An interaction relational inference method for a coal-mining equipment system
Abstract Multiple potential interactions occur in a coal-mining equipment system during operation, which is crucial for understanding and predicting the dynamic system evolution. Existing methods for building interaction relations in coal-mining equipment systems face problems including incomplete s...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Springer
2025-01-01
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| Series: | Complex & Intelligent Systems |
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| Online Access: | https://doi.org/10.1007/s40747-024-01765-w |
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| _version_ | 1832571160532877312 |
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| author | Xiangang Cao Jiajun Gao Xin Yang Fuyuan Zhao Boyang Cheng |
| author_facet | Xiangang Cao Jiajun Gao Xin Yang Fuyuan Zhao Boyang Cheng |
| author_sort | Xiangang Cao |
| collection | DOAJ |
| description | Abstract Multiple potential interactions occur in a coal-mining equipment system during operation, which is crucial for understanding and predicting the dynamic system evolution. Existing methods for building interaction relations in coal-mining equipment systems face problems including incomplete selection of system nodes and difficulty in defining interaction-relation types and distinguishing interaction-relation weights. This study proposes an interaction-relation inference method EMIFC-CIRI for coal-mining equipment systems. EMIFC-CIRI first builds a monitoring index system for coal-mining equipment based on evidence and then accurately selects system nodes. The interaction constructor of the CIRI interaction inference model in this method introduces Gumbel-softmax technology, which autonomously generates multiple types of interaction relations based on several probability matrices. CIRI’s interaction optimizer introduces an attention mechanism to assign weights to interaction relations, and it predicts future system states based on device-monitoring data and interaction relations, optimizing the types and weights of interaction relations between nodes by reducing prediction errors. The study included experiments on relevant datasets. The results show that EMIFC-CIRI successfully built various interaction relations of different strengths, with a 156.17% improvement in interaction-relation quality and a 68.17% improvement in dynamic modeling performance compared with state-of-the-art comparison methods. This study provides a new perspective for research in the field of interaction reasoning of coal-mining equipment systems. |
| format | Article |
| id | doaj-art-43bd0bfa23b14ab683bfe241308ba1c1 |
| institution | Kabale University |
| issn | 2199-4536 2198-6053 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Springer |
| record_format | Article |
| series | Complex & Intelligent Systems |
| spelling | doaj-art-43bd0bfa23b14ab683bfe241308ba1c12025-02-02T12:49:25ZengSpringerComplex & Intelligent Systems2199-45362198-60532025-01-0111111810.1007/s40747-024-01765-wAn interaction relational inference method for a coal-mining equipment systemXiangang Cao0Jiajun Gao1Xin Yang2Fuyuan Zhao3Boyang Cheng4School of Mechanical Engineering, Xi’an University of Science and TechnologySchool of Mechanical Engineering, Xi’an University of Science and TechnologySchool of Mechanical Engineering, Xi’an University of Science and TechnologySchool of Mechanical Engineering, Xi’an University of Science and TechnologySchool of Mechanical Engineering, Xi’an University of Science and TechnologyAbstract Multiple potential interactions occur in a coal-mining equipment system during operation, which is crucial for understanding and predicting the dynamic system evolution. Existing methods for building interaction relations in coal-mining equipment systems face problems including incomplete selection of system nodes and difficulty in defining interaction-relation types and distinguishing interaction-relation weights. This study proposes an interaction-relation inference method EMIFC-CIRI for coal-mining equipment systems. EMIFC-CIRI first builds a monitoring index system for coal-mining equipment based on evidence and then accurately selects system nodes. The interaction constructor of the CIRI interaction inference model in this method introduces Gumbel-softmax technology, which autonomously generates multiple types of interaction relations based on several probability matrices. CIRI’s interaction optimizer introduces an attention mechanism to assign weights to interaction relations, and it predicts future system states based on device-monitoring data and interaction relations, optimizing the types and weights of interaction relations between nodes by reducing prediction errors. The study included experiments on relevant datasets. The results show that EMIFC-CIRI successfully built various interaction relations of different strengths, with a 156.17% improvement in interaction-relation quality and a 68.17% improvement in dynamic modeling performance compared with state-of-the-art comparison methods. This study provides a new perspective for research in the field of interaction reasoning of coal-mining equipment systems.https://doi.org/10.1007/s40747-024-01765-wCoal-mining equipmentInteraction relationsSystem dynamicsGraph neural network |
| spellingShingle | Xiangang Cao Jiajun Gao Xin Yang Fuyuan Zhao Boyang Cheng An interaction relational inference method for a coal-mining equipment system Complex & Intelligent Systems Coal-mining equipment Interaction relations System dynamics Graph neural network |
| title | An interaction relational inference method for a coal-mining equipment system |
| title_full | An interaction relational inference method for a coal-mining equipment system |
| title_fullStr | An interaction relational inference method for a coal-mining equipment system |
| title_full_unstemmed | An interaction relational inference method for a coal-mining equipment system |
| title_short | An interaction relational inference method for a coal-mining equipment system |
| title_sort | interaction relational inference method for a coal mining equipment system |
| topic | Coal-mining equipment Interaction relations System dynamics Graph neural network |
| url | https://doi.org/10.1007/s40747-024-01765-w |
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