形态分量分析在滚动轴承故障诊断中的应用

滚动轴承局部故障振动信号中的周期性冲击是识别故障的关键特征.形态分量分析在由多种形态原子组成的过完备字典基础上提取信号中的不同形态成分,基于这种思想提出了一种基于新型过完备复合字典的形态分量分析方法.依据滚动轴承故障振动信号中分量间的形态差异性,改进字典后该方法可以更具针对性地提取出包含故障特征的冲击分量,配合包络谱分析准确提取故障特征频率,诊断滚动轴承局部故障.对比基于快速谱峭度法的轴承故障诊断方法,该方法可以避免人为选择共振带产生的不准确性和非最优问题,提高了故障诊断效果.通过轴承仿真信号和故障实验信号分析验证了该方法的有效性. 

Application of morphological component analysis for rolling element bearing fault diagnosis

Periodical impulses in vibration signals are key features in rolling element bearing fault diagnosis. Based on an overcomplete dictionary composed of different morphological atoms, morphological component analysis can be used to extract the signal components of different types of morphologies. A new morphological component analysis method based on a novel over-completed dictionary was proposed herein. According to morphological differences between components in rolling element bearing fault vibration signal, the method after improved dictionary could more targeted to extract impulse components containing fault feature. Then through envelope spectrum analysis, the fault characteristic frequency was extracted accurately, and rolling element bearing local faults were diagnosed. Compared with the Fast Kurtogram method for bearing fault diagnosis, the new method could avoid non-accuracy and non-optimality problems caused by artificial choice of resonance band, and improve the effectiveness of fault diagnosis. By analyzing both the simulation signal and the experimental dataset of rolling element bearing faults, the proposed method is validated.