Compara mètodes

	Anàlisi robusta d'arbres de fallada	Anàlisi de Modes i Efectes de Fallada Robusta
Camp	Disseny experimental	Disseny experimental
Família	Process / pipeline	Process / pipeline
Any d'origen	1980s–2000s (robustness extensions to classical FTA ca. 1961)	1980s–1990s
Autor original	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	Extension of traditional FMEA (MIL-P-1629, 1949) integrated with Taguchi robust design philosophy (Genichi Taguchi, 1980s)
Tipus	Quantitative reliability and safety analysis with uncertainty propagation	Risk analysis with variability quantification
Font seminal	Vesely, W. E., Goldberg, F. F., Roberts, N. H., & Haasl, D. F. (1981). Fault Tree Handbook. U.S. Nuclear Regulatory Commission, NUREG-0492.	Stamatis, D. H. (2003). Failure Mode and Effect Analysis: FMEA from Theory to Execution (2nd ed.). ASQ Quality Press. ISBN: 978-0873895989
Àlies	Robust FTA, Uncertainty-aware FTA, FTA with interval analysis, Imprecise probability FTA	Robust FMEA, Noise-Aware FMEA, Variability-Integrated FMEA, Robustness-Based FMEA
Relacionats	6	4
Resum	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 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.