Bayesian Deep Learning for Remaining Useful Life Estimation via Stein
  Variational Gradient Descent
Luca Della Libera Jacopo Andreoli Davide Dalle Pezze Mirco Ravanelli Gian Antonio Susto
A crucial task in predictive maintenance is estimating the remaining useful
life of physical systems. In the last decade, deep learning has improved
considerably upon traditional model-based and statistical approaches in terms
of predictive performance. However, in order to optimally plan maintenance
operations, it is also important to quantify the uncertainty inherent to the
predictions. This issue can be addressed by turning standard frequentist neural
networks into Bayesian neural networks, which are naturally capable of
providing confidence intervals around the estimates. Several methods exist for
training those models. Researchers have focused mostly on parametric
variational inference and sampling-based techniques, which notoriously suffer
from limited approximation power and large computational burden, respectively.
In this work, we use Stein variational gradient descent, a recently proposed
algorithm for approximating intractable distributions that overcomes the
drawbacks of the aforementioned techniques. In particular, we show through
experimental studies on simulated run-to-failure turbofan engine degradation
data that Bayesian deep learning models trained via Stein variational gradient
descent consistently outperform with respect to convergence speed and
predictive performance both the same models trained via parametric variational
inference and their frequentist counterparts trained via backpropagation.
Furthermore, we propose a method to enhance performance based on the
uncertainty information provided by the Bayesian models. We release the source
code at https://github.com/lucadellalib/bdl-rul-svgd.