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Journal of Applied Mathematics
Volume 2014 (2014), Article ID 283606, 6 pages
http://dx.doi.org/10.1155/2014/283606
Research Article

Application of EMD-Based SVD and SVM to Coal-Gangue Interface Detection

School of Information and Electronics Engineering, Shandong Institute of Business and Technology, Yantai 264005, China

Received 1 November 2013; Accepted 21 March 2014; Published 14 April 2014

Academic Editor: Feng Gao

Copyright © 2014 Wei Liu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Coal-gangue interface detection during top-coal caving mining is a challenging problem. This paper proposes a new vibration signal analysis approach to detecting the coal-gangue interface based on singular value decomposition (SVD) techniques and support vector machines (SVMs). Due to the nonstationary characteristics in vibration signals of the tail boom support of the longwall mining machine in this complicated environment, the empirical mode decomposition (EMD) is used to decompose the raw vibration signals into a number of intrinsic mode functions (IMFs) by which the initial feature vector matrices can be formed automatically. By applying the SVD algorithm to the initial feature vector matrices, the singular values of matrices can be obtained and used as the input feature vectors of SVMs classifier. The analysis results of vibration signals from the tail boom support of a longwall mining machine show that the method based on EMD, SVD, and SVM is effective for coal-gangue interface detection even when the number of samples is small.