Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Надёжный анализ дерева событий× | Робастное Анализ видов и последствий отказов× | |
|---|---|---|
| Область | Планирование эксперимента | Планирование эксперимента |
| Семейство | Process / pipeline | Process / pipeline |
| Год появления≠ | 1960s (ETA); robust extensions ~1990s–2000s | 1980s–1990s |
| Автор метода≠ | H.E. Lambert / Nuclear industry (ETA); robust extensions developed through aerospace and nuclear risk research | Extension of traditional FMEA (MIL-P-1629, 1949) integrated with Taguchi robust design philosophy (Genichi Taguchi, 1980s) |
| Тип≠ | Probabilistic risk assessment with uncertainty propagation | Risk analysis with variability quantification |
| Основополагающий источник≠ | Bedford, T., & Cooke, R. M. (2001). Probabilistic Risk Analysis: Foundations and Methods. Cambridge University Press. ISBN: 9780521773201 | Stamatis, D. H. (2003). Failure Mode and Effect Analysis: FMEA from Theory to Execution (2nd ed.). ASQ Quality Press. ISBN: 978-0873895989 |
| Другие названия | Robust ETA, uncertainty-aware event tree analysis, ETA with uncertainty quantification, robust probabilistic event tree | Robust FMEA, Noise-Aware FMEA, Variability-Integrated FMEA, Robustness-Based FMEA |
| Связанные≠ | 6 | 4 |
| Сводка≠ | Robust Event Tree Analysis (Robust ETA) extends classical event tree analysis by explicitly accounting for uncertainty in the probability estimates assigned to each branch. Rather than treating branch probabilities as precise point values, the robust approach represents them as intervals, probability distributions, or imprecise probabilities, then propagates that uncertainty through the tree to produce outcome frequency ranges instead of single numbers. This gives decision-makers a clearer picture of the confidence in risk estimates under realistic conditions of incomplete or conflicting information. | Robust Failure Mode and Effects Analysis extends the classical FMEA framework by explicitly incorporating noise factors, parameter variability, and environmental variation into the risk assessment process. Rather than treating failure likelihood as a single deterministic estimate, it uses robust design principles — most notably from Taguchi's quality engineering — to evaluate how process variability and uncontrollable noise factors influence the probability and severity of each failure mode, yielding risk priority numbers that reflect real-world variability. |
| ScholarGateНабор данных ↗ |
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