Confronta i metodi

	Analisi di Affidabilità Assistita da Simulazione	Analisi ad Albero dei Guasti (Fault Tree Analysis, FTA)
Campo	Disegno sperimentale	Affidabilità
Famiglia	Process / pipeline	Process / pipeline
Anno di origine	1940s–1980s (Monte Carlo foundations ~1940s; simulation-reliability integration ~1970s–1980s)	1981
Ideatore	Enrico Fermi, John von Neumann, Stanislaw Ulam (Monte Carlo foundations); Freudenthal (structural reliability); Melchers (simulation integration)	Vesely et al. (US NRC Fault Tree Handbook)
Tipo	Quantitative probabilistic engineering method	Deductive top-down failure analysis
Fonte seminale	Melchers, R. E., & Beck, A. T. (2018). Structural Reliability Analysis and Prediction (3rd ed.). Wiley. ISBN: 978-1119266075	Vesely, W. E., Goldberg, F. F., Roberts, N. H., & Haasl, D. F. (1981). Fault Tree Handbook (NUREG-0492). U.S. Nuclear Regulatory Commission.
Alias	SARA, Monte Carlo reliability analysis, simulation-based reliability assessment, virtual reliability testing	FTA, Fault Tree Method, Top-Down Reliability Analysis, Hata Ağacı Analizi
Correlati	6	3
Sintesi	Simulation-assisted reliability analysis combines probabilistic reliability theory with computational simulation — most commonly Monte Carlo methods or finite-element models — to estimate the probability that a system, component, or structure will perform its intended function under uncertain operating conditions. Rather than relying solely on closed-form analytical solutions, it propagates uncertainty through high-fidelity numerical models to quantify failure risk across complex, nonlinear, or multi-failure-mode systems.	Fault Tree Analysis (FTA) is a top-down, deductive reliability method that begins with an undesired top-level failure event and systematically traces backward through chains of contributing causes using Boolean logic gates (AND, OR). First formalized by Watson at Bell Telephone Laboratories in 1961 and later standardized by Vesely, Goldberg, Roberts, and Haasl in the landmark 1981 NRC Fault Tree Handbook, FTA has become a cornerstone of quantitative risk assessment in nuclear, aerospace, and industrial safety engineering.