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Defect diagnostics of SUAV gas turbine engine using hybrid SVM-artificial neural network method Sang-Myeong Lee
The Journal of Mechanical Science and Technology, vol. 23, no. 2, pp.559-568, 2009
Abstract : 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 SUAV gas turbine engine. 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 because the SVM classifies the defect location and reduces the
learning data range. The results of test data have shown that the hybrid method is more reliable and suitable algorithm
than the general ANN for the defect diagnosis of the gas turbine engine.
Keyword :
Defect Diagnostics; Hybrid method; Support vector machine; Artificial neural network; Gas turbine engine
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