Projects funded 2022-2023

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PROMETHUS project funded

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PROMETHUS project funded

2022·11·23

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The PROMETHEUS project, Advanced product design and predictive maintenance service for special lifting cranes based on digital twin, is funded by MCIN/AEI/10.13039/501100011033 and by the European Union "Next GenerationEU"/PRTR".. Reference: CPP2021-008893

 

Project Objectives

Cranes are a widely used solution to lift and manipulate heavy products, tooling or devices in industry. There exists a wide type of cranes such as overhead cranes, gantry cranes, jig cranes or open winch cranes among others. In the last years, the high productivity process cranes, which require a singular elevation solution to fulfil the specific needs and productivity requirements of the customer, are gaining traction. Therefore, crane companies must face the following challenges to remain competitive in the market:

  • The development of the crane itself, which architecture, modular subsystems and dimensioning have to be specifically designed or adapted to the particular production requirements
  • To provide and efficient predictive maintenance service of a specifically developed singular product with specific architecture. In this type of singular solutions there is a lack of historical maintenance information
  • To develop a fully integrable crane operative digital twin layer to be implemented in the full digital twin of the production plant of the customer

Therefore, in this new scenario it is key for crane manufacturer companies to develop methodologies and high-fidelity assessment tools in order to offer customers integral turnkey lift solutions and efficient predictive maintenance service.

For this reason, the aim of PROMETHEUS project is to develop the methodologies and guidelines to build the specific modules of each of the layers that compose the full elevation solution digital twin, as well as to build and characterize the standard modules of each layer in a range of configurations and load conditions where a broad number of solutions may lay on. In addition, the developed digital twin will present learning capability to improve its accuracy based on experimental data and maintenance historic data along the product life.