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Evaluation methods for estimation of Weibull parameters used in Monte Carlo simulations for safety analysis of pressure vessels

  • The test data for static burst strength and load cycle fatigue strength of pressure vessels can often be well described by Gaussian normal or Weibull distribution functions. There are various approaches which can be used to determine the parameters of the Weibull distribution function; however, the performance of these methods is uncertain. In this study, six methods are evaluated by using the criterion of OSL (observed significance level) from Anderson-Darling (AD) goodness of Fit (GoF), These are: a) the norm-log based method, b) least squares regression, c) weighted least squares regression, d) a linear approach based on good linear unbiased estimators, e) maximum likelihood estimation and f) method of moments estimation. In addition, various approaches of ranking function are considered. The results show that there are no outperforming methods which can be identified clearly, primarily due to the limitation of the small sample size of the test data used for Weibull analysis. ThisThe test data for static burst strength and load cycle fatigue strength of pressure vessels can often be well described by Gaussian normal or Weibull distribution functions. There are various approaches which can be used to determine the parameters of the Weibull distribution function; however, the performance of these methods is uncertain. In this study, six methods are evaluated by using the criterion of OSL (observed significance level) from Anderson-Darling (AD) goodness of Fit (GoF), These are: a) the norm-log based method, b) least squares regression, c) weighted least squares regression, d) a linear approach based on good linear unbiased estimators, e) maximum likelihood estimation and f) method of moments estimation. In addition, various approaches of ranking function are considered. The results show that there are no outperforming methods which can be identified clearly, primarily due to the limitation of the small sample size of the test data used for Weibull analysis. This randomness resulting from the sampling is further investigated by using Monte Carlo simulations, concluding that the sample size of the experimental data is more crucial than the exact method used to derive Weibull parameters. Finally, a recommendation is made to consider the uncertainties of the limitations due to the small size for pressure vessel testing and also for general material testing.zeige mehrzeige weniger

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Metadaten
Autor*innen:Bin Wang, Georg MairORCiD, F. Islam
Dokumenttyp:Zeitschriftenartikel
Veröffentlichungsform:Verlagsliteratur
Sprache:Englisch
Titel des übergeordneten Werkes (Englisch):Material Testing
Jahr der Erstveröffentlichung:2021
Organisationseinheit der BAM:3 Gefahrgutumschließungen; Energiespeicher
3 Gefahrgutumschließungen; Energiespeicher / 3.5 Sicherheit von Gasspeichern
Verlag:De Gruyter
Jahrgang/Band:63
Ausgabe/Heft:4
Erste Seite:279
Letzte Seite:385
DDC-Klassifikation:Technik, Medizin, angewandte Wissenschaften / Ingenieurwissenschaften / Angewandte Physik
Freie Schlagwörter:Monte Carlo simulation; Randomness; Safety assessment; Sample size; Weibull distribution parameters
Themenfelder/Aktivitätsfelder der BAM:Energie
DOI:10.1515/mt-2020-0058
Verfügbarkeit des Dokuments:Datei im Netzwerk der BAM verfügbar ("Closed Access")
Datum der Freischaltung:18.08.2021
Referierte Publikation:Ja
Datum der Eintragung als referierte Publikation:18.08.2021
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