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Article

Cutting Pattern Identification for Coal Mining Shearer through Sound Signals Based on a Convolutional Neural Network

1
Marine Equipment and Technology Institute, Jiangsu University of Science and Technology, No. 2 Mengxi Road, Zhenjiang 212003, China
2
School of Mechatronic Engineering, China University of Mining and Technology, No.1 Daxue Road, Xuzhou 221116, China
*
Author to whom correspondence should be addressed.
Received: 8 November 2018 / Revised: 2 December 2018 / Accepted: 4 December 2018 / Published: 10 December 2018
Recently, sound-based diagnosis systems have been given much attention in many fields due to the advantages of their simple structure, non-touching measurement style, and low-power dissipation. In order to improve the efficiency of coal production and the safety of the coal mining process, accurate information is always essential. It is indicated that the sound signal produced during the cutting process of the coal mining shearer contains much cutting pattern identification information. In this paper, the original acoustic signal is first collected through an industrial microphone. To analyze the signal deeply, an adaptive Hilbert–Huang transform (HHT) was applied to decompose the sound to several intrinsic mode functions (IMFs) to subsequently acquire 1024 Hilbert marginal spectrum points. The 1024 time-frequency nodes were reorganized as a 32 × 32 feature map. Moreover, the LeNet-5 convolutional neural network (CNN), with three convolution layers and two sub-sampling layers, was used as the cutting pattern recognizer. A simulation example, with 10,000 training samples and 2000 testing samples, was conducted to prove the effectiveness of the proposed method. Finally, 1971 testing sound series were recognized accurately through the trained CNN and the proposed method achieved an identification rate of 98.55%. View Full-Text
Keywords: cutting pattern recognition; sound signal; Hilbert–Huang transform; convolutional neural network; deep learning cutting pattern recognition; sound signal; Hilbert–Huang transform; convolutional neural network; deep learning
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MDPI and ACS Style

Xu, J.; Wang, Z.; Tan, C.; Lu, D.; Wu, B.; Su, Z.; Tang, Y. Cutting Pattern Identification for Coal Mining Shearer through Sound Signals Based on a Convolutional Neural Network. Symmetry 2018, 10, 736. https://0-doi-org.brum.beds.ac.uk/10.3390/sym10120736

AMA Style

Xu J, Wang Z, Tan C, Lu D, Wu B, Su Z, Tang Y. Cutting Pattern Identification for Coal Mining Shearer through Sound Signals Based on a Convolutional Neural Network. Symmetry. 2018; 10(12):736. https://0-doi-org.brum.beds.ac.uk/10.3390/sym10120736

Chicago/Turabian Style

Xu, Jing, Zhongbin Wang, Chao Tan, Daohua Lu, Baigong Wu, Zhen Su, and Yanbing Tang. 2018. "Cutting Pattern Identification for Coal Mining Shearer through Sound Signals Based on a Convolutional Neural Network" Symmetry 10, no. 12: 736. https://0-doi-org.brum.beds.ac.uk/10.3390/sym10120736

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