方法对比
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| 敏感性分析与可靠性分析× | 灵敏度分析与故障树分析× | |
|---|---|---|
| 领域 | 实验设计 | 实验设计 |
| 方法族 | Process / pipeline | Process / pipeline |
| 起源年份≠ | 1969 (importance measures); 2000s (global SA integration) | 1961 (FTA); sensitivity integration formalised 1970s–1980s |
| 提出者≠ | Birnbaum (importance measures, 1969); Saltelli et al. (global SA formalization, 2000s) | H. A. Watson (Bell Labs, FTA, 1961); integrated sensitivity extensions developed through nuclear safety research (Vesely et al., 1981) |
| 类型≠ | Quantitative integrated engineering method | Quantitative reliability and risk analysis technique |
| 开创性文献≠ | 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 | Vesely, W. E., Goldberg, F. F., Roberts, N. H., & Haasl, D. F. (1981). Fault Tree Handbook. US Nuclear Regulatory Commission, NUREG-0492. link ↗ |
| 别名 | SA-RA, reliability sensitivity analysis, importance measures in reliability, reliability-based sensitivity analysis | FTA-SA, fault tree sensitivity analysis, FTA with importance measures, probabilistic sensitivity analysis in fault trees |
| 相关≠ | 5 | 3 |
| 摘要≠ | 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. | Sensitivity analysis integrated with fault tree analysis (FTA-SA) is a quantitative reliability engineering method that first models how system failure can occur through a hierarchical Boolean logic tree, then systematically varies the probability of each basic event to determine which components drive overall system failure risk most strongly. Widely used in nuclear, aerospace, chemical, and safety-critical system design, it prioritises mitigation effort and reveals which uncertainty in input data matters most. |
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