[1]ZHAO Zhenbing,WANG Rui,ZHAO Wenqing,et al.Pin-missing bolts recognition method for transmission lines based on graph knowledge reasoning[J].CAAI Transactions on Intelligent Systems,2023,18(2):372-380.[doi:10.11992/tis.202205004]
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Pin-missing bolts recognition method for transmission lines based on graph knowledge reasoning

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 18 Number of periods: 2023 2 Page number: 372-380 Column: 学术论文—机器感知与模式识别 Public date: 2023-05-05
Title:
Pin-missing bolts recognition method for transmission lines based on graph knowledge reasoning
Author(s):
ZHAO Zhenbing1; 2; 3;  WANG Rui1;  ZHAO Wenqing2; 4;  ZHANG Ke1; 3;  ZHAI Yongjie4
1. Department of Electronic and Communication Engineering, North China Electric Power University, Baoding 071003, China;
2. Engineering Research Center of Intelligent Computing for Complex Energy Systems, Ministry of Education, North China Electric Power University, Baoding 071003, China;
3. Hebei Key Laboratory of Power Internet of Things Technology, North China Electric Power University, Baoding 071003, China;
4. School of Control and Computer Engineering, North China Electric Power University, Baoding 071003, China
Keywords:
transmission line; bolt; pin-missing recognition; graph knowledge reasoning; knowledge expression; tag dependencies; graph convolutional network; category representation
CLC:
TP18
DOI:
10.11992/tis.202205004
Abstract:
Aiming to address the challenges of visual indistinguishability and sample imbalance in identifying missing bolts in transmission lines, we propose a method that relies on graph knowledge reasoning. First, the knowledge expression module learns class representations of various bolts with discriminative characteristics. Then, the label co-occurrence information is extracted through the analysis of correlations among bolt types in the bolt dataset. Finally, the knowledge inference module performs dissemination and enhancement of graph knowledge by taking the category representation as an input feature and using static and dynamic graphs, representing label co-occurrence information, as adjacency matrices. This leads to the identification of missing bolts. In the experimental phase, six bolt types on three selected fittings were selected as test subjects. The results show that our method outperforms other methods in identifying missing bolts, improving accuracy by 9.13% compared to the original model. The results of the ablation experiment demonstrate that the category representation, static graph, and dynamic graph information of bolts extracted in our method all contribute to improved recognition of missing bolts.
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