ESR 5

ESR 5 – Simulation-based Runtime Testing and Adaptation of Cyber Physical Systems using digital twins

Project Description

A digital twin is a digital replica of an industrial asset, process or system that enables companies to better understand and predict future behaviour and performance of their machines. The digital representation provides both the elements and the dynamics of how an asset operates and lives throughout its life cycle. Digital twins integrate artificial intelligence, machine learning and software analytics with data to create living digital simulation models that update and change as their physical counterparts change. In Industrie 4.0 digital twins could be used for optimizing the operation and maintenance of physical assets, systems and manufacturing processes, virtual commissioning, machine process simulation, user or operator training, simulation-based design, etc. A physical asset can have a virtual copy running in the cloud that gets richer with every second of operational data.  

Digital Twins could be used for improving the design and testing of Cyber-Physical Systems in Industrie 4.0. This will be focused on: (i) Co-simulation, (ii) Simulation-based Runtime Testing, (iii) Usage of historical operation data, (iv) Tracking mode simulation and (v) Functional Safety.
The research will address a co-simulation scenario where simulators of relevant engineering disciplines (processes, assembly, electronics and electrical, information systems, etc.) of the cyber-phsycal system are used online and parallel with its real counterpart and a large amount of historical operational data is available. In this context, simulation-based testing using historical data and machine learning may allow to predict future behavior and performance even in uncertain scenarios using the digital twin to explore next situations and alternatives. Moreover, tracking mode simulation allows the model adjustment to real data.

Host Institution

Mondragon Goi Eskola Politeknikoa (MGEP), Spain

Planned Secondments

KTH, UNOT, UES, UNINOVA, IDEKO

Lead Supervisors

MGEP - Dr Miren Illarramendi & Dr Goiuria Sagardui