Key technologies of China high-speed comprehensive inspection train: CIT450

Abstract

The China comprehensive inspection train (CIT) is designed for evaluating railway infrastructure to ensure safe railway operations. The CIT integrates an array of inspection devices, capable of simultaneously assessing railway health condition parameters. The CIT450, representing the second generation, can reach a top speed of 450 km/h with inspection on the infrastructure. This paper begins by outlining the global evolution of inspection trains. It then focuses on the critical technologies underlying the CIT450, which include: (1) real-time inspection data acquisition with spatial and temporal synchronization; (2) intelligent fusion and centralized management of multi-source inspection data, enabling remote supervision of the inspection process; (3) technologies in inspecting track, train–track interaction, catenary, signalling systems, and train operating environment; and (4) AI-driven analysis and correlation of inspection data. The future developmental directions for comprehensive inspection trains are discussed finally. The CIT450’s approach to real-time railway health monitoring can enrich traditional inspection means, operational, and maintenance methods by enhancing inspection efficiency and automating railway maintenance.

Keywords

Railway infrastructure / Non-destructive testing / Track inspection vehicle / Track geometry car / High-speed railway / Axle box acceleration / Wheel–rail contact force / Overhead catenary system inspection / Signalling system inspection / Information and Computing Sciences / Artificial Intelligence and Image Processing

References

[1.] Sun Y, Zhao H, Yang H et al (2022) Research and design of a multi-track daily inspection robot for urban rail transit. In: 14th International Conference on Measuring Technology and Mechatronics Automation (ICMTMA). Changsha, pp 528–531

[2.] JingG, QinX, WangH, et al. . Developments, challenges, and perspectives of railway inspection robots. Autom Constr, 2022, 138. 104242

[3.] Li D, Read D, Thompson H et al (2010) Evaluation of ground penetrating radar technologies for assessing track substructure conditions. In: Proceedings of AREMA 2010 Annual Conference. Orlando.

[4.] WangH, SilvastM, MarkineV, et al. . Analysis of the dynamic wheel loads in railway transition zones considering the moisture condition of the ballast and subballast. Appl Sci, 2017, 7121208.

[5.] Rahimi M, Liu H, Rahman M et al (2021) Localisation and navigation framework for autonomous railway robotic inspection and repair system. In: Proceedings of the 10th International Conference on Through-Life Engineering Services, online event.

[6.] LiQ, ShiZ, ZhangH, et al. . A cyber-enabled visual inspection system for rail corrugation. Future Gener Comput Syst, 2018, 79: 374-382.

[7.] TakagiK. Development of high-speed railways in China. Japan Railw Trans Rev, 2011, 57: 36-41

[8.] China Railway (2024) The length of China's high-speed railways in operation has reached 45,000 km. http://www.china-railway.com.cn/xwzx/zhxw/202401/t20240112_132652.html. Assessed Aug 4th 2024

[9.] KimS-S, ParkC, KimY-G, et al. . Parameter characteristics of rail inspection measurement system of HSR-350x. J Mech Sci Technol, 2009, 2341019-1022.

[10.] XiongL, JingG, WangJ, et al. . Detection of rail defects using NDT methods. Sensors, 2023, 23104627.

[11.] Andani M (2016) The application of Doppler LIDAR technology for rail inspection and track geometry assessment. Dissertation, Virginia Polytechnic Institute and State University

[12.] NezuK, MatsumuraI, AboshiM, et al. . Contactless measuring method of overhead contact line positions by stereo image measurement and laser distance measurement. Q Rep RTRI, 2015, 563181-186.

[13.] GaoG, YanX, YangZ, et al. . Pantograph–catenary arcing detection based on electromagnetic radiation. IEEE Trans Electromagn Compat, 2019, 614983-989.

[14.] NiuL, YangF, DengX, et al. . An assessment method of rail corrugation based on wheel–rail vertical force and its application for rail grinding. J Civ Struct Health Monit, 2023, 1341131-1150.

[15.] KaewunruenS, IshidaM, MarichS. Dynamic wheel–rail interaction over rail squat defects. Acoust Aust, 2015, 43197-107.

[16.] WangS, LiuG, JingG, et al. . State-of-the-art review of Ground Penetrating Radar (GPR) applications for railway ballast inspection. Sensors, 2022, 2272450.

[17.] SilvastM, NurmikoluA, WiljanenB, et al. . An Inspection of Railway Ballast Quality Using Ground Penetrating Radar in Finland. Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit, 2010, 2245345-351.

[18.] YangY, ZhaoW. Curvelet transform-based identification of void diseases in ballastless track by ground-penetrating radar. Struct Control Health Monit, 2019, 264. e2322

[19.] Al-QadiIL, XieW, RobertsR. Scattering analysis of ground-penetrating radar data to quantify railroad ballast contamination. NDT E Int, 2008, 416441-447.

[20.] HouZ, ZhaoW, YangY. Identification of railway subgrade defects based on ground penetrating radar. Sci Rep, 2023, 13: 6030.

[21.] ZhangJ, LuY, YangZ, et al. . Recognition of void defects in airport runways using ground-penetrating radar and shallow CNN. Autom Constr, 2022, 138. 104260

[22.] LiuH, WangS, JingG, et al. . Combined CNN and RNN neural networks for GPR detection of railway subgrade diseases. Sensors (Basel), 2023, 23125383.

[23.] De Bold RP (2011) Non-destructive evaluation of railway trackbed ballast. Dissertation, University of Edinburgh

[24.] Balouchi F, Bevan A, Formston R (2016) Detecting railway under-track voids using multi-train in-service vehicle accelerometer. In: 7th IET Conference on Railway Condition Monitoring 2016 (RCM 2016). Birmingham, pp 1–6

[25.] UnluogluHA, BrysonLS, RoseJG. Stress distribution in a railroad track at the crosstie–ballast interface. J Transp Eng Part A Syst, 2023, 149810.

[26.] WuY, ChenP, QinY, et al. . Automatic railroad track components inspection using hybrid deep learning framework. IEEE Trans Instrum Meas, 2023, 72: 5011415

[27.] FengH, JiangZ, XieF, et al. . Automatic fastener classification and defect detection in vision-based railway inspection systems. IEEE Trans Instrum Meas, 2014, 634877-888.

[28.] LiuJ, HuangY, ZouQ, et al. . Learning visual similarity for inspecting defective railway fasteners. IEEE Sens J, 2019, 19166844-6857.

[29.] DattaD, ScaleaFL. High-Speed inspection of rails by passive ultrasonic monitoring. Journal of Nondestructive Evaluation, Diagnostics and Prognostics of Engineering Systems, 2022, 54. 041007

[30.] ZhengJ, WangL, LiuJ, et al. . An inspection method of rail head surface defect via bimodal structured light sensors. Int J Mach Learn Cybern, 2023, 1451903-1920.

[31.] LiQ, RenS. A real-time visual inspection system for discrete surface defects of rail heads. IEEE Trans Instrum Meas, 2012, 6182189-2199.

[32.] Li HR, Shi YH, Gao B et al (2021) Dynamic electromagnetic thermography system for rail inspection. In: 2021 IEEE Far East NDT New Technology & Application Forum (FENDT). Kunming, pp 99–103

[33.] ThomasHM, HeckelT, HanspachG. Advantage of a combined ultrasonic and eddy current examination for railway inspection trains. Insight Non Destr Test Cond Monit, 2007, 496341-344.

[34.] RenS, GuS, XuG, et al. . A new track inspection car based on a laser camera system. Chin Opt Lett, 2011, 9331202-31205.

[35.] Wrobel, S.A (2013) Multi-function LIDAR sensors for non-contact speed and track geometry measurement in rail vehicles. Dissertation, Virginia Tech

[36.] HaoX, YangJ, YangF, et al. . Track geometry estimation from vehicle–body acceleration for high-speed railway using deep learning technique. Veh Syst Dyn, 2023, 611239-259.

[37.] HsiehCC, LinYW, TsaiLH, et al. . Offline deep-learning-based defective track fastener detection and inspection system. Sens Mater, 2020, 32103429

[38.] HsiehCC, HsuTY, HuangWH. An online rail track fastener classification system based on YOLO models. Sensors, 2022, 22249970.

[39.] GeH, HuatDCK, KohGC, et al. . Guided wave–based rail flaw detection technologies: state-of-the-art review. Struct Health Monit, 2021, 2131287-1308.

[40.] FischerS, LiegnerN, BoczP, et al. . Investigation of track gauge and alignment parameters of ballasted railway tracks based on real measurements using signal processing techniques. Infrastructures, 2023, 8226.

[41.] ZhaoW, QiangW, YangF, et al. . Data-driven ballast layer degradation identification and maintenance decision based on track geometry irregularities. Int J Rail Transp, 2024, 124581-603.

[42.] Dai P, Wang S, Huang Y et al (2017) Visual indexing of large scale train-borne video for rail condition perceiving. IEICE Trans Inf & Syst E100.D(9):2017–2026

[43.] ChangC, LingL, ChenS, et al. . Dynamic performance evaluation of an inspection wagon for urban railway tracks. Measurement, 2021, 170. 108704

[44.] HouY, WangX, WeiJ, et al. . Measured dynamic load distribution within the in situ axlebox bearing of high-speed trains under polygonal wheel–rail excitation. Railway Engineering Science, 2024.

[45.] Martínez-LlopPG, de Dios Sanz Bobi J, Solano Jiménez Á, et al. . Condition-based maintenance for normal behaviour characterisation of railway car-body acceleration applying neural networks. Sustainability, 2021, 132112265.

[46.] LedermanG, ChenS, GarrettJH, et al. . A data fusion approach for track monitoring from multiple in-service trains. Mech Syst Signal Process, 2017, 95: 363-379.

[47.] XuX, LiuJ, SunS, et al. . Dynamic diagnosis method and quantitative characterization of rail corrugation. Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit, 2022, 2373297-308.

[48.] Xu X, Sun S, Yang F, et al (2022) A study on dynamic response character of high-speed railway joint. In: 7th International Conference on Intelligent Transportation Engineering (ICITE). Beijing, pp 122–127

[49.] HuangW, ZhangW, DuY, et al. . Detection of rail corrugation based on fiber laser accelerometers. Meas Sci Technol, 2013, 249. 094014

[50.] Xiao B, Mao X, Liu J et al (2021) An improved marginal index method to diagnose poor welded joints of heavy-haul railway. In: 2021 Global Reliability and Prognostics and Health Management (PHM-Nanjing). Nanjing, pp 1–7

[51.] ChoiS. Identifying parametric models used to estimate track irregularities of a high-speed railway. Machines, 2022, 1116.

[52.] YanT-H, CostaMDA, CormanF. Developing and extending status prediction models for railway tracks based on on-board monitoring data. Transportation Research Record: Journal of the Transportation Research Board, 2023, 26776708-719.

[53.] ZhongJ, LiuZ, HanZ, et al. . A CNN-based defect inspection method for catenary split pins in high-speed railway. IEEE Trans Instrum Meas, 2019, 6882849-2860.

[54.] Zhang W, Mu M, Wang J et al (2021 ) Research on Contact Wire Uplift of Typical High-speed Railway at 300km/h and 350km/h. In: 2nd China International Youth Conference on Electrical Engineering (CIYCEE). Chengdu, pp 1–6

[55.] WangJ. Research on wireless communication scheme of train control system compatible with 5G-R and GSM-R interoperability. Railway Signalling & Communication Engineering, 2021, 18542

[56.] Yao L, Qiu J, Gao S et al (2021) Defect detection in high-speed railway overhead contact system: importance, challenges, and methods. In: International Conference on Security, Pattern Analysis, and Cybernetics(SPAC). Chengdu, pp 76–80

[57.] DuX, WuD. Visual inspection system for trackside communication and signal infrastructure. Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit, 2020, 2351121-130.

[58.] Xu Q, Meng J, Luo Y et al (2021) Uplink transmission performance evaluation and prediction of railway balise based on AHP-WNN. In: 21st International Conference on Software Quality, Reliability and Security Companion (QRS-C). Hainan, pp 169–176

[59.] LiuJ, ChenS, LedermanG, et al. . Dynamic responses, GPS positions and environmental conditions of two light rail vehicles in Pittsburgh. Sci Data, 2019, 61146.

[60.] WangY, LiS, WangP, et al. . A multifunctional electromagnetic device for vibration energy harvesting and rail corrugation sensing. Smart Mater Struct, 2021, 3012. 125012

[61.] JiangX, WangS. Railway panorama: a fast inspection method for high-speed railway infrastructure monitoring. IEEE Access, 2021, 9: 150889-150902.

[62.] Li R, Bai Z, Chen B et al (2020) High-speed railway track integrated inspecting by GNSS-INS multisensor. In: 2020 IEEE/ION Position, Location and Navigation Symposium (PLANS). Portland, pp 798–809

[63.] Hou W (2010) Comprehensive inspection technologies of high-speed railways. In: 2010 Joint Rail Conference. Urbana, pp 323–328

[64.] YangJ, BaiX, ZhangZ, et al. . Research on the application of BDS/GIS/RS technology in the high-speed railway infrastructure maintenance. IOP Conf Ser: Earth Environ Sci, 2021, 7831. 012168

[65.] LiuC, WangD, LinP, et al. . Research on combined location method of dual rail inspection vehicle based on adaptive Kalman filter. J Phys: Conf Ser, 2020, 16261012098

[66.] Liu J, Wei Y, Bergés M et al (2019) Detecting anomalies in longitudinal elevation of track geometry using train dynamic responses via a variational autoencoder. In: Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2019. Denver

[67.] HisaT, KanayaM, SakaiM, et al. . Rail and contact line inspection technology for safe and reliable railway traffic. Hitachi Review, 2012, 617325-330

[68.] MatsudaH, TakikawaM, NanmokuT, et al. . Track test monitoring system using a multipurpose experimental train. WIT Transactions on The Built Environment, 2010, 114: 701-708.

[69.] WestonP, RobertsC, YeoG, et al. . Perspectives on railway track geometry condition monitoring from in-service railway vehicles. Veh Syst Dyn, 2015, 5371063-1091.

[70.] RobertsC, GoodallRM. Strategies and techniques for safety and performance monitoring on railways. IFAC Proceedings Volumes, 2009, 428746-755.

[71.] Marijolovic, K (2022) A diamond on the tracks: FS reveals new predictive diagnostics train. https://decode39.com/3045/diamond-fs-diagnostics-train-luigi-ferraris/. Assessed Aug 4th 2024

[72.] EgelkrautK. On-Site ultrasonic rail testing. Non-Destr Test, 1968, 15297-305.

[73.] WeiZ, SunX, YangF, et al. . Carriage interior noise-based inspection for rail corrugation on high-speed railway track. Appl Acoust, 2022, 196. 108881

[74.] HanY, LiuZ, LyuY, et al. . Deep learning-based visual ensemble method for high-speed railway catenary clevis fracture detection. Neurocomputing, 2020, 396: 556-568.

[75.] HeR, AiB, ZhongZ, et al. . 5G for railways: next generation railway dedicated communications. IEEE Commun Mag, 2022, 6012130-136.

[76.] XuW, DaiX, ZhaoC, et al. . Parallel testing for centralized traffic control systems of intelligent railways. IEEE Trans Intell Veh, 2023, 894249-4262.

[77.] Zhao H, Li Y, Wu S, et al (2022) An MPI programming model for fast bird nest detection on the railway catenary. In: 13th International Symposium on Parallel Architectures, Algorithms and Programming (PAAP). Beijing, pp 1–6

[78.] GaoL, JiuY, WeiX, et al. . Anomaly detection of trackside equipment based on GPS and image matching. IEEE Access, 2020, 8: 17346-17355.

[79.] MaS, LiuX, ZhangB, et al. . Differential deformation identification of high-speed railway substructures based on dynamic inspection of longitudinal level. Sensors (Basel), 2022, 231219.

[80.] XiaoX, XuH, YangY. Analysis of the influence of track irregularity on high-speed train ride comfort. Veh Syst Dyn, 2023, 6271658-1685.

[81.] XuX, SunS, NiuL, et al. . An approach for the estimation of vertical wheel/rail force using dynamic signals. Veh Syst Dyn, 2024, 6241022-1036.

[82.] Xu X, Liu J, Sun S et al (2018) Track short wave irregularity of high-speed railway insulated joint evaluation based on track impact index method. In: Proceedings of the Asia-Pacific Conference on Intelligent Medical 2018 & International Conference on Transportation and Traffic Engineering 2018. Beijing, pp 6–10

[83.] Liu T, He D, Guan K et al (2022) Channel characterization for 5G-R indoor communication at 2.1 GHz. In: 16th European Conference on Antennas and Propagation (EuCAP). Madrid, pp 1–5