विधियों की तुलना करें

	मजबूत मूल कारण विश्लेषण	सुदृढ़ दोष वृक्ष विश्लेषण
क्षेत्र	प्रयोगात्मक अभिकल्प	प्रयोगात्मक अभिकल्प
परिवार	Process / pipeline	Process / pipeline
उद्भव वर्ष	1990s–2000s	1980s–2000s (robustness extensions to classical FTA ca. 1961)
प्रवर्तक	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
प्रकार	Hybrid quality-engineering diagnostic method	Quantitative reliability and safety analysis with uncertainty propagation
मौलिक स्रोत	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.
उपनाम	Robust RCA, Robustness-Integrated Root Cause Analysis, RRCA	Robust FTA, Uncertainty-aware FTA, FTA with interval analysis, Imprecise probability FTA
संबंधित	6	6
सारांश	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.