IMAIN
A Novel Decision Support System for Intelligent Maintenance
| Coordinatore | FRAUNHOFER-GESELLSCHAFT ZUR FOERDERUNG DER ANGEWANDTEN FORSCHUNG E.V
Organization address
address: Hansastrasse 27C contact info |
| Nazionalità Coordinatore | Germany [DE] |
| Totale costo | 4˙871˙256 € |
| EC contributo | 3˙433˙448 € |
| Programma | FP7-NMP
Specific Programme "Cooperation": Nanosciences, Nanotechnologies, Materials and new Production Technologies |
| Code Call | FP7-2012-NMP-ICT-FoF |
| Funding Scheme | CP-TP |
| Anno di inizio | 2012 |
| Periodo (anno-mese-giorno) | 2012-09-01 - 2015-08-31 |
Partecipanti
| # | ||||
|---|---|---|---|---|
| 1 |
FRAUNHOFER-GESELLSCHAFT ZUR FOERDERUNG DER ANGEWANDTEN FORSCHUNG E.V
Organization address
address: Hansastrasse 27C contact info |
DE (MUENCHEN) | coordinator | 1˙319˙196.00 |
| 2 |
IMC MESSSYSTEME GMBH
Organization address
address: VOLTASTRASSE 5 contact info |
DE (BERLIN) | participant | 489˙512.00 |
| 3 |
LULEA TEKNISKA UNIVERSITET
Organization address
address: University Campus, Porsoen contact info |
SE (LULEA) | participant | 434˙200.00 |
| 4 |
RUBICO CONSULTING AB
Organization address
address: AURORUM 6 contact info |
SE (LULEA) | participant | 336˙800.00 |
| 5 |
ADVANTIC SISTEMAS Y SERVICIOS SL
Organization address
address: AVENIDA DE EUROPA 14 contact info |
ES (MADRID) | participant | 278˙600.00 |
| 6 |
OPTIMIZACION ORIENTADA A LA SOSTENIBILIDAD SL
Organization address
address: AVENIDA LEONARDO DA VINCI 18 PISO 2 contact info |
ES (SEVILLA) | participant | 226˙400.00 |
| 7 |
"Gorenje Orodjarna, d.o.o., Velenje, Partizanska 12"
Organization address
address: Partizanska cesta 12 contact info |
SI (Velenje) | participant | 221˙740.00 |
| 8 |
LITOSTROJ RAVNE PODJETJE ZA PROIZVODNJO STISKALNIC STROJNIH DELOV IN NAPRAV DOO
Organization address
address: KOROSKA CESTA 14 contact info |
SI (RAVNE NA KOROSKEM) | participant | 127˙000.00 |
| 9 |
OFFICINE S GIACOMO SRL
Organization address
address: VIA ANTONIO MEUCCI 14 contact info |
IT (VITTORIO VENETO) | participant | 0.00 |
Mappa
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Obiettivo del progetto (Objective)
'“iMain” is an European level research project aiming to develop a novel decision support system for predictive maintenance. To that end, a multi-layer solution integrating embedded information devices and artificial intelligence techniques for knowledge extraction and novel reliability & maintainability practices will be developed. The resulting solution will provide extended capabilities compared to those achievable with current state-of-the-art maintenance practices, increasing system lifetime of the production equipment at least 30%, energy efficiency at least 20%, maintenance cost at least 40% and availability of whole process at least 30%.
As for maximizing project impact, “iMain” project is strongly committed to deployment issues, including innovation and implementation actions focused on value chains and bridging the gap from research to market. To that end, “iMain” emphasizes on the commercialization of results, taking also into account the needs of post-project monitoring of commercialization, which will be conducted after the end of the project in order to assess the achievement of the requested funding and for promoting the project as an effective innovation mechanism.
As a step towards the Europe 2020 strategy, “iMain” project will thus make a contribution in terms of R&D investment, employment and resource efficiency, aiming to assist EU manufacturers, particularly SMEs, to adapt to global competitive pressures by increasing the technological base of EU manufacturing through the development and integration of the enabling technologies of the future, specifically engineering technologies for novel predictive maintenance solutions.'
Introduzione (Teaser)
Predictive maintenance systems that detect malfunctions of machines, equipment and even entire plants have their shortcomings. An EU initiative is developing a cutting-edge system enabling real-time online monitoring with advanced capabilities.
Descrizione progetto (Article)
Existing technology has limitations in the implementation of predictive maintenance strategies, particularly the condition monitoring systems of presses or forming machines. Robust and flexible industrial technological solutions equipped with smart self-monitoring functions are needed that will allow companies and operators to more effectively plan when maintenance activities are necessary or when components have to be replaced. This will result in reduced downtime, costs and energy consumption.
The EU-funded project 'A novel decision support system for intelligent maintenance' (http://www.imain-project.eu/ (IMAIN)) is developing an advanced cloud-based monitoring and predictive maintenance solution for forming machines. The system will integrate embedded information devices, artificial intelligence methods and an eMaintenance cloud for collecting data with novel reliability and maintenance practices.
Work began with an analysis of production equipment and key components of the overall system, followed by the creation of a condition and energy monitoring plan.
Simulation models have been developed for the virtual sensors. These innovative sensors are expected to provide an entirely holistic and novel approach to predictive maintenance. They will support sensors currently fitted in forming machines by delivering an accurate and optimal way to virtually monitor stress and strain.
Project partners have defined the hardware and software architecture of the embedded condition and energy monitoring system (ECEM). They also delivered prototypes and chose condition and energy evaluation parameters for both components. The self-sufficient ECEM will be part of the envisaged predictive maintenance system.
The team is developing the required information technology infrastructure and interface that will be included in ECEM. This will also support the IMAIN system.
Work is also underway on a cloud solution for the sharing and storage of monitored data like mechanical stresses, guidance temperatures, bearing vibrations, oil parameters, air and energy consumptions as well as technological parameters like ram tilting and forming forces. In the eMaintenance cloud these data will be long-term evaluated regarding trends as well as remaining useful life (RUL). A major benefit of the cloud approach is the possibility to learn from differently located machines for improved RUL estimation. The overall system architecture has been developed and the hardware and software have been specified.
IMAIN will ultimately lead to increased system lifetime for production equipment, lower maintenance costs, and greater reliability of the entire operation, production and maintenance process.