In this paper an integrated heath monitoring platform is proposed and developed for performance analysis and degradation diagnostics of gas turbine engines. In a first approach the numerical tool is able to predict engine measurable data from flight data, in order to create a dataset of expected values. Then, in the case of a mismatch between expected values and measured data coming from a real engine, a second part of the tool can be activated to detect the component under degradation. In order to evaluate the performance prediction artificial neural networks (ANN) have been implemented. The tool is able to recognize the degradation due to compressor fouling and turbine erosion. Synthetic data generation has been carried out to show how the degradation effects can affect the engine performance. The used data have been generated with a model based on gas path analysis. The training of the model is focused on components deterioration due to a combination of fouling and erosion. Different scenarios have been compared in order to carry out a sensitivity analysis and to choose the best parameters for the network input and output. Obviously the knowledge of the real engine health status can be crucial for maintenance and fleet management decisions.
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