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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

Clicked: 1628

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Hui-fang Wang

http://orcid.org/0000-0002-1483-364X

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

http://doi.org/10.1631/FITEE.1800260


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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Abstract: 
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.

基于知识图谱技术的电力设备缺陷记录错误识别方法

摘要:为实时检测缺陷记录的录入错误,提出一种基于知识图谱技术的电力设备缺陷记录错误识别方法。针对电力缺陷文本特点,通过优化构建知识图谱的一般流程,提出利用现有电力设备缺陷记录语料构建电力设备缺陷知识图谱的方法。然后,基于所构建的知识图谱,针对电力设备缺陷记录的各种错误类型,提出利用图搜索识别缺陷记录错误。最后,对比所提方法和机器学习方法在缺陷记录错误识别上的查准率、查全率、F1分数、准确率和效率,并分析影响各种方法错误识别效果的因素。比较结果表明,所提方法在缺陷记录错误识别效果上有明显优势,识别效率满足实时性要求。

关键词:错误识别;电力设备缺陷记录;知识图谱;机器学习

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