Details
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 30th European Safety and Reliability Conference and the 15th Probabilistic Safety Assessment and Management Conference |
| Editors | Piero Baraldi, Francesco Di Maio, Enrico Zio |
| Pages | 4791-4798 |
| Number of pages | 8 |
| Publication status | Published - 2020 |
| Event | 30th European Safety and Reliability Conference, ESREL 2020 and 15th Probabilistic Safety Assessment and Management Conference, PSAM15 2020 - Venice, Italy Duration: 1 Nov 2020 → 5 Nov 2020 |
Abstract
The reliability of complex systems, e.g. infrastructure networks or turbines, and further its proper prediction, is of paramount importance to our modern society. The concept of survival signature enables to entirely separate the structure of a network and its components probabilistic properties. As a consequence, once survival signature of a network has been computed, a reliability analysis can be performed by evaluating only the probabilistic part of the network. It is precisely this advantage that makes the analysis particularly efficient and clearly distinguishes the concept from traditional approaches. In reality, epistemic uncertainties, as vague or varying expert knowledge on a component’s future behavior, impede a proper reliability analysis. Considering and quantifying these in a plausible manner is therefore of major interest to decision-makers. Fuzzy probabilities offer a naturally fitting approach for this purpose. Despite the advantageous efficiency of the survival signature, analyzing complex systems under considertaion of epistemic uncertainties implies high computational effort. In order to counteract these efforts, two extended Monte Carlo simulation methods, originally utilized for imprecise structural reliability problems, are adapted into the context of system reliability. By integrating fuzzy probabilities into the probability structure of the survival signature and employing advanced Monte Carlo methods, this paper provides an efficient approach to quantify the reliability of a complex system taking into account epistemic uncertainties. Beyond the merging of the theoretical aspects, the approach is applied to a functional model of an axial compressor, in order to demonstrate its usability.
Keywords
- Epistemic uncertainty, Extended Monte Carlo methods, Fuzzy probabilities, Network reliability, Non-intrusive Imprecise Stochastic Simulation, Reliability analysis, Survival signature, System reliability
ASJC Scopus subject areas
- Engineering(all)
- Safety, Risk, Reliability and Quality
- Social Sciences(all)
- Safety Research
Cite this
- Standard
- Harvard
- Apa
- Vancouver
- BibTeX
- RIS
Proceedings of the 30th European Safety and Reliability Conference and the 15th Probabilistic Safety Assessment and Management Conference. ed. / Piero Baraldi; Francesco Di Maio; Enrico Zio. 2020. p. 4791-4798.
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Efficient Reliability Analysis of an Axial Compressor in Consideration of Epistemic Uncertainty
AU - Salomon, Julian
AU - Winnewisser, Niklas
AU - Wei, Pengfei
AU - Broggi, Matteo
AU - Beer, Michael
N1 - Funding Information: Funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) ? SFB 871/3 ? 119193472
PY - 2020
Y1 - 2020
N2 - The reliability of complex systems, e.g. infrastructure networks or turbines, and further its proper prediction, is of paramount importance to our modern society. The concept of survival signature enables to entirely separate the structure of a network and its components probabilistic properties. As a consequence, once survival signature of a network has been computed, a reliability analysis can be performed by evaluating only the probabilistic part of the network. It is precisely this advantage that makes the analysis particularly efficient and clearly distinguishes the concept from traditional approaches. In reality, epistemic uncertainties, as vague or varying expert knowledge on a component’s future behavior, impede a proper reliability analysis. Considering and quantifying these in a plausible manner is therefore of major interest to decision-makers. Fuzzy probabilities offer a naturally fitting approach for this purpose. Despite the advantageous efficiency of the survival signature, analyzing complex systems under considertaion of epistemic uncertainties implies high computational effort. In order to counteract these efforts, two extended Monte Carlo simulation methods, originally utilized for imprecise structural reliability problems, are adapted into the context of system reliability. By integrating fuzzy probabilities into the probability structure of the survival signature and employing advanced Monte Carlo methods, this paper provides an efficient approach to quantify the reliability of a complex system taking into account epistemic uncertainties. Beyond the merging of the theoretical aspects, the approach is applied to a functional model of an axial compressor, in order to demonstrate its usability.
AB - The reliability of complex systems, e.g. infrastructure networks or turbines, and further its proper prediction, is of paramount importance to our modern society. The concept of survival signature enables to entirely separate the structure of a network and its components probabilistic properties. As a consequence, once survival signature of a network has been computed, a reliability analysis can be performed by evaluating only the probabilistic part of the network. It is precisely this advantage that makes the analysis particularly efficient and clearly distinguishes the concept from traditional approaches. In reality, epistemic uncertainties, as vague or varying expert knowledge on a component’s future behavior, impede a proper reliability analysis. Considering and quantifying these in a plausible manner is therefore of major interest to decision-makers. Fuzzy probabilities offer a naturally fitting approach for this purpose. Despite the advantageous efficiency of the survival signature, analyzing complex systems under considertaion of epistemic uncertainties implies high computational effort. In order to counteract these efforts, two extended Monte Carlo simulation methods, originally utilized for imprecise structural reliability problems, are adapted into the context of system reliability. By integrating fuzzy probabilities into the probability structure of the survival signature and employing advanced Monte Carlo methods, this paper provides an efficient approach to quantify the reliability of a complex system taking into account epistemic uncertainties. Beyond the merging of the theoretical aspects, the approach is applied to a functional model of an axial compressor, in order to demonstrate its usability.
KW - Epistemic uncertainty
KW - Extended Monte Carlo methods
KW - Fuzzy probabilities
KW - Network reliability
KW - Non-intrusive Imprecise Stochastic Simulation
KW - Reliability analysis
KW - Survival signature
KW - System reliability
UR - http://www.scopus.com/inward/record.url?scp=85107263402&partnerID=8YFLogxK
U2 - 10.3850/978-981-14-8593-0_3685-cd
DO - 10.3850/978-981-14-8593-0_3685-cd
M3 - Conference contribution
AN - SCOPUS:85107263402
SN - 9789811485930
SP - 4791
EP - 4798
BT - Proceedings of the 30th European Safety and Reliability Conference and the 15th Probabilistic Safety Assessment and Management Conference
A2 - Baraldi, Piero
A2 - Di Maio, Francesco
A2 - Zio, Enrico
T2 - 30th European Safety and Reliability Conference, ESREL 2020 and 15th Probabilistic Safety Assessment and Management Conference, PSAM15 2020
Y2 - 1 November 2020 through 5 November 2020
ER -