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.
Highlights
Top-coal caving on a fully mechanized mining face is a coal mining method for gently inclined extra-thick coal seams or steeply inclined extra-thick coal seams [1], [2]
The ensemble empirical mode decomposition (EEMD) algorithm and energy entropy theory are applied to extract the vibration signal features of the tail boom support, which can be used for top-coal and coal-gangue caving state classification
The energy of vibration signals will change in different frequency bands when the top-coal fall down or the coal-gangue fall down, so we applied EEMD energy entropy to distinguish the two caving states
Summary
Top-coal caving on a fully mechanized mining face is a coal mining method for gently inclined extra-thick coal seams or steeply inclined extra-thick coal seams [1], [2]. Song et al proposed a new multi-class characteristic selection method based on vibration and acoustic signal and designed an effective minimum enclosing ball algorithm for rapid detection of coal-gangue in the caving process [8]–[10]. The vibration signals produced under two typical caving states of top-coal and coal-gangue are decomposed by EEMD, and the natural IMFs with frequencies arranged from high to low are obtained, which effectively suppresses the mode mixing in these IMFs. Since the energy of the vibration signal in different coal caving states varies with the frequency distribution, a new coal-gangue interface detection method combining the EEMD energy entropy feature and Mahalanobis Distance is proposed to describe this change quantitatively according to the information entropy theory.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
