Journal of Computer Applications ›› 2024, Vol. 44 ›› Issue (9): 2651-2659.DOI: 10.11772/j.issn.1001-9081.2023091280

• Artificial intelligence •     Next Articles

Overview of research and application of knowledge graph in equipment fault diagnosis

Jie WU1, Ansi ZHANG1,2(), Maodong WU3, Yizong ZHANG2, Congbao WANG2   

  1. 1.State Key Laboratory of Public Big Data (Guizhou University),Guiyang Guizhou 550025,China
    2.School of Mechanical Engineering,Guizhou University,Guiyang Guizhou 550025,China
    3.Unit 75841 of Chinese People’s Liberation Army,Guiyang Guizhou 550000,China
  • Received:2023-09-19 Revised:2023-12-15 Accepted:2023-12-20 Online:2024-02-20 Published:2024-09-10
  • Contact: Ansi ZHANG
  • About author:WU Jie, born in 1998, M. S. candidate. His research interests include knowledge graph, fault diagnosis.
    WU Maodong, born in 1989, engineer. His research interests include network security, deep learning.
    ZHANG Yizong, born in 1996, Ph. D. candidate. His research interests include intelligent fault diagnosis of mechanical equipment.
    WANG Congbao, born in 1996, M. S. candidate. His research interests include anomaly detection of UAV flight data.
  • Supported by:
    National Key Research and Development Program of China(2020YFB1713300);National Natural Science Foundation of China(52365061);Guizhou Science and Technology Plan (QianKeHe Basic-ZK[2023] General 059);Higher Education Integrated Research Platform Project of Guizhou Provincial Department of Education (QianJiaoHe KY [2020] 005)

知识图谱在装备故障诊断领域的研究与应用综述

武杰1, 张安思1,2(), 吴茂东3, 张仪宗2, 王从宝2   

  1. 1.公共大数据国家重点实验室(贵州大学), 贵阳 550025
    2.贵州大学 机械工程学院, 贵阳 550025
    3.中国人民解放军75841部队, 贵阳 550000
  • 通讯作者: 张安思
  • 作者简介:武杰(1998—),男,贵州毕节人,硕士研究生,主要研究方向:知识图谱、故障诊断
    张安思(1980—),男,贵州贵阳人,副教授,博士,主要研究方向:制造大数据、故障智能诊断
    吴茂东(1989—),男,贵州毕节人,工程师,主要研究方向:网络安全、深度学习
    张仪宗(1996—),男,贵州黔西南人,博士研究生,主要研究方向:机械设备故障智能诊断
    王从宝(1996—),男,贵州黔西南人,硕士研究生,主要研究方向:无人机飞行数据异常检测。
  • 基金资助:
    国家重点研发计划项目(2020YFB1713300);国家自然科学基金资助项目(52365061);贵州省科技计划项目(黔科合基础-ZK[2023]一般059);贵州省教育厅高等学校集成攻关大平台项目(黔教合KY字[2020]005)

Abstract:

Useful knowledge can be extracted from equipment fault diagnosis data for construction of a knowledge graph, which can effectively manage complex equipment fault diagnosis information in the form of triples (entity, relationship, entity). This enables the rapid diagnosis of equipment faults. Firstly, the related concepts of knowledge graph for equipment fault diagnosis were introduced, and the framework of knowledge graph for equipment fault diagnosis domain was analyzed. Secondly, the research status at home and abroad about several key technologies, such as knowledge extraction, knowledge fusion and knowledge reasoning for equipment fault diagnosis knowledge graph, was summarized. Finally, the applications of knowledge graph in equipment fault diagnosis were summarized, some shortcomings and challenges in the construction of knowledge graph in this field were proposed, and some new ideas were provided for the field of equipment fault diagnosis in the future.

Key words: knowledge graph, equipment fault diagnosis, knowledge graph construction, knowledge reasoning, knowledge extraction

摘要:

知识图谱从装备故障诊断数据中提取有用的知识,通过(实体,关系,实体)的三元组方式,对复杂装备的故障诊断信息进行有效管理,实现装备故障的快速诊断。首先,介绍装备故障诊断知识图谱的相关概念,分析装备故障诊断领域知识图谱的构建框架;其次,归纳国内外装备故障诊断知识图谱的知识抽取、知识融合以及知识推理等几个关键技术的研究现状;最后,对目前装备故障诊断知识图谱应用进行总结,提出该领域知识图谱构建的不足和面临的挑战,并对未来装备故障诊断领域提供一些新的思路。

关键词: 知识图谱, 装备故障诊断, 知识图谱构建, 知识推理, 知识抽取

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