计算机工程

所属专题: 智能交通专题

• 轨道交通专题 • 上一篇    下一篇

基于EEMD的高速列车转向架故障诊断

秦 娜,金炜东,黄 进,李智敏,刘景波   

  1. (西南交通大学电气工程学院,成都 610031)
  • 收稿日期:2013-05-15 出版日期:2013-12-15 发布日期:2013-12-13
  • 作者简介:秦 娜(1978-),女,博士研究生,主研方向:智能信息处理,模式识别;金炜东,教授、博士生导师;黄 进、李智敏,讲师;刘景波,博士研究生
  • 基金项目:
    国家自然科学基金资助重点项目(61134002);国家自然科学基金资助项目(61075104);中央高校基本科研业务费专项基金资助项目(SWJTU11BR039, SWJTU11ZT06)

Fault Diagnosis of High Speed Train Bogie Based on Ensemble Empirical Mode Decomposition

QIN Na, JIN Wei-dong, HUANG Jin, LI Zhi-min, LIU Jing-bo   

  1. (School of Electrical Engineering, Southwest Jiaotong University, Chengdu 610031, China)
  • Received:2013-05-15 Online:2013-12-15 Published:2013-12-13

摘要: 高速列车的转向架机械故障会引起转向架和车体振动信号的变化,严重影响高速列车运行的安全性和舒适度。为此,提出一种基于聚合经验模态分解的高速列车转向架故障诊断方法。针对转向架空气弹簧失气、抗蛇形减振器失效、横向减振器失效和原车4种工况进行仿真实验,得到列车不同位置的振动信号。信号经聚合经验模态分解得到一系列固有模态函数,分别提取能量矩特征,反映不同尺度上能量随时间的分布规律。将第2阶~第6阶经验模态能量矩构成的5维特征矢量作为支持向量机分类器的输入,在列车行驶200 km/h的速度下进行转向架故障识别,结果表明,该方法的识别正确率可达到95%以上。

关键词: 转向架, 故障诊断, 特征提取, 聚合经验模态分解, 能量矩, 支持向量机

Abstract: Mechanical fault of bogie seriously affects the security and comfort of the high speed train. Vibration signal of bogie and car body change with the fault occurrence, therefore, this paper proposes a fault diagnosis method of high speed train bogie based on Ensemble Empirical Mode Decomposition(EEMD). There are four typical working conditions in simulation experiment, such as air spring fault, yaw damper fault, lateral damper fault and normal condition. Vibration signal becomes several intrinsic mode functions after ensemble empirical mode decomposition. Energy moment feature is extracted to reflect the time distribution rule of energy. The 2nd to 6th energy moment are chosen to constitute 5-dimension eigenvector. In speed of 200 km/h, the Support Vector Machine(SVM) gets recognition rate. Simulation experimental result shows that the correct recognition rate of this method can achieve more than 95%.

Key words: bogie, fault diagnosis, feature extraction, Ensemble Empirical Mode Decomposition(EEMD), energy moment, Support Vector Machine(SVM)

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