摘要
套管是变压器的关键设备。目前,运行人员已积累大量文字、图片等套管运行数据,如何对其有效利用实现套管故障的预测和原因推演是提升套管运维效率的关键。该文提出一种基于跨模态数据的变压器套管故障知识图谱构建方法。首先,采用自顶向下的方法进行知识建模,构建套管故障知识图谱本体层;其次,采用ALBERT(a lite bidirectional encoder representations from transformers)-BiLSTM(bidirectional long short term memory)-CRF(conditional random field)模型和ALBERT-FC(fully connected)模型对变压器套管故障文本进行实体和关系抽取,F1值分别达到96.60%和98.99%;然后,通过ResNet(residual network)-50模型对套管故障图像进行特征提取,结合BADGE(batch active learning by diverse gradient embeddings)主动学习采样策略,实现基于少量训练样本的变压器套管故障图像的分类,分类结果的F1值达到92.11%;最后,将文本转换为词向量,并通过语义相似度计算,将文本知识和图像知识关联融合,构建包含文本、图像信息的变压器套管故障知识图谱,并在现场案例中进行应用,推理出变压器套管故障的产生原因和演变过程。
Bushings are key equipment in transformers.At present,operators have accumulated a large amount of cross-modal data for bushing operation,including text and images.How to effectively use the above data to predict bushing failures and identify causes is critical to improving bushing operation and maintenance efficiency.This paper proposes a method for constructing a knowledge graph of transformer bushing faults based on cross-modal data.First,a top-down method is used for knowledge modeling to construct the ontology layer.Second,the ALBERT(A Lite Bidirectional Encoder Representations from Transformers)-BiLSTM(Bidirectional Long Short Term Memory)-CRF(Conditional Random Field)model and the ALBERT-FC(Fully Connected)model are used to extract entities and relations from text,and their F1 scores reach 96.60%and 98.99%respectively.Then,the ResNet(Residual Network)-50 model is used to extract features from the bushing fault image,and combined with the BADGE(Batch Active learning by Diverse Gradient Embeddings)-based active learning sampling strategy to achieve accurate fault image classification with a small number of training samples.Its F1 score reaches 92.11%.Finally,the text is converted into word vectors,and the text knowledge and image knowledge are associated and fused by calculating the semantic similarity.A transformer bushing fault knowledge graph containing cross-modal data is constructed and applied in field cases to infer the causes and evolution process of faults.
作者
张禹方
袁之康
高硕杰
张颖瑶
金立军
ZHANG Yufang;YUAN Zhikang;GAO Shuojie;ZHANG Yingyao;JIN Lijun(College of Electronics and Information Engineering,Tongji University,Yangpu District,Shanghai 201804,China)
出处
《中国电机工程学报》
北大核心
2025年第22期9064-9074,I0033,共12页
PROCEEDINGS OF THE CHINESE SOCIETY FOR ELECTRICAL ENGINEERING
基金
国家重点研发计划项目(2021YFF0901300)。
关键词
变压器套管
知识图谱
跨模态
知识抽取
transformer bushing
knowledge graph
cross-modal
knowledge extraction
