Intellectualization of Dynamic Monitoring Technologies for Railway Rolling Stock in China

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.