Remaining-Useful-Life Prediction and Uncertainty Quantification using LSTM Ensembles for Aircraft Engines

Abstract

This paper introduces the innovative use of the "Deep Ensemble" technique in building a regression model to predict the Remaining Useful Life (RUL) of aircraft engines, utilizing the renowned run-to-failure turbo engine degradation dataset. Addressing the overlooked yet crucial aspect of uncertainty estimation in previous research, this project revamps the LSTM architecture to facilitate uncertainty estimates, employing Negative Log Likelihood (NLL) as the training criterion. Through a series of experiments, the model demonstrated self-awareness of its uncertainty levels, correlating high confidence with low prediction errors and vice versa. This initiative not only enhances predictive maintenance strategies but also significantly improves the safety and reliability of aviation assets by offering a more nuanced understanding of predictive uncertainties. To the best of our knowledge, this is a pioneering work in this application domain.