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SUstainable PREdictive Maintenance for manufacturing Equipment (SUPREME)
Date du début: 1 sept. 2012, Date de fin: 31 août 2015 PROJET  TERMINÉ 

Productivity improvements have major impact on EU economy and competitiveness. Industrial maintenance contributes largely to this competitiveness through reliability and availability of production equipments. The EU market of industrial maintenance can be estimated at 32 Bn€/year, in which outsourced maintenance represents 1/3. In continuous production industries (energy, chemical, food, cement or paper sectors) the ratio “maintenance costs/added value product” is even higher than 25%. In these industries, default component or process failure stop the whole production, therefore predictive maintenance is a critical issue.The objectives of SUPREME are- to develop and use most advanced signal and data processing dedicated to predictive maintenance and energy consumption reduction- to implement these tools in an industrial demonstrator- to develop, exploit and diffuse new tools for predictive maintenanceSUPREME’s main challenging results will be:-Innovative reference models for residual life prediction and optimal predictive maintenance of deteriorating system-Embedded advanced signal acquisition and features extractions for varying operating conditions machines-Real time data fusion (vibrations, acoustic emission, motor current, torque,…)-Off line data mining and self learning failure mode pattern-Automated loop for monitoring optimal machine stabilization-Dynamically updated condition monitoring software module-Specific dissemination toolsThe project impact will be the proof of predictive maintenance efficiency, reduction of down-time and energy consumption in manufacturing industry, demonstrated in a coated paper mill.To reach excellence on predictive maintenance, SUPREME consortium integrates key technical players on the maintenance added value chain, gathering technology and service providing SMEs. Partners specialised in SME technology transfer will ensure the exploitation of innovative predictive maintenance concepts in EU manufacturing SMEs.



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