Porovnat metody

	Analýza stromu poruch s asistencí simulace	Analýza stromu poruch založená na riziku
Obor	Plánování experimentů	Plánování experimentů
Rodina	Process / pipeline	Process / pipeline
Rok vzniku	1970s–1980s (widespread adoption in nuclear and aerospace industries)	1961 (FTA origin); risk-based integration formalised 1975–1981
Tvůrce	Fault tree analysis: H. A. Watson (Bell Labs, 1961); Monte Carlo integration in reliability: Herman Kahn / Stanislaw Ulam (RAND, late 1940s); combination formalized in reliability engineering literature from the 1970s onward	H.A. Watson (Bell Labs) and developed further by Boeing/U.S. Air Force; risk-based extension via NRC probabilistic risk assessment programs
Typ	Quantitative reliability and risk analysis technique	Quantitative safety and reliability analysis
Původní zdroj	Vesely, W. E., Goldberg, F. F., Roberts, N. H., & Haasl, D. F. (1981). Fault Tree Handbook. US Nuclear Regulatory Commission, NUREG-0492.	Vesely, W. E., Goldberg, F. F., Roberts, N. H., & Haasl, D. F. (1981). Fault Tree Handbook. U.S. Nuclear Regulatory Commission, NUREG-0492.
Další názvy	SA-FTA, Monte Carlo FTA, simulation-based FTA, stochastic fault tree analysis	RB-FTA, risk-informed FTA, quantitative fault tree analysis, probabilistic fault tree analysis
Příbuzné	6	6
Shrnutí	Simulation-assisted fault tree analysis (SA-FTA) combines the logical structure of classical fault tree analysis with Monte Carlo or discrete-event simulation to estimate the probability and timing of an undesired top event when component failures follow complex, non-exponential, or correlated probability distributions. The approach overcomes the analytical limitations of Boolean algebra-based FTA and is widely used in nuclear, aerospace, chemical process, and manufacturing reliability engineering.	Risk-based fault tree analysis (RB-FTA) combines classical fault tree analysis with explicit quantitative risk assessment. Starting from an undesired top event, the analyst decomposes it into contributing causes using AND/OR logic gates, assigns failure probabilities to basic events from reliability databases or historical data, and then propagates those probabilities through the tree to compute top-event likelihood. The result is expressed as risk — probability weighted by consequence severity — enabling prioritisation of safety interventions by their actual risk reduction impact.