• Intelligent operation and maintenance system for urban railway
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Intelligent operation and maintenance system for urban railway

The intelligent operation and maintenance system for urban railways is applicable to subway lines, light rail networks, and urban rail transit hubs. This system can monitor the condition of tracks, train components, and station equipment in real time, predict malfunctions and schedule maintenance work, ensuring the safety and stability of railway operations.

    Intelligent Operation and Maintenance (O&M) for Urban Rail Transit encompasses asset data management of rail transit electromechanical infrastructure, fault prediction for rail transit electromechanical equipment, accurate fault location for rail transit electromechanical equipment, fault cause analysis for rail transit electromechanical equipment, fault classification for rail transit electromechanical equipment, fault management for rail transit electromechanical equipment, and full life cycle data management.

     

    Asset data management of rail transit electromechanical infrastructure takes the asset management of rail transit electromechanical infrastructure as its core. Based on relevant standards, specifications, monographs, and expert experience, and integrated with construction archives, standard coding for electromechanical facilities, standard decomposition of electromechanical facility components, and geographic coordinates, it establishes professional knowledge databases for rail transit electromechanical equipment fault monitoring—including an electromechanical equipment database, a fault information database, a fault cause database, and a fault handling database.

     

    Fault prediction for rail transit electromechanical equipment involves the accurate acquisition and analysis of rail transit electromechanical equipment data. It preprocesses data through methods such as data cleaning, aggregation, conversion, and matching to obtain multi-source heterogeneous data in a standardized format, thereby establishing a topic-based fault data warehouse for rail transit electromechanical equipment. Based on this data warehouse, fault equipment information is classified, and a fault prediction model algorithm is constructed using information such as fault type, fault frequency, fault cause, and equipment operating status. This algorithm assists in formulating preventive maintenance plans and provides effective decision support for maintenance personnel of rail transit electromechanical equipment.

     

    Accurate fault location for rail transit electromechanical equipment is implemented by establishing a logical upstream-downstream relationship model of electromechanical equipment in the system according to the on-site status of rail transit electromechanical equipment. This model enables logical upstream-downstream correlation of electromechanical equipment, and the correlated information is integrated into the digital twin system. A qualitative impact relationship network for the electromechanical system is also established: for various types of electromechanical equipment, the output characteristics of each fault are identified, and faults are classified based on self-induced faults and non-self-induced faults (caused by input anomalies, etc.). Hierarchical fault reasoning is realized, and fault traceability and location are conducted from a system-wide perspective.

     

    Fault cause analysis for rail transit electromechanical equipment combines historical fault information of equipment and real-time status of relevant components, uses fault diagnosis models to analyze potential fault causes, and provides fault handling suggestions. Meanwhile, according to the degree of impact of faults on train operation, incident types are divided into general incidents and emergency incidents, which are subject to daily maintenance management or emergency dispatch command respectively.

     

    Fault classification for rail transit electromechanical equipment accurately distinguishes the priority of different frequent faults. Based on the network topology characteristics of the rail transit electromechanical system and the degree of impact on rail transit operation, it evaluates the urgency of fault resource repair, establishes fault classification standards for rail transit electromechanical equipment, and enables limited resources to be used efficiently to address frequent faults, thereby improving the level of reliability management.

     

    Fault management for rail transit electromechanical equipment records details of faults occurring in operational rail transit electromechanical equipment, including fault occurrence time, reporting time, fault content, and fault cause. Each fault is coded and stored in a fault database; meanwhile, an effective maintenance plan is developed for each fault and stored in a dedicated fault handling database. These form the enterprise’s equipment O&M knowledge database, and the integration of the fault database, knowledge database, and other resources ultimately constitutes the enterprise’s knowledge assets.

     

    Full life cycle data management involves constructing a full life cycle archive database for equipment, which includes basic equipment data (such as equipment code, equipment model, production date and manufacturer, commissioning date, and expected service life) as well as dynamically updated monitoring data, early warning data, diagnosis data, and maintenance data during equipment operation. On this basis, a full life cycle data management function for urban rail transit operation is established, enabling real-time monitoring of O&M data at any stage.


    FAQ‍‌‍‍‌‍‌‍‍‌‍‌‍‍‌ – Frequently Asked Questions

    1. When and where will the Expo be held?

    Han fair is set for May 13-15, 2026 at Hall C, Xiamen International Conference and Exhibition Center (XICEC), Xiamen, China.

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    Over 80+ professional forums and events will discuss the issues of smart mobility, transportation communication, safety, and sustainable development.

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    Where to conference around the world come from 80+ countries and regions, thus making it a global summit for intelligent transportation innovation.

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