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.