数学形态学和HHT在轴承故障诊断中的应用

Application of Mathematical Morphology and HHT in Fault Diagnosis of Bearings

数学形态学滤波算法具有很强的抑制脉冲干扰的能力,但滤除白噪声的能力却不及小波算法。针对这一不足,在对信号进行形态滤波之前先进行小波消噪,再进行HHT分析提取故障特征频率。通过仿真和示例证实了该方法可以有效地消除信号干扰噪声,提取轴承故障特征,达到对滚动轴承故障诊断的目的。

The mathematical morphological filtering algorithm has strong impulse suppression ability,but not as well as the wavelet algorithms on filtering out white noises.Aiming at the shortage,the wavelet denoising combining with mathematical morphology transform is taken as the filter process unit,and then the processed signal is analyzed by HHT to extract the fault characteristic frequency.The result shows that the method is able to eliminate efficiently noise jamming of vibration signals and extract bearing fault characteristic,the objective of fault diagnosis of rolling bearing is achieved.