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.
Through its unique bundle of a predictive maintenance management framework and an integrated platform, UPTIME will prevent critical asset failure, deploy resources more cost-effectively, maximize equipment uptime and enhance operations, while accelerating the quality and supply chain processes. By better diagnosing, anticipating and acting upon the asset performance and product quality, the UPTIME novel predictive maintenance framework is expected to increase in-service efficiency by at least 10% in the 3 pilot cases through combined KPIs measurements.
By investigating and demonstrating the applicability of the UPTIME predictive maintenance system in three pilot cases in different manufacturing sectors, UPTIME will contribute to a more widespread adoption of predictive maintenance and demonstrate more accurate, secure and trustworthy techniques at component, machine and system level.
Comment: By developing a novel, integrated approach to providing customisable product-services based on product-usage information feedback, FALCON will contribute to lengthening the lifecycles of physical product-service components by being able to offer customers new and customised services around the core product throughout its use.
Outcomes of the project creates base for the improvement of the existing design of the water quench and will be used for the development of the new generation of the nozzles.
Manufacturing SMEs are empowered to compute and solve problems that cannot be tackled without Cloud and HPC technology, making them more competitive by reducing development times for innovative products with better performance.
Comment: Future of medicine is driven by the concepts of early, remote and patient-managed diagnostic, teleasistance, minimal invasive and TIC enhanced treatment, to improve service, effectivity and reduce costs. All these advances rely on novel biomedical devices, which are currently being designed and developed, but which are complex and expensive to manufacture with current technologies.
Advanced microrreplication developed in FaBiMed will allow to produce such novel devices in a cost effective way, while keeping the flexibility required for patient-specific functionalization.
Comment: Direct fabrication of toolings by modular inserts and additive manufacturing, will allow resource efficient tooling, and resource efficient mould reconfiguration for reducing the cost of small batch fabrication.
Comment: CAxMan aim at using less material by offering new shape design methods for efficient design of voids and cavities. CAxMan also address the design of better and leaner support structures (use of less material) based analysis addressing thermal stresses and distortions during the additive process.
Comment: MEMAN project tries to give a response to the 2 major drawbacks that have difficult the improvement of the energy and resource efficiency of industrial companies in Europe::
1) Optimisation approaches typically focus on only one resource energy or raw materials, and usually energy due to its relevance for greenhouse gas emissions.
2) Optimisation approaches mainly focus on single company or single process optimisation; only most recently research approaches have been made to optimise elongated process chains in an integrated way.
In this context MEMAN project is arguing for a Next wave of eco-innovation, addressing resource efficiency optimisation of whole manufacturing value chains instead of isolated single company / single process optimisations and, looking at resource efficiency with a holistic view that includes energy, raw materials as well as other supplies in an integrated optimisation approach.
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.