CEEMD与广义形态差值滤波结合的故障诊断方法研究

发布时间：2018-04-18 08:37

  本文选题：CEEMD + 广义形态差值滤波器　； 参考：《华中师范大学学报(自然科学版)》2017年03期

【摘要】：为了提取滚动轴承早期微弱故障特征信息,提出一种互补总体平均经验模态分解(Complementary Ensemble Empirical Mode Decomposition,CEEMD)与广义形态差值滤波结合的故障诊断方法.该方法首先对振动信号进行CEEMD分解成若干不同尺度的本征模函数(Intrinsic Mode Function,IMF)分量,利用相关系数-峭度准则来选取故障信息丰富的IMF分量信号,并对其进行重构;然后采用广义形态差值滤波器对重构后的信号进行滤波,以滤除噪声干扰;最后利用Teager能量算子(Teager-Kaiser Energy Operator,TKEO)对去噪后的振动信号进行分析,提取振动信号的故障特征.滚动轴承振动信号分析试验结果证明了本文方法的有效性.
[Abstract]:In order to extract the weak fault feature information of rolling bearings in the early stage, a fault diagnosis method based on complementary Ensemble Empirical Mode error filtering and generalized morphological difference filtering is proposed.In this method, the vibration signal is firstly decomposed into intrinsic Mode function (IMF) components of different scales by CEEMD. The correlation-kurtosis criterion is used to select and reconstruct the IMF component signal with abundant fault information.Then the reconstructed signal is filtered by the generalized morphological difference filter to filter the noise interference. Finally, the vibration signal after denoising is analyzed by using the Teager energy operator Teager-Kaiser Energy operator TKEO, and the fault characteristics of the vibration signal are extracted.The result of vibration signal analysis of rolling bearing proves the effectiveness of this method.
【作者单位】： 昆明理工大学信息工程与自动化学院;云南省矿物管道输送工程技术研究中心;
【基金】：国家自然科学基金项目(61563024,51169007,61663017) 云南省科技计划项目(2015ZC005)
【分类号】：TH133.33
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本文编号：1767601