Reference number : GC-2017-Q4-01094, amendment number : , agreement type : G, recipient legal name : Liu, Zheng (The University of British Columbia), recipient country : CA, recipient province : nan, recipient city : British Columbia|Colombie-Britannique, recipient postal code : nan, program name english : Collaborative Research and Development Grants - Project, program name french : Subventions de recherche et développement coopérative - projet, program purpose english : The non-destructive inspection of oil & gas pipelines is essential for pipeline integrity management and critical_x000D_
for environmental protection from pipeline leakage and failure. The in-line inspection (ILI) with magnetic flux_x000D_
leakage (MFL) method is a key technology. The use of differently configured inspection systems, such as axial_x000D_
MFL and circumferential MFL, can offer a comprehensive assessment of pipeline conditions. However, the_x000D_
uncertainty associated with inspection results remains a challenge for maintenance decision making due to the_x000D_
inherent heterogeneities with varied inspection techniques._x000D_
This research project is to develop machine learning algorithms to identify the consistency between the ILI data_x000D_
by exploring the common feature patterns from multiple inspections. A comprehensive evaluation with least_x000D_
uncertainty can be achieved by integrating multi-modal in-line inspection data. Thus, this research will enable_x000D_
efficient pipeline integrity management for pipeline operation. The research outcomes will be in the form of_x000D_
software tools, which can be delivered to the industrial partner for commercializing and better serving the_x000D_
needs of Canada's oil & gas industry._x000D_, program purpose french : The non-destructive inspection of oil & gas pipelines is essential for pipeline integrity management and critical_x000D_
for environmental protection from pipeline leakage and failure. The in-line inspection (ILI) with magnetic flux_x000D_
leakage (MFL) method is a key technology. The use of differently configured inspection systems, such as axial_x000D_
MFL and circumferential MFL, can offer a comprehensive assessment of pipeline conditions. However, the_x000D_
uncertainty associated with inspection results remains a challenge for maintenance decision making due to the_x000D_
inherent heterogeneities with varied inspection techniques._x000D_
This research project is to develop machine learning algorithms to identify the consistency between the ILI data_x000D_
by exploring the common feature patterns from multiple inspections. A comprehensive evaluation with least_x000D_
uncertainty can be achieved by integrating multi-modal in-line inspection data. Thus, this research will enable_x000D_
efficient pipeline integrity management for pipeline operation. The research outcomes will be in the form of_x000D_
software tools, which can be delivered to the industrial partner for commercializing and better serving the_x000D_
needs of Canada's oil & gas industry._x000D_, agreement title english : Magnetic flux leakage in-line inspection data analysis and integration, agreement title french : Magnetic flux leakage in-line inspection data analysis and integration, agreement number : CRDPJ, agreement value : 105600.0, agreement start date : 2018-03-07, agreement end date : nan, description english : nan, description french : nan, expected results english : nan, expected results french : nan, additional information english : Grant or Award spanning more than one fiscal year (2017-2018 to 2020-2021)., additional information french : Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier (2017-2018 à 2020-2021)., owner organization : nserc-crsng, owner organization title : Natural Sciences and Engineering Research Council of Canada | Conseil de recherches en sciences naturelles et en génie du Canada