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Coal–Rock Interface Recognition Based on Permutation Entropy of LMD and Supervised Kohonen Neural Network


Affiliations
1 School of Mechatronic Engineering, China University of Mining and Technology, Xuzhou - 221116, China
2 School of Mechatronic Engineering, China University of Mining and Technology, Xuzhou - 221116, India
3 College of Mechanical and Electrical Engineering, Hohai University, Changzhou - 213022, China
 

Owing to the difficulty in coal-rock interface recognition during the process of coal mining, the shearer is damaged at a high frequency. To avoid this problem, a method is proposed for coal-rock interface recognition based on permutation entropy calculated using the local mean decomposition (LMD) method and supervised Kohonen neural network (SKNN) by performing sound signal analysis. The complex and nonstationary sound signal is adaptively decomposed by LMD. Given that the decomposed product function (PF) components contain the main information of the features, permutation entropy (PE) is used to reflect the complexity and irregularity in each PF component and is defined as the input of the SKNN model. Finally, the optimal SKNN model is obtained by training the samples. The experimental results show that the comprehensive recognition rate of a coal-rock interface is up to 89%. A coal-rock interface can be recognized effectively by sound signal analysis.

Keywords

Coal–Rock Recognition, Local Mean Decomposition, Permutation Entropy, Supervised Kohonen Neural Network, Sound Signal.
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  • Coal–Rock Interface Recognition Based on Permutation Entropy of LMD and Supervised Kohonen Neural Network

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Authors

Yong Li
School of Mechatronic Engineering, China University of Mining and Technology, Xuzhou - 221116, China
Gang Cheng
School of Mechatronic Engineering, China University of Mining and Technology, Xuzhou - 221116, India
Xihui Chen
College of Mechanical and Electrical Engineering, Hohai University, Changzhou - 213022, China
Chang Liu
College of Mechanical and Electrical Engineering, Hohai University, Changzhou - 213022, China

Abstract


Owing to the difficulty in coal-rock interface recognition during the process of coal mining, the shearer is damaged at a high frequency. To avoid this problem, a method is proposed for coal-rock interface recognition based on permutation entropy calculated using the local mean decomposition (LMD) method and supervised Kohonen neural network (SKNN) by performing sound signal analysis. The complex and nonstationary sound signal is adaptively decomposed by LMD. Given that the decomposed product function (PF) components contain the main information of the features, permutation entropy (PE) is used to reflect the complexity and irregularity in each PF component and is defined as the input of the SKNN model. Finally, the optimal SKNN model is obtained by training the samples. The experimental results show that the comprehensive recognition rate of a coal-rock interface is up to 89%. A coal-rock interface can be recognized effectively by sound signal analysis.

Keywords


Coal–Rock Recognition, Local Mean Decomposition, Permutation Entropy, Supervised Kohonen Neural Network, Sound Signal.

References





DOI: https://doi.org/10.18520/cs%2Fv116%2Fi1%2F96-103