Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Анализ дерева отказов на основе оценки риска× | Статистическое управление процессами× | |
|---|---|---|
| Область | Планирование эксперимента | Планирование эксперимента |
| Семейство | Process / pipeline | Process / pipeline |
| Год появления≠ | 1961 (FTA origin); risk-based integration formalised 1975–1981 | 1924–1931 |
| Автор метода≠ | H.A. Watson (Bell Labs) and developed further by Boeing/U.S. Air Force; risk-based extension via NRC probabilistic risk assessment programs | Walter A. Shewhart |
| Тип≠ | Quantitative safety and reliability analysis | Process monitoring and quality control method |
| Основополагающий источник≠ | Vesely, W. E., Goldberg, F. F., Roberts, N. H., & Haasl, D. F. (1981). Fault Tree Handbook. U.S. Nuclear Regulatory Commission, NUREG-0492. link ↗ | Shewhart, W. A. (1931). Economic Control of Quality of Manufactured Product. Van Nostrand. ISBN: 978-0873890762 |
| Другие названия | RB-FTA, risk-informed FTA, quantitative fault tree analysis, probabilistic fault tree analysis | SPC, statistical quality control, process control charting, Shewhart control |
| Связанные | 6 | 6 |
| Сводка≠ | 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. | Statistical Process Control (SPC) is a data-driven quality method that uses statistical techniques — primarily control charts — to monitor a manufacturing or service process over time. By distinguishing natural process variation (common cause) from unusual, actionable variation (special cause), SPC enables practitioners to maintain processes in a stable, predictable state and to detect problems early, before defective output reaches customers. |
| ScholarGateНабор данных ↗ |
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