Coal and Rock Hardness Identification Based on EEMD and Multi-Scale Permutation Entropy

Abstract

This study offers an efficient hardness identification approach to address the problem of poor real-time performance and accuracy in coal and rock hardness detection. To begin, Ensemble Empirical Mode Decomposition (EEMD) was performed on the current signal of the cutting motor to obtain a number of Intrinsic Mode Functions (IMFs). Further, the target signal was selected among the IMFs to reconstruct the current signal according to the energy density and correlation coefficient criteria. After that, the Multi-scale Permutation Entropy (MPE) of the reconstructed signal was trained by the Adaboost improved Back Propagation (BP) neural network, in order to establish the hardness recognition model. Finally, the cutting arm’s swing speed and the cutting head’s rotation speed were adjusted based on the coal and rock hardness. The simulation results indicated that using the energy density and correlation criterion to reconstruct the signal can successfully filter out noise interference. Compared to the BP model, the relative root-mean-square error of the Adaboost-BP model decreased by 0.0633, and the prediction results were more accurate. Additionally, the speed control strategy based on coal and rock hardness can ensure the efficient cutting of the roadheader.

Experimental Investigation and Fault Diagnosis for Buckled Wet Clutch Based on Multi-Speed Hilbert Spectrum Entropy

“…Nguyen et al [ 11 ] constructed a speed invariant, stacked, sparse, autoencoder deep neural network for the gearbox with improved computational capabilities, and the results outperformed the conventional methods. Liu et al [ 12 ] proposed an efficient approach to identify the real-time problem based on multi-scale permutation entropy. Sun et al [ 13 ] developed a deep neural network for classification of induction motor faults, in which a sparse autoencoder is applied to improve the robustness of feature representation.…”