Multiple defect diagnostics of gas turbine engine using SVM and RCGA-based ANN algorithms

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.