PREDICTION OF CUTTING LOAD OF DRUM SHEARER BASED ON DEEP BELIEF NETWORK
A prediction model based on Deep Belief Network（ DBN） was proposed to accurately predict the cutting load of the shearer’s spiral drum. The DBN model uses 7 characteristic parameters which include 2 guiding boots,2 smooth boots,rocker arm vibration,idler shaft,and cutting motor current as visual inp...
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| Format: | Article |
| Language: | zho |
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Editorial Office of Journal of Mechanical Strength
2020-01-01
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| Series: | Jixie qiangdu |
| Subjects: | |
| Online Access: | http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2020.02.003 |
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| _version_ | 1841535777524154368 |
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| author | MAO Jun GUO Hao CHEN HongYue |
| author_facet | MAO Jun GUO Hao CHEN HongYue |
| author_sort | MAO Jun |
| collection | DOAJ |
| description | A prediction model based on Deep Belief Network（ DBN） was proposed to accurately predict the cutting load of the shearer’s spiral drum. The DBN model uses 7 characteristic parameters which include 2 guiding boots,2 smooth boots,rocker arm vibration,idler shaft,and cutting motor current as visual input. By means of unsupervised level greedy learning,the higher level features are represented,the intelligence of the identification process is enhanced,and the complexity and imprecision of artificial features extraction are avoided. The test results show that the proposed method is suitable for predicting the load of the spiral drum of coal miner,which has strong characteristic extraction ability and better performance than BP neural network. |
| format | Article |
| id | doaj-art-5d4e6b1b687d4e8fa72b38f5c14b530f |
| institution | Kabale University |
| issn | 1001-9669 |
| language | zho |
| publishDate | 2020-01-01 |
| publisher | Editorial Office of Journal of Mechanical Strength |
| record_format | Article |
| series | Jixie qiangdu |
| spelling | doaj-art-5d4e6b1b687d4e8fa72b38f5c14b530f2025-01-15T02:28:05ZzhoEditorial Office of Journal of Mechanical StrengthJixie qiangdu1001-96692020-01-014227027530607287PREDICTION OF CUTTING LOAD OF DRUM SHEARER BASED ON DEEP BELIEF NETWORKMAO JunGUO HaoCHEN HongYueA prediction model based on Deep Belief Network（ DBN） was proposed to accurately predict the cutting load of the shearer’s spiral drum. The DBN model uses 7 characteristic parameters which include 2 guiding boots,2 smooth boots,rocker arm vibration,idler shaft,and cutting motor current as visual input. By means of unsupervised level greedy learning,the higher level features are represented,the intelligence of the identification process is enhanced,and the complexity and imprecision of artificial features extraction are avoided. The test results show that the proposed method is suitable for predicting the load of the spiral drum of coal miner,which has strong characteristic extraction ability and better performance than BP neural network.http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2020.02.003ShearerSpiral drumCutting loadDeep belief network |
| spellingShingle | MAO Jun GUO Hao CHEN HongYue PREDICTION OF CUTTING LOAD OF DRUM SHEARER BASED ON DEEP BELIEF NETWORK Jixie qiangdu Shearer Spiral drum Cutting load Deep belief network |
| title | PREDICTION OF CUTTING LOAD OF DRUM SHEARER BASED ON DEEP BELIEF NETWORK |
| title_full | PREDICTION OF CUTTING LOAD OF DRUM SHEARER BASED ON DEEP BELIEF NETWORK |
| title_fullStr | PREDICTION OF CUTTING LOAD OF DRUM SHEARER BASED ON DEEP BELIEF NETWORK |
| title_full_unstemmed | PREDICTION OF CUTTING LOAD OF DRUM SHEARER BASED ON DEEP BELIEF NETWORK |
| title_short | PREDICTION OF CUTTING LOAD OF DRUM SHEARER BASED ON DEEP BELIEF NETWORK |
| title_sort | prediction of cutting load of drum shearer based on deep belief network |
| topic | Shearer Spiral drum Cutting load Deep belief network |
| url | http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2020.02.003 |
| work_keys_str_mv | AT maojun predictionofcuttingloadofdrumshearerbasedondeepbeliefnetwork AT guohao predictionofcuttingloadofdrumshearerbasedondeepbeliefnetwork AT chenhongyue predictionofcuttingloadofdrumshearerbasedondeepbeliefnetwork |