@inproceedings{
deb2023remainingusefullife,
title={Remaining-Useful-Life Prediction and Uncertainty Quantification using {LSTM} Ensembles for Aircraft Engines},
author={Oishi Deb and Emmanouil Benetos and Philip Torr},
booktitle={Workshop on Advancing Neural Network Training: Computational Efficiency, Scalability, and Resource Optimization (WANT@NeurIPS 2023)},
year={2023},
url={https://openreview.net/forum?id=tKqUiPUE6H}
}
This paper proposes "LSTM (Long Short Term Memory) Ensemble" technique in building a regression model to predict the Remaining-Useful-Life (RUL) of aircraft engines along with uncertainty quantification, utilising the well-known run-to-failure turbo engine degradation dataset. This paper addressed the overlooked yet crucial aspect of uncertainty estimation in previous research, by revamping 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 from a non-Bayesian approach.
Remaining-Useful-Life Prediction and Uncertainty Quantification using LSTM Ensembles for Aircraft Engines
Oishi Deb Emmanouil Benetos Philip Torr