Fleet-based data analytics, integrative modelling, active learning, transfer learning, root cause analysis, performance monitoring
Nowadays many companies operate and manage large groups of complex industrial assets which are similar in kind but different in terms of their concrete specifications. Assets can be geographically distributed but often subsets of assets are co-located. Examples are photovoltaic (PV) equipment organised within plants, wind turbines arranged within wind power plants, compressors and pumps in industrial surroundings, or fleets of utility vehicles.
Usually the enormous amount of data that is generated by such a fleet of assets is underexploited. Most often, this data is merely used by maintenance and service personnel for reactive maintenance. Some lead user companies have recently shifted to proactive maintenance, but even in these cases the analysis is realised at individual machine level. This leaves a huge potential of untapped exploitation opportunities at fleet level: traditional AI methods do not exploit important intrinsic characteristics associated to the fleet aspect, i.e. the immense richness of the data originating from the heterogeneity inherent to a fleet, and the hierarchical composition present between the various components in an asset and the various assets in a fleet.
FleetAId aims to create an AI-enriched research environment that supports and facilitates exploitation of fleet-based data for maximising the fleet performance. The realised AI technologies will be demonstrated through the development of several industrial prototypes for the PV domain.
January 2018 - August 2021
Projet subsidié par la Region de Bruxelles-Capital - Innoviris/project gesubsidieerd door het Brussels Hoofdstedelijk (Project subsidized by the Brussels Capital Region - Innoviris):