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A modern aircraft’s turbofan engine is a complex mechanical system with numerous components that need to be properly maintained to continue its safe and profitable operation. As the components deteriorate they need to be replaced or repaired which drive the engine off wing for often time consuming overhaul [8] and creates a cost burden requiring proper engine fleet management to continue the aircraft operation [18]. Aircraft engine components condition is assessed on recurring inspections and compared to the limits provided by the engine manufacturer which constitute the Instructions for Continued Airworthiness approved and controlled by the regulatory agency in a form of an engine manual [16]. The engine manual limits proposed by the engine manufacturer are based upon understanding of the physics behind the particular wear out scheme and the condition progression until the part cannot be operated any longer and has to be replaced. With the complexities of loads that parts are exposed to a variety of competing failure modes occuring at different stages of part’s age and progresing at different rates comes with significant impact of enThe article proposes an approach based on deep and machine learning models to predict a component failure as an enhancement of condition based maintenance scheme of a turbofan engine and reviews currently used prognostics approaches in the aviation industry. Component degradation scale representing its life consumption is proposed and such collected condition data are combined with engines sensors and environmental data. With use of data manipulation techniques, a framework for models training is created and models' hyperparameters obtained through Bayesian optimization. Models predict the continuous variable representing condition based on the input. Best performed model is identified by detemining its score on the holdout set. Deep learning models achieved 0.71 MSE score (ensemble meta-model of neural networks) and outperformed significantly machine learning models with their best score at 1.75. The deep learning models shown their feasibility to predict the component condition within less than 1 unit of the error in the rank scale. Highlights Abstract

Predictive modelling of turbofan engine components condition using machine and deep learning methods