Design and implementation of an SMT intelligent fault diagnosis model based on knowledge graph
CUI Geng-shen
LI Shu-yi
HUANG Chun-yue
LIANG Ying
ZHANG Huai-quan
CAO Zhi-qin
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.
Keywords：surface assembly productionintelligent fault diagnosisknowledge mappingfault entity extraction
Publication Date：2026-02-28
Online Publishing Date：2026-04-08(First online date of this platform, not the publication date of the document)
Pages：11( 305-315 )