-of-things/immersive-analytics-digital-twin/.
[64] THADURI A, GALAR D, KUMAR U. Railway assets: a potential domain for big data analytics[J]. Procedia Computer Science, 2015, 53: 457-467.
[65] ZHANG Da-lin. High-speed train control system big data analysis based on the fuzzy RDF model and uncertain reasoning[J]. International Journal of Computers Communications and Control, 2017, 12(4): 577-591.
[66] JAMSHIDI A, HAJIZADEH S, SU Z, et al. A decision
support approach for condition-based maintenance of rails based on big data analysis[J]. Transportation Research Part C: Emerging Technologies, 2018, 95: 185-206.
[67] FINK O, WANG Q, SVENSÉN M, et al. Potential, challenges and future directions for deep learning in prognostics and health management applications[J]. Engineering Applications of Artificial Intelligence, 2020, 92: 103678.
[68] JAMSHIDI A, FAGHIH-ROOHI S, HAJIZADEH S, et al. A big data analysis approach for rail failure risk assessment[J]. Risk Analysis, 2017, 37(8): 1495-1507.
[69] NUNEZ A, HENDRIKS J, LI Z, et al. Facilitating
maintenance decisions on the Dutch railways using big data: the ABA case study[C]∥IEEE. 2014 IEEE International Conference on Big Data. New York: IEEE, 2014: 48-53.


Last Update: 2021-03-20