小波分析在轴承故障特征信号降噪中的应用

The application of wavelet analysis to bearing fault characteristic signal de-noise

为有效识别滚动轴承故障特征,需对含噪的实际信号进行降噪.基于小波具有多分辨分析,可有效区分信号中噪声的特点,采用Matlab将滚动轴承内圈故障信号进行小波分解,对分解后的系数进行分层软阈值降噪.为验证降噪的有效性,将去噪后的信号进行频域分析,经验证与实际相一致,证明小波在信号降噪方面有着非常大的优越性.

In order to effectively identify the failure characteristics of rolling bearing,de-noise is needed to the signal which contains noise.Based on the characteristic of multi-resolution analysis of wavelet analysis,it is effective in distinguishing noise in signals.In this paper,Mat-lab is adopted for simulation.The fault signals of rolling bearing inner race are decomposed by wavelet.Then the decomposed coefficients will be layered using soft threshold value de-noising method.To verify the effectiveness of noise reduction,the de-noised signals are analyzed by frequency domain analysis.The results are consistent with the reality,indicating that wavelet has many advantages in the signal de-noise.