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针对煤矿安全生产中的综放工作面煤矸界面探测问题,提出利用煤矸下落冲击钢板的振动特征来探测煤矸界面的方法。煤矸振动信号表现出非平稳特征,采用经验模态分解方法将复杂矿井环境下的煤矸振动信号分解成固有模态分量。选择包含煤矸振动特征的前7个本征模函数(IMF)分量,通过Hilbert变换得到Hilbert谱。分析不同放煤状态下钢板振动信号的Hilbert谱发现,顶煤下落时的Hilbert谱分布较均匀,而煤矸混放时的Hilbert谱呈现不均匀分布。根据信息熵理论,提出了基于Hilbert谱信息熵的煤矸振动特征提取方法。试验结果表明,顶煤下落时的Hilbert谱信息熵要大于煤矸混放时的Hilbert谱信息熵,因此,煤矸振动的Hilbert谱信息熵特征能够准确地反映放煤状态。
Aiming at the problem of coal gangue interface detection in fully mechanized coal caving mining face in coal mine safety production, a method to detect coal gangue interface by utilizing the vibration characteristics of coal gangue falling impact plate is proposed. The coal gangue vibration signal shows non-stationary characteristics, and the empirical mode decomposition method is used to decompose the coal gangue vibration signal into the natural modal component under complicated mine environment. The first seven intrinsic mode function (IMF) components containing the coal gangue vibration characteristics are selected and Hilbert spectra are obtained by Hilbert transform. Hilbert spectrum of steel vibration signals under different caving conditions was analyzed. It was found that the Hilbert spectrum of the top coal fell more evenly, while the Hilbert spectrum of mixed coal gangue presented uneven distribution. According to the theory of information entropy, a method to extract vibration characteristics of coal gangue based on Hilbert spectrum entropy is proposed. The experimental results show that the entropy of Hilbert spectrum at the top coal fall is larger than the entropy of Hilbert spectrum at coal gangue mixing. Therefore, the Hilbert spectral information entropy characteristics of gangue vibration can accurately reflect the coal discharge status.