摘要
由于电网规模增长,直升机、无人机巡线的大量应用,产生的航拍图像数量剧增,其中螺栓的缺陷因数量众多和体积较小,故由输电线路螺栓缺陷引发的事故频频发生。另外,现有输电线路螺栓缺陷分类方法仅限于表面特征提取而忽略目标间关联和受复杂环境的影响大等问题。针对以上情况,该文提出利用螺栓螺母之间的关联组成栓母对,然后使用卷积神经网络提取栓母对特征初始化图网络节点和结合栓母对的先验知识表示栓母对缺陷与栓母对语义对象的关联,并以此来建立栓母对知识图谱指导栓母对缺陷分类。在此基础上,将输电线路上与螺栓相关的缺陷划分为栓母对缺陷,并建立粗级缺陷数据集和细级缺陷数据集。通过使用栓母对知识图谱来指导栓母对的缺陷分类实验,并以此来验证栓母对知识图谱的有效性和可行性。实验结果表明,该栓母对知识图谱实现了栓母对先验知识的有效运用,完成了栓母对粗级缺陷和细级缺陷的高效分类。
Due to the increase in the scale of the power grid, the large number of applications of helicopters and unmanned aerial vehicles, the number of aerial images produced has increased dramatically. Among them, due to the large number and small size of bolt defects, accidents caused by bolt defects on transmission lines occur frequently. In addition, the existing bolt defect classification methods for transmission lines are limited to surface feature extraction while ignoring problems such as correlation between targets and great influence from complex environments. In view of the above, this paper proposes to use the association between bolts and nuts to form a bolt and nut pair, and then use the convolutional neural network to extract the bolt and nut pair feature initialization graph network nodes and combine the prior knowledge of the bolt and nut pair to indicate the bolt and nut pair defects and bolt and nut pair. Associate the semantic objects, and use this to establish a bolt and nut pair knowledge map to guide bolt and nut pair defect classification. If the lack of bolts and bolt-related defects on power transmission lines are divided into bolt and nut pair defects, a coarse-level defect data set and a fine-level defect data set are established. Through the use of bolt and nut pair knowledge graph to guide the defect classification experiment of bolt and nut pair, and to verify the effectiveness and feasibility of bolt and nut pair knowledge graph. The experimental results show that the bolt and nut pair knowledge graph realizes the effective use of the bolt and nut pair prior knowledge, and completes the efficient classification of the bolt and nut pair coarse and fine defects.
作者
赵振兵
段记坤
孔英会
张东霞
ZHAO Zhenbing;DUAN Jikun;KONG Yinghui;ZHANG Dongxia(School of Electrical and Electronic Engineering,North China Electric Power University,Baoding 071003,Hebei Province,China;China Electric Power Research Institute,Haidian District,Beijing 100192,China)
出处
《电网技术》
EI
CSCD
北大核心
2021年第1期98-106,共9页
Power System Technology
基金
国家自然科学基金项目(61871182)
北京市自然科学基金项目(4192055)
中央高校基本科研业务费专项资金项目(2020YJ006)。
关键词
栓母对
知识图谱
缺陷分类
门控图神经网络(GGNN)
bolt and nut pair
knowledge graph
defect classification
gated graph neural network(GGNN)
