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Supervision and prognosis architecture based on dynamical classification method for the predictive maintenance of dynamical evolving systems

Author

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  • Traore, M.
  • Chammas, A.
  • Duviella, E.

Abstract

In this paper, we are concerned by the improvement of the safety, availability and reliability of dynamical systems’ components subjected to slow degradations (slow drifts). We propose an architecture for efficient Predictive Maintenance (PM) according to the real time estimate of the future state of the components. The architecture is built on supervision and prognosis tools. The prognosis method is based on an appropriated supervision technique that consists in drift tracking of the dynamical systems using AUDyC (AUto-adaptive and Dynamical Clustering), that is an auto-adaptive dynamical classifier. Thus, due to the complexity and the dynamical of the considered systems, the Failure Mode Effect and Criticity Analysis (FMECA) is used to identify the key components of the systems. A component is defined as an element of the system that can be impacted by only one failure. A failure of a key component causes a long downtime of the system. From the FMECA, a Fault Tree Analysis (FTA) of the system are built to determine the propagation laws of a failure on the system by using a deductive method. The proposed architecture is implemented for the PM of a thermoregulator. The application on this real system highlights the interests and the performances of the proposed architecture.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:reensy:v:136:y:2015:i:c:p:120-131
    DOI: 10.1016/j.ress.2014.12.005
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    References listed on IDEAS

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    1. Carnero, MaCarmen, 2006. "An evaluation system of the setting up of predictive maintenance programmes," Reliability Engineering and System Safety, Elsevier, vol. 91(8), pages 945-963.
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    Cited by:

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    3. Meng, Huixing & Liu, Xuan & Xing, Jinduo & Zio, Enrico, 2022. "A method for economic evaluation of predictive maintenance technologies by integrating system dynamics and evolutionary game modelling," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    4. Catelani, M. & Ciani, L. & Venzi, M., 2018. "Failure modes, mechanisms and effect analysis on temperature redundant sensor stage," Reliability Engineering and System Safety, Elsevier, vol. 180(C), pages 425-433.
    5. Belkacem, Lobna & Simeu-Abazi, Zineb & Dhouibi, Hedi & Gascard, Eric & Messaoud, Hassani, 2017. "Diagnostic and prognostic of hybrid dynamic systems: Modeling and RUL evaluation for two maintenance policies," Reliability Engineering and System Safety, Elsevier, vol. 164(C), pages 98-109.

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