Russian Federation
Russian Federation
Objective: to identify the patterns and main directions in the intellectualization of dynamic monitoring technologies for railway rolling stock in China; to determine the role of the 5T system in the transition from periodic static inspection to continuous condition assessment during operation; and to establish the role of TFDS in integrating dynamic monitoring with machine vision and intelligent recognition technologies. Methods: an analytical review was carried out of scientific, technical, and regulatory sources devoted to non-destructive testing, dynamic monitoring, and intelligent diagnostics of railway rolling stock. The sources were systematized according to inspection objects, monitored parameters, physical diagnostic principles, and the level of intelligence in data processing. On this basis, the structure and functional features of the 5T system, the interrelations among its subsystems, and the specific features of implementing machine vision, multi-source data fusion, and intelligent recognition methods, primarily in TFDS-type systems, were examined. Results: it is shown that a multi-level dynamic monitoring system for rolling stock has been established on China’s railways, with the 5T system serving as its core. It was found that the development of these technologies is characterized by a transition from autonomous monitoring of individual parameters and manual interpretation of results to network-based integration of subsystems, multichannel information analysis, and automated anomaly ecognition. It was also revealed that the most pronounced features of intellectualization are observed in visual diagnostic systems, primarily in TFDS, where image processing has become the basis for defect detection, condition assessment of components, and reduction of the labor intensity of operational data analysis. The potential of integrating TFDS with data from other 5T subsystems to improve diagnostic reliability, reduce false alarms, and move from defect detection to risk prediction is also demonstrated. Practical significance: the results obtained make it possible to clarify the role of dynamic monitoring within the overall system of technical condition assessment of railway rolling stock and to identify promising directions for its integration with non-destructive testing methods for mechanical components and machine vision technologies. The study may be used as a theoretical and review-analytical basis for further research in the fields of operational safety, technical condition diagnostics, and intelligent maintenance of rolling stock.
railway rolling stock, dynamic monitoring, 5T system, machine vision, intelligent diagnostics, nondestructive testing
1. A Review on Condition Monitoring Technologies for Railway Rolling Stock / P. Kundu [et al.] // Proceedings of the PHM Society European Conference. 2018. Vol. 13. Pp. 1–15.
2. Kostrzewski M., Mel'nik R. Condition Monitoring of Rail Transport Systems: A Bibliometric Performance Analysis and Systematic Literature Review // Sensors. 2021. Vol. 21 (14). P. 4710.
3. Kitayskaya zheleznodorozhnaya korporaciya. Pravila tehnicheskogo obsluzhivaniya i remonta oborudovaniya sistemy monitoringa bezopasnosti ekspluatacii podvizhnogo sostava (5T) = 车辆运行安全监控系统设备检修维护管理规则: TG/CL 210–2015. Pekin, 2015. [Na kit. yaz.]
4. Research and Application of the Railway Vehicle Operation Safety Monitoring (5t) System / Jiang Hui [et al.] // Journal of Highway and Transportation Research and Development. 2009. No. 1. Pp. 1–6.
5. Gosudarstvennoe zheleznodorozhnoe upravlenie KNR. Gosudarstvennoe zheleznodorozhnoe upravlenie KNR opublikovalo dva otraslevyh standarta, vklyuchaya «Normy proektirovaniya sistemy monitoringa bezopasnosti ekspluatacii zheleznodorozhnyh vagonov». 04.01.2022. URL: https://www.nra.gov.cn/ (data obrascheniya: 20.01.2026).
6. A Survey of the Application of Machine Vision in Rail Transit System Inspection / Wei Xiukun [et al.] // Control and Decision. 2021. Vol. 36, no. 2. Pp. 257–282.
7. TFDS Truck Fault Image Application System / Wang Deming [et al.] // Railway Computer Application, 2024. Vol. 33 (9). Pp. 59–67.
8. Mu Xin. Intelligent Recognition of TFDS Faults Based on Large Model Technology // Railway Computer Application. 2025.Vol. 34 (11). Pp. 8–14.
9. Pudovikov S. A. Bezrazbornaya tehnologiya ul'trazvukovogo kontrolya osey kolesnyh par pri remonte podvizhnogo sostava // Izvestiya Peterburgskogo universiteta putey soobscheniya. 2005. № 1. S. 108–113.
10. Korosteleva E. Yu., Tolmachev I. I. Primenenie magnitoporoshkovogo i vihretokovogo metodov kontrolya dlya detaley i uzlov lokomotivov i motorvagonnogo podvizhnogo sostava // Izvestiya Tomskogo politehnicheskogo universiteta. Inzhiniring georesursov. 2008. T. 312, № 2S. S. 264–267.
11. Ahmedzhanov R. A., Makarochkin V. V., Rodchenko L. A. O sovershenstvovanii ul'trazvukovogo kontrolya osi kolesnoy pary vagona // Izvestiya Transsiba. 2014. № 2 (18). S. 7–18.
12. Vorob'ev A. A., Karpov V. A. Analiz nadezhnosti podvizhnogo sostava po rezul'tatam nerazrushayuschego kontrolya // Vestnik Saratovskogo gosudarstvennogo tehnicheskogo universiteta. 2015. T. 3, № 1 (80). S. 65–70.
13. Maryuhnenko V. S., Pul'tyakov A. V. Osobennosti kontrolya tehnicheskogo sostoyaniya podvizhnogo sostava na hodu poezda // Avtomatika na transporte. 2016. T. 2, № 2. S. 272–287.
14. Davydov Yu. A., Plyaskin A. K., Kushniruk A. S. Kontrol' fakticheskogo tehnicheskogo sostoyaniya lokomotivov na osnove diagnostiki // Sovremennye tehnologii. Sistemnyy analiz. Modelirovanie. 2018. № 3 (59). S. 38–47.
15. Analiz metodov izmereniy silovogo vozdeystviya podvizhnogo sostava na put' i sistem tehnicheskogo kontrolya koles pri dvizhenii poezda / Yu. P. Boronenko [i dr.] // Izvestiya Peterburgskogo universiteta putey soobscheniya. 2020. T. 17, № 3. S. 324–344.
16. Monitoring tehnicheskogo sostoyaniya gruzovyh vagonov na hodu poezda / V. V. Popov [i dr.] // Transport Rossiyskoy Federacii. Zhurnal o nauke, praktike, ekonomike. 2021. № 1–2 (92–93). S. 52–56.
17. Baranov L. A., Burchenkov V. V. Tehnologiya monitoringa podvizhnogo sostava na osnove distancionnogo akusticheskogo zondirovaniya // Avtomatika na transporte. 2022. T. 8, № 1. S. 90–100.
18. Research and Application of the Railway Vehicle Operation Safety Monitoring (5T) System / Jiang Hui [et al.] // Journal of Highway and Transportation Research and Development. 2009. No S1. Pp. 1–6.
19. Design of the TPDS Big Data Analysis System / Shi Xiaolei [et al.] // Railway Locomotive & Car. 2022. Vol. 42, no. 1. Pp. 95–98.
20. Construction Techniques for an Intelligent Recognition Dataset of Railway Freight Car Fault Images / Qi Miaomiao [et al.] // China Railway, 2025.
21. Caplin A. E. Diagnostika uzlov mehanicheskoy chasti podvizhnogo sostava s primeneniem kompleksa mashinnogo zreniya // Izvestiya Peterburgskogo universiteta putey soobscheniya. 2011. № 2. S. 41–49.
22. Kazanskiy N. L., Popov S. B. Raspredelennaya sistema tehnicheskogo zreniya registracii zheleznodorozhnyh sostavov // Komp'yuternaya optika. 2012. T. 36, № 3. S. 419–428.
23. A Review of Computer Vision for Railways / B. Olivier [et al.] // IEEE Transactions on Intelligent Transportation Systems, 2025.
24. Machine Vision-Based Method for TFDS Freight Car Fault Image Recognition / Sun Yinong [et al.]. 2025.
25. A Review of Machine Vision Applications in State Detection of Rail Transit Systems / Wei Xiukun [et al.] // Control and Decision. 2021. Vol. 36 (2). Pp. 257–282
