  One of the key challenges in predictive maintenance is to predict the
impending downtime of an equipment with a reasonable prediction horizon so that
countermeasures can be put in place. Classically, this problem has been posed
in two different ways which are typically solved independently: (1) Remaining
useful life (RUL) estimation as a long-term prediction task to estimate how
much time is left in the useful life of the equipment and (2) Failure
prediction (FP) as a short-term prediction task to assess the probability of a
failure within a pre-specified time window. As these two tasks are related,
performing them separately is sub-optimal and might results in inconsistent
predictions for the same equipment. In order to alleviate these issues, we
propose two methods: Deep Weibull model (DW-RNN) and multi-task learning
(MTL-RNN). DW-RNN is able to learn the underlying failure dynamics by fitting
Weibull distribution parameters using a deep neural network, learned with a
survival likelihood, without training directly on each task. While DW-RNN makes
an explicit assumption on the data distribution, MTL-RNN exploits the implicit
relationship between the long-term RUL and short-term FP tasks to learn the
underlying distribution. Additionally, both our methods can leverage the
non-failed equipment data for RUL estimation. We demonstrate that our methods
consistently outperform baseline RUL methods that can be used for FP while
producing consistent results for RUL and FP. We also show that our methods
perform at par with baselines trained on the objectives optimized for either of
the two tasks.
