| Multiple defect diagnostics of gas turbine engine using SVM and RCGA-based ANN algorithms
Youngho Kim, Junyoung Jang, Wanjo Kim, Tae-Seong Roh* and Dong-Whan Choi
The Journal of Mechanical Science and Technology, vol. 26, no. 5, pp.1623-1632, 2012
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."
Keyword : Diagnostics; Turbo-shaft engine; SVM; ANN; RCGA |
