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The asset administration shell as enabler for predictive maintenance: a review

Author

Listed:
  • Jhonny Rodriguez Rahal

    (Universidad de Valladolid
    Comlet Verteilte Systeme GmbH)

  • Alexander Schwarz

    (Universidad de Valladolid
    Comlet Verteilte Systeme GmbH)

  • Benjamín Sahelices

    (Universidad de Valladolid)

  • Ronny Weis

    (Comlet Verteilte Systeme GmbH)

  • Simon Duque Antón

    (Comlet Verteilte Systeme GmbH)

Abstract

The emergence of the Internet of Things and the interconnection of systems and machines enables the idea of Industry 4.0, a new industrial paradigm with a strong focus on interaction and communication between physical and digital entities, leading to the creation of cyber-physical systems. The digital twin and the standard for the Asset Administration Shell are concepts derived from Industry 4.0 that exploit the advantages of connecting the physical and virtual domains, improving the management and display of the collected data. Furthermore, the increasing availability of data has enabled the implementation of data-driven approaches, such as machine and deep learning models, for predictive maintenance in industrial and automotive applications. This paper provides a two-dimensional review of the Asset Administration Shell and data-driven methods for predictive maintenance, including fault diagnosis and prognostics. Additionally, a digital twin architecture combining the Asset Administration Shell, predictive maintenance and data-driven methods is proposed within the context of the WaVe project.

Suggested Citation

  • Jhonny Rodriguez Rahal & Alexander Schwarz & Benjamín Sahelices & Ronny Weis & Simon Duque Antón, 2025. "The asset administration shell as enabler for predictive maintenance: a review," Journal of Intelligent Manufacturing, Springer, vol. 36(1), pages 19-33, January.
    • Handle: RePEc:spr:joinma:v:36:y:2025:i:1:d:10.1007_s10845-023-02236-8
    DOI: 10.1007/s10845-023-02236-8
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    References listed on IDEAS

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    1. Pan, Haihong & Lü, Zhiqiang & Wang, Huimin & Wei, Haiyan & Chen, Lin, 2018. "Novel battery state-of-health online estimation method using multiple health indicators and an extreme learning machine," Energy, Elsevier, vol. 160(C), pages 466-477.
    2. Zhu, Rong & Chen, Yuan & Peng, Weiwen & Ye, Zhi-Sheng, 2022. "Bayesian deep-learning for RUL prediction: An active learning perspective," Reliability Engineering and System Safety, Elsevier, vol. 228(C).
    3. Charles R. Harris & K. Jarrod Millman & Stéfan J. Walt & Ralf Gommers & Pauli Virtanen & David Cournapeau & Eric Wieser & Julian Taylor & Sebastian Berg & Nathaniel J. Smith & Robert Kern & Matti Picu, 2020. "Array programming with NumPy," Nature, Nature, vol. 585(7825), pages 357-362, September.
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