Defect Analysis of Power Equipment Based on Information Extraction Model

Konferenz: ISCTT 2021 - 6th International Conference on Information Science, Computer Technology and Transportation
26.11.2021 - 28.11.2021 in Xishuangbanna, China

Tagungsband: ISCTT 2021

Seiten: 4Sprache: EnglischTyp: PDF

Persönliche VDE-Mitglieder erhalten auf diesen Artikel 10% Rabatt

Mu, Jinshuang; Gao, Zhanjun; Zhou, Fengyuan (Key Laboratory of Power System Intelligent Dispatch and Control Ministry of Education, Shandong University, Jinan, Shandong, China)

In operation and maintenance, State Grid has accumulated a large amount of historical equipment defect text data. For problems such as difficulty in text data mining. This paper established a text information extraction model at first. Extract important information such as equipment defect phenomena and defect locations from historical defect data. Based on it, establishing convolutional neural network (CNN) to achieve defect level classification. By comparing classification effect before and after information extraction, results indicate that text information extraction model can improve accuracy of defect level classification model.