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Optimization-assisted failure mode and effects analysis×Planification d'Expériences×
DomainePlans d'expériencesPlans d'expériences
FamilleProcess / pipelineProcess / pipeline
Année d'origine1949 (FMEA origin); optimization-assisted variants: 1990s–2000s1935
Auteur d'origineExtension of FMEA (U.S. Military, MIL-STD-1629, 1949); optimization integration developed in reliability and quality engineering literature from the 1990s onwardRonald A. Fisher
TypeReliability and risk analysis technique with embedded optimizationExperimental planning framework
Source fondatriceStamatis, D. H. (2003). Failure Mode and Effect Analysis: FMEA from Theory to Execution (2nd ed.). ASQ Quality Press. ISBN: 978-0873895989Fisher, R. A. (1935). The Design of Experiments. Oliver and Boyd. link ↗
AliasOptimization-assisted FMEA, FMEA with optimization, OA-FMEA, Optimized risk priority rankingDOE, experimental design, factorial experimentation, planned experimentation
Apparentées63
RésuméOptimization-assisted FMEA extends classical Failure Mode and Effects Analysis by embedding mathematical optimization algorithms — such as linear programming, multi-objective optimization, or metaheuristics — into the risk prioritization step. Rather than relying solely on the Risk Priority Number (RPN = Severity × Occurrence × Detectability), the approach frames corrective-action selection and resource allocation as an optimization problem, enabling more defensible, constraint-aware ranking and mitigation of failure modes.Design of Experiments (DOE) is a systematic framework for planning, conducting, and analyzing controlled experiments to determine how multiple input factors simultaneously affect one or more responses. Introduced by Ronald A. Fisher in 1935, DOE allows researchers and engineers to identify causal relationships, quantify factor effects, and find optimal settings efficiently — using far fewer runs than one-factor-at-a-time approaches. It is foundational in engineering, manufacturing, agriculture, and applied sciences.
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ScholarGateComparer des méthodes: Optimization-assisted failure mode and effects analysis · Design of experiments. Consulté le 2026-06-19 sur https://scholargate.app/fr/compare