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Defect Diagnostics of Gas Turbine with Altitude Variation Using Hybrid SVM-Artificial Neural Network  

Lee, Sang-Myeong (인하대학교 항공공학과)
Choi, Won-Jun (인하대학교 항공공학과)
Roh, Tae-Seong (인하대학교 항공공학과)
Choi, Dong-Whan (인하대학교 항공공학과)
Publication Information
Journal of the Korean Society of Propulsion Engineers / v.11, no.1, 2007 , pp. 43-50 More about this Journal
Abstract
In this study, Hybrid Separate Learning Algorithm(SLA) consisting of Support Vector Machine(SVM) and Artificial Neural Network(ANN) has been used for developing the defect diagnostic algorithm of the aircraft turbo-shaft engine in the off-design range considering altitude variation. Although the number of teaming data and test data highly increases more than 6 times compared with those required for the design condition, the proposed defect diagnostics of gas turbine engine using SLA was verified to give the high defect classification accuracy in the off-design range considering altitude variation.
Keywords
Defect Diagnostics; Support Vector Machine; Artificial Neural Network; Separate Learning Algorithm; Altitude Variation;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
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