ECT Image Recognition of Pipe Plugging Flow Patterns Based on Broad Learning System in Mining Filling
The process of mining filling, when the slurry is transported to the goaf by the filling pipeline, is very important to find the location and size of the caking in the filling pipeline in time for the safe and stable operation of the mine filling pipeline. It is an important research work to detect...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Advances in Civil Engineering |
| Online Access: | http://dx.doi.org/10.1155/2021/6677639 |
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| _version_ | 1832550098916081664 |
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| author | Xuebin Qin ChenChen Ji Yutong Shen Pai Wang Mingqiao Li Junle Zhang |
| author_facet | Xuebin Qin ChenChen Ji Yutong Shen Pai Wang Mingqiao Li Junle Zhang |
| author_sort | Xuebin Qin |
| collection | DOAJ |
| description | The process of mining filling, when the slurry is transported to the goaf by the filling pipeline, is very important to find the location and size of the caking in the filling pipeline in time for the safe and stable operation of the mine filling pipeline. It is an important research work to detect different flow patterns after two-dimensional section reconstruction in closed filling pipeline based on ECT (electrical capacitance tomography) visualization method. Slurry flow in pipeline is regarded as a two-phase flow, and the multishape distribution was reconstructed into images by ECT and intelligently recognized by broad learning system (BLS) algorithm. BLS is a feedforward neural network with few optimization parameters and fast training speed. In this paper, three features of two-phase sample images, the number of regional blocks, the roundness of regional blocks, and barycenter of regional blocks, are combined with network structure of BLS to recognize different flow patterns. Through the simulation, the recognition accuracy of two-phase fillback image is more than 99%. This conclusion indicates the effectiveness of BLS to predict different two-phase flow patterns; it also provides a new solution for the pattern recognition of the flow pattern in the mining filling pipeline. |
| format | Article |
| id | doaj-art-c0d90596579b4bb3a90b1ef5317b4276 |
| institution | Kabale University |
| issn | 1687-8086 1687-8094 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Civil Engineering |
| spelling | doaj-art-c0d90596579b4bb3a90b1ef5317b42762025-02-03T06:07:43ZengWileyAdvances in Civil Engineering1687-80861687-80942021-01-01202110.1155/2021/66776396677639ECT Image Recognition of Pipe Plugging Flow Patterns Based on Broad Learning System in Mining FillingXuebin Qin0ChenChen Ji1Yutong Shen2Pai Wang3Mingqiao Li4Junle Zhang5College of Electrical and Control Engineering, Xi’an University of Science and Technology, 58 Yanta Road, Xi’an 710054, ChinaCollege of Electrical and Control Engineering, Xi’an University of Science and Technology, 58 Yanta Road, Xi’an 710054, ChinaCollege of Electrical and Control Engineering, Xi’an University of Science and Technology, 58 Yanta Road, Xi’an 710054, ChinaCollege of Electrical and Control Engineering, Xi’an University of Science and Technology, 58 Yanta Road, Xi’an 710054, ChinaCollege of Electrical and Control Engineering, Xi’an University of Science and Technology, 58 Yanta Road, Xi’an 710054, ChinaShaanxi Institute of Metrology Science, Dongyi Road, Yanta District, Xi’an 710065, ChinaThe process of mining filling, when the slurry is transported to the goaf by the filling pipeline, is very important to find the location and size of the caking in the filling pipeline in time for the safe and stable operation of the mine filling pipeline. It is an important research work to detect different flow patterns after two-dimensional section reconstruction in closed filling pipeline based on ECT (electrical capacitance tomography) visualization method. Slurry flow in pipeline is regarded as a two-phase flow, and the multishape distribution was reconstructed into images by ECT and intelligently recognized by broad learning system (BLS) algorithm. BLS is a feedforward neural network with few optimization parameters and fast training speed. In this paper, three features of two-phase sample images, the number of regional blocks, the roundness of regional blocks, and barycenter of regional blocks, are combined with network structure of BLS to recognize different flow patterns. Through the simulation, the recognition accuracy of two-phase fillback image is more than 99%. This conclusion indicates the effectiveness of BLS to predict different two-phase flow patterns; it also provides a new solution for the pattern recognition of the flow pattern in the mining filling pipeline.http://dx.doi.org/10.1155/2021/6677639 |
| spellingShingle | Xuebin Qin ChenChen Ji Yutong Shen Pai Wang Mingqiao Li Junle Zhang ECT Image Recognition of Pipe Plugging Flow Patterns Based on Broad Learning System in Mining Filling Advances in Civil Engineering |
| title | ECT Image Recognition of Pipe Plugging Flow Patterns Based on Broad Learning System in Mining Filling |
| title_full | ECT Image Recognition of Pipe Plugging Flow Patterns Based on Broad Learning System in Mining Filling |
| title_fullStr | ECT Image Recognition of Pipe Plugging Flow Patterns Based on Broad Learning System in Mining Filling |
| title_full_unstemmed | ECT Image Recognition of Pipe Plugging Flow Patterns Based on Broad Learning System in Mining Filling |
| title_short | ECT Image Recognition of Pipe Plugging Flow Patterns Based on Broad Learning System in Mining Filling |
| title_sort | ect image recognition of pipe plugging flow patterns based on broad learning system in mining filling |
| url | http://dx.doi.org/10.1155/2021/6677639 |
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