Explore the words cloud of the IMPROVE project. It provides you a very rough idea of what is the project "IMPROVE" about.
The following table provides information about the project.
|Coordinator Country||Germany [DE]|
|Total cost||4˙148˙554 €|
|EC max contribution||4˙148˙554 € (100%)|
1. H2020-EU.2.1.1. (INDUSTRIAL LEADERSHIP - Leadership in enabling and industrial technologies - Information and Communication Technologies (ICT))
2. H2020-EU.184.108.40.206. (Technologies for Factories of the Future)
|Duration (year-month-day)||from 2015-09-01 to 2018-08-31|
Take a look of project's partnership.
|1||HOCHSCHULE OSTWESTFALEN-LIPPE||DE (LEMGO)||coordinator||508˙000.00|
|2||TECHNISCHE UNIVERSITAET MUENCHEN||DE (MUENCHEN)||participant||744˙670.00|
|3||UNIVERSITA DEGLI STUDI DI MODENA E REGGIO EMILIA||IT (MODENA)||participant||372˙500.00|
|4||REIFENHAEUSER REICOFIL GMBH & CO. KG||DE (Troisdorf)||participant||329˙800.00|
|5||EURICE EUROPEAN RESEARCH AND PROJECT OFFICE GMBH||DE (SAARBRUCKEN)||participant||307˙287.00|
|6||BRÜCKNER MASCHINENBAU GMBH & CO.KG||DE (Siegsdorf)||participant||290˙625.00|
|7||FRAUNHOFER GESELLSCHAFT ZUR FOERDERUNG DER ANGEWANDTEN FORSCHUNG E.V.||DE (MUNCHEN)||participant||289˙199.00|
|8||XCELGO AS||DK (RY)||participant||279˙815.00|
|9||OCME SRL||IT (Parma)||participant||272˙625.00|
|10||MARMARA UNIVERSITY||TR (ISTANBUL)||participant||249˙500.00|
|11||ARCELIK A.S.||TR (ISTANBUL)||participant||238˙000.00|
|12||TRANSITION TECHNOLOGIES SA||PL (WARSZAWA)||participant||178˙688.00|
|13||BERNECKER + RAINER INDUSTRIE-ELEKTRONIK GESELLSCHAFT MBH||AT (EGGELSBERG)||participant||87˙843.00|
The rise of the system complexity, the rapid changing of consumers demand require the European industry to produce more customized products with a better use of resources. The main objective of IMPROVE is to create a virtual Factory of the Future, which provides services for user support, especially on optimization and monitoring. By monitoring anomalous behaviour will be detected before it leads to a breakdown. Thereby, anomalous behaviour is detected automatically by comparing sensor observation with an automatically generated model, learned out of observations. Learned models will be complemented with expert knowledge because models cannot learn completely. This will ensure and establish a cheap and accurate model creation instead of manual modelling. Optimization will be performed and results will be verified through simulations. Therefore, the operator has a broad decision basis as well as a suggestion of a DSS (Decision Support System), which will improve the manufacturing system. Operator interaction will be done by a new developed HMI (Human Machine Interface) providing the huge amount of data in a reliable manner. To reach this aim, every step of the research process is covered by a minimum of two experienced consortium partners, who conclude the results of the project using four demonstrators. The basis for IMPROVE are industrial use-cases, which are transferable to various industrial sectors. Main challenges are reducing ramp-up phases, optimizing production plants to increase the cost-efficiency, reducing time to production with condition monitoring techniques and optimise supply chains including holistic data. Consequently, the resource consumption, especially the energy consumption in manufacturing activities, can be reduced. The optimized plants and supply chains enhance the productivity of the manufacturing during different phases of production. Furthermore, the industrial competitiveness and sustainability in EU will be strengthened.
Work performed, outcomes and results: advancements report(s)
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The information about "IMPROVE" are provided by the European Opendata Portal: CORDIS opendata.
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