So sánh phương pháp

	Phân tích độ nhạy kết hợp phân tích độ tin cậy	Phân tích độ nhạy với Phân tích cây lỗi (FTA-SA)
Lĩnh vực	Thiết kế thí nghiệm	Thiết kế thí nghiệm
Họ	Process / pipeline	Process / pipeline
Năm ra đời	1969 (importance measures); 2000s (global SA integration)	1961 (FTA); sensitivity integration formalised 1970s–1980s
Người khởi xướng	Birnbaum (importance measures, 1969); Saltelli et al. (global SA formalization, 2000s)	H. A. Watson (Bell Labs, FTA, 1961); integrated sensitivity extensions developed through nuclear safety research (Vesely et al., 1981)
Loại	Quantitative integrated engineering method	Quantitative reliability and risk analysis technique
Công trình gốc	Saltelli, 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	Vesely, W. E., Goldberg, F. F., Roberts, N. H., & Haasl, D. F. (1981). Fault Tree Handbook. US Nuclear Regulatory Commission, NUREG-0492.
Tên gọi khác	SA-RA, reliability sensitivity analysis, importance measures in reliability, reliability-based sensitivity analysis	FTA-SA, fault tree sensitivity analysis, FTA with importance measures, probabilistic sensitivity analysis in fault trees
Liên quan	5	3
Tóm tắt	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.	Sensitivity analysis integrated with fault tree analysis (FTA-SA) is a quantitative reliability engineering method that first models how system failure can occur through a hierarchical Boolean logic tree, then systematically varies the probability of each basic event to determine which components drive overall system failure risk most strongly. Widely used in nuclear, aerospace, chemical, and safety-critical system design, it prioritises mitigation effort and reveals which uncertainty in input data matters most.