摘要
柴油机声信号包含了丰富的运行状态信息,为了能有效地提取特征参数,需要对柴油机声信号进行去噪处理。针对传统小波阈值去噪和经验模态分解(EMD)去噪的不足,提出了一种将小波阈值与EMD相结合的去噪方法。借助EMD的自适应分解特性,在原始信号分解的基础上,利用相关系数法确定信号主导和噪声主导本征模函数(IMF)分量的分界点,将改进的小波阈值函数对噪声主导的IMF分量进行阈值去噪,再进行信号重构。仿真实验和实测结果表明,该方法去噪效果更优,适合非线性非平稳信号去噪,能够保留柴油机声信号的原貌特征。
Diesel engine acoustic signal contains abundant running status information. In order to effectively extract characteristic parameters, need to deal with the noise of diesel engine signals. In view of the shortcomings of traditional wavelet threshold denoising and empirical mode decomposition （EMD） denoising, this paper proposes a denoising method based on wavelet threshold and EMD. Using the EMD decomposition of adaptive characteristics, on the basis of the original signal decomposition, using correlation coefficient method to determine the boundary of signal and noise dominate intrinsic mode function （IMF） component. The improved wavelet threshold function is used to carry out the threshold denoising, to reconstruct the signal. Simulation results and the measured results show that the method has better denoising effect and is suitable for nonlinear nonstationary signal denoising, can preserve the original characteristics of diesel engine acoustic signal.
出处
《微型机与应用》
2016年第17期72-75,79,共5页
Microcomputer & Its Applications
关键词
小波变换
EMD
阈值函数
去噪处理
wavelet transform
empirical mode decomposition
threshold function
denoising processing
