The natural gas transmission pipeline network in the United States is a key component of the nation's energy supply infrastructure and extends for over 280,000 miles and has an average age of over 60 years. The integrity of the pipeline is maintained by periodic inline inspections using magnetic or ultrasonic pigs. Defect characterization algorithms developed using current pigging data are hampered by the fact that single inspection techniques (either magnetic or ultrasonic) do not yield sufficient information for accurately and repeatably characterizing defects. This thesis demonstrates that defect characterization algorithms using multiple inspection techniques can accomplish this task. In particular, it is shown that the varying depth of a surface breaking pipeline defect can be precisely determined using a combination of multiple inspection methods. Also the precise location of such defects can be predicted using dissimilar interrogation methods. A judicious combination of signal and image processing strategies, including geometric transformations, radial basis function approximations and Parzen windows density estimations, have been used to fuse data from both homogeneous and heterogeneous sensors. Application results using data from laboratory experiments demonstrate the consistency and efficacy of the proposed approach.
","abstract_html":"<p>The natural gas transmission pipeline network in the United States is a key component of the nation's energy supply infrastructure and extends for over 280,000 miles and has an average age of over 60 years. The integrity of the pipeline is maintained by periodic inline inspections using magnetic or ultrasonic pigs. Defect characterization algorithms developed using current pigging data are hampered by the fact that single inspection techniques (either magnetic or ultrasonic) do not yield sufficient information for accurately and repeatably characterizing defects. This thesis demonstrates that defect characterization algorithms using multiple inspection techniques can accomplish this task. In particular, it is shown that the varying depth of a surface breaking pipeline defect can be precisely determined using a combination of multiple inspection methods. Also the precise location of such defects can be predicted using dissimilar interrogation methods. A judicious combination of signal and image processing strategies, including geometric transformations, radial basis function approximations and Parzen windows density estimations, have been used to fuse data from both homogeneous and heterogeneous sensors. Application results using data from laboratory experiments demonstrate the consistency and efficacy of the proposed approach.</p>","abstract_has_math":false,"creators":["Oagaro, Joseph Anthony"],"institution":null,"degree_name":"M.S. in Engineering","degree_level":"Thesis","degree_discipline":"Electrical & Computer Engineering","degree_department":null,"school":null,"contributors":["Mandayam, Shreekanth"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2004,"date_issued":"2004-12-31T08:00:00Z","date_published":"2004-12-31T08:00:00Z","updated_at":"2026-07-24T04:14:04Z","subjects":["Natural gas pipelines--Maintenance and repair; Natural gas pipelines--United States","Electrical and Computer Engineering"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://rdw.rowan.edu/etd/1206","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Mandayam, Shreekanth"]},{"key":"dc:creator","label":"Author","values":["Oagaro, Joseph Anthony"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2016-04-26T07:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical & Computer Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S. in Engineering"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Natural gas pipelines--Maintenance and repair; Natural gas pipelines--United States","Electrical and Computer Engineering"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://rdw.rowan.edu/etd/1206"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["The natural gas transmission pipeline network in the United States is a key component of the nation's energy supply infrastructure and extends for over 280,000 miles and has an average age of over 60 years. The integrity of the pipeline is maintained by periodic inline inspections using magnetic or ultrasonic pigs. Defect characterization algorithms developed using current pigging data are hampered by the fact that single inspection techniques (either magnetic or ultrasonic) do not yield sufficient information for accurately and repeatably characterizing defects. This thesis demonstrates that defect characterization algorithms using multiple inspection techniques can accomplish this task. In particular, it is shown that the varying depth of a surface breaking pipeline defect can be precisely determined using a combination of multiple inspection methods. Also the precise location of such defects can be predicted using dissimilar interrogation methods. A judicious combination of signal and image processing strategies, including geometric transformations, radial basis function approximations and Parzen windows density estimations, have been used to fuse data from both homogeneous and heterogeneous sensors. Application results using data from laboratory experiments demonstrate the consistency and efficacy of the proposed approach.
"]},{"key":"dc:title","label":"Title","values":["Heterogeneous multi-sensor data fusion using geometric transformations and Parzen windows for the nondestructive evaluation of gas transmission pipelines"]}]}],"canonical_facts":{"dc:contributor":["Mandayam, Shreekanth"],"dc:creator":["Oagaro, Joseph Anthony"],"dc:date.available":["2016-04-26T07:00:00Z"],"dc:description.abstract":["The natural gas transmission pipeline network in the United States is a key component of the nation's energy supply infrastructure and extends for over 280,000 miles and has an average age of over 60 years. The integrity of the pipeline is maintained by periodic inline inspections using magnetic or ultrasonic pigs. Defect characterization algorithms developed using current pigging data are hampered by the fact that single inspection techniques (either magnetic or ultrasonic) do not yield sufficient information for accurately and repeatably characterizing defects. This thesis demonstrates that defect characterization algorithms using multiple inspection techniques can accomplish this task. In particular, it is shown that the varying depth of a surface breaking pipeline defect can be precisely determined using a combination of multiple inspection methods. Also the precise location of such defects can be predicted using dissimilar interrogation methods. A judicious combination of signal and image processing strategies, including geometric transformations, radial basis function approximations and Parzen windows density estimations, have been used to fuse data from both homogeneous and heterogeneous sensors. Application results using data from laboratory experiments demonstrate the consistency and efficacy of the proposed approach.
"],"dc:identifier":["https://rdw.rowan.edu/etd/1206"],"dc:subject":["Natural gas pipelines--Maintenance and repair; Natural gas pipelines--United States","Electrical and Computer Engineering"],"dc:title":["Heterogeneous multi-sensor data fusion using geometric transformations and Parzen windows for the nondestructive evaluation of gas transmission pipelines"],"thesis:degree_discipline":["Electrical & Computer Engineering"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S. in Engineering"]},"updated_at":"2026-07-24T04:14:04Z"}