Comparar métodos

	Análise de Causa Raiz Robusta	Análise Robusta de Árvore de Falhas
Área	Delineamento experimental	Delineamento experimental
Família	Process / pipeline	Process / pipeline
Ano de origem	1990s–2000s	1980s–2000s (robustness extensions to classical FTA ca. 1961)
Autor original	Synthesised from RCA practice (Kepner-Tregoe, 1960s) and Taguchi robustness principles (1980s–1990s)	Extended from classical FTA (Watson, 1961; Bell Labs / U.S. Air Force); robustness extensions developed through reliability engineering and uncertainty quantification research from the 1980s onward
Tipo	Hybrid quality-engineering diagnostic method	Quantitative reliability and safety analysis with uncertainty propagation
Fonte seminal	Andersen, B., & Fagerhaug, T. (2006). Root Cause Analysis: Simplified Tools and Techniques (2nd ed.). ASQ Quality Press. ISBN: 978-0873896924	Vesely, W. E., Goldberg, F. F., Roberts, N. H., & Haasl, D. F. (1981). Fault Tree Handbook. U.S. Nuclear Regulatory Commission, NUREG-0492.
Outros nomes	Robust RCA, Robustness-Integrated Root Cause Analysis, RRCA	Robust FTA, Uncertainty-aware FTA, FTA with interval analysis, Imprecise probability FTA
Relacionados	6	6
Resumo	Robust Root Cause Analysis (Robust RCA) integrates classical root cause investigation techniques — such as the 5-Whys, Ishikawa diagrams, and fault trees — with Taguchi's robustness thinking to identify not only the primary cause of a failure but also the noise factors and variability sources that allow the failure to occur repeatedly. The result is corrective actions that eliminate the root cause and make the system inherently insensitive to future variation.	Robust Fault Tree Analysis (Robust FTA) extends classical fault tree analysis by explicitly representing and propagating uncertainty in component failure probabilities. Rather than assigning single point estimates to basic events, it uses probability distributions, interval bounds, or imprecise probabilities, then propagates these through the logical tree structure to obtain bounds or distributions on the top-event failure probability. This makes risk conclusions defensible under incomplete or variable data.