Addressing Gaps For Reliability Assessments Using Non-Hierarchical Cluster Analysis

Paper No. IPC2022-87145

This paper addresses challenges in pipeline facility reliability assessments when essential data is missing. Traditional methods often use conservative assumptions, inflating risk assessments. The paper proposes using K-means clustering to group equipment data and fill gaps with reasonable estimates. This method balances conservatism and accuracy, prioritizing equipment with confirmed attributes while considering those with limited data. Practical applications, including dimensionality reduction and sensitivity analysis, are also discussed.