Future factories and facilities run by digital twins

The concept of digital twins has been around but the Internet of Things (IoT) has enabled its cost-effective implementation. Digital twins refer to a virtual representation of a physical product or process. The EU-funded IoTwins project plans to build testbeds for digital twins in the manufacturing and facility management sectors. The digital models will integrate data from various sources such as data APIs, historical data, embedded sensors and open data.

This will give manufacturers an unprecedented view into how their products are performing. In facility management, the technology will be instrumental in improving the way buildings and their systems operate and in preventing prospective problems.

The IOTWINS project 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.

IOTWINS proposes a hierarchical organization of digital twins modeling manufacturing production plants and facility management deployment environments at increasing accuracy levels:

• IoT twins: featuring lightweight models of specific components performing big-data stream processing and local control for quality management operations (low latency and high reliability);
• Edge twins: deployed at plant gateways and/or at emerging Multi-access Edge Computing nodes, providing higher level control knobs and orchestrating IoT sensors and actuators in a production locality, thus fostering local optimizations and interoperability;
• Cloud twins: performing time-consuming and typically off-line parallel simulation and deep-learning, feeding the edge twin with pre-elaborated predictive models to be efficiently executed in the premises of the production plant for monitoring/control/tuning purposes.

Partners

• BONFIGLIOLI SPA
• BARCELONA SUPERCOMPUTING CENTER-CENTRO NACIONAL DE SUPERCOMPUTACION
• ESI GROUP
• ESI UK LIMITED
• ESI ITI GMBH
• FRAUNHOFER GESELLSCHAFT ZUR FORDERUNG DER ANGEWANDTEN FORSCHUNG EV
• TECHNISCHE UNIVERSITAT BERLIN
• FUTBOL CLUB BARCELONA ASOCIACION
• MARPOSS SOCIETA PER AZIONI
• ALMA MATER STUDIORUM - UNIVERSITA DI BOLOGNA
• ISTITUTO NAZIONALE DI FISICA NUCLEARE
• CINECA CONSORZIO INTERUNIVERSITARIO
• ART-ER-SOCIETA CONSORTILE PER AZIONI
• WAVESTONE LUXEMBOURG SA 
• ETXE-TAR, S.A.
• AINGURA IIOT SL
• KK WIND SOLUTIONS AS
• BEWARRANT
• WARRANT HUB SPA
• ECOLE NATIONALE SUPERIEURE D'ARTS ET METIERS
• GCL INTERNATIONAL
• GUALA CLOSURES SPA
• SIEMENS AKTIENGESELLSCHAFT OESTERREICH
• SIEMENS AKTIENGESELLSCHAFT
• TTTECH COMPUTERTECHNIK AG
• THALES SIX GTS FRANCE SAS
• CENTRE TECHNIQUE DES INDUSTRIES MECANIQUES
• FILL GESELLSCHAFT MBH
• TTTECH INDUSTRIAL AUTOMATION AG

Start date: 1 September 2019 - End date: 31 August 2022

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 857191.