综放工作面煤矸界面识别理论与方法研究
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摘要
本文对综采放顶煤开采过程中的煤矸界面识别问题进行了系统研究。阐述了利用振动方法探测煤矸界面的基本原理,建立了煤矸界面识别模型,确定了煤矸识别实验系统的组成。运用经典时域、频域分析及时频联合分析方法,提取了煤矸振动信号的时频域特征,给出了其直观的谱图模型。详细研究了基于Hilbert-Huang变换的煤矸振动信号分析方法,针对Hilbert-Huang变换的端点效应,给出了基于相关系数的IMF分量选择算法；通过对Hilbert-Huang变换的结果——IMF分量、Hilbert谱和Hilbert边际谱的分析,提出了基于IMF分量的能量、IMF分量矩阵奇异值分解、Hilbert谱能量熵以及Hilbert边际谱能量等四种煤矸振动特征提取方法。在特征提取基础上,提出了基于Mahalanobis距离、BP神经网络和支持向量机的三种模式分类方法,进行了煤矸界面识别仿真实验,并对实验结果进行了分析和比较。最后探讨了基于煤矸界面识别理论的综采放顶煤工艺。
Coal-gangue interface recognition (CIR) in fully mechanized caving face was systematically studied in this thesis. Firstly, the basic principles of detect coal-gangue interface using vibration method and the structure of CIR experimental system were introduced. The theoretical model of CIR was also established. Then, the vibration features of coal-gangue were extracted by classic time-domain analysis and spectrum analysis. In addition, the intuitive time-frequency spectrum was given by STFT, Wigner-Ville distribution and wavelet transform techniques. The feature extraction methods of coal-gangue vibration signals based on Hilbert-Huang transform (HHT) were discussed emphatically. Considering the end effect of HHT, the selection algorithm of intrinsic mode functions (IMFs) based on correlation coefficient was given. Consequently, the four feature extraction methods based on IMFs energy, singular value of IMFs matrix, information entropy of Hilbert spectrum and marginal spectrum energy were proposed respectively. The simulation experiments of CIR were conducted under different feature extraction methods, which were proved that the classifiers based on Mahalanobis distance, BP neural network and support vector machine all possessed good classification performance. Lastly, the top-caving technology based on CIR was presented.
Coal-gangue interface recognition (CIR) in fully mechanized caving face was systematically studied in this thesis. Firstly, the basic principles of detect coal-gangue interface using vibration method and the structure of CIR experimental system were introduced. The theoretical model of CIR was also established. Then, the vibration features of coal-gangue were extracted by classic time-domain analysis and spectrum analysis. In addition, the intuitive time-frequency spectrum was given by STFT, Wigner-Ville distribution and wavelet transform techniques. The feature extraction methods of coal-gangue vibration signals based on Hilbert-Huang transform (HHT) were discussed emphatically. Considering the end effect of HHT, the selection algorithm of intrinsic mode functions (IMFs) based on correlation coefficient was given. Consequently, the four feature extraction methods based on IMFs energy, singular value of IMFs matrix, information entropy of Hilbert spectrum and marginal spectrum energy were proposed respectively. The simulation experiments of CIR were conducted under different feature extraction methods, which were proved that the classifiers based on Mahalanobis distance, BP neural network and support vector machine all possessed good classification performance. Lastly, the top-caving technology based on CIR was presented.
引文
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