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CLC number: TM76; TP181

On-line Access: 2019-12-10

Received: 2018-04-24

Revision Accepted: 2018-09-14

Crosschecked: 2019-11-12

Cited: 0

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Citations:  Bibtex RefMan EndNote GB/T7714


Hui-fang Wang


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Frontiers of Information Technology & Electronic Engineering  2019 Vol.20 No.11 P.1564-1577


An error recognition method for power equipment defect records based on knowledge graph technology

Author(s):  Hui-fang Wang, Zi-quan Liu

Affiliation(s):  College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China

Corresponding email(s):   huifangwang@zju.edu.cn

Key Words:  Error recognition, Power equipment defect record, Knowledge graph, Machine learning

Hui-fang Wang, Zi-quan Liu. An error recognition method for power equipment defect records based on knowledge graph technology[J]. Frontiers of Information Technology & Electronic Engineering, 2019, 20(11): 1564-1577.

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A1 - Hui-fang Wang
A1 - Zi-quan Liu
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 20
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SP - 1564
EP - 1577
%@ 2095-9184
Y1 - 2019
PB - Zhejiang University Press & Springer
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DOI - 10.1631/FITEE.1800260

To recognize errors in the power equipment defect records in real time, we propose an error recognition method based on knowledge graph technology. According to the characteristics of power equipment defect records, a method for constructing a knowledge graph of power equipment defects is presented. Then, a graph search algorithm is employed to recognize different kinds of errors in defect records, based on the knowledge graph of power equipment defects. Finally, an error recognition example in terms of transformer defect records is given, by comparing the precision, recall, F1-score, accuracy, and efficiency of the proposed method with those of machine learning methods, and the factors influencing the error recognition effects of various methods are analyzed. Results show that the proposed method performs better in error recognition of defect records than machine learning methods, and can satisfy real-time requirements.




Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


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