Objective
The advent of Industrie4.0 provides opportunities for adopting predictive maintenance (PdM), which represents the ultimate maintenance vision for manufacturers and machine vendors. Nevertheless, there are still barriers to successful deployment including the issues of data fragmentation, limited data interoperability, poor deployment of advanced analytics and lack of effective integration with other systems at the enterprise and field levels. PROPHESY will deliver and validate (in two complex demonstrators) in real plants a PdM services platform, which will alleviate these issues based on the following innovations:
• A CPS platform optimized for PdM activities (PROPHESY-CPS), which will enable maintenance driven real-time control, large scale distributed data collection and processing, as well as improved production processes driven by maintenance predictions and FMECA activities.
• Novel Machine Learning and Statistical Data processing techniques for PdM (PROPHESY-ML), which will be able to identify invisible patterns associated with machine degradation and assets depreciation, while at the same time using them to optimize FMECA activities.
• Visualization, knowledge sharing and augmented reality (AR) services (PROPHESY-AR), which will enable remotely supported maintenance that can optimize maintenance time and costs, while increasing the safety of maintenance tasks.
• A PdM service optimization engine (PROPHESY-SOE), which will enable composition of optimal PdM solutions based on the capabilities provided by PROPHESY-CPS, PROPHESY-ML and PROPHESY-AR. Service optimization aspects will consider the whole range of factors that impact PdM effectiveness (e.g. OEE, EOL, MTBF and more).
PROPHESY will establish and expand an ecosystem of PdM stakeholders around the PROPHESY-SOE, which will serve as a basis for the wider update of the project’s results, as it will offer to the CPS manufacturing community access to innovative, turn-key solutions for PdM operations.
Fields of science
- engineering and technologymechanical engineeringmanufacturing engineering
- social scienceseconomics and businessbusiness and managementbusiness models
- natural sciencesbiological sciencesecologyecosystems
- natural sciencescomputer and information sciencesartificial intelligencemachine learning
- natural sciencescomputer and information sciencesdata sciencedata processing
- natural sciencescomputer and information sciencessoftwaresoftware applicationssimulation software
Programme(s)
Funding Scheme
IA - Innovation actionCoordinator
1050 Bruxelles
Belgium
See on map
Participants (14)
Legal entity other than a subcontractor which is affiliated or legally linked to a participant. The entity carries out work under the conditions laid down in the Grant Agreement, supplies goods or provides services for the action, but did not sign the Grant Agreement. A third party abides by the rules applicable to its related participant under the Grant Agreement with regard to eligibility of costs and control of expenditure.
1253 Luxembourg
See on map
5656 AG Eindhoven
See on map
21272 Egestorf
See on map
CV3 4LF Coventry
See on map
7000 Mons
See on map
The organization defined itself as SME (small and medium-sized enterprise) at the time the Grant Agreement was signed.
52074 Aachen
See on map
The organization defined itself as SME (small and medium-sized enterprise) at the time the Grant Agreement was signed.
8500-794 Portimao
See on map
The organization defined itself as SME (small and medium-sized enterprise) at the time the Grant Agreement was signed.
80686 Munchen
See on map
2829 516 Caparica
See on map
20500 Arrasate
See on map
Participation ended
11525 Athens
5612 AE Eindhoven
See on map
73054 Eislingen
See on map
18346 Athina
See on map
The organization defined itself as SME (small and medium-sized enterprise) at the time the Grant Agreement was signed.