基于形态分量分析的滚动轴承故障诊断方法

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。本文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，本文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。

A New Method for Fault Diagnosis of Rolling bearings Based on Morphological Component Analysis

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