RUL Prediction with Encoding and Spatial-Temporal Deep Neural Networks

The objective of this paper is to design and develop
an approach to estimate the Remaining Useful Life (RUL) of an
industrial equipment evolving in a Cyber-Physical System (CPS).
To do so, this work aims to predict failures and malfunctions
of an industrial equipment, as well as evaluating all the main
underlying causes. The system will also identify the actions to
be taken in order to maintain the system at a certain level
of performance and guarantee that it will operate in the most
efficient way, which implies a study of all aspects: reliability,
availability and maintenance. Existing works are often limited to
the estimation phase of the RUL, with only a few case studies
(such as ball bearings and Proof Of Concept(PoC)) available
and a significant lack of data. The innovation of this research
work will be to design and develop diagnostic and prognostic
approaches based on Deep Learning (DL)(e.g. Boltzmann Ma-
chine, Recurrent Networks and Echo State Network (ESN)) for
the maintenance and health management of industrial equipment
evolving in a CPS