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Formalisation of a new prognosis model for supporting proactive maintenance implementation on industrial system

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  • Muller, Alexandre
  • Suhner, Marie-Christine
  • Iung, Benoît

Abstract

The importance of the maintenance function has increased because of its role in keeping and improving system availability and safety, as well as product quality. To support this role, the maintenance concept has undergone several major developments that have led to proactive considerations mainly based on a prognosis process, which normally allows selection of the best maintenance action to be carried out. This paper proposes the deployment and experimentation of a prognosis process within an e-maintenance architecture. The deployment follows a methodology based on the combination of both a probabilistic approach for modelling the degradation mechanism and of an event one for dynamical degradation monitoring. The feasibility and benefits of this new prognosis process is investigated with an experiment using a manufacturing TELMA (TELe-MAintenance) platform supporting the unwinding of metal bobbins.

Suggested Citation

  • Muller, Alexandre & Suhner, Marie-Christine & Iung, Benoît, 2008. "Formalisation of a new prognosis model for supporting proactive maintenance implementation on industrial system," Reliability Engineering and System Safety, Elsevier, vol. 93(2), pages 234-253.
    • Handle: RePEc:eee:reensy:v:93:y:2008:i:2:p:234-253
    DOI: 10.1016/j.ress.2006.12.004
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    References listed on IDEAS

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    2. Singh, Gurmeet & Anil Kumar, T.Ch. & Naikan, V.N.A., 2019. "Efficiency monitoring as a strategy for cost effective maintenance of induction motors for minimizing carbon emission and energy consumption," Reliability Engineering and System Safety, Elsevier, vol. 184(C), pages 193-201.
    3. Monnin, Maxime & Iung, Benoit & Sénéchal, Olivier, 2011. "Dynamic behavioural model for assessing impact of regeneration actions on system availability: Application to weapon systems," Reliability Engineering and System Safety, Elsevier, vol. 96(3), pages 410-424.
    4. Meysam MALEKI & Virgilio CRUZ MACHADO, 2013. "Generic Integration of Lean, Agile, Resilient, and Green Practices in Automotive Supply Chain," REVISTA DE MANAGEMENT COMPARAT INTERNATIONAL/REVIEW OF INTERNATIONAL COMPARATIVE MANAGEMENT, Faculty of Management, Academy of Economic Studies, Bucharest, Romania, vol. 14(2), pages 237-248, May.
    5. Rebello, Sinda & Yu, Hongyang & Ma, Lin, 2018. "An integrated approach for system functional reliability assessment using Dynamic Bayesian Network and Hidden Markov Model," Reliability Engineering and System Safety, Elsevier, vol. 180(C), pages 124-135.
    6. González, Esteban Le Maitre & Desforges, Xavier & Archimède, Bernard, 2018. "Assessment method of the multicomponent systems future ability to achieve productive tasks from local prognoses," Reliability Engineering and System Safety, Elsevier, vol. 180(C), pages 403-415.
    7. El Hassene Ait Mokhtar & Radouane Laggoune & Alaa Chateauneuf, 2016. "Utility-Based Maintenance Optimization for Complex Water-Distribution Systems Using Bayesian Networks," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(12), pages 4153-4170, September.
    8. Özgür-Ünlüakın, Demet & Türkali, Busenur, 2021. "Evaluation of proactive maintenance policies on a stochastically dependent hidden multi-component system using DBNs," Reliability Engineering and System Safety, Elsevier, vol. 211(C).
    9. Liang, Zhenglin & Parlikad, Ajith Kumar & Srinivasan, Rengarajan & Rasmekomen, Nipat, 2017. "On fault propagation in deterioration of multi-component systems," Reliability Engineering and System Safety, Elsevier, vol. 162(C), pages 72-80.
    10. Medina-Oliva, G. & Weber, P. & Iung, B., 2013. "PRM-based patterns for knowledge formalisation of industrial systems to support maintenance strategies assessment," Reliability Engineering and System Safety, Elsevier, vol. 116(C), pages 38-56.
    11. Traore, M. & Chammas, A. & Duviella, E., 2015. "Supervision and prognosis architecture based on dynamical classification method for the predictive maintenance of dynamical evolving systems," Reliability Engineering and System Safety, Elsevier, vol. 136(C), pages 120-131.
    12. Le Son, Khanh & Fouladirad, Mitra & Barros, Anne & Levrat, Eric & Iung, Benoît, 2013. "Remaining useful life estimation based on stochastic deterioration models: A comparative study," Reliability Engineering and System Safety, Elsevier, vol. 112(C), pages 165-175.

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