针对铁路货车轴承结构复杂,早期微弱故障往往淹没于强烈的背景噪声中而难于提取的特性,提出了基于EEMD降噪和谱峭度法的共振解调技术.首先,将轮对跑合实验台上测得的振动信号进行EEMD分解;然后,根据峭度、标准相关系数选取包含故障信息较多且与原信号相关性较大的IMF分量,分别对每个IMF分量进行谱峭度分析,确定带通滤波器的最佳中心频率和带宽;最后,将滤波后的IMF分量重构并进行包络解调和频谱分析.通过对轴承内、外圈故障的实验研究,验证了该方法的有效性和可行性.该方法在铁路货车轴承早期故障诊断中具有较好的实际应用价值.%Considering railway wagon bearing structure complex and early weak fault is difficult to extract in strong background noise,a resonance demodulation method based on ensemble empirical mode decomposition(EEMD)and spectral kurtosis is proposed.First,the bearing fault signal is decomposed by EEMD method.Then according to kurtosis and standard correlation coefficient,the IMF component is selected,which contains more fault information and better relevance.Each selected IMF component is analyzed with spectral kurtosis,based on this,a band-pass filter is designed to conduct filtering processing to each selected IMF component.Finally,the frequency of the impact composition is extracted by Hilbert transformation and spectrum analysis is made of the filtered composite signal.Through the experimental study on the bearing inner and outer ring fault,the method can eliminate the background noise's interference,and can effectively diagnose the incipient fault of bearing.This method has good practical application values for early fault diagnosis of railway wagon bearing.
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