“Novel design and predictive maintenance technologies for increased operating life of production systems”
This cluster is comprised by the EU funded projects implemented under the FOF-09 Topic of the H2020-IND-CE-2016-17 Call and targets on the establishement of an extended community in PdM technologies and beyond PROPHESY. These projects are
SERENA project will build upon these needs for saving time and money, minimizing the costly production downtimes. The proposed solutions are covering the requirements for versatility, transferability, remote monitoring and control by a) a plug-and-play cloud based communication platform for managing the data and data processing remotely, b) advanced IoT system and smart devices for data collection and monitoring of machinery conditions, c) artificial intelligence methods for predictive maintenance and planning of maintenance and production activities, d) AR based technologies for supporting the human operator for maintenance activities and monitoring of the production machinery status.
UPTIME aims to design a unified predictive maintenance framework and an associated unified information system in order to enable the predictive maintenance strategy implementation in manufacturing industries. The UPTIME predictive maintenance system will extend and unify the new digital, e-maintenance services and tools and will incorporate information from heterogeneous data sources to more accurately estimate the process performances.
The main scope of the project is the development of Strategies and Predictive Maintenance models wrapped around physical production systems for minimizing unexpected breakdowns and maximizing operating life of production systems.
The main objectives of this project are to develop a model-based prognostics method integrating the FMECA and PRM approaches for the smart prediction of equipment condition, a novel MDSS tool for smart industries maintenance strategy determination and resource management integrating ERP support, and the introduction of an MSP tool to share information between involved personnel. The proposers’ approach is able to improve overall business effectiveness with respect to the following perspectives: increasing Availability and Overall Equipment Effectiveness, continuously monitoring the criticality of system components, building physical-based models of the components, determining an optimal strategy for the maintenance activities, providing in a machine condition monitoring system, developing an Intra Factory Information Service. The production and maintenance schedule of complete production lines and entire plants will run with real-time flexibility in order to perform at the required level of efficiency, optimize resources and plan repair interventions.
The project will deploy and test a predictive cognitive maintenance decision-support system able to identify and localize damage, assess damage severity, predict damage evolution, assess remaining asset life, reduce the probability of false alarms, provide more accurate failure detection, issue notices to conduct preventive maintenance actions and ultimately increase in-service efficiency of machines by at least 10%.