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Review, analysis and synthesis of prognostic-based decision support methods for condition based maintenance

Author

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  • Alexandros Bousdekis

    (National Technical University of Athens (NTUA))

  • Babis Magoutas

    (National Technical University of Athens (NTUA))

  • Dimitris Apostolou

    (University of Piraeus)

  • Gregoris Mentzas

    (National Technical University of Athens (NTUA))

Abstract

In manufacturing enterprises, maintenance is a significant contributor to the total company’s cost. Condition based maintenance (CBM) relies on prognostic models and uses them to support maintenance decisions based on the predicted condition of equipment. Although prognostic-based decision support for CBM is not an extensively explored area, there exist methods which have been developed in order to deal with specific challenges such as the need to cope with real-time information, to predict the health state of equipment and to continuously update maintenance-related recommendations. The current work aims at providing a literature review for prognostic-based decision support methods for CBM. We analyse the literature in order to identify combinations of methods for prognostic-based decision support for CBM, propose a practical technique for selecting suitable combinations of methods and set the guidelines for future research.

Suggested Citation

  • Alexandros Bousdekis & Babis Magoutas & Dimitris Apostolou & Gregoris Mentzas, 2018. "Review, analysis and synthesis of prognostic-based decision support methods for condition based maintenance," Journal of Intelligent Manufacturing, Springer, vol. 29(6), pages 1303-1316, August.
    • Handle: RePEc:spr:joinma:v:29:y:2018:i:6:d:10.1007_s10845-015-1179-5
    DOI: 10.1007/s10845-015-1179-5
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    References listed on IDEAS

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    Cited by:

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    2. Mikhail, Mina & Ouali, Mohamed-Salah & Yacout, Soumaya, 2024. "A data-driven methodology with a nonparametric reliability method for optimal condition-based maintenance strategies," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    3. Duan, Chaoqun & Gong, Ting & Yan, Liangwen & Li, Xinmin, 2024. "Bi-level corrected residual life-based maintenance for deteriorating systems under competing risks," Reliability Engineering and System Safety, Elsevier, vol. 247(C).
    4. Rodrigo Barbosa de Santis & Marcelo Azevedo Costa, 2020. "Extended Isolation Forests for Fault Detection in Small Hydroelectric Plants," Sustainability, MDPI, vol. 12(16), pages 1-16, August.
    5. Florian, Eleonora & Sgarbossa, Fabio & Zennaro, Ilenia, 2021. "Machine learning-based predictive maintenance: A cost-oriented model for implementation," International Journal of Production Economics, Elsevier, vol. 236(C).
    6. Barbosa de Santis, Rodrigo & Silveira Gontijo, Tiago & Azevedo Costa, Marcelo, 2021. "Condition-based maintenance in hydroelectric plants: A systematic literature review," MPRA Paper 115912, University Library of Munich, Germany.
    7. Patrick Zschech, 2023. "Beyond descriptive taxonomies in data analytics: a systematic evaluation approach for data-driven method pipelines," Information Systems and e-Business Management, Springer, vol. 21(1), pages 193-227, March.

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