基于EEMD形态谱和支持向量机复合的滚动轴承故障诊断方法

Fault diagnosis of ball bearing based on EEMD morphological spectrum and support vector machine

  • 摘要: 针对滚动轴承的内圈、外圈和滚动体故障提出了一种新的诊断方法,该方法融合了集总经验模态分解(EEMD)、形态谱和支持向量机(SVM)三种方法的优势.首先,利用经验模态分解对滚动轴承故障振动信号进行分解,得到若干个具有物理意义的内禀模态分量(IMF);其次,基于最大能量法筛选出含有故障特征信息最丰富的一个内禀模态分量为故障诊断数据源;再次,对数据源在选定尺度范围内进行形态谱的提取,从而构造故障特征向量;最后,利用支持向量机对滚动轴承的三种故障进行诊断.研究结果表明,该方法能够有效地诊断出滚动轴承的三种故障,且具有很高的故障诊断正确率.

     

    Abstract: Aiming at fault diagnosis of inner race,outer race and rolling element of ball bearing,a fusion method based on ensemble empirical mode decomposition(EEMD),morphological spectrum,and support vector machine(SVM) was proposed. Firstly,the vibration signal was decomposed by EEMD to get several intrinsic mode functions(IMFs) which have physical meanings. Secondly,the IMF which was rich in fault features was selected as the data source based on power maximum of IMFs. Thirdly,morphological spectrums in some scales of the IMF were extracted,and then they were adopted as the fault eigenvectors. Lastly,the three faults of ball bearing faults were diagnosed by the use of SVM. The conclusion is that the proposed method can diagnosis the faults of the ball bearing with high accuracy.

     

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