Coal-Rock Recognition in Top Coal Caving Using Bimodal Deep Learning and Hilbert-Huang Transform
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 learn...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2017-01-01
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| Series: | Shock and Vibration |
| Online Access: | http://dx.doi.org/10.1155/2017/3809525 |
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| _version_ | 1849403769866944512 |
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| author | Guoxin Zhang Zengcai Wang Lei Zhao Yazhou Qi Jinshan Wang |
| author_facet | Guoxin Zhang Zengcai Wang Lei Zhao Yazhou Qi Jinshan Wang |
| author_sort | Guoxin Zhang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-8305ec654c6d4cd0bab8ebd7840245eb |
| institution | Kabale University |
| issn | 1070-9622 1875-9203 |
| language | English |
| publishDate | 2017-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Shock and Vibration |
| spelling | doaj-art-8305ec654c6d4cd0bab8ebd7840245eb2025-08-20T03:37:11ZengWileyShock and Vibration1070-96221875-92032017-01-01201710.1155/2017/38095253809525Coal-Rock Recognition in Top Coal Caving Using Bimodal Deep Learning and Hilbert-Huang TransformGuoxin Zhang0Zengcai Wang1Lei Zhao2Yazhou Qi3Jinshan Wang4School of Mechanical Engineering, Shandong University, No. 17923 Jingshi Road, Jinan 250061, ChinaSchool of Mechanical Engineering, Shandong University, No. 17923 Jingshi Road, Jinan 250061, ChinaSchool of Mechanical Engineering, Shandong University, No. 17923 Jingshi Road, Jinan 250061, ChinaSchool of Mechanical Engineering, Shandong University, No. 17923 Jingshi Road, Jinan 250061, ChinaSchool of Mechanical Engineering, Shandong University, No. 17923 Jingshi Road, Jinan 250061, ChinaThis 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.http://dx.doi.org/10.1155/2017/3809525 |
| spellingShingle | Guoxin Zhang Zengcai Wang Lei Zhao Yazhou Qi Jinshan Wang Coal-Rock Recognition in Top Coal Caving Using Bimodal Deep Learning and Hilbert-Huang Transform Shock and Vibration |
| title | Coal-Rock Recognition in Top Coal Caving Using Bimodal Deep Learning and Hilbert-Huang Transform |
| title_full | Coal-Rock Recognition in Top Coal Caving Using Bimodal Deep Learning and Hilbert-Huang Transform |
| title_fullStr | Coal-Rock Recognition in Top Coal Caving Using Bimodal Deep Learning and Hilbert-Huang Transform |
| title_full_unstemmed | Coal-Rock Recognition in Top Coal Caving Using Bimodal Deep Learning and Hilbert-Huang Transform |
| title_short | Coal-Rock Recognition in Top Coal Caving Using Bimodal Deep Learning and Hilbert-Huang Transform |
| title_sort | coal rock recognition in top coal caving using bimodal deep learning and hilbert huang transform |
| url | http://dx.doi.org/10.1155/2017/3809525 |
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