Design and Realization of Urban Rail Transit AFC Intelligent Operation and Maintenance System Architecture Driven by Dual-core of Data and Model

Abstract

Objective The traditional urban rail transit AFC (automatic fare collection) system suffers from maintenance problems such as slow speed, outdated method, unreasonable plan and high cost. It is necessary to adopt digital and intelligent active monitoring and maintenance methods, and build an urban rail transit AFC intelligent operation and maintenance system architecture drive by dual-core of data and model, transforming periodic scheduled maintenance into condition-based and predictive maintenance, so as to improve the efficiency of operation and maintenance and reduce the cost. Method The architecture and main functions of AFC intelligent operation and maintenance system for urban rail transit is introduced. Firstly, the terminal equipment data are collected through multi-sensor fusion and uploaded to the big data platform for processing. Then, the collected data are analyzed by deep learning algorithm and BAS (beetle antennae search algorithm), and the model is optimized to further identify the failure mode and predict the potential equipment failure. In addition, the system uses automated workflow management tools to analyze the execution efficiency of the operation and maintenance tasks, and optimize the routine maintenance and emergency response strategies. Result & Conclusion The system performs well in the trial operation on Shanghai Metro Line 3, and it can realize intelligent maintenance, dynamic equipment history management, remote monitoring, mobile terminal, paperless maintenance and other functions, featuring stable running, convenient operating, and smooth workflow. The benefit analysis shows that the system effectively improves the maintenance efficiency and reduces the operation cost.

Keywords

• urban rail transit
• automatic fare collection system
• intelligent operation and maintenance
• economic benefit