Subventions et des contributions :

Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier (2017-2018 à 2020-2021).

The non-destructive inspection of oil & gas pipelines is essential for pipeline integrity management and criticalx000D
for environmental protection from pipeline leakage and failure. The in-line inspection (ILI) with magnetic fluxx000D
leakage (MFL) method is a key technology. The use of differently configured inspection systems, such as axialx000D
MFL and circumferential MFL, can offer a comprehensive assessment of pipeline conditions. However, thex000D
uncertainty associated with inspection results remains a challenge for maintenance decision making due to thex000D
inherent heterogeneities with varied inspection techniques.x000D
This research project is to develop machine learning algorithms to identify the consistency between the ILI datax000D
by exploring the common feature patterns from multiple inspections. A comprehensive evaluation with leastx000D
uncertainty can be achieved by integrating multi-modal in-line inspection data. Thus, this research will enablex000D
efficient pipeline integrity management for pipeline operation. The research outcomes will be in the form ofx000D
software tools, which can be delivered to the industrial partner for commercializing and better serving thex000D
needs of Canada's oil & gas industry.x000D