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Jet engine degradation prognostic using artificial neural networks

Maria Grazia De Giorgi (Department of Engineering for innovation, Università del Salento, Lecce, Italy)
Antonio Ficarella (Department of Engineering for innovation, Università del Salento, Lecce, Italy)
Laura De Carlo (Department of Engineering for innovation, Università del Salento, Lecce, Italy)

Aircraft Engineering and Aerospace Technology

ISSN: 0002-2667

Article publication date: 16 January 2019

Issue publication date: 19 March 2020

281

Abstract

Purpose

The purpose of this paper is to propose and develop artificially intelligent methodologies to discover degradation trends through the detection of engine’s status. The objective is to predict these trends by studying their effects on the engine measurable parameters.

Design/methodology/approach

The method is based on the implementation of an artificial neural network (ANN) trained with well-known cases referred to real conditions, able to recognize degradation because of two main gas turbine engine deterioration effects: erosion and fouling. Three different scenarios are considered: compressor fouling, turbine erosion and presence of both degraded conditions. The work consists of three parts: the first one contains the mathematical model of real jet engine in healthy and degraded conditions, the second step is the optimization of ANN for engine performance prediction and the last part deals with the application of ANN for prediction of engine fault.

Findings

This study shows that the proposed diagnostic approach has good potential to provide valuable estimation of engine status.

Practical implications

Knowledge of the true state of the engine is important to assess its performance capability to meet the operational and maintenance requirements and costs.

Originality/value

The main advantage is that the engine performance data for model validation were obtained from real flight conditions of the engine VIPER 632-43.

Keywords

Acknowledgements

The authors wish to thank the 10th Aircraft Maintenance Unit of Italian Air Force for its support to the present research.

Citation

De Giorgi, M.G., Ficarella, A. and De Carlo, L. (2018), "Jet engine degradation prognostic using artificial neural networks", Aircraft Engineering and Aerospace Technology, Vol. 92 No. 3, pp. 296-303. https://doi.org/10.1108/AEAT-01-2018-0054

Publisher

:

Emerald Publishing Limited

Copyright © 2019, Emerald Publishing Limited

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