Economic growth is driven by innovation and technological improvements. In everything we do, we strive for sustainable relationships and helping our society and our customers to drive innovation and efficiency. This to have a positive impact on people’s lives and to help our customers reduce production costs and enable a higher output.
PROPHESY’s vision is to act as a catalyst for the wider deployment and uptake of next generation, optimal, adaptive and self-configurable PdM services. Several challenges and new age trends in Industrie 4.0 and PdM Adoption increase the necessity of a viable route to market for a novel PdM platform, which will enable end-to-end development, deployment and operationalization of adaptive self-configurable PdM services.
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 766994
By developing & applying a Big Data approach as basic infrastructure to:
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under Manunet Reference Number MNET17/ICT-1140 - Manunet Funding agencies: Lombardy and Wallonia region
provide access to Industry 4.0 technologies to the Food sector by reducing production losses due to unplanned maintenance on monitored assets and to extend the lifetime of battery-powered IIoT sensors.
European Union’s Horizon 2020 Programme This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 777455
Based on a "cloud" structure, networks of intelligent sensors and a portable data collector using embedded artificial intelligence algorithms, the aim is to make field engineers more competitive and to extend the range of products for Industry 4.0. Smart-R4F will also induce significant economic benefits (jobs, sustainable development) for our partners and for Wallonia.
Performance for Assets has developed a unique hybrid model that combines Online Monitoring with Physical Models and Historic Data. The hybrid model improves machine behavior knowledge, increases model robustness and identifies causes more precisely.