Coal–Rock Cutting Sound Denoising Based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise and an improved Fruit Fly Optimization Algorithm

Abstract

The cutting sound signal of a coal mining shearer is an important signal source for identifying the coal–rock cutting mode and load state. However, the coal–rock cutting sound signal directly collected from the industrial field always contains a large amount of background noise, which is not conducive to the subsequent feature extraction and recognition. Therefore, efficient noise elimination for the original signal is required. An intelligent processing method based on an improved complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) denoising algorithm is constructed for the cutting sound signal in this paper. CEEMDAN first decomposes the sound to generate a series of intrinsic modal functions (IMFs). Because the denoising threshold of each IMF is usually obtained by an experimental test or an empirical formula in the traditional CEEMDAN method, obtaining an optimal threshold set for each IMF is difficult. The processing effect is often restricted. To overcome this problem, the fruit fly optimization algorithm (FOA) was introduced for CEEMDAN threshold determination. Moreover, in the basic FOA, the scouting bee mutation operation and adaptive dynamic adjustment search strategy are applied to maintain the convergence speed and global search ability. The simulation result shows that the signal waveform processed by the improved CEEMDAN denoising algorithm is smoother than the other four typical eliminate noise signal algorithms. The output signal’s signal-to-noise ratio and mean square error are significantly improved. Finally, an industrial application of a shearer in a coal mining working face is performed to demonstrate the practical effect.