基于循环自相关的滚动轴承故障特征提取研究

A study on fault feature extraction of rolling element bearing based on cyclic autocorrelation

滚动轴承在工作过程中产生的振动信号既有周期性又有随机性。周期性信号来源于滚动轴承的周期运转方式,这种周期性本质上是一种近似周期的冲击性振动;随机性信号来源于滚珠的滑移、制造误差等多种因素。因此,对于滚动轴承的故障诊断来说,理论上用循环平稳模型来描述故障特征比单纯用周期性模型描述更加合适。以循环平稳模型为基础,提出一种基于循环自相关的滚动轴承故障特征提取方法,通过理论分析以及滚动轴承故障仿真和试验,证明了循环频率可以反映故障特征频率。用循环自相关函数谱图与包络频谱图进行对比分析,说明在提取滚动轴承故障特征时,利用循环自相关函数法能够很好地抑制噪声。所提出的方法对于滚动轴承故障的精细诊断具有重要的意义。

Vibration signals produced from rolling element bearings in use are both periodic and random. Perio- dicity comes from almost periodic shock vibrations in nature tot its inherent periodic mode of operation, and randomness comes from uncertainty factors such as slip of the balls, manufacturing errors, and so on. The fault model of rolling element bearing is theoretically depicted in cyclostationary model better than in periodic model due to the above reasons. A method of fault teature extraction of rolling element bearing based on cyclic auto- correlation was presented. It proved that cyclic frequency was capable of reflecting the teature frequency of the faulty rolling element bearing by theoretical analysis, computation simulations and experiments. What＇s more, the cyclic autocorrelation function method could suppress the noise better than the conventional envelope spec- trum method could do when extracting fauh teatures of the rolling element bearings. The proposed method was of great significance tot the fault fine diagnosis of rolling element bearings.