摘要
为了实现机械故障的有效诊断,准确提取在强背景噪声中的微弱信号特征,提出一种基于平衡正交多小波的降噪方法。多小波具有多个尺度函数和小波函数,具备单小波无法同时满足的优良性质,可以匹配信号中不同的特征信息。平衡正交多小波可以有效地消除Gibbs现象,并且避免了多小波的预处理过程,减小数据冗余。给出了平衡正交多小波算法和该降噪法的实施步骤,并将该算法和传统多小波算法的降噪效果进行了实验对比,实验证明该法能有效提取微弱信号,保留其信息特征。
In order to extract fault features of weak signal from the strong noise, a new denoising method based on balance orthogonal multiwavelet is proposed. Muhiwavelet has several scaling functions and wavelet functions, and matches different characteristics of signals, and possesses the excellent properties that scalar wavelet cannot satisfy simultaneously. Moreover, the balance orthogonal muhiwavelet can avoid the Gibbs phenomena and their processes have the advantages in denoising. The algorithm and the implementation steps of this denoising method are de scribed. The experimental comparison of the denoising effect between this method and the traditional multiwavelet algorithm was done. The experiments indicate that this method can extract the fault feature submerged in a heavy noise and maintain signal smoothness.
出处
《机械科学与技术》
CSCD
北大核心
2012年第6期996-998,共3页
Mechanical Science and Technology for Aerospace Engineering
基金
河南省科技攻关项目(112102210128)
河南省教育厅自然科学研究项目(2011C510005)资助
关键词
正交平衡多小波
信号降噪
弱信号提取
故障诊断
balance orthogonal multiwavelets
signal denoising
extracting signal features
fault diagnosis
