Least Squares Support Vector Machine Based Real-Time Fault Diagnosis Model for Gas Path Parameters of Aero Engines

被引：0
|
作者：
王旭辉 [1 ]
黄圣国 [1 ]
王烨 [1 ]
刘永建 [1 ,2 ]
舒平 [2 ]
机构：
[1] College of Civil Aviation,Nanjing University of Aeronautics and Astronautics
[2] General Civil Aviation Administration of China,Center of Aviation Safety Technology Aviation Safety Institute Technology Lab
来源：
Railway Engineering Science | 2009年 / 17卷 / 01期
基金：
国家高技术研究发展计划(863计划);
关键词：
Engine diagnosis; Gas path; Least squares support vector machine; Pattern search;
D O I：
暂无
中图分类号：
V263.6 [故障分析及排除];
学科分类号：
082503 ;
摘要：
Least squares support vector machine(LS-SVM) is applied in gas path fault diagnosis for aero engines.Firstly,the deviation data of engine cruise are analyzed.Then,model selection is conducted using pattern search method.Finally,by decoding aircraft communication addressing and reporting system(ACARS) report,a real-time cruise data set is acquired,and the diagnosis model is adopted to process data.In contrast to the radial basis function(RBF) neutral network,LS-SVM is more suitable for real-time diagnosis of gas turbine engine.
引用
收藏
页码：22 / 26
页数：5
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