Defect diagnostics of gas turbine engine using hybrid SVM-ANN with module system in off-design condition

A hybrid method of an artificial neural network (ANN) and a support vector machine (SVM) has been used for a health monitoring algorithm of a gas turbine engine. The method has the advantage of reducing learning data and converging time without any loss of estimation accuracy, because the SVM classifies the defect location and reduces the learning data range. In off-design condition, however, the operation region of the engine becomes wide and the nonlinearity of learning data increases considerably. Therefore, an improved hybrid method with the module system and the advanced SVM has been suggested to solve the problems. The module system divides the whole operating region into reasonably small-sized sections, and the advanced SVM has two steps of the classification. The proposed algorithm has been proven to reliably and effectively diagnose the simultaneous defects of the triple components as well as the defects of the single and dual components of the gas turbine engine in off-design condition. This paper was recommended for publication in revised form by Associate Editor Tong Seop 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 Engi-neering at Inha University in Incheon, Korea. His research interests are in the area of combustion instabilities, rocket and jet propulsions, interior ballistics, and gas turbine 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 served three years as a President of Korea Aerospace Research Institute from 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 gas turbine defect diagnostics.