A denoising method of coal charge signal under load based on IWT and ICEEMDAN algorithm

Abstract

With the continuous improvement of signal noise reduction performance requirements, in view of the noise existing in the electromagnetic radiation signal acquisition process, the wavelet threshold function of traditional denoising methods has problems such as non-progressiveness and discontinuity at the threshold value. In order to achieve better signal noise reduction effect, this paper proposes a collection based on improved wavelet threshold (IWT) function and improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) combined denoising algorithm. Firstly, the algorithm is used to decompose the noisy signal, and calculate the intrinsic mode function (IMF) and correlation coefficient of each order to distinguish the noise from the correlated signal. Then, the IMF component dominated by the signal is reconstructed to complete the denoising. Simulations and experiments show that the algorithm can effectively remove the noise in the charge sensing signal, and its signal-to-noise ratio (SNR) is improved by 2.4062 compared with six similar algorithms such as the improved threshold function, CEEMD algorithm, and the root mean square error (RMSE) is reduced by 0.0866, and its noise-to-noise ratio(Rnn) is reduced by 3.103 compared with four algorithms such as the improved threshold function, the improved threshold function combined with the ensemble empirical mode decomposition (EEMD), and has a good noise reduction performance, and the research results provide a new method for capturing real signals. Decomposition (EEMD) combined with denoising and other four algorithms is reduced by 3.103, which has better noise reduction performance. The research results provide a new method for collecting real signals, This indicates the feasibility of the joint denoising algorithm.