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Towards online adaptation of digital twins

Authors
  • Nikula, Riku-Pekka1
  • Paavola, Marko2
  • Ruusunen, Mika1
  • Keski-Rahkonen, Joni3
  • 1 Control Engineering, Environmental and Chemical Engineering, University of Oulu, P.O. Box 4300, 90014 , (Finland)
  • 2 VTT Technical Research Centre of Finland, P.O. Box 1100, 90571 , (Finland)
  • 3 Kongsberg Maritime Finland Oy, P.O. Box 220, 26101 , (Finland)
Type
Published Article
Journal
Open Engineering
Publisher
De Gruyter Open
Publication Date
Aug 23, 2020
Volume
10
Issue
1
Pages
776–783
Identifiers
DOI: 10.1515/eng-2020-0088
Source
De Gruyter
Keywords
License
Green

Abstract

Digital twins have gained a lot of attention in modern day industry, but practical challenges arise from the requirement of continuous and real-time data integration. The actual physical systems are also exposed to disturbances unknown to the real-time simulation. Therefore, adaptation is required to ensure reliable performance and to improve the usability of digital twins in monitoring and diagnostics. This study proposes a general approach to the real-time adaptation of digital twins based on a mechanism guided by evolutionary optimization. The mechanism evaluates the deviation between the measured state of the real system and the estimated state provided by the model under adaptation. The deviation is minimized by adapting the model input based on the differential evolution algorithm. To test the mechanism, the measured data were generated via simulations based on a physical model of the real system. The estimated data were generated by a surrogate model, namely a simplified version of the physical model. A case study is presented where the adaptation mechanism is applied on the digital twin of a marine thruster. Satisfactory accuracy was achieved in the optimization during continuous adaptation. However, further research is required on the algorithms and hardware to reach the real-time computation requirement.

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