Towards online adaptation of digital twins

Riku Pekka Nikula (Corresponding Author), Marko Paavola, Mika Ruusunen, Joni Keski-Rahkonen

Research output: Contribution to journalArticleScientificpeer-review

7 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)776-783
Number of pages8
JournalOpen Engineering
Issue number1
Publication statusPublished - 23 Aug 2020
MoE publication typeA1 Journal article-refereed
EventAutomaatiopäivät23 - Oulu, Finland
Duration: 15 May 201916 May 2019


  • adaptation
  • differential evolution
  • digital twin
  • optimization
  • surrogate model


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