摘要
减振器是轨道车辆的重要部件,在目前的研究中,缺少对轨道车辆一系垂向减振器劣化阶段进行辨识的研究。为了实现对减振器的劣化阶段进行辨识,提出了一种基于粒子群优化支持向量机(PSO-SVM)模型的减振器性能劣化辨识方法。首先通过仿真软件获得减振器的加速度信号,提取特征后利用SVM模型对特征进行筛选融合,其次使用粒子群寻优算法对SVM模型的参数进行寻优处理,最后实现了对减振器服役性能劣化状态的辨识。通过实验验证了该方法的有效性,实验结果表明:SVM模型对减振器进行劣化辨识具有可行性,采用寻优算法能够提升方法的辨识率。
Shock absorber is an important part of rail vehicles,while the research on identifying the deterioration stage of the primary vertical shock absorbers of rail vehicles is insufficient.In order to realize the identification of the deterioration stage of the shock absorber,a method for identifying the performance degradation of shock absorbers based on the PSO-SVM model is proposed.First,the acceleration signal of the shock absorber is obtained through the simulation software.After extracting the features,the SVM model is used to screen and fuse the features.Then,the particle swarm optimization algorithm is used to optimize the parameters of the SVM model.Finally,the identification of the deterioration state of the service performance of the shock absorber is realized.The effectiveness of the method is verified by the experiment,and the results show that:The SVM model is feasible to identify the deterioration of the shock absorber.The optimization algorithm can improve the identification rate of the method.
作者
姜良奎
卢昌宏
方柳川
JIANG Liangkui;LU Changhong;FANG Liuchuan(CRRC Qingdao Sifang Co.Ltd.,Qingdao 266111,China;School of Mechanical Engineering,Southwest Jiaotong University,Chengdu 610031,China)
出处
《机械》
2021年第3期32-38,共7页
Machinery
基金
国家重点研发计划(2017YFB1201201)。
关键词
减振器
劣化辨识
PSO
SVM
shock absorber
deterioration identification
PSO
SVM
