Research on Event Logic Knowledge Graph Construction Method of Robot Transmission System Fault Diagnosis
Knowledge graph technology has important guiding significance for efficient and orderly fault diagnosis of robot transmission system. Taking the historical robot maintenance logs of robot transmission system as the research object, a top-down fault diagnosis event logic knowledge graph construction...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9709362/ |
| _version_ | 1828866063293480960 |
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| author | Jianfeng Deng Tao Wang Zhuowei Wang Jiale Zhou Lianglun Cheng |
| author_facet | Jianfeng Deng Tao Wang Zhuowei Wang Jiale Zhou Lianglun Cheng |
| author_sort | Jianfeng Deng |
| collection | DOAJ |
| description | Knowledge graph technology has important guiding significance for efficient and orderly fault diagnosis of robot transmission system. Taking the historical robot maintenance logs of robot transmission system as the research object, a top-down fault diagnosis event logic knowledge graph construction method is proposed. Firstly, we define event arguments of fault phenomenon and fault cause events, define event argument classes and relation between classes, and construct an event logic knowledge ontology model. According to the event logic knowledge ontology, the fault diagnosis event argument entity and relation in the corpus are labeled, and an event logic knowledge extraction dataset is formed. Secondly, an event argument entity and relation joint extraction model is proposed. Using stacked bidirectional long short-term memory(BiLSTM) to obtain deep context features of text. As a supplement to stacked BiLSTM, self-attention mechanism extracts character dependency features from multiple subspaces, and uses conditional random field(CRF) to realize entity recognition. The character dependency features are mapped to the entity label weight embedding, and spliced with deep context features to extract relations. Bidirectional graph convolutional network(BiGCN) is introduced for relation inference, graph convolution features are used to update deep context features to perform joint extraction in the second phase. Experimental results show that this method can improve the effect of event argument entity and relation joint extraction and is better than other methods. Finally, an event logic knowledge graph of robot transmission system fault diagnosis is constructed, which provides decision support for autonomous fault diagnosis of robot transmission system. |
| first_indexed | 2024-12-13T04:42:34Z |
| format | Article |
| id | doaj.art-22947ede42c049a090f21321ab39816e |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-13T04:42:34Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-22947ede42c049a090f21321ab39816e2022-12-21T23:59:15ZengIEEEIEEE Access2169-35362022-01-0110176561767310.1109/ACCESS.2022.31504099709362Research on Event Logic Knowledge Graph Construction Method of Robot Transmission System Fault DiagnosisJianfeng Deng0https://orcid.org/0000-0002-6397-4334Tao Wang1https://orcid.org/0000-0002-6907-4142Zhuowei Wang2https://orcid.org/0000-0001-6479-5154Jiale Zhou3Lianglun Cheng4School of Automation, Guangdong University of Technology, Guangzhou, ChinaSchool of Automation, Guangdong University of Technology, Guangzhou, ChinaSchool of Computer, Guangdong University of Technology, Guangzhou, ChinaSchool of Automation, Guangdong University of Technology, Guangzhou, ChinaSchool of Computer, Guangdong University of Technology, Guangzhou, ChinaKnowledge graph technology has important guiding significance for efficient and orderly fault diagnosis of robot transmission system. Taking the historical robot maintenance logs of robot transmission system as the research object, a top-down fault diagnosis event logic knowledge graph construction method is proposed. Firstly, we define event arguments of fault phenomenon and fault cause events, define event argument classes and relation between classes, and construct an event logic knowledge ontology model. According to the event logic knowledge ontology, the fault diagnosis event argument entity and relation in the corpus are labeled, and an event logic knowledge extraction dataset is formed. Secondly, an event argument entity and relation joint extraction model is proposed. Using stacked bidirectional long short-term memory(BiLSTM) to obtain deep context features of text. As a supplement to stacked BiLSTM, self-attention mechanism extracts character dependency features from multiple subspaces, and uses conditional random field(CRF) to realize entity recognition. The character dependency features are mapped to the entity label weight embedding, and spliced with deep context features to extract relations. Bidirectional graph convolutional network(BiGCN) is introduced for relation inference, graph convolution features are used to update deep context features to perform joint extraction in the second phase. Experimental results show that this method can improve the effect of event argument entity and relation joint extraction and is better than other methods. Finally, an event logic knowledge graph of robot transmission system fault diagnosis is constructed, which provides decision support for autonomous fault diagnosis of robot transmission system.https://ieeexplore.ieee.org/document/9709362/Event logic knowledge graphfault diagnosis ontologyevent argument knowledge extractionstacked BiLSTMself-attentionBiGCN |
| spellingShingle | Jianfeng Deng Tao Wang Zhuowei Wang Jiale Zhou Lianglun Cheng Research on Event Logic Knowledge Graph Construction Method of Robot Transmission System Fault Diagnosis IEEE Access Event logic knowledge graph fault diagnosis ontology event argument knowledge extraction stacked BiLSTM self-attention BiGCN |
| title | Research on Event Logic Knowledge Graph Construction Method of Robot Transmission System Fault Diagnosis |
| title_full | Research on Event Logic Knowledge Graph Construction Method of Robot Transmission System Fault Diagnosis |
| title_fullStr | Research on Event Logic Knowledge Graph Construction Method of Robot Transmission System Fault Diagnosis |
| title_full_unstemmed | Research on Event Logic Knowledge Graph Construction Method of Robot Transmission System Fault Diagnosis |
| title_short | Research on Event Logic Knowledge Graph Construction Method of Robot Transmission System Fault Diagnosis |
| title_sort | research on event logic knowledge graph construction method of robot transmission system fault diagnosis |
| topic | Event logic knowledge graph fault diagnosis ontology event argument knowledge extraction stacked BiLSTM self-attention BiGCN |
| url | https://ieeexplore.ieee.org/document/9709362/ |
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