Abstract—Recently, the advanced condition monitoring methods such as the model-based method and the artificial intelligent method have been applied to maximize the availability as well as to minimize the maintenance cost of the aircraft gas turbines. Among them the non-linear GPA(Gas Path Analysis) method and the GA(Genetic Algorithms) have lots of advantages to diagnose the engines compared to other advanced condition monitoring methods such as the linear GPA, fuzzy logic and neural networks. Therefore this work applies the linear GPA, the non-linear GPA and the GA to diagnose AE3007 turbofan engine for an aircraft, and the GA method shows good diagnostic results on all the fault cases not only single and multiple fault cases but also consideration of sensor noise and biases.
Index Terms—Engine condition monitoring, non linear GPA, genetic algorithms, 2-spool turbofan engine.
C. D. Kong, M. C. Kang, and G. L. Park are with the Chosun University, 375 Seosuk-dong, Dong-gu, Gwangju, 501-759 , Rep. of Korea (e-mail: email@example.com)
Cite:Changduk Kong, Myoungcheol Kang, and Gwanglim Park, "Engine Using Non-Linear Gas Path Analysis Method and Genetic Algorithms," International Journal of Materials, Mechanics and Manufacturing vol. 1, no. 2, pp. 214-220, 2013.