摘要
针对城轨列车的滚动轴承故障诊断问题,提出了一种经验模态分解(E M D,E m p i r i c a l Mode Deco-mposition)与支持向量机(SVM,Support Vector Machine)相结合的故障诊断方法。对采集到的振动信号进行小波消噪,利用经验模态分解将振动信号分解为一组本征模态函数(IMF,Intrinsic Mode Functions),并计算其能量从而获得信号的特征向量。采用支持向量机实现了滚动轴承故障分类。实验结果表明,本文提出的方法能够准确有效地识别城轨列车滚动轴承的工作状态和故障类型。
Aiming at the problem of fault diagnosis for rolling bearing of metro vehicle, a method combined empirical mode decomposition （EMD）, with support vector machine （SVM） was proposed. Firstly, the collected vibration signal was de-noised by using wavelet method. Then, the obtained signals were decomposed into a ifnite number of intrinsic mode functions （IMF） whose energy feature parameters were calculated to construct feature vectors. Finally, a certain SVM classiifer was built to recognize the fault pattern.The experiment results indicated that the proposed method could identify fault patterns for ruling bearing accurately and effectively.
出处
《铁路计算机应用》
2015年第8期1-4,15,共5页
Railway Computer Application
基金
国家"八六三"计划项目(2011AA110506)
