Search

Research on Vibration Patterns and Online Monitoring System of Heavy-Haul Railway Catenary

Author(s)

Yang Liu1, Yicheng Xiang2,*, Ming Zeng2, Jinfa Guan2, Xiaoyu Tian1

Affiliation(s)

1Guoneng Shuohuang Railway Development Co.,LTD., Beijing, China 2Southwest Jiaotong University, Chengdu, Sichuan, China *Corresponding Author

Abstract

This study aims to address the issue of severe vibration in the catenary induced by heavy-haul railways - characterized by large transport volume and high axle load - when trains traverse bridge sections. Such vibrations significantly increase the risk of cumulative fatigue damage to catenary components. The research focuses on developing accurate methods for load identification and analysis. By precisely capturing and evaluating the dynamic loads sustained by the catenary during operation, this work provides essential data support and a theoretical basis for assessing the operational status of the system, predicting the service life of key components, and optimizing structural design. Research conclusions: (1) This study successfully developed an integrated online monitoring system capable of multi-parameter acquisition, wireless transmission, and intelligent analysis. The system adopts an integrated mast design, complies with IP65 protection standards, and features wide-temperature operational capability, ensuring stable performance in harsh railway environments. Its vehicle-triggered automatic activation and low-power sleep mechanism significantly enhance engineering application efficiency and reliability.(2) Through multi-sensor collaborative data acquisition and Kalman filter-based data fusion, this study accurately characterized the dynamic response of the catenary system. Field measurements revealed that the contact wire uplift fluctuates within ±5 mm under wind load when no train is present, while the peak uplift during pantograph passage ranges between 20 and 25 mm. This work represents the first quantitative characterization of the operational load spectrum for heavy-haul trains.(3) Utilizing deep learning algorithms, this research achieved intelligent recognition of pantograph abnormal states. Integrated with train number identification, a "one-pantograph-one-file" management system was established. The structured correlation of multi-source data and response spectrum analysis significantly improved the safety management level of the power supply system.(4) This study provides valuable references and a solid foundation for further investigation into vibration patterns and intelligent maintenance of heavy-haul railway catenary systems.

Keywords

Railway Communication; Online Monitoring; Sensors; Heavy-Haul Railway; Catenary

References

[1]Bardhan, Abidhan. Probabilistic assessment of heavy-haul railway track using multi-gene genetic programming[J].Applied Mathematical Modelling, Volume 125, Pages 687-720, January 2024.. [2]Eckert, J. J., Pavani Teodoro, Ícaro, etc. Multi-objective optimization of electro-pneumatic braking process with fuzzy logic control for heavy haul railway applications[J]. International Journal of Rail Transportation, 13(2), 332–351. [3]Lihua Li,Tuo Ning,Wang Mao, etc. Study on Dynamic Performance of Coarse-grained Soil Under Different Reinforcement Materials [J]. Journal of Railway Engineering Society, 2025, 42(05): 1-6+31. [4]Yahui Zhang,Yong Liu,Naijie Han, etc. Analysis on Effect of Jet Grouting Pile Reinforcement for Loess Area Heavy-haul Railway Tunnel Foundation [J]. High Speed Railway Technology, 2024, 15(06): 104-110. [5]Xuanming Liang,Haoling Chen,Bing Zhang. Design of a Wireless Transmission Communication System for Train Braking Signals [J]. Journal of Railway Science and Engineering, 2024, 21(04): 1579-1590.. [6]Haitao Deng,Chun Wu,Xilong Chen. Multi-channel Data Acquisition System Based on FPGA and Gigabit Ethernet Transmission [J]. Instrument Technique and Sensor, 2025, (02): 52-56. [7]Yuhuan Fei,Qichen Song,Ruichuan Li, etc. Research on Fuzzy PID Valve Core Control Based on Kalman Filtering [J]. Journal of Chinese Agricultural Mechanization, 2025, 46(12): 261-267.