Analysis of aeronautical engines based on machine learning

Abstract

The aviation industry depends heavily on the availability of planes and their mechanical components, specially their engines. In addition, the fuel consumption of the engine is the highest operating expense for airlines. In the last decade Machine Learning (ML) has had a big impact in Prognostics and Health Management (PHM), hence making it a suitable option for creating models that deliver interesting outputs related to the maintenance of machinery. The main objective of this thesis is to create a model able to identify when the features of the engine are similar to the ones obtained when the engine needs a maintenance. Data is analyzed during the take off of the plane, being that during this window of time the consumption rate of fuel reaches its maximum. This model is conceived using machine learning techniques. The specific objectives accomplished in this thesis are: The identification of sections of interest from the original data set. In this sections an estimation of the fuel consumption of the engine is performed. Finally an anomaly detector that identifies differences between predicted and true fuel consumption is presented. This anomaly detector labels as anomalies the bigger differences between predicted and true fuel consumption. Results show a clear relation between degradation over time and fuel consumption in the engines. For the prediction of fuel consumption 4 models were used: polynomial regression, decision trees, gradient boosting and long short term memory. The best results were obtained by gradient boosting, with an error below 0.7%. Evaluation metrics show accuracies of 92 % for engines AIY 2 and AHD 1 and 83 % for engine AHD 2.