The Application of Support Vector Machines to Gas Turbine Performance Diagnosis

SVMs (support vector machines) is a new artificial intelligence methodology derived from Vapnik's statistical learning theory, which has better generalization than artificial neural network. A C-support vector classifiers Based Fault Diagnostic Model (CBFDM) which gives the 3 most possible fault causes is constructed in this paper. Fivefold cross validation is chosen as the method of model selection for CBFDM. The simulated data are generated from PW4000-94 engine influence coefficient matrix at cruise, and the results show that the diagnostic accuracy of CBFDM is over 93% even when the standard deviation of noise is 3 times larger than the normal. This model can also be used for other diagnostic problems.