基于自适应粒子群算法的轨下基础病害识别  

Sub-rail Foundation Disease Identification Based on Adaptive Particle Swarm Optimization Algorithm

作　　者：伍伟嘉 杨俭[1] 袁天辰[1] 邵志慧 WU Weijia;YANG Jian;YUAN Tianchen;SHAO Zhihui(School of Urban Railway Transportation,Shanghai University of Engineering Science,201620,Shanghai,China)

机构地区：[1]上海工程技术大学城市轨道交通学院,上海201620

出　　处：《城市轨道交通研究》2023年第1期12-16,共5页Urban Mass Transit

基　　金：国家自然科学基金项目(11802170);上海市晨光计划项目(18CG66);上海市自然科学基金项目(19ZR1421700)。

摘　　要：基于车辆-轨道耦合动力学模型,对不同轨下基础病害情况下的轨枕振动响应进行仿真分析。提出利用支持向量机算法和粒子群算法对轨下基础病害进行识别。为了提高粒子群算法的收敛速度,提出一种自适应粒子群算法,并将所提方法应用于轨下基础病害识别仿真,分析不同病害条件下的轨枕振动特征。研究表明:所提算法的病害识别准确率≥80%,且其算法收敛速度有明显提升。Based on vehicle-track coupling dynamics model,the simulation analysis of the sleeper vibration response under different sub-rail foundation disease conditions is carried out.It is proposed to adopt SVM(support vector machine)algorithm and PSO(particle swarm optimization)algorithm to identify the sub-rail foundation basic diseases.To improve the convergence speed of PSO,an APSO(adaptive particle swarm optimization)algorithm is proposed,and the proposed method is applied to the identification and simulation of sub-rail foundation basic diseases,so as to analyze the vibration characteristics of sleepers under different disease conditions.The research shows that the disease identification accuracy rate of the proposed algorithm can achieve over 80%,and the convergence speed of the algorithm is significantly improved.

关 键 词：轨道交通 轨下基础 病害识别 

分 类 号：U213.213[交通运输工程—道路与铁道工程]