Abstract
The objective of this research is to present a model to predict failure of two categories of critical aircraft engine components; nonrotating components such as valves and gearboxes, and rotating components such as engine turbines. The work utilizes Weibull regression and artificial neural networks employing Back Propagation (BP) as well as Radial Basis Functions (RBF). The model utilizes training failure data collected from operators of turboprop aircraft working in harsh desert conditions, where sand erosion is a detrimental factor in reducing turbine life. Accordingly, the model is more suited for accurate prediction of life of critical components of such engines. The algorithm, which uses Radial Basis Function (RBF) NN, uses a closest point specifier. The activation is based on the deviation of the earlier prototype from the input vector. Two earlier models are used for comparison purposes; namely Weibull regression modeling and Feed-Forward BP network. Comparison results show that the failure times represented by RBF are in better compromise with actual failure data than both earlier modeling methods. Moreover, the technique has comparatively higher efficiency as the neuron’s number in each layer of ANN is reduced, to decrease computation time, with minimum effect on the accuracy of results.
Highlights
Reliability of modern aircraft engines is highly affected by failure prediction methods of their critical parts
Actual failure data over a period of thirty years was provided by local aviation operator working within the Arabian Gulf Area contained both the Time Since Overhaul (T.S.O.)
It is clear from the table that there is negligible mean percentage error of around 1.09E-15 % when Radial based function neural network was used for prediction of failure rate for turbines that requires overhaul maintenance
Summary
Reliability of modern aircraft engines is highly affected by failure prediction methods of their critical parts. When these engines are operated in severe desert conditions, the high temperature, pressure, and velocity of the intake air may increase the effect of sand erosion on the rotating engine critical components, such as high-pressure turbine blades, and gearboxes. To better schedule cost-effective preventive maintenance, more accurate modeling and prediction of component life is important. This helps to enhance both aircraft safety and reliability. The type of engine system which is currently under consideration comprises a 4-stage turbine subjected to extreme high pressure and temperature as it draws air energy from the combustion chamber. The maximum Turbine Inlet Temperature (TIT) can reach up to
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