Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | Proceedings of the 29th European Safety and Reliability Conference, ESREL 2019 |
| Herausgeber/-innen | Michael Beer, Enrico Zio |
| Erscheinungsort | Singapur |
| Seiten | 2719-2726 |
| Seitenumfang | 8 |
| ISBN (elektronisch) | 9789811127243 |
| Publikationsstatus | Veröffentlicht - 2019 |
| Veranstaltung | 29th European Safety and Reliability Conference, ESREL 2019 - Leibniz University Hannover, Hannover, Deutschland Dauer: 22 Sept. 2019 → 26 Sept. 2019 |
Abstract
The Probability Density Evolution Method (PDEM) is a relatively novel tool to approximate the time-dependent joint Probability Distribution Function of multidimensional stochastic systems. The PDEM enables the possibility to give a statement about the time-dependent behaviour of a target physical quantity acquired from a deterministically solvable system. This has been utilized to assess the performance of systems in the face of reliability statements, specifically first passage problems. For now, the PDEM requires a solving strategy that includes the selection of points of interest that cover a large area in the random space, based on direct Monte Carlo simulation and Sobol set establishment. This approach often neglects the existence of rare events which could trigger critical behaviours of the system. This neglection can lead to a system assessment that is too much generalized. A new approach is presented that utilizes the features of the advanced Monte Carlo method Subset sampling (SuS) with regard to a first passage failure criteria. This enables the identification of random parameter configurations that result in rare event behaviour which lead to a failure. This combination and the novel formulation of the selected points that cover a rare event space could lead to further understandings of rare event behaviour of specific systems and additionally increase the PDEM efficiency and accuracy when dealing with a higher order of random dimensions.
ASJC Scopus Sachgebiete
- Ingenieurwesen (insg.)
- Sicherheit, Risiko, Zuverlässigkeit und Qualität
- Sozialwissenschaften (insg.)
- Sicherheitsforschung
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Proceedings of the 29th European Safety and Reliability Conference, ESREL 2019. Hrsg. / Michael Beer; Enrico Zio. Singapur, 2019. S. 2719-2726.
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - Rare event modelling for stochastic dynamic systems approximated by the probability density evolution method
AU - Bittner, Marius
AU - Broggi, Matteo
AU - Beer, Michael
N1 - Funding information: The authors are deeply grateful for the support given by the group for stochastic dynamic analysis and concrete damage analysis, Institute of Modern Concrete Structure and Lifeline Engineering at the Tongji University, under Prof. Dr. Jian-Bing Chen, where M. Bittner has been hosted during part of his studies.
PY - 2019
Y1 - 2019
N2 - The Probability Density Evolution Method (PDEM) is a relatively novel tool to approximate the time-dependent joint Probability Distribution Function of multidimensional stochastic systems. The PDEM enables the possibility to give a statement about the time-dependent behaviour of a target physical quantity acquired from a deterministically solvable system. This has been utilized to assess the performance of systems in the face of reliability statements, specifically first passage problems. For now, the PDEM requires a solving strategy that includes the selection of points of interest that cover a large area in the random space, based on direct Monte Carlo simulation and Sobol set establishment. This approach often neglects the existence of rare events which could trigger critical behaviours of the system. This neglection can lead to a system assessment that is too much generalized. A new approach is presented that utilizes the features of the advanced Monte Carlo method Subset sampling (SuS) with regard to a first passage failure criteria. This enables the identification of random parameter configurations that result in rare event behaviour which lead to a failure. This combination and the novel formulation of the selected points that cover a rare event space could lead to further understandings of rare event behaviour of specific systems and additionally increase the PDEM efficiency and accuracy when dealing with a higher order of random dimensions.
AB - The Probability Density Evolution Method (PDEM) is a relatively novel tool to approximate the time-dependent joint Probability Distribution Function of multidimensional stochastic systems. The PDEM enables the possibility to give a statement about the time-dependent behaviour of a target physical quantity acquired from a deterministically solvable system. This has been utilized to assess the performance of systems in the face of reliability statements, specifically first passage problems. For now, the PDEM requires a solving strategy that includes the selection of points of interest that cover a large area in the random space, based on direct Monte Carlo simulation and Sobol set establishment. This approach often neglects the existence of rare events which could trigger critical behaviours of the system. This neglection can lead to a system assessment that is too much generalized. A new approach is presented that utilizes the features of the advanced Monte Carlo method Subset sampling (SuS) with regard to a first passage failure criteria. This enables the identification of random parameter configurations that result in rare event behaviour which lead to a failure. This combination and the novel formulation of the selected points that cover a rare event space could lead to further understandings of rare event behaviour of specific systems and additionally increase the PDEM efficiency and accuracy when dealing with a higher order of random dimensions.
KW - Advanced Monte Carlo Methods
KW - First Passage Problem
KW - Non-Linear Dynamic Response
KW - Probability Density Evolution Method
KW - Probability of Failure
KW - Spectral Representation
KW - Stochastic Process
KW - Subset Sampling
KW - Uncertainty Quantification
UR - http://www.scopus.com/inward/record.url?scp=85089181515&partnerID=8YFLogxK
U2 - 10.3850/978-981-11-2724-3_0735-cd
DO - 10.3850/978-981-11-2724-3_0735-cd
M3 - Conference contribution
AN - SCOPUS:85089181515
SP - 2719
EP - 2726
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 -