空军工程大学学报2024，Vol.25Issue(4)：5-12,8.DOI:10.3969/j.issn.2097-1915.2024.04.002
基于知识图谱与模糊贝叶斯推理的航空发动机故障诊断
Fault Diagnosis of Aero-Engine Based on KG-FBN Inference
摘要
Abstract
Aimed at the problems that structure and function of aero-engine are complex,construction of Bayesian network is difficult,and it is difficult to obtain the exact value of node conditional probability,in this paper,a knowledge graph with fuzzy Bayesian network(KG-FBN)inference fault diagnosis method is proposed.Firstly,on the basis of large-scale historical fault data,an aero-engine fault knowledge graph is constructed by using the knowledge graph technology.Secondly,a mapping method of"knowledge graph-Bayesian network"is proposed to rapidly construct Bayesian network,and introduce fuzzy set theory to solve the uncertainty problem of probability parameters in engineering practice.Finally,an example is giv-en to verify the feasibility of the proposed method.The results show that the proposed method can im-prove the efficiency of Bayesian network construction and achieve uncertain inference in fault diagnosis,can be also used for optimizing diagnostic strategies,and can improve equipment reliability,and is strong in engineering application value.关键词
航空发动机/知识图谱/模糊贝叶斯网络/故障诊断Key words
aero-engine/knowledge graph/fuzzy Bayesian network/fault diagnosis分类
航空航天引用本文复制引用
张亮,吴闯,贾宇航,谢小月,唐希浪..基于知识图谱与模糊贝叶斯推理的航空发动机故障诊断[J].空军工程大学学报,2024,25(4):5-12,8.基金项目
国家自然科学基金(72201276) （72201276）
西安市科协青年人才托举计划(959202313098) （959202313098）
陕西省自然科学基础研究计划(2023-JC-QN-0059) （2023-JC-QN-0059）
