摘要
空气制动广泛应用于高铁车辆的制动,空气制动系统的可靠性和稳定性直接影响行车安全。文中基于多领域的物理建模软件AMESim,针对高速列车制动系统产品,提出应用试验设计的方法进行模型优化设计。减压阀是高速列车制动系统的关键组成部件,对高速列车的行车安全非常重要。因此以减压阀为对象,以提高减压阀的稳定性及可靠性为目标,进行减压阀的试验设计。首先,提出所有可能影响减压阀的稳定性和可靠性的因子,通过试验设计(DOE)方法,进行全组合的参数研究,根据线性回归系数以及pareto图找出影响减压阀稳定性和可靠性的关键因子;确定减压阀优化设计的目标量,以关键因子作为输入,以目标量作为输出,最终确定关键因子的值;对不可控因子进行统计学分析,验证减压阀的可靠性和稳定性的设计目标。
Air braking is widely used in High-Speed railway vehicle brake.The reliability and stability of air braking system directly affect the traffic safety.Based on the multi-domain physical modeling software AMESim,this paper proposes the method of applying Design of Experiment(DOE)to accomplish the goal of model optimal design for braking system products on High-Speed train.Pressure Reducing Valve is the key component of braking system of High-Speed train,which is very important for the safety of High-Speed train.Therefore,to improve the stability and reliability of Pressure-Reducing Valve,the method of DOE for pressure-reducing valve is carried out.Firstly,all the factors may affect the stability and reliability of Pressure-Reducing Valve are put forward.Through the method of Design of Experiment(DOE),the whole combination of Parameters Study is carried out to find out the key factors that affect the stability and reliability of Pressure-Reducing Valve by analyzing the Linear Regression coefficient and Pareto diagram.Identify the object of the optimal design of the pressure Reducing valve,take the key factor as input,take the object value as output,and finally determine the value of key factors.Statistical Analysis of uncontrollable factors is carried out to verify the reliability and stability of Pressure Reducing valve.
作者
韩朝霞
李邦国
王群伟
HAN Zhaoxia;LI Bangguo;WANG Qunwei(Beijing Zongheng Electro-Mechanical Technology Co.,Ltd.,Beijing 100094,China;Locomotive&Car Research Institute,China Academy of Railway Sciences Corporation Limited,Beijing 100081,China)
出处
《铁道机车车辆》
北大核心
2021年第5期149-155,共7页
Railway Locomotive & Car
关键词
减压阀
试验设计
参数研究
线性回归
统计学分析
pressure reducing valve
DOE
parameter study
linear regression
statistical analysis