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An adaptive model is proposed based on genetic algorithm to predict the characteristic map of components. The difference functions, of the primary performance parameters between numerical model and test data, are taken as objective function. The coupled factors of component characteristics" map as optimized parameters are considered. The difference of the main performance parameters and process parameters between the adaptive model and the test data are shown to be within the range of 0.05%.Meanwhile, the section"s total temperature and pressure are controlled within 1%. Furthermore, a fault diagnosis model is developed by the small deviation equation method in which the gas path analysis is implemented and the symptom and measuring parameters represent engine performance parameters" variation. It shows that the selection and relatively variable value of symptom parameter have great effect on fault diagnosis error, and the best selection of value is 1/3 of threshold. The relative error of variable value between the symptom parameter of fault diagnosis model and the real fault can be found to be controlled within 5% and it can do the correct evaluation of fault type. And the fault diagnosis model has no misdiagnosis in all the performed conditions.