A Bayesian Approach for Effective Use of Multiple Measurements of Crack Depths

Abstract

A Bayesian methodology was applied to use data from multiple inline inspection (ILI) runs and field measurements with non-destructive examination (NDE) tools to increase confidence in crack size estimates. Multiple crack depth measurements were used in two different ways — namely, to improve the characterization of ILI sizing error bias and to update the maximum depth distribution of individual crack features. This methodology was applied to selected datasets from an industrywide database for crack ILI data collected over a series of Pipeline Research Council International (PRCI) projects. The results of the approach are presented for two datasets, showing reduced variance in sizing error bias and improved confidence in crack depth estimates. In addition to the PRCI datasets, an additional dataset was collected and used to investigate the effect of multiple ILI runs on estimates of rate of detection and depth distribution of undetected features. The results of this analysis are also summarized.