DEFECT DIAGNOSTICS OF POWER PLANT GAS TURBINE USING HYBRID SVM-ANN METHOD

A hybrid method of an artificial neural network (ANN) combined with a support vector machine (SVM) has been developed for the defect diagnostic system applied to the power plant gas turbine. This method has been suggested to overcome the demerits of the general ANN with the local minima problem and low classification accuracy in case of many nonlinear data. This hybrid approach takes advantage of the reduction of learning data and converging time without any loss of estimation accuracy therefore it has been applied for the power plant monitoring system in order to detect fails and status of compressors and turbines in detail. The results have shown the suggested defect diagnostic algorithm has reliable and suitable efficiency estimation accuracy.