Chengshi guidao jiaotong yanjiu (Jan 2024)

Exploration and Practice of Intelligent Operation-maintenance Platform for Hang-Hai Intercity Railway Track Hospital

  • Feng HUANG

DOI
https://doi.org/10.16037/j.1007-869x.2024.01.043
Journal volume & issue
Vol. 27, no. 1
pp. 234 – 238

Abstract

Read online

[Objective] Aimed at the OM (operation-maintenance) pain points and intelligent OM requirements of Hang-Hai (Hangzhou to Haining) Intercity Railway, to explore a path for intelligent OM high-quality development, the investigation and construction of Hang-Hai Intercity Railway track hospital intelligent OM platform is needed, and development suggestions for the future digital, automated, and intelligent OM are proposed. [Method] By analyzing the existing OM pain points and intelligent demands of railway lines, the overall architecture and functional aspects of the Hang-Hai Intercity Railway track hospital intelligent OM platform version 1.0 are introduced. From aspects such as on-route train status monitoring, critical component analysis, optimization of maintenance procedures and schedules, machine-assisted manual work, and intelligent OM management empowerment, the construction and practical effects of the track hospital intelligent OM platform are elucidated. Targeted development recommendations are also provided. [Result & Conclusion] Version 1.0 of the track hospital intelligent OM platform achieves certain results in supporting ground-assisted train main line fault handling, train critical component analysis, optimization of maintenance procedures and schedules, and OM management empowerment. To achieve highly intelligent development of the track hospital intelligent OM platform, targeted development suggestions are put forward, including enhancing the machine learning capabilities of intelligent sensing devices, integrating multiple professional system interfaces and decoupling analyses, establishing expert knowledge bases and specialized capacity mechanisms, and strengthening the integration of human and machine practices.

Keywords