Jet engine degradation prognostic using artificial neural networks

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.