Automated matching of pipeline corrosion features from in-line inspection data

• Published in: Reliability engineering & system safety Vol. 162; pp. 40 - 50
• Main Authors: Dann, Markus R., Dann, Christoph
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.06.2017
• Subjects: Corrosion; Feature matching; In-line inspection; Integrity assessment; Pipeline; Feature matching; Corrosion; Integrity assessment; Pipeline; In-line inspection
• ISSN: 0951-8320; 1879-0836
• DOI: 10.1016/j.ress.2017.01.008

The integrity assessment of corroded pipelines is often based on in-line inspection (ILI) results. Before determining the corrosion growth for the integrity assessment, the detected corrosion features from two or more ILIs need to be matched with respect to their location in the pipeline. The objective of this paper is to introduce a framework for automated feature matching. The input for the framework is the locations of all detected corrosion features and girth welds from each ILI. Using a multi-step approach, the size of several ILIs with a possibly large number of features is reduced to a set of independent smaller problems to match efficiently the corrosion features. The results include the matched features for the subsequent corrosion growth analysis and the identification of outliers that cannot be matched. The applied probabilistic matching assigns to each feature pair a probability of being a match to reflect the inherent uncertainty in the matching process. The proposed framework replaces manual matching, which can be time intensive and prone to errors, particularly for internal corrosion with high feature densities. It reliably matches features in pipelines and supports the integrity and risk assessment of pipeline systems. •New framework for automated matching of corrosion defects from pipeline inspections.•Quantification of matching uncertainties for improved risk assessment of pipelines.•Decomposition of 3D defect matching into independent 2D point matching problems.•Computationally efficient method handling large inspection data with many outliers.