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A predictive maintenance model for health assessment of an assembly robot based on machine learning in the context of smart plant

Author

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  • Ayoub Chakroun

    (University Paris VIII Vincennes, University Institute of Technology
    University of Sfax, National School of Engineers of Sfax)

  • Yasmina Hani

    (University Paris VIII Vincennes, University Institute of Technology)

  • Abderrahmane Elmhamedi

    (University Paris VIII Vincennes, University Institute of Technology)

  • Faouzi Masmoudi

    (University of Sfax, National School of Engineers of Sfax)

Abstract

This paper introduces a predictive maintenance model based on Machine Learning (ML) in the context of a smart factory. It addresses a critical aspect within factories which is the health assessment of vital machinery. This case study specifically focuses on two brass accessories assembly robots and predicts the degradation of their power transmitters, which operate under severe mechanical and thermal conditions. The paper presents a predictive model based on ML and Artificial Intelligence (the Discrete Bayes Filter) to estimate and foresee the gradual deterioration of robots’ power transmitters. It aims at empowering operators to make informed decisions regarding maintenance interventions. The model is based on a Discrete Bayesian Filter (DBF) in comparison to a model based on Naïve Bayes Filter (NBF). The findings indicate that the DBF model demonstrates superior predictive performance compared to the NBF model. The predictive model’s investigation results were validated during testing on robots. This model enables the company to establish an informed and efficient schedule for maintenance interventions.

Suggested Citation

  • Ayoub Chakroun & Yasmina Hani & Abderrahmane Elmhamedi & Faouzi Masmoudi, 2024. "A predictive maintenance model for health assessment of an assembly robot based on machine learning in the context of smart plant," Journal of Intelligent Manufacturing, Springer, vol. 35(8), pages 3995-4013, December.
    • Handle: RePEc:spr:joinma:v:35:y:2024:i:8:d:10.1007_s10845-023-02281-3
    DOI: 10.1007/s10845-023-02281-3
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    References listed on IDEAS

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    1. Kans, Mirka & Ingwald, Anders, 2008. "Common database for cost-effective improvement of maintenance performance," International Journal of Production Economics, Elsevier, vol. 113(2), pages 734-747, June.
    2. Qinming Liu & Ming Dong & Wenyuan Lv & Chunming Ye, 2019. "Manufacturing system maintenance based on dynamic programming model with prognostics information," Journal of Intelligent Manufacturing, Springer, vol. 30(3), pages 1155-1173, March.
    3. Riccardo Rosati & Luca Romeo & Gianalberto Cecchini & Flavio Tonetto & Paolo Viti & Adriano Mancini & Emanuele Frontoni, 2023. "From knowledge-based to big data analytic model: a novel IoT and machine learning based decision support system for predictive maintenance in Industry 4.0," Journal of Intelligent Manufacturing, Springer, vol. 34(1), pages 107-121, January.
    4. Zio, Enrico, 2022. "Prognostics and Health Management (PHM): Where are we and where do we (need to) go in theory and practice," Reliability Engineering and System Safety, Elsevier, vol. 218(PA).
    5. Da Wen & Pan Ershun & Wang Ying & Liao Wenzhu, 2016. "An economic production quantity model for a deteriorating system integrated with predictive maintenance strategy," Journal of Intelligent Manufacturing, Springer, vol. 27(6), pages 1323-1333, December.
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    Cited by:

    1. Apostolos Giannoulidis & Anastasios Gounaris & Athanasios Naskos & Nikodimos Nikolaidis & Daniel Caljouw, 2025. "Engineering and evaluating an unsupervised predictive maintenance solution: a cold-forming press case-study," Journal of Intelligent Manufacturing, Springer, vol. 36(3), pages 2121-2139, March.
    2. Alexandre Dolgui & Hichem Haddou Benderbal & Fabio Sgarbossa & Simon Thevenin, 2024. "Editorial for the special issue: AI and data-driven decisions in manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 35(8), pages 3599-3604, December.

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