Intelligent O&M Solutions
The locomotive operation safety and fire monitoring system (model YDVS-6Z) enables the driver to keep abreast of the situation inside and outside the locomotive and to make audio and video records of the locomotives operating status through multiple cameras installed in the front, rear, inside, roof and hook of the locomotive.
The locomotive automatic video monitoring and recording sub-system (model YDVS-01) is developed on the basis of the locomotive operation safety and fire monitoring system, combined with the general requirements of the locomotive on-board safety protection system (6A system).
The Traveling Section Failure Monitoring System (Model YZD-2) is an online real-time monitoring device developed to ensure the safety and quality of vehicle operation and to assess the health status of components.
Through the detection of physical quantities such as smoke, heat and light, it can provide a continuous and reliable determination of whether a fire has occurred in an environment where multiple sources of false alarms exist, providing fire monitoring information to the platform and realizing fire alarm video linkage.
The Chinese locomotive remote monitoring and diagnosis system (CMD system) is one of the basic subsystems of the railway locomotive management information system, which can realize locomotive positioning, dynamically track the "human-vehicle map", and conduct condition monitoring, remote diagnosis and troubleshooting of locomotive equipment in transit.
As an important part of the CMD system, the CMD segment-level application system solves the shortcomings of the CMD system in terms of the small amount of real-time data transmission and the analysis and application of locomotive data through the construction of high-speed WLAN network and ground analysis system.
By creating a crew monitoring and early warning system, it can effectively prevent accidents caused by fatigue driving, improve crew management, enhance the enterprises safety management capability and improve the comprehensive operation management level.
The system uses the existing information resources of the machine section, which has the effect of reducing staff and increasing efficiency, ensuring safety and improving labor conditions, and is an effective way to realize the automation and informationization of the machine section management, which has been promoted and used in the whole road.
The safety interlocking monitoring system for inspection operations addresses the safety problems of misfeeding of the contact network, operators mistakenly entering the electrified area and mistakenly hanging the ground wire during the daily inspection of locomotives.
Taking the preparation yard as the center, we integrate resources to realize the information management of locomotive preparation operation management, locomotive preparation quality control and locomotive quality analysis control, and establish the preparation information center to achieve the requirement of "data preparation".
Focusing on the management of fixed capital equipment and tools, it has strengthened the management of equipment production process such as equipment overhaul, inspection, provisional repair, spot check and maintenance, explored the application of IOT technology in the process data collection of equipment operation status and equipment status monitoring, and realized the whole life cycle management and health maintenance of equipment.
Based on the overhaul process of the locomotive section/overhaul section, combined with the overhaul operation management process, it realizes the information management of locomotive overhaul process and parts overhaul process such as C1-C6 repair and provisional repair.
Ensure the effective collection, management and analysis of D-level operation and 181 quality information by the bureau/section, and realize the interoperability and sharing of information on locomotive operations.
Automatic identification and alerting of abnormalities in key train components is achieved through artificial intelligence technologies such as computer vision, deep learning and data-driven hierarchical alerting based on The aim is to improve operational efficiency, reduce the amount of manual maintenance and lower O&M costs.