Explore the words cloud of the UPTIME project. It provides you a very rough idea of what is the project "UPTIME" about.
The following table provides information about the project.
Coordinator |
BIBA - BREMER INSTITUT FUER PRODUKTION UND LOGISTIK GMBH
Organization address contact info |
Coordinator Country | Germany [DE] |
Total cost | 6˙248˙367 € |
EC max contribution | 4˙847˙836 € (78%) |
Programme |
1. H2020-EU.2.1.5.1. (Technologies for Factories of the Future) |
Code Call | H2020-FOF-2017 |
Funding Scheme | IA |
Starting year | 2017 |
Duration (year-month-day) | from 2017-09-01 to 2020-08-31 |
Take a look of project's partnership.
# | ||||
---|---|---|---|---|
1 | BIBA - BREMER INSTITUT FUER PRODUKTION UND LOGISTIK GMBH | DE (BREMEN) | coordinator | 1˙045˙305.00 |
2 | INSTITUTE OF COMMUNICATION AND COMPUTER SYSTEMS | EL (ATHINA) | participant | 534˙625.00 |
3 | MEWS INNOVATION | FR (TOULOUSE) | participant | 498˙312.00 |
4 | FFT PRODUKTIONSSYSTEME GMBH & CO. KG | DE (FULDA) | participant | 440˙562.00 |
5 | RINA CONSULTING SPA | IT (GENOVA) | participant | 435˙312.00 |
6 | UBITECH LIMITED | CY (LIMASSOL) | participant | 430˙062.00 |
7 | PUMACY TECHNOLOGIES AG | DE (BERNBURG) | participant | 394˙187.00 |
8 | SUITE5 DATA INTELLIGENCE SOLUTIONS LIMITED | CY (LIMASSOL) | participant | 335˙687.00 |
9 | WHIRLPOOL EMEA SPA | IT (PERO) | participant | 305˙812.00 |
10 | ISADEUS | FR (PARIS) | participant | 219˙187.00 |
11 | M.J.MAILLIS S.A. INDUSTRIAL PACKAGING SYSTEMS AND TECHNOLOGIES | EL (KIFISSA) | participant | 171˙281.00 |
12 | SUITE5 DATA INTELLIGENCE SOLUTIONS LIMITED | IE (Rathcoole) | participant | 37˙499.00 |
UPTIME will seek to reframe predictive maintenance strategy by proposing a unified framework and to create an associated unified information system in alignment to the aforementioned framework. Therefore, UPTIME will extend and unify new digital, e-maintenance services and tools in order to exploit the full potential of a predictive maintenance strategy with the UPTIME solution, will deploy and validate the UPTIME solution in the manufacturing companies participating in the UPTIME consortium and will diffuse the UPTIME solution in the manufacturing community. UPTIME will enable manufacturing companies having installed sensors to fully exploit the availability of huge amounts of data with respect to the implementation of a predictive maintenance strategy. Moreover, production, quality and logistics operations driven by predictive maintenance will benefit from UPTIME. UPTIME will enable manufacturing companies to reach Gartner’s level 4 of data analytics maturity (“optimized decision-making”) in order to improve physically-based models and to synchronise maintenance with quality management, production planning and logistics options. In this way, it will optimize in-service efficiency through reduced failure rates and downtime due to repair, unplanned plant/production system outages and extension of component life. Moreover, it will contribute to increased accident mitigation capability since it will be able to avoid crucial breakdown with significant consequences. Consequently, UPTIME will exploit the full potential of predictive maintenance management and its interactions with other industrial operations by investigating a unified methodology and by implementing a unified information system addressing the predictive maintenance strategy.
FFT Business Case, Conceptualization and Evaluation Strategy | Documents, reports | 2020-02-13 17:07:08 |
Whirlpool Business Case, Conceptualization and Evaluation Strategy | Documents, reports | 2020-02-13 17:07:07 |
Market Research | Documents, reports | 2020-02-13 17:07:08 |
MAILLIS Business Case, Conceptualization and Evaluation Strategy | Documents, reports | 2020-02-13 17:07:07 |
Catalogue of PM Models, Techniques, Platforms | Other | 2020-02-13 17:07:07 |
Dissemination, Communication and Data Management Plan | Documents, reports | 2020-02-13 17:07:08 |
Take a look to the deliverables list in detail: detailed list of UPTIME deliverables.
year | authors and title | journal | last update |
---|---|---|---|
2017 |
Alexandros Bousdekis, Nikos Papageorgiou, Babis Magoutas, Dimitris Apostolou, Gregoris Mentzas Information Processing for Generating Recommendations Ahead of Time in an IoT-Based Environment published pages: 38-62, ISSN: 2166-7241, DOI: 10.4018/IJMSTR.2017100103 |
International Journal of Monitoring and Surveillance Technologies Research 5 10 | 2020-02-13 |
2018 |
Alexandros Bousdekis, Nikos Papageorgiou, Babis Magoutas, Dimitris Apostolou, Gregoris Mentzas Enabling condition-based maintenance decisions with proactive event-driven computing published pages: 173-183, ISSN: 0166-3615, DOI: 10.1016/j.compind.2018.04.019 |
Computers in Industry 100 | 2020-02-13 |
2018 |
Karl Hribernik, Moritz von Stietencron, Alexandros Bousdekis, Bernd Bredehorst, Gregoris Mentzas, Klaus-Dieter Thoben Towards a Unified Predictive Maintenance System - A Use Case in Production Logistics in Aeronautics published pages: 131-138, ISSN: 2351-9789, DOI: 10.1016/j.promfg.2018.10.168 |
Procedia Manufacturing 16 | 2020-02-13 |
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The information about "UPTIME" are provided by the European Opendata Portal: CORDIS opendata.