基于磨耗数据驱动模型的轮对镟修策略优化和剩余寿命预报

基于广州地铁车辆轮对的磨耗实测数据, 首先针对踏面直径和轮缘厚度两个型面参数以及镟修比例系数, 给出了一种轮对磨耗的数据驱动模型. 根据轮对应用要求, 提出一种轮对镟修的控制限策略. 在前述轮对磨耗模型的基础上, 给出了该镟修策略的蒙特卡罗仿真步骤. 然后应用蒙特卡罗仿真方法, 实现以期望费用率最小为目标的轮对镟修策略优化, 并给出轮对剩余寿命仿真预报. 研究结果表明: 当轮缘厚度减少到27 mm至27.5 mm时, 通过镟修将轮缘厚度恢复到29mm至30mm, 这样的镟修策略能降低轮对镟修期望费用率, 并延长轮对期望使用寿命; 提出的轮对剩余寿命仿真预报法能够给出某时刻轮对剩余寿命的概率密度分布.

Optimization of the re-profiling strategy and remaining useful life prediction of wheels based on a data-driven wear model

Based on the wheel wear data of Guangzhou Metro,a new data-driven model of wheel wear is given in terms of the variations of wheel diameter,flange thickness,and the re-profiling gain.A control limit policy for the wheel re-profiling decision is also proposed.According to the wheel wear model,the Monte Carlo simulation 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 minimize the expected long-run cost...