最小熵反褶积的数学形态法在滚动轴承故障特征提取中的应用

针对强噪声背景下滚动轴承故障特征提取,提出了基于最小熵反褶积的数学形态法。该方法先应用最小熵反褶积算法加强信号中的冲击特性,再利用数学形态法进行故障特征提取,其中选取具有双向脉冲提取能力的DIF滤波器作为形态算子,并以峭度值作为结构元素长度选取依据。仿真信号和滚动轴承的内外故障实例分析表明该方法具有较好的特征提取效果。通过对比发现:最小熵反褶积算法能够增大信号中峭度值,有效加强信号脉冲特性。

Applications of Mathematical Morphology Method to Fault Feature Extraction of Rolling Bearings Based on Minimum Entropy Deconvolution

Aiming at the extractions of fault features of rolling bearings under the strong noise background, a novel method, called mathematical morphology based on minimum entropy deconvolution (MMBMED) was proposed herein. In this method, MED was first introduced to enhance the impact behaviors of the signals. And the fault characteristics of defective bearings were extracted by MM method, where the DIF filter with catching the bidirectional pulses was adopted as morphological operator, and kurtosis was employed as the criterion to the length selection of structural elements. The effectiveness of the new method was validated by both of simulation signals and vibration signals of rolling bearings with the outer and inner race faults. Finally, the results disclose that MED is effective to increase signal kurtosis and to strengthen the impulsive characteristics.