摘要
针对航空发动机气路部件故障样本特点,提出了一种基于灰色关联聚类的特征提取算法,论述了邓氏、相对变率和斜率三种灰色关联度分析方法,并以利用灰色关联度作为动态聚类欧氏距离的思想,构建灰色关联聚类特征提取模型.以某型涡扇发动机常见气路部件故障为例,利用ESVR算法验证特征提取能满足故障诊断要求.仿真结果表明:该方法可以提取特征传感器.使核与约简更精确.
Considering the common fault samples of aero-engine,a feature extraction algorithm of clustering based on grey relational theory was proposed.The three kinds of grey correlation analysis methods, containing Deng,relative change rate,and slope correlation degree were discussed.The idea of taking advantage of Grey correlation degree as Euclidean distance of dynamic clustering was given,and the feature extraction model was built.According to the simulation experiment of aero-engine gas components fault, the precision of fault diagnosis meet the requirements.The results showed that this method could be used to extract feature sensors,and made the ultimate core and reduction more accurate.
出处
《系统工程理论与实践》
EI
CSSCI
CSCD
北大核心
2012年第4期872-876,共5页
Systems Engineering-Theory & Practice
基金
南京航空航天大学基本科研业务费项目(V1042-021)
关键词
航空发动机
气路部件故障
特征提取
灰色关联度
聚类
aero-engine
gas component fault
feature extraction
grey relational analysis
clustering method
