Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | 2023 IEEE Symposium Series on Computational Intelligence (SSCI) |
| Seiten | 506-511 |
| Seitenumfang | 6 |
| ISBN (elektronisch) | 978-1-6654-3065-4 |
| Publikationsstatus | Veröffentlicht - 2023 |
| Veranstaltung | 2023 IEEE Symposium Series on Computational Intelligence - Mexico City, Mexiko Dauer: 5 Dez. 2023 → 8 Dez. 2023 https://doi.org/10.1109/SSCI52147.2023 |
Abstract
of the survival signature for very large systems.
In recent years, the survival signature has seen promising
applications for the reliability analysis of critical infrastructures.
It outperforms traditional techniques by allowing for complex
modelling of dependencies, common causes of failures and
imprecision. However, as an inherently combinatorial method, the
survival signature suffers greatly from the curse of dimensionality.
Computation for very large systems, as needed for critical
infrastructures, is mostly infeasible. New advancements have
applied Monte Carlo simulation to approximate the signature instead
of performing a full evaluation. This allows for significantly
larger systems to be considered. Unfortunately, these approaches
will also quickly reach their limits with growing network size
and complexity. In this work, instead of approximating the full
survival signature, we will strategically select key values of the
signature to accurately approximate it using a surrogate radial
basis function network. This surrogate model is then extended to
an interval predictor model (IPM) to account for the uncertainty
in the prediction of the remaining unknown values. In contrast to
standard models, IPMs return an interval bounding the survival
signature entry. The resulting imprecise survival signature is then
fed into the reliability analysis, yielding upper and lower bounds
on the reliability of the system. This new method provides a
significant reduction in numerical effort enabling the analysis of
larger systems where the required computational demand was
previously prohibitive.
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2023 IEEE Symposium Series on Computational Intelligence (SSCI). 2023. S. 506-511.
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - Imprecise Survival Signature Approximation Using Interval Predictor Models
AU - Behrensdorf, Jasper
AU - Broggi, Matteo
AU - Beer, Michael
PY - 2023
Y1 - 2023
N2 - This paper presents a novel technique for the approximationof the survival signature for very large systems.In recent years, the survival signature has seen promisingapplications for the reliability analysis of critical infrastructures.It outperforms traditional techniques by allowing for complexmodelling of dependencies, common causes of failures andimprecision. However, as an inherently combinatorial method, thesurvival signature suffers greatly from the curse of dimensionality.Computation for very large systems, as needed for criticalinfrastructures, is mostly infeasible. New advancements haveapplied Monte Carlo simulation to approximate the signature insteadof performing a full evaluation. This allows for significantlylarger systems to be considered. Unfortunately, these approacheswill also quickly reach their limits with growing network sizeand complexity. In this work, instead of approximating the fullsurvival signature, we will strategically select key values of thesignature to accurately approximate it using a surrogate radialbasis function network. This surrogate model is then extended toan interval predictor model (IPM) to account for the uncertaintyin the prediction of the remaining unknown values. In contrast tostandard models, IPMs return an interval bounding the survivalsignature entry. The resulting imprecise survival signature is thenfed into the reliability analysis, yielding upper and lower boundson the reliability of the system. This new method provides asignificant reduction in numerical effort enabling the analysis oflarger systems where the required computational demand waspreviously prohibitive.
AB - This paper presents a novel technique for the approximationof the survival signature for very large systems.In recent years, the survival signature has seen promisingapplications for the reliability analysis of critical infrastructures.It outperforms traditional techniques by allowing for complexmodelling of dependencies, common causes of failures andimprecision. However, as an inherently combinatorial method, thesurvival signature suffers greatly from the curse of dimensionality.Computation for very large systems, as needed for criticalinfrastructures, is mostly infeasible. New advancements haveapplied Monte Carlo simulation to approximate the signature insteadof performing a full evaluation. This allows for significantlylarger systems to be considered. Unfortunately, these approacheswill also quickly reach their limits with growing network sizeand complexity. In this work, instead of approximating the fullsurvival signature, we will strategically select key values of thesignature to accurately approximate it using a surrogate radialbasis function network. This surrogate model is then extended toan interval predictor model (IPM) to account for the uncertaintyin the prediction of the remaining unknown values. In contrast tostandard models, IPMs return an interval bounding the survivalsignature entry. The resulting imprecise survival signature is thenfed into the reliability analysis, yielding upper and lower boundson the reliability of the system. This new method provides asignificant reduction in numerical effort enabling the analysis oflarger systems where the required computational demand waspreviously prohibitive.
KW - survival signature
KW - interval predictor models
KW - radial basis function networks
UR - http://www.scopus.com/inward/record.url?scp=85182935850&partnerID=8YFLogxK
U2 - 10.1109/SSCI52147.2023.10371939
DO - 10.1109/SSCI52147.2023.10371939
M3 - Conference contribution
SN - 978-1-6654-3064-7
SP - 506
EP - 511
BT - 2023 IEEE Symposium Series on Computational Intelligence (SSCI)
T2 - 2023 IEEE Symposium Series on Computational Intelligence
Y2 - 5 December 2023 through 8 December 2023
ER -