Abstract:

In view of the difficulties of fault feature extraction from strong
background noise in actual fault diagnosis,a method of bearing fault diagnosis based
on LMD was presented.First,SR was employed as the pretreatment to remove noise in bearing vibration signals by virtue of its good effect in enhancing the signal-to-noise ratio; then the de-noised signals were decomposed by LMD.Through calculating the amplitude spectrums of the PF,fault frequency of bearing was found.The experimental results show that this method can improve signal-to-noise ratio,and realize the weak signal detection,and apply to the bearing fault diagnosis more effectively.

Key words: fault diagnosis, stochastic resonance(SR), local mean decomposition(LMD), product function(PF), signal-to-noise ratio

摘要：

针对实际机械故障诊断中强噪声背景下难以提取故障特征的情况，提出了一种基于随机共振消噪(SR)和局域均值分解(LMD)的轴承故障诊断方法。首先，将轴承振动信号进行随机共振消噪，利用噪声增强振动信号的信噪比;然后，将消噪的信号再进行LMD分解，通过求取乘积函数(PF)幅值谱从而发现轴承故障频率。实验结果表明，该方法可以提高信噪比，实现微弱信号的检测,可有效地应用于轴承的故障诊断。
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关键词: 故障诊断, 随机共振, 局域均值分解, 乘积函数, 信噪比