Noise Elimination for Coalcutter Vibration Signal Based on Ensemble Empirical Mode Decomposition and an Improved Harris Hawks Optimization Algorithm

Abstract

The vibration signal of the shearer is one of the important signals for coal and rock cutting mode recognition and fault diagnosis. However, the signal collected in the field contains a large amount of background noise, which is not conducive to subsequent analysis and processing. Therefore, a noise elimination method for coalcutter vibration signal based on Ensemble Empirical Mode Decomposition (EEMD) and an Improved Harris Hawks Optimization (HHO) algorithm is proposed in this paper. The vibration signal is first decomposed by EEMD to generate a series of intrinsic mode functions (IMF). The HHO algorithm was introduced to determine the optimal denoising threshold of each IMF. In addition, the original HHO has been improved to use the natural constant as the base exponential function to determine the escape energy trend line. Simulation results show that compared with the other four denoising methods, the signal waveform processed by this method is smoother. Under different types of signals and the same intensity of noise, the SNR increases by 70.9%, 6.7%, 2.6%, and 10.53% on average, respectively. The MSE decreases by 67.6%, 12.7%, 4.5%, and 5.42% on average. Under the same type of signal and different intensity of noise environment, the SNR is improved by 74.62%, 37.70%, 5.24%, and 39.72% on average, respectively. MSE decreased by 77.38%, 53.10%, 9.88%, and 54.67% on average. Finally, the method is applied to the shearer working state diagnosis system, and its actual effect is verified.