经验模态分解在电力绝缘气体SF6红外光谱去噪中的应用

经验模态分解(EMD)是以信号极值特征尺度为度量的时空滤波器,它充分保留了信号本身的非线性和非平稳特征,在信号去噪中具有较大的优势。本文以电力绝缘气体SF6为研究对象,在介绍E MD分解方法的基础上,首先对含噪的SF6光谱信号做EMD分解,得到各阶本征模态函数(IMF),然后对高频的IMF分量用阈值法进行处理,把经过阈值处理后的高频I MF分量与低频IMF分量叠加重构得到去噪后的信号。分析了在不同噪声水平上与小波阈值去噪方法的处理效果。实验结果表明EMD阈值去噪法有效地去除了噪声,较好地保留了光谱的细节信息,与小波阈值去噪方法相比较具有自适应的优势。

The Application of EMD De-noising on Infrared Spectrum of Power Insolated Gas SF6

The empirical mode decomposition is a temporal and spatial filtering based on the extremum characteristic scale of the signal.This method can well preserve the nonlinearity and non-stability of the signal,and has a potential superiority in de-noising.In this paper,the power insulated gas SF6 is served as the target and the method of EMD decomposition is introduced.First,infrared spectrum of SF6 polluted by white noise is decomposed into several intrinsic mode function(IMF) components.Then,the IMF components of high frequencies are preprocessed using the threshold method,whose results are added to the IMF components of low frequencies to achieve de-noised signal.On various noise levels,the effects of wavelet threshold de-noising and EMD threshold de-noising are analyzed by processing the noisy infrared spectra.The results show that the EMD threshold de-nosing method eliminates the noise effectively and preserves the detailed information of the original spectra well.Compared with the wavelet threshold de-noising method,the EMD threshold de-noising has adaptive advantage.