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

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振动与冲击 ›› 2011, Vol. 30 ›› Issue (10) : 104-108.

论文

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

李兵1,2; 张培林1; 刘东升2; 米双山2 ;任国全1

作者信息 +

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

Li Bing1,2; Zhang Pei-lin1; Mi Shuan-shan2; Liu Dong-sheng2; Ren Guo-quan1

Author information +

文章历史 +

摘要

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的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，本文提出了一种自适应多尺度形态梯度变换（AMMG）方法，它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析，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。

Abstract

Impulsive modulated type signal is the characteristic response of a defected roller bearing. How to extract the impulsive signal from the noised vibration signal becomes the key step for fault diagnosis of bearing. A novel method named Adaptive Multi-scale Morphological Gradient (AMMG) based on the mathematical morphology is proposed for feature extraction of roller bearing fault signal in this study. The AMMG technique has the advantage to depressing the noise as well as keeping the detail components of the signal. Comparing study is done with the most used envelope demodulation and the morphological close method. Results of application to the simulated signal and the real bearing fault signal demonstrated that the proposed AMMG technique can extracts the impulse signal more effectively from the original signal disturbed with strong background noise. Moreover, the computation of AMMG is relatively simple and it has provided one an effective and fast way to feature extraction for fault diagnosis of roller bearing.

导出引用

李兵;张培林;刘东升;米双山;任国全. 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[J]. 振动与冲击, 2011, 30(10): 104-108

Li Bing;Zhang Pei-lin;Mi Shuan-shan;Liu Dong-sheng;Ren Guo-quan. Feature extraction for roller bearing fault diagnosis based on the adaptive multi-scale morphological gradient transform [J]. Journal of Vibration and Shock, 2011, 30(10): 104-108