基于小波包自相关的能量算子旋转机械故障诊断

Fault diagnosis of rotating machinery based on autocorrelation and wavelet packet energy demodulation

针对在强噪声背景下轴承振动信号的非线性,非平稳性以及信号出现的复杂调制现象,提出一种基于小波包熵与自相关函数相结合的能量算子解调故障诊断方法。该方法首先根据信号的小波包熵值对信号小波包降噪,其次用自相关函数分析的方法进一步抑制噪声对提取特征频率的干扰,最后对降噪处理过的信号进行能量算子解调,从而实现提取轴承的故障信号的幅值和频率信息。对机械故障振动信号进行实验分析表明,相对于单纯的小波包分析预处理存在的降噪效果不理想以及普通Hilbert解调法的运算精度满足不了诊断需求的情况,该方法能够有效解调出故障频率信息,实现对故障类别的推断。

In view of the nonlinear,non-stationary and complex modulation of the bearing vibration signal in a strong noise background,a new method is proposed for fault diagnosis based on wavelet packet entropy and autocorrelation function. Firstly,wavelet packet de-noising signal is performed by means of the signal of wavelet packet entropy. Then,the interference of noise to the characteristic frequency is suppressed by the method of correlation function analysis,and the energy operator demodulation of the signal is carried out,so as to realize the amplitude and frequency information of the fault signal of the bearing. The experimental analysis of mechanical fault vibration signal shows that the noise reduction effect is not ideal and the accuracy and the ordinary Hilbert demodulation method cannot meet the needs of the diagnosis compared with the wavelet packet analysis. The proposed method can effectively process the fault frequency information and realize the inference of fault types.