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| 위험 기반 신뢰성 분석× | 위험 기반 고장 모드 및 영향 분석(RBFMEA)× | |
|---|---|---|
| 분야 | 실험설계 | 실험설계 |
| 계열 | Process / pipeline | Process / pipeline |
| 기원 연도≠ | 1960s–1990s (risk-informed frameworks codified ~1980s–1990s) | 1949 (FMEA origins); risk-prioritised RPN framework circa 1977–1980s (AIAG automotive) |
| 창시자≠ | Multiple contributors; formalized in reliability engineering literature from the 1960s onward (MIL-HDBK-217, IEC 60300 series) | U.S. Department of Defense / MIL-STD-1629A; formalised in IEC 60812 |
| 유형≠ | Quantitative / semi-quantitative engineering analysis | Quantitative risk-prioritisation technique |
| 원전≠ | Modarres, M., Kaminskiy, M., & Krivtsov, V. (2006). Reliability Engineering and Risk Analysis: A Practical Guide (2nd ed.). CRC Press. ISBN: 978-0849392016 | International Electrotechnical Commission. (2018). IEC 60812:2018 — Failure modes and effects analysis (FMEA and FMECA). IEC. link ↗ |
| 별칭 | RBRA, risk-informed reliability analysis, risk-based dependability analysis, probabilistic risk and reliability assessment | RBFMEA, Risk-based FMEA, Risk-prioritised FMEA, Quantitative FMEA |
| 관련 | 6 | 6 |
| 요약≠ | 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 failure mode and effects analysis (RBFMEA) is a structured engineering technique that identifies every way a system or process can fail, assesses the risk of each failure mode using a numerical Risk Priority Number (RPN = Occurrence × Severity × Detectability), and prioritises corrective actions accordingly. Rooted in MIL-STD-1629A and standardised in IEC 60812:2018, it is the dominant proactive reliability and safety tool in aerospace, automotive, pharmaceutical, and manufacturing industries. |
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