Ensemble Neural Networks for Remaining Useful Life (RUL) Prediction
This work addresses the need for probabilistic predictions in maintenance planning for systems like jet engines, offering an incremental improvement by decoupling uncertainties compared to existing methods.
The paper tackled the problem of probabilistic remaining useful life (RUL) prediction by proposing ensemble neural networks that decouple aleatoric and epistemic uncertainties, tested on NASA's CMAPSS dataset to model and interpret these uncertainties.
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.