  Remaining Useful Life (RUL) estimation is the problem of inferring how long a
certain industrial asset can be expected to operate within its defined
specifications. Deploying successful RUL prediction methods in real-life
applications is a prerequisite for the design of intelligent maintenance
strategies with the potential of drastically reducing maintenance costs and
machine downtimes. In light of their superior performance in a wide range of
engineering fields, Machine Learning (ML) algorithms are natural candidates to
tackle the challenges involved in the design of intelligent maintenance
systems. In particular, given the potentially catastrophic consequences or
substantial costs associated with maintenance decisions that are either too
late or too early, it is desirable that ML algorithms provide uncertainty
estimates alongside their predictions. However, standard data-driven methods
used for uncertainty estimation in RUL problems do not scale well to large
datasets or are not sufficiently expressive to model the high-dimensional
mapping from raw sensor data to RUL estimates. In this work, we consider Deep
Gaussian Processes (DGPs) as possible solutions to the aforementioned
limitations. We perform a thorough evaluation and comparison of several
variants of DGPs applied to RUL predictions. The performance of the algorithms
is evaluated on the N-CMAPSS (New Commercial Modular Aero-Propulsion System
Simulation) dataset from NASA for aircraft engines. The results show that the
proposed methods are able to provide very accurate RUL predictions along with
sensible uncertainty estimates, providing more reliable solutions for
(safety-critical) real-life industrial applications.
