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煤矿综采设备故障知识图谱构建

蔡安江 张妍 任志刚

蔡安江,张妍,任志刚. 煤矿综采设备故障知识图谱构建[J]. 工矿自动化,2023,49(5):46-51.  doi: 10.13272/j.issn.1671-251x.2023020005
引用本文: 蔡安江,张妍,任志刚. 煤矿综采设备故障知识图谱构建[J]. 工矿自动化,2023,49(5):46-51.  doi: 10.13272/j.issn.1671-251x.2023020005
CAI Anjiang, ZHANG Yan, REN Zhigang. Fault knowledge graph construction for coal mine fully mechanized mining equipment[J]. Journal of Mine Automation,2023,49(5):46-51.  doi: 10.13272/j.issn.1671-251x.2023020005
Citation: CAI Anjiang, ZHANG Yan, REN Zhigang. Fault knowledge graph construction for coal mine fully mechanized mining equipment[J]. Journal of Mine Automation,2023,49(5):46-51.  doi: 10.13272/j.issn.1671-251x.2023020005

煤矿综采设备故障知识图谱构建

doi: 10.13272/j.issn.1671-251x.2023020005
基金项目: 工信部物联网集成创新与融合应用项目(2018-470);榆林市科技计划项目(CXY-2022-172)。
详细信息
    作者简介:

    蔡安江(1965—),男,安徽舒城人,教授,博士研究生导师,研究方向为人工智能及智能制造,E-mail:cai_aj@163.com

  • 中图分类号: TD632

Fault knowledge graph construction for coal mine fully mechanized mining equipment

  • 摘要: 现有煤矿综采设备故障诊断方法缺乏对综采设备历史故障数据的系统化管理及应用,针对该问题,引入知识图谱技术对综采设备故障数据进行系统化管理。采用自顶而下的方法对综采设备故障知识进行本体构建,将综采设备故障知识归纳为故障位置、故障现象、故障原因、处理方法4类,并进行规范化命名;采用通用的命名实体标注方法BIOES对综采设备故障知识进行人工标注;将双向长短期记忆(BiLSTM)和条件随机场(CRF)相结合,构建BiLSTM−CRF模型,对已标注的综采设备故障知识进行命名实体识别,并通过人工抽取实体关系,从而实现故障知识抽取;结合BiLSTM−CRF模型的实体识别结果和人工抽取的实体关系,使用Neo4j图数据库存储综采设备故障知识,构建综采设备故障知识图谱。实验结果表明,相较于BiLSTM模型和BiLSTM−Attention模型,BiLSTM−CRF模型精确率显著提高,为87%,F1值也有一定幅度上升,为69%。综采设备故障知识图谱的构建可为大规模、多域综采设备故障数据的有效分析、管理及应用提供支持。

     

  • 图  1  综采设备故障知识结构

    Figure  1.  Fault knowledge structure of fully mechanized mining equipment

    图  2  LSTM网络结构

    Figure  2.  Network structure of LSTM

    图  3  BiLSTM网络结构

    Figure  3.  Network structure of BiLSTM

    图  4  BiLSTM−CRF模型样例分析

    Figure  4.  Sample analysis of BiLSTM-CRF model

    图  5  综采设备故障知识图谱

    Figure  5.  Fault knowledge graph of fully mechanized mining equipment

    图  6  知识问答界面

    Figure  6.  Knowledge Q&A interface

    图  7  各模型训练、验证过程中精确率变化曲线

    Figure  7.  Accuracy curves of each model in training and verification process

    表  1  不同模型的实验结果对比

    Table  1.   Comparison of experimental results of different models %

    模型精确率召回率F1
    BiLSTM696768
    BiLSTM−Attention626363
    BiLSTM−CRF876969
    下载: 导出CSV
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  • 收稿日期:  2023-02-01
  • 修回日期:  2023-04-28
  • 网络出版日期:  2023-05-16

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