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Improving scheduled maintenance by missing data reconstruction: A double-loop Monte Carlo approach

Author

Listed:
  • Michele Compare
  • Francesco Di Maio
  • Enrico Zio
  • Fausto Carlevaro
  • Sara Mattafirri

Abstract

This article describes a Monte Carlo–based approach for reconstructing missing information in a dataset used by General Electric for reliability analysis, which contains data coming from field observations at inspection of gas turbine components. The approach is based on a combination of maximum likelihood estimation technique to estimate the failure model parameters, Fisher information matrix to estimate the confidence intervals on the estimated parameters, and a double-loop Monte Carlo approach to estimate the missing equivalent starts (i.e. data of turbine state without the relative equivalent starts). The proposed methodology reduces the uncertainty in the estimation of the parameters of the turbine. The results of the application of the novel approach to a real industrial dataset are discussed along with a sensitivity analysis for the quantification of the robustness of the methodology to deal with different sizes of datasets.

Suggested Citation

  • Michele Compare & Francesco Di Maio & Enrico Zio & Fausto Carlevaro & Sara Mattafirri, 2016. "Improving scheduled maintenance by missing data reconstruction: A double-loop Monte Carlo approach," Journal of Risk and Reliability, , vol. 230(5), pages 502-511, October.
    • Handle: RePEc:sae:risrel:v:230:y:2016:i:5:p:502-511
    DOI: 10.1177/1748006X16651988
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    References listed on IDEAS

    as
    1. Wang, Wenbin, 2010. "A model for maintenance service contract design, negotiation and optimization," European Journal of Operational Research, Elsevier, vol. 201(1), pages 239-246, February.
    2. Zio, Enrico & Compare, Michele, 2013. "Evaluating maintenance policies by quantitative modeling and analysis," Reliability Engineering and System Safety, Elsevier, vol. 109(C), pages 53-65.
    3. Enrico Zio, 2013. "System Reliability and Risk Analysis," Springer Series in Reliability Engineering, in: The Monte Carlo Simulation Method for System Reliability and Risk Analysis, edition 127, chapter 0, pages 7-17, Springer.
    4. Enrico Zio, 2013. "Monte Carlo Simulation: The Method," Springer Series in Reliability Engineering, in: The Monte Carlo Simulation Method for System Reliability and Risk Analysis, edition 127, chapter 0, pages 19-58, Springer.
    5. Enrico Zio, 2013. "The Monte Carlo Simulation Method for System Reliability and Risk Analysis," Springer Series in Reliability Engineering, Springer, edition 127, number 978-1-4471-4588-2, January.
    6. Murthy, D. N. P. & Asgharizadeh, E., 1999. "Optimal decision making in a maintenance service operation," European Journal of Operational Research, Elsevier, vol. 116(2), pages 259-273, July.
    7. Enrico Zio, 2013. "System Reliability and Risk Analysis by Monte Carlo Simulation," Springer Series in Reliability Engineering, in: The Monte Carlo Simulation Method for System Reliability and Risk Analysis, edition 127, chapter 0, pages 59-81, Springer.
    8. Amato, Andrea & Compare, Michele & Gallisto, Maurizio & Maccari, Augusto & Paganelli, Mauro & Zio, Enrico, 2011. "Business interruption and loss of assets risk assessment in support of the design of an innovative concentrating solar power plant," Renewable Energy, Elsevier, vol. 36(5), pages 1558-1567.
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