IDEAS home Printed from https://ideas.repec.org/a/spr/joinma/v35y2024i8d10.1007_s10845-023-02281-3.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10845-023-02281-3
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10845-023-02281-3?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Mingfei Li & Jiajian Wu & Zhengpeng Chen & Jiangbo Dong & Zhiping Peng & Kai Xiong & Mumin Rao & Chuangting Chen & Xi Li, 2022. "Data-Driven Voltage Prognostic for Solid Oxide Fuel Cell System Based on Deep Learning," Energies, MDPI, vol. 15(17), pages 1-20, August.
    2. Vincenzo Varriale & Antonello Cammarano & Francesca Michelino & Mauro Caputo, 2025. "Critical analysis of the impact of artificial intelligence integration with cutting-edge technologies for production systems," Journal of Intelligent Manufacturing, Springer, vol. 36(1), pages 61-93, January.
    3. Mohamad Javad Afzalinejad, 2025. "Application of dynamic maintenance strategy model based on group information and reliability," OPSEARCH, Springer;Operational Research Society of India, vol. 62(1), pages 55-76, March.
    4. Zhou, Haoxuan & Wang, Bingsen & Zio, Enrico & Wen, Guangrui & Liu, Zimin & Su, Yu & Chen, Xuefeng, 2023. "Hybrid system response model for condition monitoring of bearings under time-varying operating conditions," Reliability Engineering and System Safety, Elsevier, vol. 239(C).
    5. Kamei, Sayaka & Taghipour, Sharareh, 2023. "A comparison study of centralized and decentralized federated learning approaches utilizing the transformer architecture for estimating remaining useful life," Reliability Engineering and System Safety, Elsevier, vol. 233(C).
    6. Pang, Zhenan & Li, Tianmei & Pei, Hong & Si, Xiaosheng, 2023. "A condition-based prognostic approach for age- and state-dependent partially observable nonlinear degrading system," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    7. Zhang, Mingyuan & He, Chen & Huang, Chengxuan & Yang, Jianhong, 2024. "A weighted time embedding transformer network for remaining useful life prediction of rolling bearing," Reliability Engineering and System Safety, Elsevier, vol. 251(C).
    8. Waqar Ahmed Khan & Mahmoud Masoud & Abdelrahman E. E. Eltoukhy & Mehran Ullah, 2025. "Stacked encoded cascade error feedback deep extreme learning machine network for manufacturing order completion time," Journal of Intelligent Manufacturing, Springer, vol. 36(2), pages 1313-1339, February.
    9. Zuo, Jian & Cadet, Catherine & Li, Zhongliang & Bérenguer, Christophe & Outbib, Rachid, 2024. "A deterioration-aware energy management strategy for the lifetime improvement of a multi-stack fuel cell system subject to a random dynamic load," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    10. Peng, Shiliang & Fan, Lin & Zhang, Li & Su, Huai & He, Yuxuan & He, Qian & Wang, Xiao & Yu, Dejun & Zhang, Jinjun, 2024. "Spatio-temporal prediction of total energy consumption in multiple regions using explainable deep neural network," Energy, Elsevier, vol. 301(C).
    11. Costa, Nahuel & Sánchez, Luciano, 2022. "Variational encoding approach for interpretable assessment of remaining useful life estimation," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    12. Wang, Chu & Dou, Manfeng & Li, Zhongliang & Outbib, Rachid & Zhao, Dongdong & Zuo, Jian & Wang, Yuanlin & Liang, Bin & Wang, Peng, 2023. "Data-driven prognostics based on time-frequency analysis and symbolic recurrent neural network for fuel cells under dynamic load," Reliability Engineering and System Safety, Elsevier, vol. 233(C).
    13. Lewis, Austin D. & Groth, Katrina M., 2022. "Metrics for evaluating the performance of complex engineering system health monitoring models," Reliability Engineering and System Safety, Elsevier, vol. 223(C).
    14. Zheng, Rui & Najafi, Seyedvahid & Zhang, Yingzhi, 2022. "A recursive method for the health assessment of systems using the proportional hazards model," Reliability Engineering and System Safety, Elsevier, vol. 221(C).
    15. Jiabao Zou & Ping Lin, 2025. "Multichannel Attention-Based TCN-GRU Network for Remaining Useful Life Prediction of Aero-Engines," Energies, MDPI, vol. 18(8), pages 1-15, April.
    16. Zhou, Liang & Wang, Huawei, 2024. "An adaptive multi-scale feature fusion and adaptive mixture-of-experts multi-task model for industrial equipment health status assessment and remaining useful life prediction," Reliability Engineering and System Safety, Elsevier, vol. 248(C).
    17. Tianlei Zang & Shijun Wang & Zian Wang & Chuangzhi Li & Yunfei Liu & Yujian Xiao & Buxiang Zhou, 2024. "Integrated Planning and Operation Dispatching of Source–Grid–Load–Storage in a New Power System: A Coupled Socio–Cyber–Physical Perspective," Energies, MDPI, vol. 17(12), pages 1-43, June.
    18. Li, Xiao Yan & Cheng, De Jun & Fang, Xi Feng & Zhang, Chun Yan & Wang, Yu Feng, 2024. "A novel data augmentation strategy for aeroengine multitask prognosis based on degradation behavior extrapolation and diversity-usability trade-off," Reliability Engineering and System Safety, Elsevier, vol. 249(C).
    19. Yuchen Liu & Chaoxu Li & Man Li, 2025. "Data-Driven Online State Prediction Method for the Traction Motors of Electric Multiple Units (EMUs)," Sustainability, MDPI, vol. 17(9), pages 1-22, May.
    20. Zheng, Shuwen & Wang, Chong & Zio, Enrico & Liu, Jie, 2024. "Fault detection in complex mechatronic systems by a hierarchical graph convolution attention network based on causal paths," Reliability Engineering and System Safety, Elsevier, vol. 243(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:joinma:v:35:y:2024:i:8:d:10.1007_s10845-023-02281-3. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.