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논문 기본 정보

자료유형
학술저널
저자정보
Changduk Kong (Chosun University)
저널정보
한국항공우주학회 International Journal of Aeronautical and Space Sciences International Journal of Aeronautical and Space Sciences Volume.15 Number.2
발행연도
2014.6
수록면
123 - 137 (15page)

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The aviation gas turbine is composed of many expensive and highly precise parts and operated in high pressure and temperature gas. When breakdown or performance deterioration occurs due to the hostile environment and component degradation, it severely influences the aircraft operation. Recently to minimize this problem the third generation of predictive maintenance known as condition based maintenance has been developed. This method not only monitors the engine condition and diagnoses the engine faults but also gives proper maintenance advice. Therefore it can maximize the availability and minimize the maintenance cost. The advanced gas turbine health monitoring method is classified into model based diagnosis (such as observers, parity equations, parameter estimation and Gas Path Analysis (GPA)) and soft computing diagnosis (such as expert system, fuzzy logic, Neural Networks (NNs) and Genetic Algorithms (GA)). The overview shows an introduction, advantages, and disadvantages of each advanced engine health monitoring method. In addition, some practical gas turbine health monitoring application examples using the GPA methods and the artificial intelligent methods including fuzzy logic, NNs and GA developed by the author are presented.

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Abstract
1. Introduction
2. Advanced diagnostic methods
3. Application examples of advanced diagnostic methods
4. Concluding Remarks
References

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