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Data Collection and Uncertainties

In: Reliability Assessment of Safety and Production Systems

Author

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  • Jean-Pierre Signoret

    (Total Professeurs Associés)

  • Alain Leroy

Abstract

Relevant probabilistic calculations cannot be performed without taking input data uncertainties into consideration. As a matter of fact, obtained from field feedback and statistics or/and engineering judgment, input data are far from being perfectly known. Then, it is of utmost importance to estimate which is the impact on the results calculated from uncertain input parameters. Provided that their probabilistic distributions are established, this can be undertaken by Monte Carlo simulation. This chapter aims to describe how to estimate and model input data uncertainties by using several classical distributions: uniform, triangular, chi-square, gamma and lognormal—the last one proving to be particularly flexible. The Bayesian approach is briefly addressed and the main standards related to reliability data collection and general databases are also identified.

Suggested Citation

  • Jean-Pierre Signoret & Alain Leroy, 2021. "Data Collection and Uncertainties," Springer Series in Reliability Engineering, in: Reliability Assessment of Safety and Production Systems, chapter 0, pages 839-861, Springer.
  • Handle: RePEc:spr:ssrchp:978-3-030-64708-7_38
    DOI: 10.1007/978-3-030-64708-7_38
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