To address the issue of abnormal adhesion on rail surfaces and reduce the risk of slip/slide, this paper introduces an autonomous perception method for detecting adhesion characteristic parameters in subway trains. This method combines vehicle and signal data to identify adhesion parameters, and then uses the Kiencke adhesion characteristic model along with the SLSQP optimization algorithm to extract the critical traction/braking rate. Experimental results demonstrate that this approach can accurately detect low adhesion conditions and determine the critical traction/braking rate. Based on these findings, safety strategies and operational control commands can be developed to prevent trains from losing control, significantly enhancing the safety and operational efficiency of urban rail transit systems.


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    Title :

    Vehicle-Signal Fusion Based Autonomous Perception Method of Subway Train Adhesion Characteristic Parameters


    Additional title:

    Lect. Notes Electrical Eng.


    Contributors:
    Jia, Limin (editor) / Ou, Dongxiu (editor) / Liu, Hui (editor) / Zong, Fang (editor) / Wang, Pangwei (editor) / Zhang, Mingfang (editor) / Ou, Dongxiu (author) / Tong, Yunwen (author) / Wang, Yanghao (author) / Dai, Shaogang (author)

    Conference:

    International Conference on Artificial Intelligence and Autonomous Transportation ; 2024 ; Beijing, China December 06, 2024 - December 08, 2024



    Publication date :

    2025-03-31


    Size :

    10 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




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