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Experience from implementing digital twins for maintenance in industrial processes

Author

Listed:
  • Muhammad Hassan

    (University of Gävle)

  • Marcus Svadling

    (Ovako AB)

  • Niclas Björsell

    (University of Gävle)

Abstract

The capability of estimating future maintenance needs in advance and in a timely manner is a prerequisite for reliable manufacturing with high availability in a production unit. Additionally, conducting planned maintenance efforts regularly and prematurely increases the service lifetimes and utilization rates of parts, which leads to more sustainable production. The benefits of predictive maintenance are obvious, but introducing it into a facility poses various challenges. In this study, digital twins of well-functioning machines are used for predictive maintenance. The discrepancies between each physical unit and its digital twin are used to detect the maintenance needs. A thorough evaluation of the method over a period of 18 months by comparing digital twin detection results with maintenance and control system logs shows promising results. The method is successful in detecting discrepancies, and the paper describes the techniques that are used. However, not all discrepancies are related to the maintenance needs, and the evaluation identifies and discusses the most common sources of error. These are often the results of human interaction, such as parameter changes, maintenance activities and component replacement.

Suggested Citation

  • Muhammad Hassan & Marcus Svadling & Niclas Björsell, 2024. "Experience from implementing digital twins for maintenance in industrial processes," Journal of Intelligent Manufacturing, Springer, vol. 35(2), pages 875-884, February.
    • Handle: RePEc:spr:joinma:v:35:y:2024:i:2:d:10.1007_s10845-023-02078-4
    DOI: 10.1007/s10845-023-02078-4
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    References listed on IDEAS

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    1. Si, Xiao-Sheng & Wang, Wenbin & Hu, Chang-Hua & Zhou, Dong-Hua, 2011. "Remaining useful life estimation - A review on the statistical data driven approaches," European Journal of Operational Research, Elsevier, vol. 213(1), pages 1-14, August.
    2. Andrew Kusiak, 2017. "Smart manufacturing must embrace big data," Nature, Nature, vol. 544(7648), pages 23-25, April.
    3. Fei Tao & Fangyuan Sui & Ang Liu & Qinglin Qi & Meng Zhang & Boyang Song & Zirong Guo & Stephen C.-Y. Lu & A. Y. C. Nee, 2019. "Digital twin-driven product design framework," International Journal of Production Research, Taylor & Francis Journals, vol. 57(12), pages 3935-3953, June.
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