基于自适应多尺度形态梯度变换的滚动轴承故障特征提取

Feature extraction for roller bearing fault diagnosis based on adaptive multi-scale morphological gradient transformation

滚动轴承故障信号是一种典型的周期性冲击信号,如何从含有强噪声的振动信号中有效地提取出冲击特征信号是轴承故障诊断的关键。基于数学形态学理论,提出了一种自适应多尺度形态梯度变换(AMMG)方法,能够在有效抑制噪声的同时很好的保留信号的细节。仿真信号和实测轴承故障信号的分析结果表明,与常用的包络解调分析和近来提出的另一种基于数学形态学的形态闭变换方法相比较,自适应多尺度形态梯度变换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 type signal is the characteristic response of a defected roller bearing.How to extract impulsive signal from a noised vibration signal becomes the key step for bearing fault diagnosis.A novel method named adaptive multi-scale morphological gradient（AMMG） based on mathematical morphology was proposed for feature extraction of a roller bearing fault signal here.The AMMG technique had the advantage to depress noise and keep the detail of a signal.Compared with the envelope demodulation method and the morphological closed transformation one,simulation and test results demonstrated that the proposed AMMG technique can extract impulse signals more effectively from the original signals disturbed with strong background noise;moreover,the computation of AMMG is relatively simple and fast;it provides an effective way to extract features for fault diagnosis of roller bearings.