A dynamic load identification method of roadheader
For problems of varied roadheader loads and difficult real-time identification of dynamic load during roadheader working, a dynamic load identification method of roadheader was proposed which was based on multi neural networks and evidence theory. In the method, vertical, horizontal and axial compon...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | zho |
| Published: |
Editorial Department of Industry and Mine Automation
2016-10-01
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| Series: | Gong-kuang zidonghua |
| Subjects: | |
| Online Access: | http://www.gkzdh.cn/article/doi/10.13272/j.issn.1671—251x.2016.10.002 |
| _version_ | 1797868857211224064 |
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| author | WANG Wei YAN Lin JIN Tao WU Dongxun LIU Xingting ZHANG Shifeng HAN Yu ZHANG Ying |
| author_facet | WANG Wei YAN Lin JIN Tao WU Dongxun LIU Xingting ZHANG Shifeng HAN Yu ZHANG Ying |
| author_sort | WANG Wei |
| collection | DOAJ |
| description | For problems of varied roadheader loads and difficult real-time identification of dynamic load during roadheader working, a dynamic load identification method of roadheader was proposed which was based on multi neural networks and evidence theory. In the method, vertical, horizontal and axial components of vibration signal are analyzed separately by use of RBF neural network, then preliminary results from RBF neural network are fused by use of D-S evidence fusion theory, so as to identify dynamic load of roadheader real-timely. The analysis of actual example show that accuracy rate of dynamic load identification of roadheader achieves 88%. |
| first_indexed | 2024-04-10T00:03:31Z |
| format | Article |
| id | doaj.art-9095bc6ef8c543f3aa85937ac0132c0e |
| institution | Directory Open Access Journal |
| issn | 1671-251X |
| language | zho |
| last_indexed | 2024-04-10T00:03:31Z |
| publishDate | 2016-10-01 |
| publisher | Editorial Department of Industry and Mine Automation |
| record_format | Article |
| series | Gong-kuang zidonghua |
| spelling | doaj.art-9095bc6ef8c543f3aa85937ac0132c0e2023-03-17T01:41:49ZzhoEditorial Department of Industry and Mine AutomationGong-kuang zidonghua1671-251X2016-10-01421061110.13272/j.issn.1671—251x.2016.10.002A dynamic load identification method of roadheaderWANG Wei0YAN LinJIN Tao1WU DongxunLIU Xingting2ZHANG Shifeng3HAN Yu4ZHANG Ying51.State Grid Shanxi Electric Power Research Institute, Taiyuan 030001, China；2.State Grid Taiyuan Power Supply Company, Taiyuan 030012, China；3.State Grid Shanxi Economic and Technological Research1.State Grid Shanxi Electric Power Research Institute, Taiyuan 030001, China；2.State Grid Taiyuan Power Supply Company, Taiyuan 030012, China；3.State Grid Shanxi Economic and Technological Research1.State Grid Shanxi Electric Power Research Institute, Taiyuan 030001, China；2.State Grid Taiyuan Power Supply Company, Taiyuan 030012, China；3.State Grid Shanxi Economic and Technological Research1.State Grid Shanxi Electric Power Research Institute, Taiyuan 030001, China；2.State Grid Taiyuan Power Supply Company, Taiyuan 030012, China；3.State Grid Shanxi Economic and Technological Research1.State Grid Shanxi Electric Power Research Institute, Taiyuan 030001, China；2.State Grid Taiyuan Power Supply Company, Taiyuan 030012, China；3.State Grid Shanxi Economic and Technological Research1.State Grid Shanxi Electric Power Research Institute, Taiyuan 030001, China；2.State Grid Taiyuan Power Supply Company, Taiyuan 030012, China；3.State Grid Shanxi Economic and Technological ResearchFor problems of varied roadheader loads and difficult real-time identification of dynamic load during roadheader working, a dynamic load identification method of roadheader was proposed which was based on multi neural networks and evidence theory. In the method, vertical, horizontal and axial components of vibration signal are analyzed separately by use of RBF neural network, then preliminary results from RBF neural network are fused by use of D-S evidence fusion theory, so as to identify dynamic load of roadheader real-timely. The analysis of actual example show that accuracy rate of dynamic load identification of roadheader achieves 88%.http://www.gkzdh.cn/article/doi/10.13272/j.issn.1671—251x.2016.10.002roadheaderdynamic loading identificationadjustment of cutting speedmulti neural networksevidence theory |
| spellingShingle | WANG Wei YAN Lin JIN Tao WU Dongxun LIU Xingting ZHANG Shifeng HAN Yu ZHANG Ying A dynamic load identification method of roadheader Gong-kuang zidonghua roadheader dynamic loading identification adjustment of cutting speed multi neural networks evidence theory |
| title | A dynamic load identification method of roadheader |
| title_full | A dynamic load identification method of roadheader |
| title_fullStr | A dynamic load identification method of roadheader |
| title_full_unstemmed | A dynamic load identification method of roadheader |
| title_short | A dynamic load identification method of roadheader |
| title_sort | dynamic load identification method of roadheader |
| topic | roadheader dynamic loading identification adjustment of cutting speed multi neural networks evidence theory |
| url | http://www.gkzdh.cn/article/doi/10.13272/j.issn.1671—251x.2016.10.002 |
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