• Journal of the Korean Society of Propulsion Engineers
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• v.3 no.1
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• pp.24-33
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• 1999

For performance prediction and diagnostics of gas turbine engines, linear and non-linear gas path analysis are applied. In order to find optimal instrument parameters to detect the physical faults such as (outing, erosion and corrosion, non-linear gas path analysis is used. A typical industrial gas turbine engine, TB5000, is used to study the effect of physical faults on engine performance. Through comparison of RMS error between linear and non-linear gas path analysis, the optimal instrument parameters can be defined. As a result, it is found that the linear GPA has the level of error introduced by the assumption of the linear mode: can be of the same order of magnitude as the fault being soughtwhile the non-linear GPA can be solved the non-linear relationships between dependent and independent parameters using an iterative method such as the Newton-Raphson method with sufficient accuracy.

• Journal of Aerospace System Engineering
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• v.7 no.1
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• pp.8-18
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• 2013

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.

• Journal of the Korean Society of Propulsion Engineers
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• v.17 no.2
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• pp.71-83
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• 2013

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.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.49 no.4
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• pp.311-320
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• 2021

A study is performed for the real time fault diagnosis during operation and health estimation relating to performance deterioration in a turbojet engine used for an unmanned air vehicle. For this study the real time dynamic model is derived from the transient thermodynamic gas path analysis. For real fault conditions which are manipulated for the simulation, the detection techniques are applied such as Kalman filter and probabilistic decision-making approach based on statistical hypothesis test. Thereby the effectiveness is verified by showing good fault detection and isolation performances. For the health estimation with measurement parameters, it shows using an assumed performance degradation that the method by adaptive Kalman filter is feasible in practice for a condition based diagnosis and maintenance.