随机共振消噪和EMD分解在轴承故障诊断中的应用

Application of Stochastic Resonance for Denoising and EMD to Bearing Fault Diagnosis

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(stochastic resonance,SR)和经验模态分解(Empirical Mode Decomposition,EMD)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EMD分解,通过求取本征模函数(Intrinsic Mode Function,IMF)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,实现微弱信号检测,更有效地应用于轴承的故障诊断。

In view of the difficulty of fault feature extraction from strong background noise in actual fault diagnosis, a method of bearing fault diagnosis based on stochastic resonance （SR） and empirical mode decomposition （EMD） was presented. First, SR was employed as the pretreatment to remove noise in bearing vibration signal by virtue of its good effect on enhancing the signal-to-noise ratio; then the de-noised signal was decomposed by EMD. Through calculating the amplitude spectrums of the intrinsic mode functions （IMF） , fault frequency of bearing was found. The experimental results showed that this method can improve signal-to-noise ratio, realize the weak signal detection, and apply to the bearing fault diagnosis more effectively.