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http://dx.doi.org/10.6108/KSPE.2013.17.2.071

Study on Condition Monitoring of 2-Spool Turbofan Engine Using Non-Linear GPA(Gas Path Analysis) Method and Genetic Algorithms  

Kong, Changduk (조선대학교 항공우주공학과)
Kang, MyoungCheol (조선대학교 항공우주공학과)
Park, Gwanglim (조선대학교 항공우주공학과)
Publication Information
Journal of the Korean Society of Propulsion Engineers / v.17, no.2, 2013 , pp. 71-83 More about this Journal
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 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.
Keywords
Engine Condition Monitoring; Non Linear GPA; Genetic Algorithms; 2-Spool Turbofan Engine;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
연도 인용수 순위
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