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Shock and Vibration
Volume 2017, Article ID 3809525, 13 pages
Research Article

Coal-Rock Recognition in Top Coal Caving Using Bimodal Deep Learning and Hilbert-Huang Transform

1School of Mechanical Engineering, Shandong University, No. 17923 Jingshi Road, Jinan 250061, China
2Key Laboratory of High Efficiency and Clean Mechanical Manufacture, Shandong University, Ministry of Education, No. 17923 Jingshi Road, Jinan 250061, China

Correspondence should be addressed to Zengcai Wang; nc.ude.uds@czgnaw

Received 5 April 2017; Revised 11 June 2017; Accepted 19 June 2017; Published 27 July 2017

Academic Editor: Matteo Filippi

Copyright © 2017 Guoxin Zhang 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.


This study employs the mechanical vibration and acoustic waves of a hydraulic support tail beam for an accurate and fast coal-rock recognition. The study proposes a diagnosis method based on bimodal deep learning and Hilbert-Huang transform. The bimodal deep neural networks (DNN) adopt bimodal learning and transfer learning. The bimodal learning method attempts to learn joint representation by considering acceleration and sound pressure modalities, which both contribute to coal-rock recognition. The transfer learning method solves the problem regarding DNN, in which a large number of labeled training samples are necessary to optimize the parameters while the labeled training sample is limited. A suitable installation location for sensors is determined in recognizing coal-rock. The extraction features of acceleration and sound pressure signals are combined and effective combination features are selected. Bimodal DNN consists of two deep belief networks (DBN), each DBN model is trained with related samples, and the parameters of the pretrained DBNs are transferred to the final recognition model. Then the parameters of the proposed model are continuously optimized by pretraining and fine-tuning. Finally, the comparison of experimental results demonstrates the superiority of the proposed method in terms of recognition accuracy.