基于GA-LSSVM的航空发动机气路故障诊断  

Fault diagnosis of aero-engine gas path based on GA-LSSVM

作　　者：田静[1] 胡鹤翔 TIAN Jing;HU Hexiang(College of Aeronautical Engineering,CAUC,Tianjin 300300,China)

机构地区：[1]中国民航大学航空工程学院,天津300300

出　　处：《中国民航大学学报》2021年第3期29-33,共5页Journal of Civil Aviation University of China

摘　　要：针对航空发动机气路故障数据样本少、非线性的问题,将遗传算法(GA,genetic algorithm)优化的最小二乘支持向量机(LSSVM,least square support vector machine)应用于航空发动机气路故障诊断。首先对LSSVM算法中的关键参数进行分析,并用GA对LSSVM中的正则化参数(C)、核参数(g)进行优化;其次采用GALSSVM对某型双转子涡喷发动机进行气路故障诊断;最后从诊断精度、抗噪能力和训练耗时3方面与SVM、LSSVM和GA-SVM比较。结果表明:GA-LSSVM在诊断精度和抗噪强度方面都高于其他3种算法,但由于GA-LSSVM有寻优过程,故训练时间较长。Aiming at the lack of data sample and nonlinearity of aero-engine gas-path fault,the genetic algorithm(GA)optimized least square support vector machine(LSSVM)is applied to aero-engine gas-path fault diagnosis.Firstly,the key parameters of LSSVM algorithm is analyzed,and the regularization parameter(C)and kernel parameter(g)of LSSVM are optimited with GA.Secondly,GA-LSSVM is used to diagnose the gas path fault of a biaxial turbofan engine.Finally,SVM,LSSVM,GA-SVM and GA-LSSVM are compared from three aspects of diagnosis accuracy,anti-noise ability and training time.Results show that GA-LSSVM is superior to the other three algorithms in diagnosis accuracy and anti noise intensity,but its training time is longer due to the optimization process of GA-LSSVM.

关 键 词：航空发动机 气路故障诊断 最小二乘支持向量机 遗传算法 

分 类 号：V233.7[航空宇航科学与技术—航空宇航推进理论与工程]