小波变换及经验模式分解方法在电机轴承早期故障诊断中的应用

电机轴承早期故障的有效诊断是实现安全生产、避免大事故的技术前提。文中用高精度加速度传感器采集电机轴承振动信号，采用小波变换实现信噪分离，提取淹没在噪声背景中的早期故障特征信息，然后对提纯的信号进行经验模式分解（EMD）而得到若干个基本模态分量（IMF），再计算各基本模态分量的频谱。理论及试验研究结果表明：按此方法得到的各基本模态分量的频谱突显了轴承的故障特征信息，能有效诊断出轴承的早期故障。

STUDY ON THE METHOD OF INCIPIENT MOTOR BEARING FAULT DIAGNOSIS BASED ON WAVELET TRANSFORM AND EMD

Incipient fault diagnosis in bearings is the technical prerequisite for safe production and to avoiding accidents.A highly precise acceleration transducer is used to sample vibration signals in bearings.Incipient bearing fault characteristic signals obscured by noise background are extracted by using wavelet decomposition method.The extracted signals are decomposed by means of Empirical Mode Decomposition(EMD) to obtain several intrinsic mode functions(IMFs).And the frequency spectra of IMFs are calculated finally.The results of theoretical and experiments research show that the spectra of IMFs obtained by the above method reveal the characteristic information in bearings clearly,which can be used to detect incipient faults in bearings.