Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Моделирование с поддержкой анализа дерева отказов× | Метод Монте-Карло× | |
|---|---|---|
| Область≠ | Планирование эксперимента | Принятие решений |
| Семейство≠ | Process / pipeline | MCDM |
| Год появления≠ | 1970s–1980s (widespread adoption in nuclear and aerospace industries) | 1949 |
| Автор метода≠ | Fault tree analysis: H. A. Watson (Bell Labs, 1961); Monte Carlo integration in reliability: Herman Kahn / Stanislaw Ulam (RAND, late 1940s); combination formalized in reliability engineering literature from the 1970s onward | Metropolis, N., Ulam, S. |
| Тип≠ | Quantitative reliability and risk analysis technique | Robustness wrapper — Monte Carlo uncertainty propagation |
| Основополагающий источник≠ | Vesely, W. E., Goldberg, F. F., Roberts, N. H., & Haasl, D. F. (1981). Fault Tree Handbook. US Nuclear Regulatory Commission, NUREG-0492. link ↗ | Metropolis, N., Ulam, S. (1949). The Monte Carlo method. Journal of the American Statistical Association DOI ↗ |
| Другие названия≠ | SA-FTA, Monte Carlo FTA, simulation-based FTA, stochastic fault tree analysis | — |
| Связанные≠ | 6 | 0 |
| Сводка≠ | Simulation-assisted fault tree analysis (SA-FTA) combines the logical structure of classical fault tree analysis with Monte Carlo or discrete-event simulation to estimate the probability and timing of an undesired top event when component failures follow complex, non-exponential, or correlated probability distributions. The approach overcomes the analytical limitations of Boolean algebra-based FTA and is widely used in nuclear, aerospace, chemical process, and manufacturing reliability engineering. | MONTE-CARLO-SIMULATION (Monte Carlo Simulation — Stochastic uncertainty propagation through MCDM model) is a ranking multi-criteria decision-making (MCDM) method introduced by Metropolis, N., Ulam, S. in 1949. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result. |
| ScholarGateНабор данных ↗ |
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