Magnetic flux leakage in-line inspection data analysis and integration

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The non-destructive inspection of oil & gas pipelines is essential for pipeline integrity management and critical**for environmental protection from pipeline leakage and failure. The in-line inspection (ILI) with magnetic flux**leakage (MFL) method is a key technology. The use of differently configured inspection systems, such as axial**MFL and circumferential MFL, can offer a comprehensive assessment of pipeline conditions. However, the**uncertainty associated with inspection results remains a challenge for maintenance decision making due to the**inherent heterogeneities with varied inspection techniques.**This research project is to develop machine learning algorithms to identify the consistency between the ILI data**by exploring the common feature patterns from multiple inspections. A comprehensive evaluation with least**uncertainty can be achieved by integrating multi-modal in-line inspection data. Thus, this research will enable**efficient pipeline integrity management for pipeline operation. The research outcomes will be in the form of**software tools, which can be delivered to the industrial partner for commercializing and better serving the**needs of Canada's oil & gas industry.**