小波分析在轴承故障特征信号降噪中的应用

英文篇名：The application of wavelet analysis to bearing fault characteristic signal de-noise
中文刊名：山东理工大学学报(自然科学版)
英文刊名：Journal of Shandong University of Technology(Natural Science Edition)
作者：裴新才 ; 许同乐
英文作者：PEI Xin-cai ; XU Tong-le(School of Mechanical Engineering ; Shandong University of Technology ; Zibo 255091 ; China)
中文关键词：小波分析 ; 阈值降噪 ; 故障特征
英文关键词：wavelet analysis ; threshold value de-noising ; fault characteristic
出版日期：2011-03-25
机构：山东理工大学机械工程学院;
年：2011
期：02
出版单位：山东理工大学学报(自然科学版)

摘要

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.

引文

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