Details
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 29th European Safety and Reliability Conference, ESREL 2019 |
| Editors | Michael Beer, Enrico Zio |
| Place of Publication | Singapur |
| Pages | 1743-1749 |
| Number of pages | 7 |
| ISBN (electronic) | 9789811127243 |
| Publication status | Published - 2019 |
| Event | 29th European Safety and Reliability Conference, ESREL 2019 - Leibniz University Hannover, Hannover, Germany Duration: 22 Sept 2019 → 26 Sept 2019 |
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
ASJC Scopus subject areas
- Engineering(all)
- Safety, Risk, Reliability and Quality
- Social Sciences(all)
- Safety Research
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Proceedings of the 29th European Safety and Reliability Conference, ESREL 2019. ed. / Michael Beer; Enrico Zio. Singapur, 2019. p. 1743-1749.
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Components importance ranking considering the effect of epistemic uncertainty
AU - Song, Jingwen
AU - Lu, Zhenzhou
AU - Beer, Michael
PY - 2019
Y1 - 2019
N2 - 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.
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.
KW - Component importance ranking
KW - Density estimation
KW - Epistemic uncertainty
KW - Importance measure
KW - System lifetime
KW - System reliability
UR - http://www.scopus.com/inward/record.url?scp=85089187878&partnerID=8YFLogxK
U2 - 10.3850/978-981-11-2724-3_0724-cd
DO - 10.3850/978-981-11-2724-3_0724-cd
M3 - Conference contribution
AN - SCOPUS:85089187878
SP - 1743
EP - 1749
BT - Proceedings of the 29th European Safety and Reliability Conference, ESREL 2019
A2 - Beer, Michael
A2 - Zio, Enrico
CY - Singapur
T2 - 29th European Safety and Reliability Conference, ESREL 2019
Y2 - 22 September 2019 through 26 September 2019
ER -