Design of Big Data Intelligent Operation and Maintenance Platform for Urban Rail Transit Signaling System
Journal Title: Urban Mass Transit - Year 2024, Vol 27, Issue 10
Abstract
Objective The maintenance and management of urban rail transit signaling systems encompass various tasks such as equipment monitoring, production organization, and information management, each task involving one or more business systems. These systems vary in construction ages and often have overlapping or redundant functions on the same railway line. Therefore, there is a need to develop a big data IOM (intelligent operation and maintenance) platform for signaling systems that integrates data and enables analytical functions. Method Based on the current state of signaling system maintenance operations and the characteristics of signaling system data, a signaling system big data IOM platform is constructed using a layered data approach to meet practical application needs. The overall architecture of the platform is introduced, the design scheme of its three sub-platforms: data sharing, data analysis, and data application, is expounded. Result & Conclusion The signaling system big data IOM platform is piloted on Zhengzhou Metro network, which achieves the aggregation and unified storage of data from various business systems, including the centralized signal monitoring system, construction management system, and material management system. It enables the correlation analysis of business data such as production plans, maintenance records, inspection issues, fault records, monitoring alarms, electrical characteristics, and equipment ledgers. Additionally, the platform regulates standardized data interfaces and multi-dimensional data applications, such as quantitative scoring of key signal equipment quality and key maintenance tasks. The application of this platform reduces data redundancy within the signaling system, simplifies the complexity of maintaining signal equipment information, enhances resource utilization, and improves the convenience for maintenance personnel in data reviewing.
Authors and Affiliations
Chong ZHANG, Mengtao HUANG, Xin AI
Keywords
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- EP ID EP749263
- DOI 10.16037/j.1007-869x.2024.10.060
- Views 45
- Downloads 0
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