| Hilbert-Huang变换在煤矸界面探测中的应用 |
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| 引用本文： | 刘伟,华臻.Hilbert-Huang变换在煤矸界面探测中的应用[J].计算机工程与应用,2011,47(9):8-11. |
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| 作者姓名： | 刘伟 华臻 |
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| 作者单位： | 1.山东工商学院 信息与电子工程学院，山东 烟台 264005 2.中国矿业大学（北京） 机电与信息工程学院，北京 100083 |
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| 摘 要： | 为了解决煤矿综采工作面煤矸界面识别问题，将Hilbert-Huang变换应用于煤矸振动信号的特征提取。采用EMD方法可以将复杂环境下的煤矸振动加速度信号分解成固有模态分量，通过分析包含煤矸振动特征的前4个IMF分量，得到局部Hilbert边际谱和局部能量谱，进而发现当顶煤放落时，其振动信号的幅值和能量主要集中在100 Hz至600 Hz的频率范围内；而当煤矸混放时，其幅值和能量则主要集中在1 000 Hz左右，此时100 Hz至600 Hz频率范围内的幅值和能量相对有所减弱。根据上述特征定义特征函数，应用到煤矸界面识别的仿真实验中，取得了很好的识别效果。
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| 关 键 词： | 综采工作面 煤矸振动信号 Hilbert-Huang变换 经验模态分解法（EMD） 局部Hilbert边际谱 |
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Application of Hilbert-Huang transform to coal gangue interface detection |
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| LIU Wei,HUA Zhen.Application of Hilbert-Huang transform to coal gangue interface detection[J].Computer Engineering and Applications,2011,47(9):8-11. |
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| Authors: | LIU Wei HUA Zhen |
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| Affiliation: | 1.School of Information & Electronics Engineering，Shandong Institute of Business & Technology，Yantai，Shandong 264005，China 2.School of Mechanical Electronic & Information Engineering，China University of Mining & Technology，Beijing 100083，China |
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| Abstract: | A new method of vibration signal analysis of coal and gangue based on Hilbert-Huang transform is presented for coal and gangue interface recognition on fully mechanized mining face.To further extract useful information contained in response signals under complicated environment,empirical mode decomposition algorithm is used to decompose the original vibration signal of coal and gangue into the intrinsic modes.By analyzing local Hilbert marginal spectrum and local energy spectrum of the first four intrinsic mode function components,the difference of coal and rock in specific frequency interval is found that the amplitude and energy mainly distribute at frequency interval between 100 Hz and 600 Hz when coal is drawn,while the amplitude and energy mostly concentrate at 1000 Hz or so,when gangue is drawn.Furthermore,the further analysis result from marginal spectrum of each intrinsic mode function component agrees well with the conclusion above. The results show that the extracted features based on EMD and local Hilbert marginal spectrum can be served as coal and gangue interface recognition effectively. |
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| Keywords: | fully mechanized mining face vibration signal of coal and gangue Hilbert-Huang transform empirical mode decomposition local Hilbert marginal spectrum |
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