A study on separate learning algorithm using support vector machine for defect diagnostics of gas turbine engine

A separate learning algorithm with support vector machine (SVM) has been studied for the development of a defect-diagnostic algorithm applied to the gas turbine engine. The system using only an artificial neural network (ANN) falls in a local minima and its classification accuracy rate becomes low in case it is learning nonlinear data. To make up for this risk, a separate learning algorithm combining ANN with SVM has been proposed. In the separate learning algorithm, a sequential ANN learns selectively after classification of defect patterns and discrimination of defect position using SVM, resulting in higher classification accuracy rate as well as the rapid convergence by decreasing the nonlinearity of the input data. The results have shown this suggested method has reliable and suitable estimation accuracy of the defect cases of the turbo-shaft engine. This paper was recommended for publication in revised form by Associate Editor Dongsik Kim Tae-Seong Roh received his B.S. and M.S. degrees in Aeronautical Engineering from Seoul National University in 1984 and 1986. He then went on to receive his Ph.D. degree from Pennsylvania State University in 1995. Dr. Roh is currently a Professor at the department of Aerospace Engineering at Inha University in Incheon, Korea. His research interests are in the area of combustion instabilities, rocket and jet propulsions, interior ballistics, and gasturbine engine defect diagnostics. Dong-Whan Choi received his B.S. degree in Aeronautical Engineering from Seoul National University in 1974. He then went on to receive his M.S. and Ph.D. degrees from University of Washington in 1978 and 1983. Dr. Choi had served three years as a President of Korea Aerospace Research Institute since 1999. He is currently a Professor at the department of Aerospace Engineering at Inha University in Incheon, Korea. His research interests are in the area of turbulence, jet propulsions, and gasturbine defect diagnostics.