Comparer des méthodes

	Analyse Robuste d'Arbre de Défaillances	Analyse de fiabilité robuste
Domaine	Plans d'expériences	Plans d'expériences
Famille	Process / pipeline	Process / pipeline
Année d'origine	1980s–2000s (robustness extensions to classical FTA ca. 1961)	1980s–1990s (integration formalized in engineering literature)
Auteur d'origine	Extended from classical FTA (Watson, 1961; Bell Labs / U.S. Air Force); robustness extensions developed through reliability engineering and uncertainty quantification research from the 1980s onward	Synthesized from Taguchi robust design and classical reliability theory (Kececioglu, Taguchi)
Type	Quantitative reliability and safety analysis with uncertainty propagation	Quantitative reliability engineering method
Source fondatrice	Vesely, W. E., Goldberg, F. F., Roberts, N. H., & Haasl, D. F. (1981). Fault Tree Handbook. U.S. Nuclear Regulatory Commission, NUREG-0492.	Kececioglu, D. (1991). Reliability Engineering Handbook (Vol. 1). Prentice Hall. ISBN: 978-0137720774
Alias	Robust FTA, Uncertainty-aware FTA, FTA with interval analysis, Imprecise probability FTA	RRA, reliability robustness analysis, uncertainty-aware reliability analysis, robust probabilistic reliability
Apparentées	6	4
Résumé	Robust Fault Tree Analysis (Robust FTA) extends classical fault tree analysis by explicitly representing and propagating uncertainty in component failure probabilities. Rather than assigning single point estimates to basic events, it uses probability distributions, interval bounds, or imprecise probabilities, then propagates these through the logical tree structure to obtain bounds or distributions on the top-event failure probability. This makes risk conclusions defensible under incomplete or variable data.	Robust reliability analysis is an engineering method that combines classical reliability estimation with robustness principles to quantify and improve system dependability in the presence of parameter uncertainty and variability. Rather than assuming fixed input values, it propagates distributions of noise factors through a reliability model to produce probability-of-failure estimates that remain valid across a range of operating conditions and manufacturing tolerances.