控制理论与应用2026，Vol.43Issue(2)：305-315,11.DOI:10.7641/CTA.2024.30625
一种基于知识图谱的SMT智能故障诊断模型设计与实现
Design and implementation of an SMT intelligent fault diagnosis model based on knowledge graph
摘要
Abstract
Aiming at the complexity of the surface assembly production process,the production process is prone to equipment failures and process defects,this paper designs an intelligent fault diagnosis model based on fault knowledge graph for surface assembly production.At the same time,the key technology of knowledge graph construction process-fault entity extraction is studied,and a fault entity extraction model based on BERT-Residual-BiLSTM-CRF for surface assembly production fault logs is designed and implemented.Firstly,the training and testing datasets of the fault entity extraction model are constructed based on the text of surface mount technology(SMT)fault logs,secondly,the TensorFlow framework is used to build the SMT fault entity extraction model,and finally,the trained model is used to conduct controlled experiments.The results show that the fault entity recognition accuracy,recall and mean F-value of the designed fault entity extraction model are improved by about 0.26,0.28 and 0.24 compared with the base model BERT-BiLSTM-CRF,respectively.关键词
表面组装生产/智能故障诊断/知识图谱/故障实体抽取Key words
surface assembly production/intelligent fault diagnosis/knowledge mapping/fault entity extraction引用本文复制引用
崔更申,李书漪,黄春跃,梁颖,张怀权,曹知勤..一种基于知识图谱的SMT智能故障诊断模型设计与实现[J].控制理论与应用,2026,43(2):305-315,11.基金项目
国家自然科学基金项目(62164002),广西重点研发计划项目(桂科AB23075076),四川省钒钛材料工程技术研究中心开放基金项目(2023FTGC08),桂林电子科技大学研究生教育创新计划项目(2023YCXS011,2023YCXS014,2023YCXS019)资助.Supported by the National Natural Science Foundation of China(62164002),the Guangxi Key Technologies R&D Program(AB23075076),the Open Fund Project of Sichuan Province Engineering Technology Research Center of Vanadium and Titanium Materials(2023FTGC08)and the Graduate Education Innovation Program Fund of Guilin University of Electronic Science and Technology(2023YCXS011,2023YCXS014,2023YCXS019). （62164002）
