Comparative Study of K-Means Clustering and a Novel Ranking Algorithm for Risk Priority Number Analysis in FMECA

Abstract

Failure mode, effects, and criticality analysis (FMECA) has become a fundamental tool for identifying critical failure modes and prioritizing maintenance activities. As part of the analysis, the risk priority number (RPN), a numeric assessment of the risk, has received much attention as it is computed using severity (S), occurrence (O), and detectability (D), which serve as the main criteria for criticality analysis in many practical FMECA cases. In this paper, we assemble and present a data set containing RPN evaluations from 20 real-world cases. We then apply K-Means clustering to identify the most critical failure modes and propose a novel ranking algorithm that prioritizes mitigation actions based on specific criteria for each failure mode. Our experimental results suggest that both clustering and ranking methods can provide effective prioritization for critical failure modes under given assumptions, while our novel ranking algorithm can adapt to general scenarios and provide more accurate prioritization that can help develop effective maintenance strategies to minimize equipment failure risk and optimize maintenance costs.