Сравнение методов

	Анализ надежности на основе оценки рисков	Анализ дерева отказов на основе оценки риска
Область	Планирование эксперимента	Планирование эксперимента
Семейство	Process / pipeline	Process / pipeline
Год появления	1960s–1990s (risk-informed frameworks codified ~1980s–1990s)	1961 (FTA origin); risk-based integration formalised 1975–1981
Автор метода	Multiple contributors; formalized in reliability engineering literature from the 1960s onward (MIL-HDBK-217, IEC 60300 series)	H.A. Watson (Bell Labs) and developed further by Boeing/U.S. Air Force; risk-based extension via NRC probabilistic risk assessment programs
Тип	Quantitative / semi-quantitative engineering analysis	Quantitative safety and reliability analysis
Основополагающий источник	Modarres, M., Kaminskiy, M., & Krivtsov, V. (2006). Reliability Engineering and Risk Analysis: A Practical Guide (2nd ed.). CRC Press. ISBN: 978-0849392016	Vesely, W. E., Goldberg, F. F., Roberts, N. H., & Haasl, D. F. (1981). Fault Tree Handbook. U.S. Nuclear Regulatory Commission, NUREG-0492.
Другие названия	RBRA, risk-informed reliability analysis, risk-based dependability analysis, probabilistic risk and reliability assessment	RB-FTA, risk-informed FTA, quantitative fault tree analysis, probabilistic fault tree analysis
Связанные	6	6
Сводка	Risk-based reliability analysis (RBRA) is an engineering methodology that combines classical reliability analysis — quantifying failure rates, component lifetimes, and system dependability — with risk assessment frameworks that weigh the severity and consequences of each failure mode. By ranking failures according to both their likelihood and their impact, RBRA guides engineers in allocating inspection, maintenance, and redesign resources where they matter most, rather than treating all potential failures as equally important.	Risk-based fault tree analysis (RB-FTA) combines classical fault tree analysis with explicit quantitative risk assessment. Starting from an undesired top event, the analyst decomposes it into contributing causes using AND/OR logic gates, assigns failure probabilities to basic events from reliability databases or historical data, and then propagates those probabilities through the tree to compute top-event likelihood. The result is expressed as risk — probability weighted by consequence severity — enabling prioritisation of safety interventions by their actual risk reduction impact.