Abstract—High-speed train Electric Multiple Units (EMU) ground maintenance platforms generate critical telemetry data from sub systems such as pneumatics, braking, and traction. While real-time monitoring is essential for safety, many existing ground stations rely on monolithic Relational Database Management Systems (RDBMS) and lack the infrastructure for heavy big data stacks. This paper proposes a lightweight, non-intrusive real-time post-processing architecture based on Python and standard RDBMS mechanisms which requires no modification to the original data ingestion pipeline. The system is evaluated using telemetry schemas from EMU braking systems. Results show a saturation throughput of 1600 records per second with end-to-end latency of 20 ms on total data volumes of 106 records, which is sufficient to support the monitoring of over 200 concurrent subsystem units at standard sampling rates. A comparative analysis with the industry-standard tool Debezium reveals similar performance limits under identical hardware constraints. This study validates that, for specific legacy industrial scenarios, an RDBMS-native approach offers a superior trade-off between performance, cost, and maintainability.

Keywords—digital twin, real-time processing, relational database, railway equipment maintenance, EMU

[PDF]

Cite: Tianyu Xia, Beichen Gong, Jingxian Ding, and Jianyong Zuo, "A Lightweight Real-Time Data Post-Processing Framework for Legacy High-Speed Train Ground Maintenance Systems: Design and Performance Evaluation," Journal of Software, vol. 21, no. 1, pp. 32-44, 2026.


Copyright @ 2026 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0)