手法を比較

	シミュレーション支援信頼性解析	信頼性解析との感度解析
分野	実験計画法	実験計画法
系統	Process / pipeline	Process / pipeline
提唱年	1940s–1980s (Monte Carlo foundations ~1940s; simulation-reliability integration ~1970s–1980s)	1969 (importance measures); 2000s (global SA integration)
提唱者	Enrico Fermi, John von Neumann, Stanislaw Ulam (Monte Carlo foundations); Freudenthal (structural reliability); Melchers (simulation integration)	Birnbaum (importance measures, 1969); Saltelli et al. (global SA formalization, 2000s)
種類	Quantitative probabilistic engineering method	Quantitative integrated engineering method
原典	Melchers, R. E., & Beck, A. T. (2018). Structural Reliability Analysis and Prediction (3rd ed.). Wiley. ISBN: 978-1119266075	Saltelli, A., Ratto, M., Andres, T., Campolongo, F., Cariboni, J., Gatelli, D., Saisana, M., & Tarantola, S. (2008). Global Sensitivity Analysis: The Primer. Wiley. ISBN: 978-0470059975
別名	SARA, Monte Carlo reliability analysis, simulation-based reliability assessment, virtual reliability testing	SA-RA, reliability sensitivity analysis, importance measures in reliability, reliability-based sensitivity analysis
関連	6	5
概要	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.	Sensitivity analysis integrated with reliability analysis is a quantitative engineering method that determines how uncertainty or variation in each system input — such as component failure rates, material properties, or load distributions — propagates into overall system reliability. By computing importance measures for every uncertain parameter, analysts can rank components and assumptions by their influence on system dependability, focusing improvement efforts where they matter most.