• CN:11-2187/TH
  • ISSN:0577-6686

›› 2010, Vol. 46 ›› Issue (24): 88-95.

• 论文 • 上一篇    下一篇

基于高斯过程的地铁车辆轮对磨耗建模及其镟修策略优化

许宏;员华;王凌;那文波;徐文彬;李运堂   

  1. 中国计量学院机电工程分院;广州市地下铁道总公司;上海轨道交通维护保障中心
  • 发布日期:2010-12-20

Modeling of Metro Wheel Wear and Optimization of the Wheel Re-profiling Strategy Based on Gaussian Processes

XU Hong;YUAN Hua;WANG Ling;NA Wenbo;XU Wenbin;LI Yuntang   

  1. College of Mechanical and Electrical Engineering, China Jiliang University Guangzhou Metro Operation Co. Ltd. Shanghai Rail Transit Maintenance Support Centre
  • Published:2010-12-20

摘要: 根据广州地铁车辆轮对的磨耗数据,分析轮对磨耗和镟修的特点。针对踏面直径和轮缘厚度两个形面参数,基于高斯过程建立轮对磨耗的数据模型。根据地铁车辆轮对镟修要求,提出一种轮对镟修的控制限策略。在轮对磨耗模型的基础上,给出该镟修策略的蒙特卡罗仿真模型。然后利用蒙特卡罗仿真方法,以轮对期望使用寿命和期望镟修次数为指标,对不同的轮对镟修策略进行分析,从而实现轮对镟修策略的优化。结果表明:轮对轮缘厚度较大或较小时,轮缘磨损均较严重,而踏面直径的磨损速率和轮缘厚度不具有相关性;当轮缘厚度减少到27~28.5 mm时,通过镟修将轮缘厚度恢复到30 mm,这样的镟修策略能较好地延长轮对期望使用寿命,达到约13.2~15.3年。

关键词: 地铁车辆轮对, 高斯过程, 磨耗模型, 镟修策略, 优化

Abstract: Based on the wheel wear data of Guangzhou Metro, this paper analyzes the characteristics of wheel wear and re-profiling at first. With the application of the theory of Gaussian processes, a new data-driven model of wheel wear is given in terms of the variation of wheel diameter and flange thickness. A control limit policy for the wheel re-profiling decision is also proposed. According to the wheel wear model, the Monte Carlo simulation model/procedure of the re-profiling strategy is described in detail. Utilizing the Monte Carlo simulation, this wheel re-profiling strategy is optimized in order to maximize the expected lifetime of wheel finally. The results indicate that wheel wear is higher when wheels have a thicker or thinner flange, and the decreasing rate of the wheel diameter is independent of the thickness of wheel flange. Furthermore, the better selection for the wheel re-profiling strategies is that wheels should be restored to 30 mm when the flange is worn down to 27~28.5 mm, which could extend the expected lifetime of wheels to 13.2~15.3 years.

Key words: Gaussian process, Metro wheel, Optimization, Re-profiling strategy, Wear model

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