A denoising method for loaded coal-rock charge signals based on a joint algorithm of IWT and ICEEMDAN

The electromagnetic radiation signal generated by loaded coal and rock is widely used to predict coal and rock dynamic disasters. However, due to the presence of significant electromagnetic interference at the coal mine site, the accuracy of the collected signals is insufficient. For the collected noisy charge induction signals, the wavelet threshold function of traditional denoising methods has problems such as non-progressiveness and discontinuity at the threshold. In order to achieve a better signal noise reduction effect, this paper proposes a collection based on an improved wavelet threshold (IWT) function and an improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) combined denoising algorithm. It overcomes mode aliasing and optimizes signal smoothness. 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. The simulation and experimental results show that this algorithm can effectively remove noise in charge induction signals, and its signal-to-noise ratio (SNR) is improved by 2.3482 and 0.095 compared to six algorithms such as IWT and VMD, respectively. Compared with four algorithms, including the improved threshold function and the improved threshold function combined with Ensemble Empirical Mode Decomposition (EEMD), its noise-to-noise ratio (Rnn) decreased by 3.103, showing good noise reduction performance. The results presented in this paper provide a new method for collecting real charge induction signals.