| Issue |
MATEC Web Conf.
Volume 232, 2018
2018 2nd International Conference on Electronic Information Technology and Computer Engineering (EITCE 2018)
|
|
|---|---|---|
| Article Number | 04075 | |
| Number of page(s) | 5 | |
| Section | Circuit Simulation, Electric Modules and Displacement Sensor | |
| DOI | https://doi.org/10.1051/matecconf/201823204075 | |
| Published online | 19 November 2018 | |
Research on coal-rock recognition based on sound signal analysis
1
College of Mechanical and Electrical Engineering, Hohai University, Changzhou, 213022, China
2
School of Mechatronic Engineering, China University of Mining and Technology, Xuzhou, 221116, China
a Corresponding author: Xihui Chen; Email: chenxh@hhu.edu.cn
The recognition of the cutting state of shearer is the key technology to realize variable speed cutting and mining automation. It is of great significance for improving shearer reliability, ensuring personal safety and improving coal quality. This paper proposed a coal-rock recognition method based on sound signal analysis. The original sound signal produced during the cutting process of shearer is decomposed by variational mode decomposition (VMD), and the obtained IMFs can construct a signal matrix. The signal matrix is processed by singular value decomposition (SVD), and a series of singular values can be obtained and defined as the signal features. Finally, the coal-rock recognition is realized by extreme learning machine (ELM) based on the extracted signal features. The experiment results show that the overall recognition accuracy is 91.7% under the actual cutting condition, which verifies the effectiveness of the proposed method in coal-rock recognition, and lays a theoretical foundation for the automation and intellectualization of shearer mining.
© The Authors, published by EDP Sciences, 2018
This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0 (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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