Coal-gangue interface detection based on Hilbert spectral analysis of vibrations due to rock impacts on a longwall mining machine

Abstract

Coal-gangue interface detection during top-coal caving mining is a challenging problem. This paper proposes a new empirical approach to detect the coal-gangue interface based on vibration signal analysis of the tail boom support of the longwall mining machine. Due to nonstationary characteristics in vibration signals in this complicated environment, the empirical mode decomposition is used to decompose the original vibration signals into intrinsic mode functions. The associated Hilbert transform calculates the instantaneous frequency and amplitude of the selected intrinsic mode functions, providing a novel Hilbert spectrum in the time-frequency domain. The distribution of the Hilbert spectrum of top-coal caving is found to be more uniform than that of coal-gangue caving. A method of vibration feature extraction based on the information entropy of the Hilbert spectrum is presented. The Mahalanobis distance function is used to classify the caving states. Experimental results show that the Mahalanobis distance measure applied to the information entropy of the Hilbert spectrum of vibration signals from the tail boom support of a longwall mining machine is effective for coal-gangue interface detection.