北京航空航天大学学报2026，Vol.52Issue(3)：687-697,11.DOI:10.13700/j.bh.1001-5965.2023.0777
基于R藤Copula-DBN齿轮箱故障诊断
Gearbox fault diagnosis based on R-vine Copula-DBN
摘要
Abstract
Low diagnostic accuracy results from the wide set of directed acyclic graphs that must be searched when doing structure learning on dynamic Bayesian starting networks under multidimensional input.Conventional approaches find it challenging to find the best structure.In this paper,a method is proposed to combine the R-vine Copula model with a dynamic Bayesian network(DBN)for fault diagnosis.First,the network structure space is made smaller by using the structure prediction model to filter the retrieved features and identify nodes with high correlation.Then,the first-layer tree structure of the R-vine Copula model is used combined with the transfer entropy method to construct the initial network of dynamic Bayesian network,and the DBN of the initial network is built according to the Markov process in time series for fault diagnosis,which solves the problem that it is difficult to obtain the optimal structure in the network construction under multiple features.The gearbox data of Southeast University is used for verification,and the comparison results show that the method can better learn the DBN structure,and the fit between the data and the model is high,and good diagnostic results can be obtained in fault diagnosis.关键词
故障诊断/动态贝叶斯网络/R藤Copula/结构预测模型/齿轮箱Key words
fault diagnosis/dynamic Bayesian network/R-vine Copula/structure prediction model/gearbox分类
信息技术与安全科学引用本文复制引用
王进花,刘正奇,曹洁,刘昀强,陈莉..基于R藤Copula-DBN齿轮箱故障诊断[J].北京航空航天大学学报,2026,52(3):687-697,11.基金项目
国家自然科学基金(62063020) （62063020）
国家重点研发计划(2020YFB1713600) （2020YFB1713600）
甘肃省自然科学基金(20JR5RA463) National Natural Science Foundation of China(62063020) （20JR5RA463）
National Key Research and Development Program of China(2020YFB1713600) （2020YFB1713600）
Natural Science Foundation of Gansu Province(20JR5RA463) （20JR5RA463）
