Professor of Mechanical Engineering, PhD
Project time: 2019 – 2022
Budget: 9 592 630
Funding: SIP Produktion2030
A digital twin model for improved predictive maintenance decision support, supporting service business models
The DT-SAPS project has the ambitious goal of advancing the industrial and scientific state of the art by developing an innovative, flexible and versatile digital twin model (software platform) for improved predictive maintenance decision support to support service business models. The digital twin will be based on three main focus areas, namely (i) Availability and maintenance simulation modelling for production processes and equipment, and (ii) remote capture and communication of production line monitoring data, and (iii) logical modelling of representation and presentation of data from monitoring of production processes.
The project novelty lies in the creation of a digital twin (a dynamic software model of a system that is continuously updated with real-time system operating data and feeds real-time optimisations back to the system) that provides availability prediction and maintenance optimisation for a complete multi-asset production system.
The research challenge is to enhance and combine the data collection, logical representation and analytics (smart algorithms) technologies for updating the digital twin in real-time, along with the simulation and statistical modelling technologies that empower it with predictive insight and optimisation functions.
The industrial application is Epiroc drill rigs and other mobile units such as charge robots used in underground mining, with results and developments that are applicable in both manufacturing and process industries.
Vinnova dnr: 2019-00778