基于扁平衰减三角形结构元素的滚动轴承故障诊断

Bearing Fault Diagnosis Based on Flat Attenuated Triangle Structuring Element

形态滤波性能主要取决于结构元素,目前应用中以直线形结构元素为主,其外形与实际轴承故障时的脉冲信号相似度较弱,影响了冲击特征提取。针对该问题,提出了一种扁平衰减三角形结构元素,并应用滚动轴承故障诊断。通过滚动轴承内外圈故障的振动信号分析,结果表明所建议结构元素能够提取冲击脉冲信号。同时,对比传统扁平直线结构元素应用于相同的振动信号,扁平衰减三角形结构元素能够提高脉冲提取效果。

The performance of morphological filtering is highly determined by structuring elements（ SEs）. In real applications,flat linear SEs are quite often applied. However,they are little similar to the impulsive signals sampled from fault bearings. In order to tackle this situation,flat attenuated triangle SE is presented. It is validated that the proposed SE is effective to extract the impulsive features of the bearings with an outer and an inner race faults respectively. At the same time,conventional flat linear SE is used for further comparisons. The results show that the morphological filtering can extract much more impulses due to applying the flat attenuated triangle SEs.