Abstract
An artificial neural network (ANN) based on the real coded genetic algorithm (RCGA) has been used with the support vector machine (SVM) for developing the defect diagnostics of the turbo-shaft engine of an aircraft. Nonlinearity increases due to the ascending number of input data in the off-design region. If the ANN algorithm is used by itself to determine defects under this condition, the possibility of falling in the local minima becomes high because of the large amount of learning data. To solve this problem, the expanded multi-class SVM has been used to reduce nonlinearity of input data. The RCGA, which is effective to search the global minima, has been applied to the ANN algorithm to obtain the magnitude of defects. As results, the number of learning data has been decreased and convergence and accuracy have been improved.
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Recommended by Associate Editor Tong Seop Kim.
Tae-Seong Roh received his B.S. and M.S. degrees in Aeronautical Engineering from Seoul National University, Korea, 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, Korea, 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 Aero-space Engineering at Inha University in Incheon, Korea. His research interests are in the area of turbulence, jet propulsions, and gasturbine defect diagnostics.
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Kim, Y., Jang, J., Kim, W. et al. Multiple defect diagnostics of gas turbine engine using SVM and RCGA-based ANN algorithms. J Mech Sci Technol 26, 1623–1632 (2012). https://doi.org/10.1007/s12206-012-0333-7
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DOI: https://doi.org/10.1007/s12206-012-0333-7