A novel coal-rock cutting state identification model based on the Internet of Things
The sudden change in the interface between coal-rock mass can lead to the increased abrasion of picks and the failure rate of mining machinery. The safe and efficient coal-rock identification technology is the key to realize the intelligent control of coal mining machinery. To realize the real-time...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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KeAi Communications Co., Ltd.
2023-06-01
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| Series: | International Journal of Cognitive Computing in Engineering |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666307423000189 |
| _version_ | 1797377847201890304 |
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| author | Dong Song Chitra Venugopal |
| author_facet | Dong Song Chitra Venugopal |
| author_sort | Dong Song |
| collection | DOAJ |
| description | The sudden change in the interface between coal-rock mass can lead to the increased abrasion of picks and the failure rate of mining machinery. The safe and efficient coal-rock identification technology is the key to realize the intelligent control of coal mining machinery. To realize the real-time perception and accurate recognition of coal-rock cutting state information, a novel coal-rock cutting state identification model based on the Internet of Things (IoT) is explored. Specifically, by using the virtual prototype technology, multi-source heterogeneous data from acquisition, processing and identification of coal-rock cutting state information are fused and analyzed. First, the paper analyzes the physical and mechanical characteristics of coal-rock mass, and takes the cutting drum of bolter miner as the research object to theoretically analyze its load, which provides a foundation for the research on coal-rock cutting state identification. Second, a rigid-flexible coupling virtual prototype model of the cutting drum and coal-rock models under different cutting conditions are established. The simulation process is implemented by employing the discrete element method (DEM) to ensure the real-time transmission of motion information and state characteristic signals. Ultimately, the dynamic information of drum load during the coal-rock cutting process is obtained. Finally, LVQ (Learning Vector Quantization) and PSO-BP (Particle Swarm Optimization-Back Propagation) neural networks are created, and the variation coefficient, waveform factor, and peak factor of load curves of cutting drums in the coal-rock mass with different firmness coefficients are input into the neural networks as feature vectors for state recognition. The experimental results show that LVQ and PSO-BP neural networks can be used for coal-rock cutting state identification, and PSO-BP network has faster convergence speed and higher recognition efficiency, which provides a new scheme for coal-rock cutting state identification to improve the safety and efficiency of coal mining machinery. |
| first_indexed | 2024-03-08T19:58:14Z |
| format | Article |
| id | doaj.art-80c6f1b18ba742a8a9d4d6aa441d0a30 |
| institution | Directory Open Access Journal |
| issn | 2666-3074 |
| language | English |
| last_indexed | 2024-03-08T19:58:14Z |
| publishDate | 2023-06-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | International Journal of Cognitive Computing in Engineering |
| spelling | doaj.art-80c6f1b18ba742a8a9d4d6aa441d0a302023-12-24T04:46:40ZengKeAi Communications Co., Ltd.International Journal of Cognitive Computing in Engineering2666-30742023-06-014179186A novel coal-rock cutting state identification model based on the Internet of ThingsDong Song0Chitra Venugopal1China Coal Research Institute, Beijing 100013, China; Taiyuan Institute of China Coal Technology and Engineering Group, Taiyuan 030006, China; Shanxi Tiandi Coal Machinery Co. Ltd, Taiyuan 030006, China; National Engineering Laboratory for Coal Mining & Excavation Machinery Equipment, Taiyuan 030006, China; Corresponding author at: China Coal Research Institute, Beijing 100013, China.Department of Electrical and Renewable Energy Oregon, Institute of Technology, Oregon 97001, USAThe sudden change in the interface between coal-rock mass can lead to the increased abrasion of picks and the failure rate of mining machinery. The safe and efficient coal-rock identification technology is the key to realize the intelligent control of coal mining machinery. To realize the real-time perception and accurate recognition of coal-rock cutting state information, a novel coal-rock cutting state identification model based on the Internet of Things (IoT) is explored. Specifically, by using the virtual prototype technology, multi-source heterogeneous data from acquisition, processing and identification of coal-rock cutting state information are fused and analyzed. First, the paper analyzes the physical and mechanical characteristics of coal-rock mass, and takes the cutting drum of bolter miner as the research object to theoretically analyze its load, which provides a foundation for the research on coal-rock cutting state identification. Second, a rigid-flexible coupling virtual prototype model of the cutting drum and coal-rock models under different cutting conditions are established. The simulation process is implemented by employing the discrete element method (DEM) to ensure the real-time transmission of motion information and state characteristic signals. Ultimately, the dynamic information of drum load during the coal-rock cutting process is obtained. Finally, LVQ (Learning Vector Quantization) and PSO-BP (Particle Swarm Optimization-Back Propagation) neural networks are created, and the variation coefficient, waveform factor, and peak factor of load curves of cutting drums in the coal-rock mass with different firmness coefficients are input into the neural networks as feature vectors for state recognition. The experimental results show that LVQ and PSO-BP neural networks can be used for coal-rock cutting state identification, and PSO-BP network has faster convergence speed and higher recognition efficiency, which provides a new scheme for coal-rock cutting state identification to improve the safety and efficiency of coal mining machinery.http://www.sciencedirect.com/science/article/pii/S2666307423000189Cutting state identificationVirtual prototypeNeural networkInternet of Things |
| spellingShingle | Dong Song Chitra Venugopal A novel coal-rock cutting state identification model based on the Internet of Things International Journal of Cognitive Computing in Engineering Cutting state identification Virtual prototype Neural network Internet of Things |
| title | A novel coal-rock cutting state identification model based on the Internet of Things |
| title_full | A novel coal-rock cutting state identification model based on the Internet of Things |
| title_fullStr | A novel coal-rock cutting state identification model based on the Internet of Things |
| title_full_unstemmed | A novel coal-rock cutting state identification model based on the Internet of Things |
| title_short | A novel coal-rock cutting state identification model based on the Internet of Things |
| title_sort | novel coal rock cutting state identification model based on the internet of things |
| topic | Cutting state identification Virtual prototype Neural network Internet of Things |
| url | http://www.sciencedirect.com/science/article/pii/S2666307423000189 |
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