Highlight Projects in CNERC-Rail
CNERC-Rail conducts research on intelligent operation and maintenance technologies for high-speed railway systems. By integrating advanced sensing, structural health monitoring, data-driven analysis and intelligent diagnosis, the research supports condition assessment, predictive maintenance and life-cycle management of critical railway infrastructure.
CNERC-Rail develops and implements intelligent health monitoring systems for metro, high-speed rail and maglev transportation. Advanced sensing and data analysis technologies are employed to monitor the operational condition and dynamic responses of trains and railway infrastructure, providing technical support for safe and reliable rail transit operations.
CNERC-Rail develops vibration and noise control technologies for railway systems, with applications covering Hong Kong MTR, Shenzhen Metro, Hangzhou Metro and maglev systems. The research addresses railway-induced vibration and noise through source identification, dynamic analysis and innovative mitigation technologies.
CNERC-Rail explores advanced meta-materials and structural designs for railway noise mitigation. By tailoring the acoustic and dynamic characteristics of materials and structures, the research aims to develop innovative solutions for reducing railway-generated noise and improving the environmental performance of rail transit systems.
CNERC-Rail develops energy harvesting technologies to convert vibration and mechanical energy generated during railway operation into electrical energy. Rolling tests on train bogies and related experiments are conducted to evaluate energy harvester performance and support the development of self-powered sensing and monitoring systems.
CNERC-Rail develops machine vision and LiDAR-based systems for automatic railway inspection and obstacle intrusion detection. By integrating intelligent sensing, image processing and artificial intelligence, these systems enable automated identification of rail defects and foreign objects, enhancing inspection efficiency and railway operational safety.
Ultrasonic guided wave techniques are developed for the detection and assessment of damage in railway components, particularly train axles. The technology supports non-destructive inspection and condition assessment by identifying damage-related changes in guided wave propagation characteristics.
CNERC-Rail conducts trackside acoustic measurements and diagnosis at railway corridors and noise-sensitive receivers. The work aims to characterise railway noise sources, evaluate their propagation and environmental impact, and provide technical support for the development and assessment of effective noise mitigation measures.
CNERC-Rail explores the development and application of large language models for railway systems and auxiliary infrastructure. The research integrates large language models with engineering knowledge and intelligent analysis tools to support information retrieval, defect analysis, engineering decision-making and the digitalisation of railway operation and maintenance.
CNERC-Rail has developed a collaborative typhoon monitoring system based on a city-scale LiDAR network for high-resolution observation of wind fields during tropical cyclones. The system supports the monitoring and analysis of the spatial and temporal evolution of typhoon winds across Hong Kong and has been applied in collaboration with the Hong Kong Observatory.
