Description: Predictive
maintenance is widely acknowledged as the "killer
application" of machine learning in Industry 4.0.
This research activity will develop machine learning
methods for prognostic maintenance, an approach that aims
not only at predicting the future maintenance necessities,
but also at describing causes and effects of future
evolutions of a system: "foresight," as opposed to
"forecast."
The activity will be carried on in collaboration
with a software company that already markets a more
traditional solution for predictive maintenance.
Therefore, the work will build on an existing, substantial
body of tools and know-how. The candidate is expected to
develop competences that are of great technical,
industrial, ans well as commercial, interest.
Link to the group or personal
Webpage:
https://www.dibris.unige.it/rovetta-stefano
Requirements: background
in computer science, bioengineering, computer engineering,
mathematics, physics or related disciplines.
Reference: Vogl, G.W., Weiss, B.A. &
Helu, M. "A review of diagnostic and prognostic capabilities
and best practices for manufacturing." J Intell Manuf (2019)
30: 79