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Workshop: 4th Workshop on Self-Supervised Learning: Theory and Practice

Uncertainty Quantification using Deep Ensembles for Safety-Critical Predictive Models

Oishi Deb · Emmanouil Benetos · Philip Torr


This paper introduces a novel approach for uncertainty quantification in safety-critical predictive models by using a deep ensemble model, hence addressing a critical problem in predictive maintenance tasks. It builds a regression model to predict the Remaining Useful Life (RUL) of aircraft engines, utilizing the well-known run-to-failure turbo engine degradation dataset. Addressing the overlooked yet crucial aspect of uncertainty estimation in previous research, this paper 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 pioneering work in this application domain.

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