Feature extraction of anode effect based on digital filter and local mean decomposition

Abstract

The fault signal is a non-stationary and nonlinear signal, and because of the complexity of the field environment, the fault signal often has a lot of noise interference. In order to reduce the noise interference to the greatest extent, a feature extraction method based on digital filter and the local mean decomposition is proposed. Firstly, the Fourier transform is used to obtain the dominant frequency of the signals. Then, an IIR low-pass digital filter is designed to achieve the effect of noise reduction. Finally, the de-noised signal is decomposed by local mean decomposition. Every component PF can be represented as the product of the envelope signals and the frequency modulated signals. The component PF1 containing the highest power is selected to conduct energy spectrum analysis, and the fault features are exacted. The results show that the method can effectively extract the fault features, proving the feasibility of the proposed method.