-
摘要: 以铁路基础设施和车辆为主要研究对象，结合智能制造涉及的前沿技术和方法，阐述了合理利用工业4.0的内涵要素进行中国下一代智能铁路数字化建设、改造与升级的重要性和必要性；按照工业4.0的基本概念、技术内涵、系统模型和技术框架的影响效果，对比分析了智能基础设施、智慧列车、智能运维及相关技术的实施过程和存在问题，并在此基础上分析了以智慧列车为核心的智能铁路数字化平台建设关键技术；概括了铁路传统制造向智能制造数字化建设的具体技术要求，整理了利用工业4.0六维模型解决人工智能、大数据、云计算和数字孪生等前沿技术与铁路传统制造业的融合问题，包括数据传输与共享、信息通信与安全技术的潜力挖掘、智能管理、技术应用、信息安全、状态智能感知等各个方面。研究结果表明：中国铁路数字信息技术和智能技术与传统制造过程存在融合不足的问题；智能制造的核心技术储备不足，状态智能感知、数据在线分析、工业控制系统等软硬件技术自主性不强；铁路系统大数据建设的数据传输和标准体系也不够完善；未来智能铁路应该加强工业4.0下铁路传统制造的标准化管理系统与数据信息安全系统的数字化设计、升级与改造；需要深刻思考和分析人工智能和大数据驱动等前沿技术与铁路的融合与实施，通过工业4.0涵盖的各项关键技术的实施和准确评估真正有效推动中国智能铁路先进数字化平台的建设和发展。Abstract: 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.
-
Key words:
- intelligent railway /
- smart train /
- Industry 4.0 /
- artificial intelligence /
- internet of things /
- big data
-
图 2 产业化时代和数字生态时代的特征
Figure 2. Characteristics of production and digital ecosystem eras
图 7 日本未来铁路的智能维护策略
Figure 7. Intelligent maintenance strategy of future railways of Japan
图 8 高速列车智能诊断与故障预测系统
Figure 8. High-speed train intelligent diagnosis and fault prediction system
图 11 增强现实技术在电机维护中的应用
Figure 11. Application of augment reality technology in motor maintenance
图 13 智能铁路的数字一体化平台框架
Figure 13. Digital integrated platform framework of intelligent railways
表 1 增强现实的类型及应用
Table 1. Types and applications of augmented realities
类型 描述 应用场景 投影 利用虚拟图像增强用户看到的内容，允许用户与显示的虚拟图像之间交互作用。 虚拟标牌或海报：在物体表面叠加虚拟信息；协作：允许多个用户查看虚拟图像并与之人机交互。 识别 识别现实世界中的对象、图案或标记，向用户提供可补充的实时虚拟信息。 3D可视化：3D信息相对环境特定对象叠加；虚拟演示：在产品制造完成之前显示产品3D表示；原位：可视化对象、标记或图案，放置在环境中的虚拟对象。 定位 基于位置的增强现实技术，利用三角测量技术的详细输入数据为用户提供相关的方向指示，通过将实时虚拟信息精确覆盖在由设备相机呈现给用户的现实世界的视图上，从而获得信息。 位置层：将虚拟信息叠加到相对于用户位置的真实世界，提供有关对象或新地点的数据；兴趣点：提供虚拟标记，指示用户感兴趣的点，传输诸如方向、距离和高度等信息；原位：可视化相对的坐标、标记信息、放置在环境中的虚拟对象。 轮廓 将人的身体或物体的轮廓与虚拟信息融合在一起，从而使用户可以拾取和操纵现实世界中不存在的物体。 培训和教育：提供有关复杂设备或工作场景的动手经验；理解系统：将增强现实用于复杂对象的内部或分解视图。 
下载: 导出CSV
-
[1] GAO Liang, SHEN Wei-ming, LI Xin-yu. New trends in intelligent manufacturing[J]. Engineering, 2019, 5(4): 619-620. doi: 10.1016/j.eng.2019.07.001 [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. doi: 10.1007%2Fs00170-019-03754-7 [3] 李培根. 浅说智能制造[J]. 科技导报, 2019, 37(08): 1. https://www.cnki.com.cn/Article/CJFDTOTAL-KJDB201908002.htmLI Pei-gen. Talking about intelligent manufacturing[J]. Science and Technology Review, 2019, 37(8): 1. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-KJDB201908002.htm [4] ROBLEK V, MESKO M, KRAPEZ A. A complex view of Industry 4.0[J]. SAGE Open, 2016, 6(2): 2158244016653987. http://www.researchgate.net/publication/301860128_A_complexity_view_of_Industry_40 [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. doi: 10.1016/J.ENG.2017.05.015 [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. doi: 10.1109/ACCESS.2018.2793265 [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. doi: 10.1080/00207543.2018.1444806 [8] ROJKO A. Industry 4.0 concept: background and overview[J]. International Journal of Interactive Mobile Technologies, 2017, 11(5): 77-90. doi: 10.3991/ijim.v11i5.7072 [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. http://www.sciencedirect.com/science/article/pii/S2212827116307909 [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. doi: 10.30699/IJRRS.2.1.4 [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. http://www.researchgate.net/publication/282791888_Smart_factory_for_industry_40_A_review [14] PRAUSE M. Challenges of Industry 4.0 technology adoption for SMEs: the case of Japan[J]. Sustainability, 2019, 11(20): 5807. doi: 10.3390/su11205807 [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. doi: 10.3969/j.issn.2096-062X.2020.08.034DELOITTE Insight. Digital twin: linking reality and the digital world[J]. Software and Integrated Circuit, 2020, 5: 78-85. (in Chinese) doi: 10.3969/j.issn.2096-062X.2020.08.034 [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. doi: 10.20965/ijat.2017.p0004 [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. doi: 10.1080/09537287.2020.1744763 [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. doi: 10.3969/j.issn.1006-9178.2020.09.001Editorial 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) doi: 10.3969/j.issn.1006-9178.2020.09.001 [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. . https://www.cnki.com.cn/Article/CJFDTOTAL-BFJD201701001.htmHU An-gang. China entering post-industrial era[J]. Journal of Beijing Jiaotong University (Social Sciences Edition), 2017, 16(1): 1-16. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-BFJD201701001.htm [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. https://www.cnki.com.cn/Article/CJFDTOTAL-ZBZA201506009.htmMIAO Wei. Made in China 2025 is similar to German Industry 4.0[J]. Equipment Manufacturing, 2015(6): 22. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-ZBZA201506009.htm [21] PICCAROZZI M, AQUILANI B, GATTI C. Industry 4.0 in management studies: a systematic literature review[J]. Sustainability, 2018, 10(10): 3821. doi: 10.3390/su10103821 [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. doi: 10.3390/s17061457 [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. doi: 10.3969/j.issn.1005-8451.2018.07.003WANG Tong-jun. Overall framework and development prospect of intelligent railway[J]. Railway Computer Application, 2018, 27(7): 1-8. (in Chinese) doi: 10.3969/j.issn.1005-8451.2018.07.003 [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. doi: 10.1088/1742-6596/1187/5/052053 [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. doi: 10.3390/s20020482 [28] ALAWAD H, KAEWUNRUEN S. Wireless sensor networks: toward smarter railway stations[J]. Infrastructures, 2018, 3(3): 24. doi: 10.3390/infrastructures3030024 [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. https://www.cnki.com.cn/Article/CJFDTOTAL-YSZH202001019.htmZHANG 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) https://www.cnki.com.cn/Article/CJFDTOTAL-YSZH202001019.htm [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. doi: 10.1016/j.eng.2019.06.007 [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. https://www.cnki.com.cn/Article/CJFDTOTAL-TLZG201904001.htmWANG Feng. Development of China's intelligent HSR building technology and its future[J]. China Railway, 2019(4): 1-8. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-TLZG201904001.htm [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. doi: 10.3969/j.issn.1006-2106.2019.04.017KANG 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). doi: 10.3969/j.issn.1006-2106.2019.04.017 [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. https://www.cnki.com.cn/Article/CJFDTOTAL-KJDB201906009.htmSHI 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) https://www.cnki.com.cn/Article/CJFDTOTAL-KJDB201906009.htm [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. https://www.cnki.com.cn/Article/CJFDTOTAL-TLZG201911009.htmWANG 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) https://www.cnki.com.cn/Article/CJFDTOTAL-TLZG201911009.htm [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. https://www.cnki.com.cn/Article/CJFDTOTAL-GSTL202002009.htmZHU 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. https://www.cnki.com.cn/Article/CJFDTOTAL-GSTL202002009.htm [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, 2020, 64(1): 7-11. https://www.cnki.com.cn/Article/CJFDTOTAL-TDBS202001005.htmWANG 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) https://www.cnki.com.cn/Article/CJFDTOTAL-TDBS202001005.htm [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. doi: 10.1016/j.adhoc.2010.04.002 [39] HALLDORSDOTTIR K, NIELSEN O A, PRATO C G. 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. doi: 10.1080/15568318.2017.1317888 [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. doi: 10.3969/j.issn.1009-1742.2015.04.011MIAO 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). doi: 10.3969/j.issn.1009-1742.2015.04.011 [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. https://www.cnki.com.cn/Article/CJFDTOTAL-JCDC201801003.htmZHANG 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) https://www.cnki.com.cn/Article/CJFDTOTAL-JCDC201801003.htm [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. doi: 10.3969/j.issn.1001-8360.2019.03.008MIAO 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) doi: 10.3969/j.issn.1001-8360.2019.03.008 [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. https://www.cnki.com.cn/Article/CJFDTOTAL-KXZZ202002005.htmLIANG Jian-ying. Start a new era of intelligent rail transit equipment[J]. Science, 2020, 73(2): 17-22. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-KXZZ202002005.htm [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. doi: 10.1016/j.eng.2020.01.008 [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. doi: 10.1109/TITS.2014.2366512 [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. http://trid.trb.org/view/1402702 [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. doi: 10.11860/j.issn.1673-0291.2019.01.007LIANG 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) doi: 10.11860/j.issn.1673-0291.2019.01.007 [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. doi: 10.1016/j.trc.2018.03.010 [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. http://www.zhangqiaokeyan.com/academic-journal-foreign_other_thesis/0204112858552.html [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. doi: 10.1016/j.proeng.2016.01.031 [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. doi: 10.1016/j.procs.2015.07.321 [55] AZPIAZU J, SILTANEN S, MULTANEN P, et al. Remote 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. doi: 10.1016/j.promfg.2015.07.333 [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. doi: 10.1080/00207543.2019.1630775 [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. doi: 10.1080/0951192X.2018.1529430 [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. doi: 10.1016/j.procs.2015.07.323 [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. doi: 10.15837/ijccc.2017.4.2914 [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. doi: 10.1016/j.trc.2018.07.007 [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. doi: 10.1016/j.engappai.2020.103678 [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. doi: 10.1111/risa.12836 [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. -
点击查看大图
图(13) / 表(1)
计量
- 文章访问数: 1231
- HTML全文浏览量: 585
- PDF下载量: 2718
- 被引次数: 0
