{"id":87,"date":"2017-10-10T13:06:18","date_gmt":"2017-10-10T13:06:18","guid":{"rendered":"https:\/\/www.uptime-h2020.eu\/?page_id=87"},"modified":"2019-12-18T12:08:33","modified_gmt":"2019-12-18T12:08:33","slug":"foresee-cluster","status":"publish","type":"page","link":"https:\/\/www.uptime-h2020.eu\/index.php\/foresee-cluster\/","title":{"rendered":"ForeSee-Cluster"},"content":{"rendered":"
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UPTIME cooperates in the European cluster for sustainable predictive maintenance solutions in the factory of the future (ForeSee Cluster<\/a>) with other five projects that are funded under the EU H2020 FoF-9 call - Novel design and predictive maintenance technologies for increased operating life of production systems. The main objective of the cluster is to create a roadmap for predictive maintenance, which may serve as a guideline for companies that want to set up predictive maintenance approaches.<\/p>\n\n\n\n\n\t\n\t\n\t\n\t\n\t
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\n<\/td>		Predictive Cognitive Maintenance Decision Support System<\/strong>
\nThe PreCoM 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%.<\/td>\n<\/tr>\n
\"\"<\/a><\/td>PROGnostics based Reliability Analysis for Maintenance Scheduling<\/strong>
\nThe main objectives of PROGRAMS 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\u2019 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.<\/td>\n<\/tr>\n
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\n<\/td>		Platform for rapid deployment of self-configuring and optimized predictive maintenance services<\/strong>
\nPROPHESY\u2019s 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.<\/td>\n<\/tr>\n
\"\"<\/a><\/td>VerSatilE plug-and-play platform enabling remote pREdictive maintenance <\/strong>
\nSERENA 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.<\/td>\n<\/tr>\n
\"\"<\/a><\/td>Strategies and Predictive Maintenance models wrapped around physical systems for Zero-unexpected-Breakdowns and increased operating life of Factories<\/strong>
\nThe main scope of the Z-BRE4K 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.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\n<\/div>\n<\/div><\/div><\/div><\/div><\/div>","protected":false},"excerpt":{"rendered":"

UPTIME cooperates in the European cluster for sustainable predictive maintenance solutions in the factory of the future (ForeSee Cluster) with other five projects that are funded under the EU H2020 FoF-9 call – Novel design and predictive maintenance technologies for increased operating life of production systems. The main objective of the cluster is to create a roadmap for predictive maintenance, […]<\/p>\n","protected":false},"author":2,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-87","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/www.uptime-h2020.eu\/index.php\/wp-json\/wp\/v2\/pages\/87","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.uptime-h2020.eu\/index.php\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/www.uptime-h2020.eu\/index.php\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/www.uptime-h2020.eu\/index.php\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.uptime-h2020.eu\/index.php\/wp-json\/wp\/v2\/comments?post=87"}],"version-history":[{"count":35,"href":"https:\/\/www.uptime-h2020.eu\/index.php\/wp-json\/wp\/v2\/pages\/87\/revisions"}],"predecessor-version":[{"id":5614,"href":"https:\/\/www.uptime-h2020.eu\/index.php\/wp-json\/wp\/v2\/pages\/87\/revisions\/5614"}],"wp:attachment":[{"href":"https:\/\/www.uptime-h2020.eu\/index.php\/wp-json\/wp\/v2\/media?parent=87"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}