摘要
针对滚动轴承早期故障冲击信号微弱,强噪声干扰下故障特征难以提取等问题,提出了基于总体平均经验模态分解(EEMD)与1.5维谱的滚动轴承故障诊断方法。由于经验模态分解(EMD)在对信号进行分解时容易产生模态混叠现象,引入总体平均经验模态分解(EEMD)。首先将最小熵解卷积(MED)作为前置滤波器,对原始信号进行降噪处理,再利用1.5维谱对经过EEMD分解得到的较为敏感的本征模态函数进行分析,得到各个分量的1.5维包络谱,最终判断轴承是否存在故障。通过仿真信号及实验信号验证了文中所论方法的可行性和有效性。
For the problems such as weak early shock signal of rolling bearing and difficulty in extracting fault features under strong noise interference,a method of rolling element bearing fault diagnosis based on EEMD and 1.5-dimensional spectrum is proposed,The empirical mode decomposition method is easy to produce false component and the mode mixing phenomenon,and the improved algorithm based on EEMD is used. First,use minimum entropy deconvolution(MED) as a prefilter to denoise the original signal,and then use 1.5-dimensional spectrum pairs to pass EEMD. The more sensitive intrinsic modal functions obtained by the decomposition are analyzed to obtain the 1.5-dimensional envelope spectrum of each component. Finally,it is judged whether the bearing has a fault. The feasibility and effectiveness of the proposed method are verified by simulation signals and experimental signals.
引文
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