Publication | Closed Access
An Assessment of RPN Prioritization in a Failure Modes Effects and Criticality Analysis
203
Citations
2
References
2004
Year
EngineeringFailoverSafety ScienceNetwork RobustnessNetwork AnalysisSystem ReliabilityCriticality AnalysisOperations ResearchReliability EngineeringAutomobile Failure ModesRisk ManagementManagementFailure AnalysisSystems EngineeringReliability AnalysisDecision TheoryStatisticsQuantitative ManagementReliabilityPotential Failure ModesRiskFailure Modes EffectsSafety EngineeringFault-tolerant NetworkSurvivable NetworkSafety AnalysisRpn PrioritizationFailure PredictionFailure Modes
The Risk Priority Number (RPN) method ranks failure modes from 1 to 10 for severity, probability, and detectability, multiplies these to produce a 1–1000 score that signals design risk. The paper proposes recommendations to improve the RPN methodology. The study finds that the RPN approach is technically flawed because it treats ordinal rankings as numeric values, lacks continuity, produces duplicate scores with differing characteristics, and is highly sensitive to small changes.
The Risk Priority Number methodology for prioritizing failure modes is an integral part of the Automobile Failure Modes and Effects Analysis (FMECA) technique. This technique consists of ranking potential failures from 1 to 10 with respect to their severity, probability of occurrence, and likelihood of detection in later tests, and multiplying the numbers. The result is a numerical ranking, called the RPN, on a scale from 1 to 1000. Potential failure modes having higher RPNs are assumed to have a higher design risk than those having lower values. Although this method is well documented and easy to apply, it is seriously flawed from a technical perspective, making the interpretation of the analysis results problematic. Problems with the methodology include: use of ordinal ranking numbers as numeric quantities; lack of continuity in the RPN measurement scale; duplicate RPN values with extremely different characteristics; and varying sensitivity to small changes. Recommendations for an improved methodology are provided.
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