基于角域经验小波变换的滚动轴承故障诊断

Fault Diagnosis of Rolling Element Bearing based on Angular Domain Empirical Wavelet Transform

针对变转速条件下滚动轴承故障特征难以提取的问题,提出了一种基于角域经验小波变换的变转速滚动轴承故障诊断方法。该方法首先利用等角度重采样将变转速下非平稳的滚动轴承故障振动信号转化为角域平稳信号,然后应用经验小波变换(Empirical mode decomposition,EWT)对角域平稳信号进行自适应分解,得到若干个经验模态分量,最后选择峭度值最大的经验模态分量进行包络谱分析,提取出滚动轴承故障的阶比特征。为提高经验小波变换的分解效率,对其频谱分割方法进行了改进。滚动轴承故障诊断实例表明,该方法能够有效地抑制噪声等干扰成分的影响,精确提取滚动轴承故障的阶比特征,为变转速条件下的滚动轴承故障诊断提供一种有效方法。

Considering the difficulty of extracting fault features of rolling element bearings, a fault diagno- sis method for rolling element bearings under variable speed conditions based on angular domain empirical wave- let transform （EWT） is proposed. Firstly, original rolling element bearing vibration signal is re -sampled with even - angular sampling so that the non - stationary signal in time - domain could be transformed into a stationa- ry one in angular - domain. Then, the stationary signal is decomposed adaptively into some empirical mode components by using the empirical wavelet transform （EWT）. Finally, the envelope spectrum analysis is car- ried on the empirical mode component that kurtosis value is maximum to extract the order ratio fault features. In order to promote the decomposition efficiency of EWT, the spectrum segmentation of EWT is improved. The rolling element bearing experimental signal analysis results show that the proposed method can effectively inhibit the influence of noise, other interference factors and extract the order ratio fault features accurately, it provides an effective method for rollering bearing fault diagnosis under variable speed.