sustainablySMART | Sustainable Smart Mobile Devices Lifecycles through Advanced Re-design, Reliability, and Re-use and Remanufacturing Technologies
01-09-2015
-31-10-2019
01-09-2015
-31-10-2019
01-09-2014
-17-11-2016
01-10-2016
-30-09-2019
01-10-2016
-31-10-2019
01-11-2016
-31-01-2020
Printed Electronics is the key step for fully digitizing electronics manufacturing, thus being able to customize the product without making changes in the infrastructures or process.
01-10-2016
-30-09-2019
01-10-2016
-30-09-2019
01-10-2016
-31-03-2021
01-10-2016
-30-09-2019
01-09-2017
-28-02-2021
UPTIME will provide a unified predictive maintenance management framework and a smart predictive maintenance information system covering the whole prognostic lifecycle. The UPTIME solution will be applicable to any production system incorporating sensors and will be based on real-time reliability-related (prognostic) information in order to reduce the equipment downtime and malfunctions with the aim to produce high-quality products with optimized losses. It will utilize sensors for measuring various parameters of the production process, provide diagnostic outcomes, i.e. the current equipment health state, generate predictions about future equipment behaviour, and recommend optimal actions at optimal times. It will also incorporate a continuous improvement mechanism for continuous learning of Diagnosis, Prognosis and Maintenance Decision Making phases triggered by sensor data during maintenance and other operational actions implementation. The elimination of unexpected failures will lead to an increased level of safety in the workplace and to improved overall operations efficiency.
UPTIME will be able to be applied in the context of the production process of any manufacturing company regardless their processes, products and physical model used. It will take advantage of predictive maintenance management, industrial IoT and big data, as well as proactive computing and the e-maintenance concept in order to reframe predictive maintenance strategy and to create a unified information system in alignment to the new predictive maintenance strategy framework and to Gartner’s 4 levels of data analytics maturity.
UPTIME will be applicable at the level of component, machine and production system, depending on the placement of sensors throughout the production lifecycle and the data availability in the manufacturing company’s systems (e.g. Enterprise Resources Planning- ERP, Manufacturing Execution System- MES). Within UPTIME, there will be interactions between the various e-maintenance services and the e-operations data and information from the manufacturing companies’ systems in order to synchronise maintenance with production, quality and logistics management. The results of the UPTIME solution will be evaluated by the manufacturing companies participating in the consortium and will be demonstrated in manufacturing companies beyond the consortium.
01-10-2017
-31-03-2021
In particular, the project aims the development of intelligent and predictive maintenance systems for the new manufacturing trends of mass customisation and individualisation. Increased reliability of production systems is considered to be crucial for securing competitive advantage for manufacturing companies. At present, maintenance in general and predictive maintenance strategies in particular are facing significant challenges in dealing with the evolution of the equipment, instrumentation and manufacturing processes they support. So, preventive maintenance strategies designed for traditional highly repetitive and stable mass production processes based on predefined components and machine behaviour models are no longer valid and more adaptive and responsive (predictive-prescriptive) maintenance strategies are needed.
Z-BRE4K will provide a modular solution for predictive maintenance that is highly customizable. Therefore, the different modules of the solution cane be sold as stand alone products or can be combined depending on the users needs. Z-BRE4K solution can be applied to both new machines and old machines that were not designed to be equipped with predictive maintenance solutions.
01-11-2019
-30-04-2023
01-11-2015
-31-10-2017
01-10-2020
-30-09-2023
01-10-2022
-30-09-2026