基于形态梯度算子的滚动轴承故障特征提取

Feature Extraction for Roller Bearing Fault Diagnosis Based on Morphological Gradient Filter

滚动轴承故障信号是一种典型的脉冲调制信号,从含有噪声的振动信号中有效地提取出冲击脉冲信号是振动轴承故障诊断的关键.提出了采用形态梯度算子提取滚动轴承冲击脉冲信号的方法,并与常用的包络解调分析和形态闭算子进行了比较分析.仿真信号和实测轴承故障信号的分析结果表明:形态梯度解调算子具有更强的噪声抑制和脉冲提取能力,并且计算简单、快速,为滚动轴承故障特征提取提供了一种有效的方法.

Impulsive modulated signal is the typical response found in defected roller bearings.The means to extract the impulsive signal from the noised vibration signal becomes the key step for fault diagnosis of bearing.A method based on the morphological gradient（MG） filter was proposed for feature extraction of roller bearing fault signal.Comparative study was conducted with the most commonly used envelope demodulation and the morphological close method. Results of both simulation and experiments show that the proposed MG filter can extract the impulse signal more effectively from the original signal with strong background noise.Moreover,the computation cost of MG filter is much less.