Coal-Gangue Interface Detection Based on Ensemble Empirical Mode Decomposition Energy Entropy

Abstract

To realize the unmanned automation of the full mechanized caving, the bottleneck problem of coal-gangue interface detection in top coal caving must be solved first. Targeting coal-gangue interface detection on fully mechanized mining face, an alternative scheme to detect coal-gangue interface based on vibration signal analysis of the tail boom support of the longwall mining machine. It is found that when coal and gangue fall, the characteristics of vibration signals generated by coal and gangue shocking the tail boom are different. First, EEMD algorithm is used to decompose the original vibration signals into intrinsic mode functions (IMFs). Each IMF represents the distribution of energy from high to low. EEMD algorithm can restrain the mode mixing phenomenon caused by empirical mode decomposition (EMD). The energy of vibration signals will change in different frequency bands when the top-coal fall down or the coal-gangue fall down. According the information theory, we define EEMD energy entropy to describe this change. Experimental results show that EEMD energy entropy of top-coal caving is always greater than that of coal-gangue caving. Thus, the Mahalanobis distance metric method based on EEMD energy entropy is proposed for coal-gangue interface detection. The results show the proposed method can be used as a robust empirical method for coal-gangue interface detection.

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“…15 In different engineering fields, for example, Shen et al 16 utilized the empirical modal decomposition (EMD) method to analyze the vibration of vehicle platforms. Liu et al 17 proposed a hybrid method based on ensemble empirical modal decomposition (EEMD) for coal-gangue interface detection. Zhang et al 18 proposed a quasi-online equivalent series resistance (ESR) identification method for fractional capacitance of the forward converter based on variational mode decomposition (VMD).…”