作者：叶运广, 宁静, 种传杰, 崔万里, 刘棋
作者单位：西南交通大学机械工程学院, 四川 成都 610031
关键词：高速列车;蛇行失稳;改进的集合经验模态分解;Hilbert变换
摘要：
为研究高速列车蛇行失稳时转向架横向、纵向和垂向的时频能特征，针对集合经验模态分解（EEMD）的模态分裂问题，提出一种改进的集合经验模态分解-Hilbert变换（MEEMD-HT）方法，首先对列车速度330～350 km/h时转向架蛇行失稳状态下的横向、纵向和垂向振动信号进行MEEMD分解，再通过HT分析MEEMD-HT能量谱和边际谱，并将MEEMD-HT分析结果与集合经验模态分解-Hilbert变换（EEMD-HT）对比。结果表明：列车蛇行失稳时，横向失稳现象最为明显，频率、能量分布高度集中，振动最为剧烈，纵向、垂向相对次之，但失稳现象显著；同时，MEEMD-HT方法能够体现出信号的时频能细节分布特征，优于传统的EEMD-HT方法。
Feature analysis of high-speed train bogie hunting instability based on modified ensemble empirical mode decomposition and Hilbert transformation
YE Yunguang, NING Jing, CHONG Chuanjie, CUI Wanli, LIU Qi
School of Mechanical Engineering, Southwest Jiaotong University, Chengdu 610031, China
Abstract: To investigate the features of lateral, longitudinal and vertical time-frequency and energy of high-speed train bogie in the hunting instability, in allusion to mode splitting problem of ensemble empirical model decomposition(EEMD), a modified ensemble empirical mode decomposition and Hilbert transformation(MEEMD-HT) method is presented in this paper. Firstly, the lateral, longitudinal and vertical vibration signals of the train bogie in the hunting instability state at 330-350 km/h were decomposed by MEEMD. Then, the MEEMD-HT energy spectrum and marginal spectrum were analyzed by HT. Meanwhile, the analysis results of MEEMD-HT were compared with that of traditional ensemble empirical mode decomposition-Hilbert transformation (EEMD-HT). The results show that when the train is in the hunting instability state, the lateral instability is the most obvious and the distribution of frequency and energy is highly centralized, the longitudinal and vertical are better than lateral despite of obvious instability. Details of time-frequency-energy of hunting motion signal will be discovered through MEEMD-HT method, which is superior to that of traditional EEMD-HT.
Keywords: high-speed train;hunting instability;modified ensemble empirical mode decomposition;Hilbert transformation
2016, 42(9): 120-125 收稿日期: 2015-12-10;收到修改稿日期: 2016-1-23
基金项目: 国家自然科学基金项目（51475387）；中央高校基本业务费专项基金项目（2682014CX033）；四川省科技创新苗子工程项目（2015102）
作者简介: 叶运广(1988-),男,重庆市人,硕士研究生,专业方向为智能化状态监测及故障诊断。
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