JORDAN-CIZELJ, Romana ;VRBANIĆ, Ivan . Modeling Uncertainties when Estimating Component Reliability. Strojniški vestnik - Journal of Mechanical Engineering, [S.l.], v. 49, n.7-8, p. 413-425, july 2017. ISSN 0039-2480. Available at: <https://www.sv-jme.eu/article/modeling-uncertainties-when-estimating-component-reliability/>. Date accessed: 16 apr. 2024. doi:http://dx.doi.org/.
Jordan-Cizelj, R., & Vrbanić, I. (2003). Modeling Uncertainties when Estimating Component Reliability. Strojniški vestnik - Journal of Mechanical Engineering, 49(7-8), 413-425. doi:http://dx.doi.org/
@article{.,
author = {Romana Jordan-Cizelj and Ivan Vrbanić},
title = {Modeling Uncertainties when Estimating Component Reliability},
journal = {Strojniški vestnik - Journal of Mechanical Engineering},
volume = {49},
number = {7-8},
year = {2003},
keywords = {probabilistic safety assessment PSA; assessment of uncertainties; modeling; },
abstract = {The extensive use of probabilistic safely assessment (PSA) methods for various risk-informed applications requires a thorough assessment of the uncertainties introduced with PSA models. Quantitative assessment is usually focused on an uncertainty analysis of the PSAs input parameters, such as initiatingevent frequencies, human-error probabilities, parameters of component-reliability models and some other special parameters. In this paper uncertainties that arise in PSA when building component-reliability models are described. Different approaches can be selected for estimating the input parameters of componentreliability models. Focus is given to the assessment of the so-called modeling uncertainties that arise when different mathematical and calculation-based approaches are used for the parameter of component-reliability model estimation. The assessment of uncertainties is presented using an example of a pump-reliability estimation. The so-called uncertainty bound is defined to assess uncertainties in the parameter estimation due to the use of different mathematical approaches for the parameter calculation. To minimize the modeling uncertainties for the component failure-rate estimation, the numerical calculation of the Bayesian updating procedure and the transformation of the resulting tabular discrete distribution into an analytical distribution seems to be most suitable.},
issn = {0039-2480}, pages = {413-425}, doi = {},
url = {https://www.sv-jme.eu/article/modeling-uncertainties-when-estimating-component-reliability/}
}
Jordan-Cizelj, R.,Vrbanić, I. 2003 July 49. Modeling Uncertainties when Estimating Component Reliability. Strojniški vestnik - Journal of Mechanical Engineering. [Online] 49:7-8
%A Jordan-Cizelj, Romana %A Vrbanić, Ivan %D 2003 %T Modeling Uncertainties when Estimating Component Reliability %B 2003 %9 probabilistic safety assessment PSA; assessment of uncertainties; modeling; %! Modeling Uncertainties when Estimating Component Reliability %K probabilistic safety assessment PSA; assessment of uncertainties; modeling; %X The extensive use of probabilistic safely assessment (PSA) methods for various risk-informed applications requires a thorough assessment of the uncertainties introduced with PSA models. Quantitative assessment is usually focused on an uncertainty analysis of the PSAs input parameters, such as initiatingevent frequencies, human-error probabilities, parameters of component-reliability models and some other special parameters. In this paper uncertainties that arise in PSA when building component-reliability models are described. Different approaches can be selected for estimating the input parameters of componentreliability models. Focus is given to the assessment of the so-called modeling uncertainties that arise when different mathematical and calculation-based approaches are used for the parameter of component-reliability model estimation. The assessment of uncertainties is presented using an example of a pump-reliability estimation. The so-called uncertainty bound is defined to assess uncertainties in the parameter estimation due to the use of different mathematical approaches for the parameter calculation. To minimize the modeling uncertainties for the component failure-rate estimation, the numerical calculation of the Bayesian updating procedure and the transformation of the resulting tabular discrete distribution into an analytical distribution seems to be most suitable. %U https://www.sv-jme.eu/article/modeling-uncertainties-when-estimating-component-reliability/ %0 Journal Article %R %& 413 %P 13 %J Strojniški vestnik - Journal of Mechanical Engineering %V 49 %N 7-8 %@ 0039-2480 %8 2017-07-07 %7 2017-07-07
Jordan-Cizelj, Romana, & Ivan Vrbanić. "Modeling Uncertainties when Estimating Component Reliability." Strojniški vestnik - Journal of Mechanical Engineering [Online], 49.7-8 (2003): 413-425. Web. 16 Apr. 2024
TY - JOUR AU - Jordan-Cizelj, Romana AU - Vrbanić, Ivan PY - 2003 TI - Modeling Uncertainties when Estimating Component Reliability JF - Strojniški vestnik - Journal of Mechanical Engineering DO - KW - probabilistic safety assessment PSA; assessment of uncertainties; modeling; N2 - The extensive use of probabilistic safely assessment (PSA) methods for various risk-informed applications requires a thorough assessment of the uncertainties introduced with PSA models. Quantitative assessment is usually focused on an uncertainty analysis of the PSAs input parameters, such as initiatingevent frequencies, human-error probabilities, parameters of component-reliability models and some other special parameters. In this paper uncertainties that arise in PSA when building component-reliability models are described. Different approaches can be selected for estimating the input parameters of componentreliability models. Focus is given to the assessment of the so-called modeling uncertainties that arise when different mathematical and calculation-based approaches are used for the parameter of component-reliability model estimation. The assessment of uncertainties is presented using an example of a pump-reliability estimation. The so-called uncertainty bound is defined to assess uncertainties in the parameter estimation due to the use of different mathematical approaches for the parameter calculation. To minimize the modeling uncertainties for the component failure-rate estimation, the numerical calculation of the Bayesian updating procedure and the transformation of the resulting tabular discrete distribution into an analytical distribution seems to be most suitable. UR - https://www.sv-jme.eu/article/modeling-uncertainties-when-estimating-component-reliability/
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volume = {49},
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TY - JOUR AU - Jordan-Cizelj, Romana AU - Vrbanić, Ivan PY - 2017/07/07 TI - Modeling Uncertainties when Estimating Component Reliability JF - Strojniški vestnik - Journal of Mechanical Engineering; Vol 49, No 7-8 (2003): Strojniški vestnik - Journal of Mechanical Engineering DO - KW - probabilistic safety assessment PSA, assessment of uncertainties, modeling, N2 - The extensive use of probabilistic safely assessment (PSA) methods for various risk-informed applications requires a thorough assessment of the uncertainties introduced with PSA models. Quantitative assessment is usually focused on an uncertainty analysis of the PSAs input parameters, such as initiatingevent frequencies, human-error probabilities, parameters of component-reliability models and some other special parameters. In this paper uncertainties that arise in PSA when building component-reliability models are described. Different approaches can be selected for estimating the input parameters of componentreliability models. Focus is given to the assessment of the so-called modeling uncertainties that arise when different mathematical and calculation-based approaches are used for the parameter of component-reliability model estimation. The assessment of uncertainties is presented using an example of a pump-reliability estimation. The so-called uncertainty bound is defined to assess uncertainties in the parameter estimation due to the use of different mathematical approaches for the parameter calculation. To minimize the modeling uncertainties for the component failure-rate estimation, the numerical calculation of the Bayesian updating procedure and the transformation of the resulting tabular discrete distribution into an analytical distribution seems to be most suitable. UR - https://www.sv-jme.eu/article/modeling-uncertainties-when-estimating-component-reliability/
Jordan-Cizelj, Romana, AND Vrbanić, Ivan. "Modeling Uncertainties when Estimating Component Reliability" Strojniški vestnik - Journal of Mechanical Engineering [Online], Volume 49 Number 7-8 (07 July 2017)
Strojniški vestnik - Journal of Mechanical Engineering 49(2003)7-8, 413-425
© The Authors, CC-BY 4.0 Int. Change in copyright policy from 2022, Jan 1st.
The extensive use of probabilistic safely assessment (PSA) methods for various risk-informed applications requires a thorough assessment of the uncertainties introduced with PSA models. Quantitative assessment is usually focused on an uncertainty analysis of the PSAs input parameters, such as initiatingevent frequencies, human-error probabilities, parameters of component-reliability models and some other special parameters. In this paper uncertainties that arise in PSA when building component-reliability models are described. Different approaches can be selected for estimating the input parameters of componentreliability models. Focus is given to the assessment of the so-called modeling uncertainties that arise when different mathematical and calculation-based approaches are used for the parameter of component-reliability model estimation. The assessment of uncertainties is presented using an example of a pump-reliability estimation. The so-called uncertainty bound is defined to assess uncertainties in the parameter estimation due to the use of different mathematical approaches for the parameter calculation. To minimize the modeling uncertainties for the component failure-rate estimation, the numerical calculation of the Bayesian updating procedure and the transformation of the resulting tabular discrete distribution into an analytical distribution seems to be most suitable.