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基于KPCA和SVM的火箭发动机试验台故障诊断方法
引用本文:朱宁,冯志刚,王祁.基于KPCA和SVM的火箭发动机试验台故障诊断方法[J].哈尔滨工业大学学报,2009,41(3):81-84,120.
作者姓名:朱宁  冯志刚  王祁
作者单位:朱宁,王祁,ZHU Ning,WANG Qi(哈尔滨工业大学,自动化测试与控制系,哈尔滨,150001);冯志刚,FENG Zhi-gang(沈阳航空工业学院,自动化学院,沈阳,110136)  
摘    要:为了解决液体火箭发动机试验台的故障诊断问题,提出了一种基于核主元分析(KPCA)特征提取和支持向量多分类机(SVM)的故障诊断方法,该方法首先利用核主元分析对试验台标准故障样本进行特征提取,通过特征分析,建立适合于试验台故障状态识别的层次多分类支持向量机,并对其进行训练,然后将试验数据在主元上投影,输入到训练好的支持向量多分类器,对试验台故障状态进行识别.该方法充分利用了核主元分析强大的非线性特征提取能力和支持向量分类机良好的小样本泛化特性,解决了试验台故障诊断中的小样本、非线性模式识别问题.对试验台的试验结果表明,该方法是有效的、可行的.

关 键 词:液体火箭发动机试验台  故障诊断  特征提取  核主元分析  层次支持向量多分类机

Fault diagnosis of rocket engine ground testing bed based on KPCA and SVM
ZHU Ning,FENG Zhi-gang,WANG Qi.Fault diagnosis of rocket engine ground testing bed based on KPCA and SVM[J].Journal of Harbin Institute of Technology,2009,41(3):81-84,120.
Authors:ZHU Ning  FENG Zhi-gang  WANG Qi
Affiliation:1(1.Dept.of Automatic Measurement and Control,Harbin Institute of Technology,Harbin 150001,China;2.Dept.of Automation,Shenyang Institute of Aeronautical engineering,Shenyang 110136,China)
Abstract:To solve the problem of fault diagnosis for liquid propellant rocket engine ground testing bed,a fault diagnosis approach based on kernel principal component analysis(KPCA) feature extraction and support vector machines(SVM) multi-classification is proposed.After extracting the feature of testing bed standard fault samples,a hierarchical support vector machine(H-SVM) for multi-classification is established and trained through feature analysis.Then,by projecting the testing data to the principal component,the fault status is identified using the trained multi-classifier.Through exhausting the capability of non-linear feature extraction of KPCA and the small sample generalization of SVM,this approach resolves the pattern recognition problem of small sample and nonlinearity in fault diagnosis of testing bed.The experimental results indicate that this approach is available and effective.
Keywords:liquid propellant rocket engine ground testing bed  fault diagnosis  feature extraction  KPCA  H-SVM for multi-classification
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