قارن الطرق

	تحليل الموثوقية بمساعدة المحاكاة	تحليل الموثوقية القوية
المجال	التصميم التجريبي	التصميم التجريبي
العائلة	Process / pipeline	Process / pipeline
سنة النشأة	1940s–1980s (Monte Carlo foundations ~1940s; simulation-reliability integration ~1970s–1980s)	1980s–1990s (integration formalized in engineering literature)
صاحب الطريقة	Enrico Fermi, John von Neumann, Stanislaw Ulam (Monte Carlo foundations); Freudenthal (structural reliability); Melchers (simulation integration)	Synthesized from Taguchi robust design and classical reliability theory (Kececioglu, Taguchi)
النوع	Quantitative probabilistic engineering method	Quantitative reliability engineering method
المصدر التأسيسي	Melchers, R. E., & Beck, A. T. (2018). Structural Reliability Analysis and Prediction (3rd ed.). Wiley. ISBN: 978-1119266075	Kececioglu, D. (1991). Reliability Engineering Handbook (Vol. 1). Prentice Hall. ISBN: 978-0137720774
الأسماء البديلة	SARA, Monte Carlo reliability analysis, simulation-based reliability assessment, virtual reliability testing	RRA, reliability robustness analysis, uncertainty-aware reliability analysis, robust probabilistic reliability
ذات صلة	6	4
الملخص	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.	Robust reliability analysis is an engineering method that combines classical reliability estimation with robustness principles to quantify and improve system dependability in the presence of parameter uncertainty and variability. Rather than assuming fixed input values, it propagates distributions of noise factors through a reliability model to produce probability-of-failure estimates that remain valid across a range of operating conditions and manufacturing tolerances.