Skip to main content
Log in

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

  • Published:
Frontiers of Information Technology & Electronic Engineering Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Hui-fang Wang.

Ethics declarations

Hui-fang WANG and Zi-quan LIU declare that they have no conflict of interest.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, Hf., Liu, Zq. An error recognition method for power equipment defect records based on knowledge graph technology. Front Inform Technol Electron Eng 20, 1564–1577 (2019). https://doi.org/10.1631/FITEE.1800260

Download citation

  • Received:

  • Revised:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1631/FITEE.1800260

Key words

CLC number

Navigation