Deep Analysis of Power Equipment Defects Based on Semantic Framework Text Mining Technology

Huifang Wang; Jing Cao; Dongyang Lin

doi:10.17775/CSEEJPES.2019.00210

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Deep Analysis of Power Equipment Defects Based on Semantic Framework Text Mining Technology

Regular Papers | 更新时间：2026-10-15

- Deep Analysis of Power Equipment Defects Based on Semantic Framework Text Mining Technology
- Deep Analysis of Power Equipment Defects Based on Semantic Framework Text Mining Technology
- 中国电机工程学会电力与能源系统学报(英文) 2022年8卷第4期页码：1157-1164
- 作者机构：
  
  1. Electrical Engineering Department, Zhejiang University,Hangzhou,China
  2. State Grid Jiangsu Electric Power Engineering Consulting Co., Ltd,Nanjing,China
- 作者简介：
- 基金信息：
- DOI：10.17775/CSEEJPES.2019.00210
  中图分类号：
- 纸质出版：2022-07-01
- 稿件说明：
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Huifang Wang, Jing Cao, Dongyang Lin. Deep Analysis of Power Equipment Defects Based on Semantic Framework Text Mining Technology[J]. 中国电机工程学会电力与能源系统学报(英文), 2022,8(4):1157-1164.

Huifang Wang, Jing Cao, Dongyang Lin. Deep Analysis of Power Equipment Defects Based on Semantic Framework Text Mining Technology[J]. CSEE Journal of Power and Energy Systems, 2022, 8(4): 1157-1164.
Huifang Wang, Jing Cao, Dongyang Lin. Deep Analysis of Power Equipment Defects Based on Semantic Framework Text Mining Technology[J]. 中国电机工程学会电力与能源系统学报(英文), 2022,8(4):1157-1164. DOI： 10.17775/CSEEJPES.2019.00210.

Huifang Wang, Jing Cao, Dongyang Lin. Deep Analysis of Power Equipment Defects Based on Semantic Framework Text Mining Technology[J]. CSEE Journal of Power and Energy Systems, 2022, 8(4): 1157-1164. DOI： 10.17775/CSEEJPES.2019.00210.

摘要

Abstract

Defect factors and their relevant rules can be analyzed in depth by processing defect records which are often expressed in the form of text data. However

considering that defect text consists of both structured and unstructured data

it is necessary to excavate structured information from unstructured data. In this paper

a text mining method based on semantic framework technology is introduced to transform unstructured defect description into structured information such as components and defect attributes. Then

a deep analyzing model of a power equipment defect is established

which provides a scheme of defect mining based on historical defect texts. Case studies prove that the proposed deep analysis method has a guiding significance for equipment upgrading

selection and maintenance.

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