MATISSE
MATISSE is a European HORIZON-KDT-JU research project bringing together over 30 partners from 7 countries in order to develop an advanced approach for efficient engineering and validation of industrial systems using Digital Twins (DTs). By integrating DTs with state-of-the-art technologies, MATISSE aims to better simulate, test, and predict system behaviours. This innovative approach helps companies optimise their industrial processes, reduce errors, and boost productivity, ultimately simplifying complex operations. To this end, MATISSE proposes to create a framework incorporating methods and tools for the efficient and continuous engineering and validation of industrial systems that are supported by DTs. The project notably leverages the advantages of model-based, data-driven, and cloud techniques to enable validation and verification services that improve productivity and quality significantly.
During the last decades, advances in digitalisation, information technologies and robotics have led to a significant increase in automation and smart technologies within industrial systems. This increase has also led to a critical need for monitoring, analysis, and diagnosis of these systems for providing more and more efficiency, correctness, reliability and availability. However, the Digital Twin (DT) solutions most efficient for these processes are both costly (e.g. in terms of time) and challenging to develop.
The MATISSE scientific and technological objectives notably include:
● A model-based cloud framework for automating the engineering and federation of DTs.
● Domain-independent DT-based services for prediction, verification, and monitoring.
● Demonstrator projects showcasing the practical applicability of the framework and services.
● The exploitation of MATISSE's results via open-source and commercial tools.
The goal of MATISSE is to provide a model-based framework and tooling for the engineering of digital twins (DTs) and their services. DTs are virtual representations of physical systems that can be used for verification, validation, prediction, and monitoring purposes. DTs require a large amount of data from the actual system, such as sensor data, usage data, environmental data, and historical data. MATISSE proposes to use Model-Driven Engineering (MDE) techniques to handle this data and create models of the DTs and their services. MDE leverages models as abstractions of systems and their environment and supports automation and integration of various software engineering activities. MATISSE will exploit MDE to provide high-level abstraction, facilitate activities automation, and support technology integration among all the covered design and development activities. In practice, the built and/or available models are used to create and validate the DTs and their services (DT Engineering). The DT services include verification and validation (V&V) services that can be applied for different scopes in different domains (DT V&V Toolkit). The MATISSE approach also supports continuous V&V throughout the DT life cycle, as well as traceability and federation of DTs.
Role of the school
The Department of Automation, Production and Computer Sciences (DAPI in French) in the NaoMod Team : Research expertise in software engineering and modeling in the context of complex and large-scale systems (e.g. Cyber-Physical Systems). Long-term experience in developping and disseminating research tools/prototypes in open source.
Hugo Bruneliere : Member of the project management team, coordinator of the French consortium, WP2 leader.
Partners
The whole project is coordinated by Mälardalen University (Sweden), the French consortium is made of IMTA (coordinator), Softeam-Docaposte, and University of Rennes.
30 partners (and sub-partners) from 7 different European countries (Austria, Finland, France, Italy, Portugal, Sweden, Turkey) :
10 academic partners (IMT Atlantique, University of Rennes, Universita degli Studi di Teramo, Mälardalen University, Johannes Kepler University Linz, Instituto Superior de Engenharia do Porto, etc.),
3 research institutes, 10 SMEs and 5 large companies (Siemens, Bombardier-Alstom, Leonardo, Softeam-Docaposte, etc.).