Components importance ranking considering the effect of epistemic uncertainty

Jingwen Song; Zhenzhou Lu; Michael Beer

doi:10.3850/978-981-11-2724-3_0724-cd

Components importance ranking considering the effect of epistemic uncertainty

Jingwen Song, Zhenzhou Lu, Michael Beer

School of Aeronautics

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

The identification of the importance of basic events or system components is an essential task in system design, which is quantified by reliability importance measure (IM). Traditional reliability IMs are typically calculated based on precisely given parameters (e.g., component failure rate) which is often inevitable when the available data is incomplete or imprecise. This paper proposes a new components importance ranking method concerning the case when the epistemic uncertainties of component parameters are represented by probability distribution (commonly established by Bayesian method). First, a new IM is defined at given working time point based on quantifying the overall difference between the probability distributions of system reliability due to the state change of one component. The larger the proposed IM, the more important the corresponding component is for improving the system reliability. Second, for given system reliability target, another IM is defined based on measuring the overall difference of the probability distribution of system lifetime when the state of one component is changed. A larger value of the second IM indicates a larger contribution of this component for increasing the system lifetime. The proposed method has two merits, i.e., (a) the effect of epistemic uncertainty in component parameters on system reliability performance is considered from the perspective of reliability and system lifetime separately; (b) it provides a simple way for ranking the components considering epistemic uncertainty.

Original language	English
Title of host publication	Proceedings of the 29th European Safety and Reliability Conference, ESREL 2019
Editors	Michael Beer, Enrico Zio
Publisher	Research Publishing Services
Pages	1743-1749
Number of pages	7
ISBN (Electronic)	9789811127243
DOIs	https://doi.org/10.3850/978-981-11-2724-3_0724-cd
State	Published - 2020
Event	29th European Safety and Reliability Conference, ESREL 2019 - Hannover, Germany Duration: 22 Sep 2019 → 26 Sep 2019

Publication series

Name	Proceedings of the 29th European Safety and Reliability Conference, ESREL 2019

Conference

Conference	29th European Safety and Reliability Conference, ESREL 2019
Country/Territory	Germany
City	Hannover
Period	22/09/19 → 26/09/19

Keywords

Component importance ranking
Density estimation
Epistemic uncertainty
Importance measure
System lifetime
System reliability

Access to Document

10.3850/978-981-11-2724-3_0724-cd

Cite this

Song, J., Lu, Z., & Beer, M. (2020). Components importance ranking considering the effect of epistemic uncertainty. In M. Beer, & E. Zio (Eds.), Proceedings of the 29th European Safety and Reliability Conference, ESREL 2019 (pp. 1743-1749). (Proceedings of the 29th European Safety and Reliability Conference, ESREL 2019). Research Publishing Services. https://doi.org/10.3850/978-981-11-2724-3_0724-cd

Song, Jingwen ; Lu, Zhenzhou ; Beer, Michael. / Components importance ranking considering the effect of epistemic uncertainty. Proceedings of the 29th European Safety and Reliability Conference, ESREL 2019. editor / Michael Beer ; Enrico Zio. Research Publishing Services, 2020. pp. 1743-1749 (Proceedings of the 29th European Safety and Reliability Conference, ESREL 2019).

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title = "Components importance ranking considering the effect of epistemic uncertainty",

abstract = "The identification of the importance of basic events or system components is an essential task in system design, which is quantified by reliability importance measure (IM). Traditional reliability IMs are typically calculated based on precisely given parameters (e.g., component failure rate) which is often inevitable when the available data is incomplete or imprecise. This paper proposes a new components importance ranking method concerning the case when the epistemic uncertainties of component parameters are represented by probability distribution (commonly established by Bayesian method). First, a new IM is defined at given working time point based on quantifying the overall difference between the probability distributions of system reliability due to the state change of one component. The larger the proposed IM, the more important the corresponding component is for improving the system reliability. Second, for given system reliability target, another IM is defined based on measuring the overall difference of the probability distribution of system lifetime when the state of one component is changed. A larger value of the second IM indicates a larger contribution of this component for increasing the system lifetime. The proposed method has two merits, i.e., (a) the effect of epistemic uncertainty in component parameters on system reliability performance is considered from the perspective of reliability and system lifetime separately; (b) it provides a simple way for ranking the components considering epistemic uncertainty.",

keywords = "Component importance ranking, Density estimation, Epistemic uncertainty, Importance measure, System lifetime, System reliability",

author = "Jingwen Song and Zhenzhou Lu and Michael Beer",

note = "Publisher Copyright: {\textcopyright} 2019 European Safety and Reliability Association. Published by Research Publishing, Singapore.; 29th European Safety and Reliability Conference, ESREL 2019 ; Conference date: 22-09-2019 Through 26-09-2019",

year = "2020",

doi = "10.3850/978-981-11-2724-3_0724-cd",

language = "英语",

series = "Proceedings of the 29th European Safety and Reliability Conference, ESREL 2019",

publisher = "Research Publishing Services",

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booktitle = "Proceedings of the 29th European Safety and Reliability Conference, ESREL 2019",

}

Song, J, Lu, Z & Beer, M 2020, Components importance ranking considering the effect of epistemic uncertainty. in M Beer & E Zio (eds), Proceedings of the 29th European Safety and Reliability Conference, ESREL 2019. Proceedings of the 29th European Safety and Reliability Conference, ESREL 2019, Research Publishing Services, pp. 1743-1749, 29th European Safety and Reliability Conference, ESREL 2019, Hannover, Germany, 22/09/19. https://doi.org/10.3850/978-981-11-2724-3_0724-cd

Components importance ranking considering the effect of epistemic uncertainty. / Song, Jingwen; Lu, Zhenzhou; Beer, Michael.
Proceedings of the 29th European Safety and Reliability Conference, ESREL 2019. ed. / Michael Beer; Enrico Zio. Research Publishing Services, 2020. p. 1743-1749 (Proceedings of the 29th European Safety and Reliability Conference, ESREL 2019).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - The identification of the importance of basic events or system components is an essential task in system design, which is quantified by reliability importance measure (IM). Traditional reliability IMs are typically calculated based on precisely given parameters (e.g., component failure rate) which is often inevitable when the available data is incomplete or imprecise. This paper proposes a new components importance ranking method concerning the case when the epistemic uncertainties of component parameters are represented by probability distribution (commonly established by Bayesian method). First, a new IM is defined at given working time point based on quantifying the overall difference between the probability distributions of system reliability due to the state change of one component. The larger the proposed IM, the more important the corresponding component is for improving the system reliability. Second, for given system reliability target, another IM is defined based on measuring the overall difference of the probability distribution of system lifetime when the state of one component is changed. A larger value of the second IM indicates a larger contribution of this component for increasing the system lifetime. The proposed method has two merits, i.e., (a) the effect of epistemic uncertainty in component parameters on system reliability performance is considered from the perspective of reliability and system lifetime separately; (b) it provides a simple way for ranking the components considering epistemic uncertainty.

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Song J, Lu Z, Beer M. Components importance ranking considering the effect of epistemic uncertainty. In Beer M, Zio E, editors, Proceedings of the 29th European Safety and Reliability Conference, ESREL 2019. Research Publishing Services. 2020. p. 1743-1749. (Proceedings of the 29th European Safety and Reliability Conference, ESREL 2019). doi: 10.3850/978-981-11-2724-3_0724-cd

Components importance ranking considering the effect of epistemic uncertainty

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