Scholarlink

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.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

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

  • 会议论文

  • 发表日期：

    2025年

查看全文
  • Dongxiu Ou,
  • Yunwen Tong,
  • Yanghao Wang,
  • Shaogang Dai,
  • Ming Chang

摘要

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.