  Reliable uncertainty quantification on RUL prediction is crucial for
informative decision-making in predictive maintenance. In this context, we
assess some of the latest developments in the field of uncertainty
quantification for prognostics deep learning. This includes the
state-of-the-art variational inference algorithms for Bayesian neural networks
(BNN) as well as popular alternatives such as Monte Carlo Dropout (MCD), deep
ensembles (DE) and heteroscedastic neural networks (HNN). All the inference
techniques share the same inception deep learning architecture as a functional
model. We performed hyperparameter search to optimize the main variational and
learning parameters of the algorithms. The performance of the methods is
evaluated on a subset of the large NASA NCMAPSS dataset for aircraft engines.
The assessment includes RUL prediction accuracy, the quality of predictive
uncertainty, and the possibility to break down the total predictive uncertainty
into its aleatoric and epistemic parts. The results show no method clearly
outperforms the others in all the situations. Although all methods are close in
terms of accuracy, we find differences in the way they estimate uncertainty.
Thus, DE and MCD generally provide more conservative predictive uncertainty
than BNN. Surprisingly, HNN can achieve strong results without the added
training complexity and extra parameters of the BNN. For tasks like active
learning where a separation of epistemic and aleatoric uncertainty is required,
radial BNN and MCD seem the best options.

Machine Learning;Machine Learning