Bolt defect classification algorithm based on knowledge graph and feature fusion
At present, there is a problem of insufficient utilization of regional features in the application of the existing knowledge graph based on GGNN in the defect classification of the bolt and nut pair of transmission lines. Therefore, a decision-making method of combining the bolt and nut pair with th...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2022-04-01
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| Series: | Energy Reports |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352484721012749 |
| _version_ | 1811332103912030208 |
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| author | Yinghui Kong Xu Liu Zhenbing Zhao Dongxia Zhang Jikun Duan |
| author_facet | Yinghui Kong Xu Liu Zhenbing Zhao Dongxia Zhang Jikun Duan |
| author_sort | Yinghui Kong |
| collection | DOAJ |
| description | At present, there is a problem of insufficient utilization of regional features in the application of the existing knowledge graph based on GGNN in the defect classification of the bolt and nut pair of transmission lines. Therefore, a decision-making method of combining the bolt and nut pair with the original regional feature and the bolt and nut pair knowledge graph feature is proposed. For this reason, a method is proposed to combine the decision-making of the bolt and nut pair on the original regional features and the bolt and nut pair on the characteristics of the knowledge graph. First, the characteristics of the bolt and nut pair knowledge graph are combined with the adaptive normalization of the bolt and nut pair to the features of the original area. Then, the classification score vector based on fusion features and the classification score vector based on the bolt and nut pair to the original area feature are derived from the classifier respectively; Finally, the classification score vector of the fusion feature and the bolt and nut pair are fused to the classification score vector of the original region feature to obtain the final classification result. On this basis, this article uses bolt and nut pair to conduct multiple sets of defect classification experiments on the data set of the knowledge graph experiment. The experimental results show that the method of decision fusion of the bolt and nut pair to the original regional feature fusion the bolt and nut pair to the knowledge graph feature is better than the bolt and nut pair to the knowledge graph average precision, precision, and recall rate. It is effective Prove the improvement of the algorithm. |
| first_indexed | 2024-04-13T16:30:52Z |
| format | Article |
| id | doaj.art-c0a4d7cd68704d7d85123b26dccd766c |
| institution | Directory Open Access Journal |
| issn | 2352-4847 |
| language | English |
| last_indexed | 2024-04-13T16:30:52Z |
| publishDate | 2022-04-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Energy Reports |
| spelling | doaj.art-c0a4d7cd68704d7d85123b26dccd766c2022-12-22T02:39:35ZengElsevierEnergy Reports2352-48472022-04-018856863Bolt defect classification algorithm based on knowledge graph and feature fusionYinghui Kong0Xu Liu1Zhenbing Zhao2Dongxia Zhang3Jikun Duan4Department of Electronic and Communication Engineering, North China Electric Power University, Hebei Province Baoding 071003, China; Hebei Key Laboratory of Power Internet of Things Technology, North China Electric Power University, Hebei Province Baoding 071003, ChinaDepartment of Electronic and Communication Engineering, North China Electric Power University, Hebei Province Baoding 071003, China; Hebei Key Laboratory of Power Internet of Things Technology, North China Electric Power University, Hebei Province Baoding 071003, ChinaDepartment of Electronic and Communication Engineering, North China Electric Power University, Hebei Province Baoding 071003, China; Hebei Key Laboratory of Power Internet of Things Technology, North China Electric Power University, Hebei Province Baoding 071003, China; Corresponding author at: Department of Electronic and Communication Engineering, North China Electric Power University, Hebei Province Baoding 071003, China.China Electric Power Research Institute, Beijing Haidian District, 100192, ChinaDepartment of Electronic and Communication Engineering, North China Electric Power University, Hebei Province Baoding 071003, China; Hebei Key Laboratory of Power Internet of Things Technology, North China Electric Power University, Hebei Province Baoding 071003, ChinaAt present, there is a problem of insufficient utilization of regional features in the application of the existing knowledge graph based on GGNN in the defect classification of the bolt and nut pair of transmission lines. Therefore, a decision-making method of combining the bolt and nut pair with the original regional feature and the bolt and nut pair knowledge graph feature is proposed. For this reason, a method is proposed to combine the decision-making of the bolt and nut pair on the original regional features and the bolt and nut pair on the characteristics of the knowledge graph. First, the characteristics of the bolt and nut pair knowledge graph are combined with the adaptive normalization of the bolt and nut pair to the features of the original area. Then, the classification score vector based on fusion features and the classification score vector based on the bolt and nut pair to the original area feature are derived from the classifier respectively; Finally, the classification score vector of the fusion feature and the bolt and nut pair are fused to the classification score vector of the original region feature to obtain the final classification result. On this basis, this article uses bolt and nut pair to conduct multiple sets of defect classification experiments on the data set of the knowledge graph experiment. The experimental results show that the method of decision fusion of the bolt and nut pair to the original regional feature fusion the bolt and nut pair to the knowledge graph feature is better than the bolt and nut pair to the knowledge graph average precision, precision, and recall rate. It is effective Prove the improvement of the algorithm.http://www.sciencedirect.com/science/article/pii/S2352484721012749Bolt and nut pairKnowledge graphFeature fusionDecision fusionDefect classification |
| spellingShingle | Yinghui Kong Xu Liu Zhenbing Zhao Dongxia Zhang Jikun Duan Bolt defect classification algorithm based on knowledge graph and feature fusion Energy Reports Bolt and nut pair Knowledge graph Feature fusion Decision fusion Defect classification |
| title | Bolt defect classification algorithm based on knowledge graph and feature fusion |
| title_full | Bolt defect classification algorithm based on knowledge graph and feature fusion |
| title_fullStr | Bolt defect classification algorithm based on knowledge graph and feature fusion |
| title_full_unstemmed | Bolt defect classification algorithm based on knowledge graph and feature fusion |
| title_short | Bolt defect classification algorithm based on knowledge graph and feature fusion |
| title_sort | bolt defect classification algorithm based on knowledge graph and feature fusion |
| topic | Bolt and nut pair Knowledge graph Feature fusion Decision fusion Defect classification |
| url | http://www.sciencedirect.com/science/article/pii/S2352484721012749 |
| work_keys_str_mv | AT yinghuikong boltdefectclassificationalgorithmbasedonknowledgegraphandfeaturefusion AT xuliu boltdefectclassificationalgorithmbasedonknowledgegraphandfeaturefusion AT zhenbingzhao boltdefectclassificationalgorithmbasedonknowledgegraphandfeaturefusion AT dongxiazhang boltdefectclassificationalgorithmbasedonknowledgegraphandfeaturefusion AT jikunduan boltdefectclassificationalgorithmbasedonknowledgegraphandfeaturefusion |