Automated Machine Learning for Remaining Useful Life Predictions
Marc-André Zöller Fabian Mauthe Peter Zeiler Marius Lindauer Marco F. Huber
Being able to predict the remaining useful life (RUL) of an engineering
system is an important task in prognostics and health management. Recently,
data-driven approaches to RUL predictions are becoming prevalent over
model-based approaches since no underlying physical knowledge of the
engineering system is required. Yet, this just replaces required expertise of
the underlying physics with machine learning (ML) expertise, which is often
also not available. Automated machine learning (AutoML) promises to build
end-to-end ML pipelines automatically enabling domain experts without ML
expertise to create their own models. This paper introduces AutoRUL, an
AutoML-driven end-to-end approach for automatic RUL predictions. AutoRUL
combines fine-tuned standard regression methods to an ensemble with high
predictive power. By evaluating the proposed method on eight real-world and
synthetic datasets against state-of-the-art hand-crafted models, we show that
AutoML provides a viable alternative to hand-crafted data-driven RUL
predictions. Consequently, creating RUL predictions can be made more accessible
for domain experts using AutoML by eliminating ML expertise from data-driven
model construction.