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

作     者：Huifang Wang Jing Cao Dongyang Lin Huifang Wang;Jing Cao;Dongyang Lin

作者机构：Electrical Engineering DepartmentZhejiang UniversityHangzhou 310058China State Grid Jiangsu Electric Power Engineering Consulting Co.Ltd.Nanjing 210000China 

出 版 物：《CSEE Journal of Power and Energy Systems》 (中国电机工程学会电力与能源系统学报（英文）)

年 卷 期：2022年第8卷第4期

页      面：1157-1164页

核心收录：

学科分类：0401[教育学-教育学] 0808[工学-电气工程] 080802[工学-电力系统及其自动化] 08[工学] 

主　　题：Age curve defect analysis defect rate factor study power equipment text mining 

摘      要：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.