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Availability Model of a PHM-Equipped Component

Author

Listed:
  • Michele Compare
  • Luca Bellani
  • Enrico Zio

    (SSEC - Chaire Sciences des Systèmes et Défis Energétiques EDF/ECP/Supélec - Ecole Centrale Paris - Ecole Supérieure d'Electricité - SUPELEC (FRANCE) - CentraleSupélec - EDF R&D - EDF R&D - EDF - EDF, LGI - Laboratoire Génie Industriel - EA 2606 - CentraleSupélec)

Abstract

—A variety of prognostic and health management (PHM) algorithms have been developed in the last years and some metrics have been proposed to evaluate their performances. However , a general framework that allows us to quantify the benefit of PHM depending on these metrics is still lacking. We propose a general , time-variant, analytical model that conservatively evaluates the increase in system availability achievable when a component is equipped with a PHM system of known performance metrics. The availability model builds on metrics of literature and is applicable to different contexts. A simulated case study is presented concerning crack propagation in a mechanical component. A simplified cost model is used to compare the performance of predictive maintenance based on PHM with corrective and scheduled maintenance. Index Terms—Availability, cost-benefit analysis, Monte Carlo (MC) simulation, prognostics and health Management (PHM) metrics.

Suggested Citation

  • Michele Compare & Luca Bellani & Enrico Zio, 2017. "Availability Model of a PHM-Equipped Component," Post-Print hal-01652232, HAL.
  • Handle: RePEc:hal:journl:hal-01652232
    DOI: 10.1109/TR.2017.2669400
    Note: View the original document on HAL open archive server: https://hal.archives-ouvertes.fr/hal-01652232
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    References listed on IDEAS

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    1. Wang, Hongzhou, 2002. "A survey of maintenance policies of deteriorating systems," European Journal of Operational Research, Elsevier, vol. 139(3), pages 469-489, June.
    2. Zio, Enrico & Di Maio, Francesco, 2010. "A data-driven fuzzy approach for predicting the remaining useful life in dynamic failure scenarios of a nuclear system," Reliability Engineering and System Safety, Elsevier, vol. 95(1), pages 49-57.
    3. Curcurù, Giuseppe & Galante, Giacomo & Lombardo, Alberto, 2010. "A predictive maintenance policy with imperfect monitoring," Reliability Engineering and System Safety, Elsevier, vol. 95(9), pages 989-997.
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    Cited by:

    1. Compare, Michele & Bellani, Luca & Zio, Enrico, 2019. "Optimal allocation of prognostics and health management capabilities to improve the reliability of a power transmission network," Reliability Engineering and System Safety, Elsevier, vol. 184(C), pages 164-180.
    2. Compare, Michele & Baraldi, Piero & Marelli, Paolo & Zio, Enrico, 2020. "Partially observable Markov decision processes for optimal operations of gas transmission networks," Reliability Engineering and System Safety, Elsevier, vol. 199(C).
    3. Mancuso, A. & Compare, M. & Salo, A. & Zio, E., 2021. "Optimal Prognostics and Health Management-driven inspection and maintenance strategies for industrial systems," Reliability Engineering and System Safety, Elsevier, vol. 210(C).

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