基于自回归最小熵反褶积的滚动轴承故障诊断

Fault Diagnosis for Rolling Bearing Based on Autoregressive Minimum Entropy Deconvolution

轴承故障信号中的周期冲击成分会受到轴承元件间碰撞产生非周期冲击成分以及工况噪声的干扰,难以提取故障特征。使用自回归最小熵反褶积方法对故障信号处理,首先用自回归模型滤除非周期冲击成分,再使用最小熵反褶积方法对周期冲击成分进行增强,通过仿真和实验信号处理结果证明了该方法的有效性。

Cycle impact component of the bearing fault signal can be influenced by the non periodic impact component and working condition noise produced by the bearing components. Fault component can be disturbed by non periodic impact component and noise. Autoregressive minimum entropy deconvolution method for fault signal processing was used. First,autoregressive model filter non- periodic impact component was used,and then the minimum entropy deconvolution method to enhance the fault component was used. Signal processing by simulation and experiment results prove the effectiveness of the proposed method.