高电压技术2024，Vol.50Issue(5)：1923-1932,10.DOI:10.13336/j.1003-6520.hve.20221712
电力设备缺陷文本的双通道语义增强网络挖掘方法
Dual-channel Semantic Enhancement Network Mining Method for Defect Text of Power Equipment
张宇波 1王有元 1梁玄鸿 1夏宇1
作者信息
- 1. 输变电装备技术全国重点实验室(重庆大学电气工程学院),重庆 400044
- 折叠
摘要
Abstract
Defect texts accumulated in the operation and maintenance of power equipment may guide the condition evaluation work and overhaul work.However,the complex structure and strong background noise of the defect records lead to the difficulty of information mining intelligently.To address this problem,this paper proposes a dual-channel se-mantic enhancement network model based on defect text mining.Firstly,the content of the defective text is analyzed,and the defect text is pre-processed by the methods of natural language processing.And the Glove word vector embedding model is used to map the defect text to the numerical space to express the semantics.Then the enhanced text of the defect text is constructed based on word moving distance,and the defect text and enhanced text features are extracted by a bidi-rectional long-short term memory neural network with an attention mechanism.The key information is enhanced by feature fusion at the end of the network to improve the model effect of classification.The examples show that the classi-fication Macro-F1 metrics of the proposed dual-channel semantic enhancement network are at least 6.2%and 5.2%higher than those of traditional machine learning methods and single-channel deep learning methods,and the proposed method provides a new idea for feature enhancement of multi-source operational data such as images and text.关键词
缺陷文本/信息智能挖掘/词移距离/双通道语义增强网络/特征融合Key words
defect text/information intelligently mining/word moving distance/dual-channel semantic enhancement network/feature fusion引用本文复制引用
张宇波,王有元,梁玄鸿,夏宇..电力设备缺陷文本的双通道语义增强网络挖掘方法[J].高电压技术,2024,50(5):1923-1932,10.
