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

摘要

The empirical mode decomposition (EMD) is a temporal and spatial filtering based on the extrema characteristic scale of the signal. This method can preserve the nonlinearity and non-stability of signal, and has 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, and these IMF components is added to the IMF components of low frequencies to achieve de-noising signal. For 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. By contrast with the wavelet threshold de-noising method, the EMD threshold de-noising has adaptive advantage.