Journal of Computer Applications ›› 2024, Vol. 44 ›› Issue (9): 2651-2659.DOI: 10.11772/j.issn.1001-9081.2023091280
• Artificial intelligence • Next Articles
Jie WU1, Ansi ZHANG1,2(), Maodong WU3, Yizong ZHANG2, Congbao WANG2
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.Supported by:
武杰1, 张安思1,2(), 吴茂东3, 张仪宗2, 王从宝2
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、故障诊断基金资助:
CLC Number:
Jie WU, Ansi ZHANG, Maodong WU, Yizong ZHANG, Congbao WANG. Overview of research and application of knowledge graph in equipment fault diagnosis[J]. Journal of Computer Applications, 2024, 44(9): 2651-2659.
武杰, 张安思, 吴茂东, 张仪宗, 王从宝. 知识图谱在装备故障诊断领域的研究与应用综述[J]. 《计算机应用》唯一官方网站, 2024, 44(9): 2651-2659.
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URL: https://www.joca.cn/EN/10.11772/j.issn.1001-9081.2023091280
类别 | 覆盖范围 | 数据来源 | 数据精度 | 应用场景 |
---|---|---|---|---|
通用领域 | 覆盖广泛,包括各种领域的知识 | 数据来源广泛 | 精度较低 | 应用场景较广,可用于各个领域 |
装备故障 诊断领域 | 覆盖单一,只针对装备故障诊断领域 | 主要依赖领域专家、相关设计文档、 故障诊断记录文档和传感器记录等 | 精度高,来源精确 | 只能应用于装备故障诊断领域 |
Tab. 1 Differences between equipment fault diagnosis knowledge graph and general domain knowledge graph
类别 | 覆盖范围 | 数据来源 | 数据精度 | 应用场景 |
---|---|---|---|---|
通用领域 | 覆盖广泛,包括各种领域的知识 | 数据来源广泛 | 精度较低 | 应用场景较广,可用于各个领域 |
装备故障 诊断领域 | 覆盖单一,只针对装备故障诊断领域 | 主要依赖领域专家、相关设计文档、 故障诊断记录文档和传感器记录等 | 精度高,来源精确 | 只能应用于装备故障诊断领域 |
类型 | 优点 | 缺点 | 名称 |
---|---|---|---|
基于机器学习的方法 | 自动处理装备 故障文本数据 | 错误的特征选择或特征变换 | TermInformer[ FDD[ |
基于深度学习的方法 | 自动提取特征 | 数据量要求多,可解释性差 | 双层BiLSTM+CFR[ MUSA-BiLSTM-CRF[ |
Tab. 2 Comparison of different entity extraction methods for equipment fault diagnosis
类型 | 优点 | 缺点 | 名称 |
---|---|---|---|
基于机器学习的方法 | 自动处理装备 故障文本数据 | 错误的特征选择或特征变换 | TermInformer[ FDD[ |
基于深度学习的方法 | 自动提取特征 | 数据量要求多,可解释性差 | 双层BiLSTM+CFR[ MUSA-BiLSTM-CRF[ |
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