基于MED和峭度准则形态滤波的滚动轴承故障诊断

Fault Diagnosis of Rolling Element Bearings Based on MED and Kurtosis Criterion Morphological Filtering

针对滚动轴承故障诊断问题,提出一种基于最小熵解卷积(Minimum entropy deconvolution,MED)和峭度准则形态滤波的滚动轴承故障诊断方法。该方法首先通过MED对滚动轴承故障信号进行降噪处理,然后设计不同长度结构元素的形态滤波器对降噪后的信号进行差值形态滤波,最后利用峭度准则筛选出峭度值最大的最佳形态滤波分量,进行幅值谱分析提取轴承故障特征频率。应用该方法分析了滚动轴承内圈故障模拟信号和实验测试信号,取得良好的分析效果,证明了该方法的有效性。

A new fault diagnosis method based on Minimum entropy deconvolution（MED） and morphological filtering is presented for rolling element bearings. The strong background noise of rolling bearing is decreased by the MED method firstly, then different morphological filters with different length of structuring elements are applied to the de-noising signal.Subsequently, bearing＇s fault characteristic frequency is extracted with the amplitude spectrum analysis of morphological filtering component has the maxim kurtosis. The proposed method is used to analyze both a simulated an experimental signal of inner ring fault bearing and the good results validated the effectiveness of the proposed method.