School of Mechanical Electronic & Information Engineering,China University of Mining and Technology (Beijing),Beijing　 100083,China

针对综放工作面垮落煤岩性状识别的技术问题,采集了综放开采现场垮落煤岩冲击液压支架后尾梁的振动信号,提出了一种基于分形盒维数和小波包能量矩的识别方法。该方法结合分形盒维数对非线性信号整体定量描述的特点和小波包能量矩对非线性信号在各频带精细描述的特点,先对振动信号进行分形特征分析,求出其盒维数,然后对振动信号进行小波包变换,并计算各频带的能量矩。以分形盒维数和小波包能量矩构造特征向量,并作为BP神经网络的输入来识别顶煤垮落和顶板岩石垮落两种工况。试验结果表明:分形盒维数和小波包能量矩构造的特征向量可用于识别垮落煤岩,识别率达到95%。

In order to recognize the collapsing coal-rock in a fully mechanized caving face,the vibration signals caused by the impact of collapsing coal-rock and hydraulic support tail beam were collected at the scene. A recognition meth- od based on fractal box dimension and Wavelet packet energy moment was proposed. In the method,combining the whole description of nonlinear signal by fractal box dimension with the detailed description at different frequency bands by Wavelet packet moment,the fractal characteristics of vibration signals are analyzed and the box dimensions are cal- culated,then the energy moments at different frequency bands are calculated after the Wavelet packet transform of the vibration signals. The combination of fractal box dimension and Wavelet packet energy moments are used as feature vectors and the input of neural network to identify the two conditions-top coal collapse and roof rock collapse. The ex- perimental results show that the feature vectors can be used to recognize the collapsing coal-rock and the identification rate reaches 95% .

国家重点基础研究发展计划(973)资助项目(2014CB046306)；

Li Yiming,Fu Shichen,Jiao Yabo,et al. Collapsing coal-rock identification based on fractal box dimension and wavelet packet energy mo- ment[J]. Journal of China Coal Society,2017,42(3):803-808.