Identification of Rock Properties of Rock Wall Cut by Roadheader Based on PSO-VMD-LSSVM
The problem of low digging efficiency and mining imbalance due to outdated digging technology and low degree of equipment intelligence has long existed in coal mine roadway excavation work. Lithology identification is the key to the intelligence of roadheading equipment. Accurate lithology identific...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-05-01
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| Series: | Frontiers in Earth Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/feart.2022.884633/full |
| _version_ | 1818252944851599360 |
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| author | Pengfei Qi Jucai Chang Xiao Chen Tuo Wang Mengyun Wu |
| author_facet | Pengfei Qi Jucai Chang Xiao Chen Tuo Wang Mengyun Wu |
| author_sort | Pengfei Qi |
| collection | DOAJ |
| description | The problem of low digging efficiency and mining imbalance due to outdated digging technology and low degree of equipment intelligence has long existed in coal mine roadway excavation work. Lithology identification is the key to the intelligence of roadheading equipment. Accurate lithology identification significantly affects the automatic control of roadheader cutting conditions. Completing the identification of lithology in the process of rock wall cutting by a roadheader involved the following steps: building a tunneling experiment platform, making four rock specimens with different lithologies, completing the tunneling simulation experiments on four lithologies, obtaining current sensor data of four lithologies cutting, and finally proposing an intelligent lithology identification method of PSO-VMD-LSSVM. The research results show that the particle swarm algorithm (PSO) optimized the variational modal decomposition (VMD) with minimum envelope information entropy as the fitness function can realize the adaptive decomposition of the current signal of truncated motors. The signal reconstruction can increase the signal-to-noise ratio of the current signal by selecting the eigenmodal components according to the energy density and correlation coefficient criterion. The multi-scale fuzzy entropy is used as the eigenvector of the reconstructed current signal as the fuzzy entropy of different lithology cut-off motor currents has better differentiation at different scales. The least-squares support vector machine (LSSVM) is used to classify the feature vectors processed by custom decomposition parameter VMD and gives a recognition rate of 87.5%. The recognition rate increases to 97.5% for the feature vectors processed by PSO-VMD. The particle swarm algorithm optimizes the noise reduction via VMD to effectively improve the lithology recognition rate. The research results can provide a methodological reference for rock property recognition during rock cutting by a roadheading machine. |
| first_indexed | 2024-12-12T16:32:14Z |
| format | Article |
| id | doaj.art-8c7444a0d66a490ca94d46069b488e6d |
| institution | Directory Open Access Journal |
| issn | 2296-6463 |
| language | English |
| last_indexed | 2024-12-12T16:32:14Z |
| publishDate | 2022-05-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Earth Science |
| spelling | doaj.art-8c7444a0d66a490ca94d46069b488e6d2022-12-22T00:18:46ZengFrontiers Media S.A.Frontiers in Earth Science2296-64632022-05-011010.3389/feart.2022.884633884633Identification of Rock Properties of Rock Wall Cut by Roadheader Based on PSO-VMD-LSSVMPengfei QiJucai ChangXiao ChenTuo WangMengyun WuThe problem of low digging efficiency and mining imbalance due to outdated digging technology and low degree of equipment intelligence has long existed in coal mine roadway excavation work. Lithology identification is the key to the intelligence of roadheading equipment. Accurate lithology identification significantly affects the automatic control of roadheader cutting conditions. Completing the identification of lithology in the process of rock wall cutting by a roadheader involved the following steps: building a tunneling experiment platform, making four rock specimens with different lithologies, completing the tunneling simulation experiments on four lithologies, obtaining current sensor data of four lithologies cutting, and finally proposing an intelligent lithology identification method of PSO-VMD-LSSVM. The research results show that the particle swarm algorithm (PSO) optimized the variational modal decomposition (VMD) with minimum envelope information entropy as the fitness function can realize the adaptive decomposition of the current signal of truncated motors. The signal reconstruction can increase the signal-to-noise ratio of the current signal by selecting the eigenmodal components according to the energy density and correlation coefficient criterion. The multi-scale fuzzy entropy is used as the eigenvector of the reconstructed current signal as the fuzzy entropy of different lithology cut-off motor currents has better differentiation at different scales. The least-squares support vector machine (LSSVM) is used to classify the feature vectors processed by custom decomposition parameter VMD and gives a recognition rate of 87.5%. The recognition rate increases to 97.5% for the feature vectors processed by PSO-VMD. The particle swarm algorithm optimizes the noise reduction via VMD to effectively improve the lithology recognition rate. The research results can provide a methodological reference for rock property recognition during rock cutting by a roadheading machine.https://www.frontiersin.org/articles/10.3389/feart.2022.884633/fullparticle swarm optimizationvariational modal decompositionminimum envelope entropymulti-scale fuzzy entropyleast square support vector machineidentify rock properties |
| spellingShingle | Pengfei Qi Jucai Chang Xiao Chen Tuo Wang Mengyun Wu Identification of Rock Properties of Rock Wall Cut by Roadheader Based on PSO-VMD-LSSVM Frontiers in Earth Science particle swarm optimization variational modal decomposition minimum envelope entropy multi-scale fuzzy entropy least square support vector machine identify rock properties |
| title | Identification of Rock Properties of Rock Wall Cut by Roadheader Based on PSO-VMD-LSSVM |
| title_full | Identification of Rock Properties of Rock Wall Cut by Roadheader Based on PSO-VMD-LSSVM |
| title_fullStr | Identification of Rock Properties of Rock Wall Cut by Roadheader Based on PSO-VMD-LSSVM |
| title_full_unstemmed | Identification of Rock Properties of Rock Wall Cut by Roadheader Based on PSO-VMD-LSSVM |
| title_short | Identification of Rock Properties of Rock Wall Cut by Roadheader Based on PSO-VMD-LSSVM |
| title_sort | identification of rock properties of rock wall cut by roadheader based on pso vmd lssvm |
| topic | particle swarm optimization variational modal decomposition minimum envelope entropy multi-scale fuzzy entropy least square support vector machine identify rock properties |
| url | https://www.frontiersin.org/articles/10.3389/feart.2022.884633/full |
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