UBITECH actively participates in the kick-off meeting, in Brussels, Belgium (September 27-28, 2017) of the UPTIME H2020 Innovation Action, officially started on September 1st, 2017 – undertaking the technical leadership of the research project’s activities. The project is funded by European Commission under Horizon 2020 Programme (Grant Agreement No. 768634) and spans on the period September 2017 – August 2020. The UPTIME project aims to reframe predictive maintenance strategy in a systematic and unified way so as to fully exploit the advancements in ICT and maintenance management by examining the potential of big data in an e-maintenance infrastructure, to deliver novel e-maintenance services and tools to support the daily work of maintenance engineers as well as the overall maintenance management with the aim to optimize in-service efficiency, and to analyse and widely exploit results generated from the use of UPTIME solution in order to show its benefits to manufacturing companies for maintenance utility maximization along with industrial operations management improvement.
In this context, UBITECH undertakes the technical and integration lead of the project’s activities, while UBITECH R&D team will heavily contribute and lead the technological choices towards the definition and design of a unified framework for predictive maintenance strategy implementation. Moreover, UBITECH will realize and incorporate real-time data-driven information processing technologies and algorithms in all steps of the unified predictive maintenance approach, i.e. the signal processing phase, the diagnosis and prognosis phase, the maintenance decision making phase, the maintenance implementation phase and the industrial operations management phase. Finally, UBITECH will deliver the UPTIME e-maintenance platform that constitutes a unified information system that will cover the whole prognostic lifecycle and interconnect industrial operations management with predictive maintenance in the context of production systems. Unification will be achieved by bringing together approaches, tools and services each one of which implements a different phase of the predictive maintenance framework in order to effectively support different enterprise management layers, i.e. operational (e.g. maintenance engineers), management (e.g. factory manager), strategic (e.g. board of directors) by aggregating and interpreting data captured from the production system and effectively sharing the massive amount of information throughout the whole organization, both horizontally and vertically.
