Power BI app for Tolerance Intervals and Reduced Major Axes Regression

Music City Center 

In-line inspection (ILI) tools that run internally in the pipe detect and characterize threats on a pipeline but are prone to inherent variability. The results of these ILI surveys are used to assess the criticality of reported anomalies, but the ILI runs should be compared to actual field excavations among other comparisons. To effectively manage the threats, correlation between ILI runs, and correlation between ILI and Field excavation results are important. Key to assessing how well results compare are tolerance intervals that with a specified confidence level cover a given proportion of the population. In this PBI app 95% confidence and 80% population coverage is used that meshes well with industry requirements. Another new added feature is least squares regression. However, often a better result for error prone data is reduced major axes regression. Both regression approaches are used to assess the relative fits of the various data types in the model.
These enhancements have been added to a PBI Bias assessment app to provide solid quality results for pipeline companies. This study used representative ILI results to create interactive, statistical, and visual analyses.

ILI

pipeline

correlation

oil

RMA

tolerance 

Section on Physical and Engineering Sciences