IoTwins | Distributed Digital Twins for industrial SMEs: a big-data platform

Based in the Emilia-Romagna Data Valley as part of the Horizon 2020 programme, IoTwins will experiment with new technologies for the digitisation of industrial processes and products. It will encompass 12 test platforms that will create ‘digital twins’, or virtual copies of industrial processes that will be used to test plants and infrastructure management tools in advance.

In addition to local partners, the project will also include international leading companies and scientific institutions such as Siemens, the Supercomputing Centre in Barcelona and the Fraunhofer Institute in Munich.

IoTwins was born within the Big Data Association and Industries, thanks to the joint efforts of the University of Bologna, INFN, Cineca, ART-ER and Bonfiglioli Riduttori, where the use of HPC with big data will enable different and new technologies through specific use cases/testbed.

Therefore IoTwins is the result of a close collaboration among public/private bodies, including academia, industry and government institutions, in synergy with those actions already active in the region for supporting data economy. Starting with the group of core members cited above, the partnership had been enlarged in order to involve further excellence in scientific research and industrial partners for the deployment of a number of testbeds and find several potential applications.

IoTwins aims at enabling SMEs in the manufacturing and facility management/service sectors to access edge-enabled and cloud-based big-data analysis services to create hybrid digital companions to improve their production process and optimize the management of their facilities.

During the next 3 years, IoTwins will deliver large-scale industrial test-beds leveraging and combining data related to the manufacturing and facility management optimization domains, coming from diverse sources, such as data APIs, historical data, embedded sensors, and Open Data sources.
The goal is to build a reference architecture for the development and deployment of distributed and edge-enabled digital twins of production plants and processes. Digital Twins collect data from manufacturing, maintenance, operations, facilities and operating environments, and use them to create a model of each specific asset, system, or process.
These models are then used to detect and diagnose anomalies, to determine an optimal set of actions that maximize key performance metrics.