  A core part of maintenance planning is a monitoring system that provides a
good prognosis on health and degradation, often expressed as remaining useful
life (RUL). Most of the current data-driven approaches for RUL prediction focus
on single-point prediction. These point prediction approaches do not include
the probabilistic nature of the failure. The few probabilistic approaches to
date either include the aleatoric uncertainty (which originates from the
system), or the epistemic uncertainty (which originates from the model
parameters), or both simultaneously as a total uncertainty. Here, we propose
ensemble neural networks for probabilistic RUL predictions which considers both
uncertainties and decouples these two uncertainties. These decoupled
uncertainties are vital in knowing and interpreting the confidence of the
predictions. This method is tested on NASA's turbofan jet engine CMAPSS
data-set. Our results show how these uncertainties can be modeled and how to
disentangle the contribution of aleatoric and epistemic uncertainty.
Additionally, our approach is evaluated on different metrics and compared
against the current state-of-the-art methods.
