Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Jutīguma analīze ar uzticamības analīzi× | Jutīguma analīze ar kļūdu koku analīzi× | |
|---|---|---|
| Nozare | Eksperimentu plānošana | Eksperimentu plānošana |
| Saime | Process / pipeline | Process / pipeline |
| Izcelsmes gads≠ | 1969 (importance measures); 2000s (global SA integration) | 1961 (FTA); sensitivity integration formalised 1970s–1980s |
| Autors≠ | 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) |
| Tips≠ | Quantitative integrated engineering method | Quantitative reliability and risk analysis technique |
| Pirmavots≠ | 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 ↗ |
| Citi nosaukumi | 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 |
| Saistītās≠ | 5 | 3 |
| Kopsavilkums≠ | 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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