Comparer des méthodes

	Analyse des modes de défaillance et de leurs effets assistée par simulation	Analyse de fiabilité assistée par simulation
Domaine	Plans d'expériences	Plans d'expériences
Famille	Process / pipeline	Process / pipeline
Année d'origine	1949 (FMEA); simulation-assisted variant: 1980s–1990s	1940s–1980s (Monte Carlo foundations ~1940s; simulation-reliability integration ~1970s–1980s)
Auteur d'origine	FMEA originates from US MIL-P-1629 (1949); simulation integration developed in reliability engineering from the 1980s–1990s	Enrico Fermi, John von Neumann, Stanislaw Ulam (Monte Carlo foundations); Freudenthal (structural reliability); Melchers (simulation integration)
Type	Reliability and risk analysis method	Quantitative probabilistic engineering method
Source fondatrice	Stamatis, D. H. (2003). Failure Mode and Effect Analysis: FMEA from Theory to Execution (2nd ed.). ASQ Quality Press. ISBN: 978-0873895989	Melchers, R. E., & Beck, A. T. (2018). Structural Reliability Analysis and Prediction (3rd ed.). Wiley. ISBN: 978-1119266075
Alias	Simulation-FMEA, Monte Carlo FMEA, Simulation-based FMEA, SA-FMEA	SARA, Monte Carlo reliability analysis, simulation-based reliability assessment, virtual reliability testing
Apparentées	6	6
Résumé	Simulation-assisted FMEA enhances the classical Failure Mode and Effects Analysis by replacing point-estimate occurrence ratings with probabilistic simulation — typically Monte Carlo — to quantify failure probability distributions across a system's components. This yields statistically grounded Risk Priority Numbers (RPNs) rather than expert guesses, enabling more rigorous identification and prioritization of critical failure modes in complex engineering systems.	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.