Data Collection and Uncertainties

Jean-Pierre Signoret () and Alain Leroy ()
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Jean-Pierre Signoret: Total Professeurs Associés

Chapter Chapter 38 in Reliability Assessment of Safety and Production Systems, 2021, pp 839-861 from Springer

Abstract: 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.

Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:spr:ssrchp:978-3-030-64708-7_38

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DOI: 10.1007/978-3-030-64708-7_38

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