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| Simulation-gestützte Zuverlässigkeitsanalyse× | Sensitivitätsanalyse mit Zuverlässigkeitsanalyse× | |
|---|---|---|
| Fachgebiet | Versuchsplanung | Versuchsplanung |
| Familie | Process / pipeline | Process / pipeline |
| Entstehungsjahr≠ | 1940s–1980s (Monte Carlo foundations ~1940s; simulation-reliability integration ~1970s–1980s) | 1969 (importance measures); 2000s (global SA integration) |
| Urheber≠ | Enrico Fermi, John von Neumann, Stanislaw Ulam (Monte Carlo foundations); Freudenthal (structural reliability); Melchers (simulation integration) | Birnbaum (importance measures, 1969); Saltelli et al. (global SA formalization, 2000s) |
| Typ≠ | Quantitative probabilistic engineering method | Quantitative integrated engineering method |
| Wegweisende Quelle≠ | Melchers, R. E., & Beck, A. T. (2018). Structural Reliability Analysis and Prediction (3rd ed.). Wiley. ISBN: 978-1119266075 | Saltelli, A., Ratto, M., Andres, T., Campolongo, F., Cariboni, J., Gatelli, D., Saisana, M., & Tarantola, S. (2008). Global Sensitivity Analysis: The Primer. Wiley. ISBN: 978-0470059975 |
| Aliasnamen | SARA, Monte Carlo reliability analysis, simulation-based reliability assessment, virtual reliability testing | SA-RA, reliability sensitivity analysis, importance measures in reliability, reliability-based sensitivity analysis |
| Verwandt≠ | 6 | 5 |
| Zusammenfassung≠ | Simulation-assisted reliability analysis combines probabilistic reliability theory with computational simulation — most commonly Monte Carlo methods or finite-element models — to estimate the probability that a system, component, or structure will perform its intended function under uncertain operating conditions. Rather than relying solely on closed-form analytical solutions, it propagates uncertainty through high-fidelity numerical models to quantify failure risk across complex, nonlinear, or multi-failure-mode systems. | Sensitivity analysis integrated with reliability analysis is a quantitative engineering method that determines how uncertainty or variation in each system input — such as component failure rates, material properties, or load distributions — propagates into overall system reliability. By computing importance measures for every uncertain parameter, analysts can rank components and assumptions by their influence on system dependability, focusing improvement efforts where they matter most. |
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