ScholarGate
Assistant

Comparer des méthodes

Examinez les méthodes sélectionnées côte à côte ; les lignes qui diffèrent sont mises en évidence.

Analyse de fiabilité assistée par simulation×Analyse de Sensibilité et Analyse de Fiabilité×
DomainePlans d'expériencesPlans d'expériences
FamilleProcess / pipelineProcess / pipeline
Année d'origine1940s–1980s (Monte Carlo foundations ~1940s; simulation-reliability integration ~1970s–1980s)1969 (importance measures); 2000s (global SA integration)
Auteur d'origineEnrico 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)
TypeQuantitative probabilistic engineering methodQuantitative integrated engineering method
Source fondatriceMelchers, R. E., & Beck, A. T. (2018). Structural Reliability Analysis and Prediction (3rd ed.). Wiley. ISBN: 978-1119266075Saltelli, 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
AliasSARA, Monte Carlo reliability analysis, simulation-based reliability assessment, virtual reliability testingSA-RA, reliability sensitivity analysis, importance measures in reliability, reliability-based sensitivity analysis
Apparentées65
RésuméSimulation-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.
ScholarGateJeu de données
  1. v1
  2. 2 Sources
  3. PUBLISHED
  1. v1
  2. 2 Sources
  3. PUBLISHED

Aller à la recherche Télécharger les diapositives

ScholarGateComparer des méthodes: Simulation-assisted reliability analysis · Sensitivity Analysis with Reliability Analysis. Consulté le 2026-06-15 sur https://scholargate.app/fr/compare