Caving coal-rock identification based on EEMD-KPCA and KL divergence

The realization of automatic top coal caving (key technology in a fully mechanized caving mining face) can enable the coal miners to be away from the working face and to remotely control the top coal caving,thereby protecting coal miners. Moreover,the effective and real-time caving coal-rock identification in the process of top coal caving can provide a theoretical basis for the control of top coal caving. Considering the real-time recognition of caving coal-rock and the efficiency of the fully mechanized caving mining,a new caving coal-rock identification method based on EE- MD-KPCA and KL divergence is proposed. The method is based on the vibration signals caused by the impact of ca- ving coal-rock and hydraulic support tail beam on the scene. Firstly,the vibration signals are decomposed by Ensemble Empirical Mode Decomposition ( EEMD) and a number of Intrinsic Mode Functions ( IMFs) are obtained. To con- struct the eigenvectors characterizing the caving coal and rock,the energy,kurtosis and sample entropy of each IMF are calculated respectively. Then the Kernel Principal Component Analysis (KPCA) is employed to reduce the dimension- ality of the feature vectors. The caving coal-rock is characterized by the KPCA low-dimensional feature of the vectors composed of the energy of IMFs,the kurtosis of IMFs,the sample entropy of IMFs respectively. Finally,the values of the KL divergence between the eigenvector of ’unknown sample’ and the eigenvector of caving coal sample or caving rock sample are calculated,the real-time recognition of caving coal-rock is achieved by comparing the values of the KL divergence,and the feature extraction time consuming and recognition effectiveness of different features characterizing caving coal-rock are compared. Then BP neural network is used to verify the validity of the eigenvectors based on EE- MD-KPCA. The results show that the identification method based on EEMD-KPCA and KL divergence can realize the real-time recognition of caving coal-rock,and greatly reduce the influence of the traditional identification method on the efficiency of the fully mechanized caving mining. The KPCA low-dimensional feature of the vectors composed of the energy and kurtosis of IMFs decomposed by EEMD is more effective than other eigenvectors for characterizing the ca- ving coal-rock.