Proyecto Platinum

Platinum Project

Horizontal platform of smart data and deep learning for Industry and application to the Manufacturing sector

In recent years, numerous solutions have proliferated Connected Industry 4.0. Currently, the solutions developed are:

  • Too specific for a specific problem in an industry.
  • The proposed platforms are too generalist, not easily adaptable to industries.
  • Most of these solutions have been limited to a descriptive analysis of the data to detect anomalies or to know the optimal modes of operation.
  • There are few solutions that support decision making and use predictive models that evolve and adapt to processes over time.

The project PLATINUM (Horizontal PLatformof smart data and deep learning for INdUstry and application to the sector Manufacturing) aims to define a platform with a architecture based on virtual organizations that, through a social machine, let build a structure in which, Each organization contributes to the resolution of the problems that arise around the optimization of processes, predictive maintenance and safety.d.

These solutions will be built on the infrastructure that this platform provides and will address all these problems. Making use of a social machine and the tools available to decompose and give an answer to each of the situations you have to face. All this infrastructure that constitutes the platform will be responsible for feeding and managing the resources from which it is nourished (both structurally, in aspects such as clustering, memory, capacity, task management, etc., as operational, data flow , information management, sampling rate, recommendation system, etc.).

PLATINUM It has been financed by the Ministry of Economy, Industry and Competitiveness and the State Research Agency, and co-financed with Structural Funds of the European Union, in accordance with the provisions of article 31 of the 27 Resolution of October of 2017, for the concession of the grants corresponding to the call RETOS COLABORACIÓN with the file number RTC-2017-6401-7.

Collaborating Partners:

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