基于形态分量分析的滚动轴承故障诊断方法

New method for fault diagnosis of rolling bearings based on morphological component analysis

在改进形态分量分析阈值去噪方法的基础上,提出了基于形态分量分析的滚动轴承故障诊断方法。形态分量分析根据信号中各组成成分的形态差异,构建不同的稀疏表示字典对各组成成分进行分离。当轴承出现局部损伤时,其振动信号往往由以包含轴承自身振动的谐振分量、包含轴承故障信息的冲击分量及随机噪声分量构成。谐振分量表现为信号中的平滑部分,而冲击分量则表现为信号中的细节部分,因此,可根据谐振分量与冲击分量的形态差异,实现二者的分离。该方法利用形态分量分析对滚动轴承故障信号中的谐振分量、冲击分量和噪声分量进行分离,然后根据冲击分量中冲击之间的时间间隔诊断滚动轴承故障。算法仿真和应用实例表明,该方法能有效地提取滚动轴承故障振动信号中的故障冲击成分。

Based on the improvement of the threshold denoising method of morphological component analysis （MCA）,a new method for fault diagnosis of rolling bearings based on MCA was proposed.According to the morphological difference of each component,different sparse dictionaries were built with MCA to separate each component from a signal. When a rolling bearing was locally damaged,its vibration signal was often composed of harmonic components with system characteristics of the rolling bearing,impulse components with fault information and random noise. The harmonic components represented the smooth part of the vibration signal,while the impulse components represented the detail part of the vibration signal,therefore,the two kinds of components could be separated according to the morphological difference .The harmonic components,impulse components and random noise component were separated from the vibration signal of a fault rolling bearing by using MCA,and the fault diagnosis of rolling bearing was carried out according to the time interval of impulses in the impulse components.The simulation and application examples proved that the proposed method is effective in extracting the fault impulse components from the vibration signal of a locally damaged rolling bearing.