Study on Fault Diagnostics Considering Sensor Noise and Bias of Mixed Flow Type 2-Spool Turbofan Engine using Non-Linear Gas Path Analysis Method and Genetic Algorithms

혼합배기가스형 2 스풀 터보팬 엔진의 가스경로 기법과 유전자 알고리즘 이용한 센서 노이즈 및 바이어스를 고려한 고장진단 연구

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 both the non-linear GPA and the GA to diagnose AE3007 turbofan engine for an aircraft, and in case of having sensor noise and bias it is confirmed that the GA is better than the GPA through the comparison of two methods.