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IMPROVE

Innovative Modeling Approaches for Production Systems to raise validatable efficiency

Total Cost €

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EC-Contrib. €

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Partnership

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Project "IMPROVE" data sheet

The following table provides information about the project.

Coordinator
TECHNISCHE HOCHSCHULE OSTWESTFALEN-LIPPE 

Organization address
address: CAMPUSALLEE 12
city: LEMGO
postcode: 32657
website: n.a.

contact info
title: n.a.
name: n.a.
surname: n.a.
function: n.a.
email: n.a.
telephone: n.a.
fax: n.a.

 Coordinator Country Germany [DE]
 Project website http://www.improve-vfof.eu/
 Total cost 4˙148˙554 €
 EC max contribution 4˙148˙554 € (100%)
 Programme 1. H2020-EU.2.1.1. (INDUSTRIAL LEADERSHIP - Leadership in enabling and industrial technologies - Information and Communication Technologies (ICT))
2. H2020-EU.2.1.5.1. (Technologies for Factories of the Future)
 Code Call H2020-FoF-2015
 Funding Scheme RIA
 Starting year 2015
 Duration (year-month-day) from 2015-09-01   to  2018-08-31

 Partnership

Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 
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

Map

 Project objective

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.

 Deliverables

List of deliverables.
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.

 Publications

year authors and title journal last update
List of publications.
2017 Eray Gençay, Peter Schüller, 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
2018 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
2018 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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