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.
TECHNISCHE HOCHSCHULE OSTWESTFALEN-LIPPE
|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.220.127.116.11. (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||TECHNISCHE 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 (ST INGBERT)||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.
|Evaluate the forecasting quality||Documents, reports||2019-05-31 17:15:59|
|Quality based HMI, which supports decision making in problematic production scenarios||Documents, reports||2019-05-31 17:15:53|
|Learned causality model which integrates expert knowledge||Documents, reports||2019-05-31 17:15:49|
|An algorithm to classify an anomaly as critical or not||Documents, reports||2019-05-31 17:15:46|
|Toolbox to understand the effects of the transition from traditional to smart production. Design learning scenarios and on-the-job training formats that help users to skilfully operate the system and to modify sociotechnical arrangements||Documents, reports||2019-05-31 17:16:00|
|An algorithm to identifying the root cause of a fault||Documents, reports||2019-05-31 17:15:58|
|Report on the overall architecture which ensures the collaboration of all WPs||Documents, reports||2019-05-31 17:15:50|
|Framework for operator knowledge collection||Documents, reports||2019-05-31 17:15:43|
|Open access summary report||Documents, reports||2019-05-31 17:15:48|
|Transfer the implementation from the lab demonstrators to three prototypes in different industries||Demonstrators, pilots, prototypes||2019-05-31 17:15:49|
|The implemented algorithms are validated on lab demonstrators||Demonstrators, pilots, prototypes||2019-05-31 17:15:52|
|An intelligent optimization algorithm which is able to find the efficient configuration of the factory||Documents, reports||2019-05-31 17:15:46|
|Summary of all requirements for all parts of the project||Documents, reports||2019-05-31 17:16:02|
|An algorithm detecting anomalies out of observation based on learned models||Documents, reports||2019-05-31 17:15:56|
|Evaluate the results of the pilot runs of three demonstrators and the numerical benefit||Documents, reports||2019-05-20 13:32:42|
|External project website||Other||2019-05-31 18:04:40|
|Behaviour model which is learned||Documents, reports||2019-05-31 18:04:41|
Take a look to the deliverables list in detail: detailed list of IMPROVE deliverables.
|year||authors and title||journal||last update|
Eray GenÃ§ay, Peter SchÃ¼ller, Esra Erdem
Applications of non-monotonic reasoning to automotive product configuration using answer set programming
published pages: 1-16, ISSN: 0956-5515, DOI: 10.1007/s10845-017-1333-3
|Journal of Intelligent Manufacturing||2019-06-19|
Benedikt Eiteneuer; Oliver Niggemann
LSTM for Model-Based Anomaly Detection in Cyber-Physical Systems
published pages: , ISSN: , DOI: 10.5281/zenodo.1409641
|DX18 29th International Workshop on Principles of Diagnostics 1||2019-05-20|
Khaled Al-Gumaei, Kornelia Schuba, Andrej Friesen, Sascha Heymann, Carsten Pieper, Florian Pethig, and Sebastian Schriegel
A Survey of Internet of Things and Big Data Integrated Solutions for Industrie 4.0
published pages: , ISSN: , DOI: 10.5281/zenodo.1446427
|2018 IEEE 23rd International Conference on Emerging Technologies and Factory Automation 1536098400||2019-05-20|
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The information about "IMPROVE" are provided by the European Opendata Portal: CORDIS opendata.
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