Nondestructive evaluation (NDE) plays a vital component in the operation and maintenance of large infrastructure such as gas transmission pipelines, nuclear power plants, aircraft, bridges and highways, etc. As this infrastructure continues to age it is essential that the inspection techniques reliably and accurately predict the integrity of these systems. No single NDE method is capable of inspecting all types of anomalies and extracting all required information – a combination of methods must be used and the resulting data fused. Moreover, newer systems that are developed are often made of composite materials that include metals and dielectrics. One interrogation modality cannot be used to inspect such components for reliability – multiple tests are always needed.
This thesis presents a technique that can be used to fuse data from multiple sensors that are employed in modem NDE applications, specifically in the in-line inspection of gas transmission pipelines. A radial basis function artificial neural network is used to perform geometric transformations on data obtained from multiple sources. The technique allows the user to define the redundant and complementary information present in the data sets. The efficacy of the algorithm is demonstrated using simulated canonical images and experimental images obtained from the NDE of a test specimen suite using magnetic flux leakage, ultrasonic and thermal imaging methods. The results presented in this thesis indicate that neural network based geometric transformation algorithms show considerable promise in multi-sensor data fusion applications.
","abstract_html":"<p>Nondestructive evaluation (NDE) plays a vital component in the operation and maintenance of large infrastructure such as gas transmission pipelines, nuclear power plants, aircraft, bridges and highways, etc. As this infrastructure continues to age it is essential that the inspection techniques reliably and accurately predict the integrity of these systems. No single NDE method is capable of inspecting all types of anomalies and extracting all required information – a combination of methods must be used and the resulting data fused. Moreover, newer systems that are developed are often made of composite materials that include metals and dielectrics. One interrogation modality cannot be used to inspect such components for reliability – multiple tests are always needed.</p> <p>This thesis presents a technique that can be used to fuse data from multiple sensors that are employed in modem NDE applications, specifically in the in-line inspection of gas transmission pipelines. A radial basis function artificial neural network is used to perform geometric transformations on data obtained from multiple sources. The technique allows the user to define the redundant and complementary information present in the data sets. The efficacy of the algorithm is demonstrated using simulated canonical images and experimental images obtained from the NDE of a test specimen suite using magnetic flux leakage, ultrasonic and thermal imaging methods. The results presented in this thesis indicate that neural network based geometric transformation algorithms show considerable promise in multi-sensor data fusion applications.</p>","abstract_has_math":false,"creators":["Kulick, Philip James"],"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":2003,"date_issued":"2003-12-31T08:00:00Z","date_published":"2003-12-31T08:00:00Z","updated_at":"2026-07-24T04:14:25Z","subjects":["Gas pipelines; Nondestructive testing","Electrical and Computer Engineering"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://rdw.rowan.edu/etd/1333","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":["Kulick, Philip James"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2016-05-09T07:00:00Z"]},{"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":["Gas pipelines; Nondestructive testing","Electrical and Computer Engineering"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://rdw.rowan.edu/etd/1333"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Nondestructive evaluation (NDE) plays a vital component in the operation and maintenance of large infrastructure such as gas transmission pipelines, nuclear power plants, aircraft, bridges and highways, etc. As this infrastructure continues to age it is essential that the inspection techniques reliably and accurately predict the integrity of these systems. No single NDE method is capable of inspecting all types of anomalies and extracting all required information – a combination of methods must be used and the resulting data fused. Moreover, newer systems that are developed are often made of composite materials that include metals and dielectrics. One interrogation modality cannot be used to inspect such components for reliability – multiple tests are always needed.
This thesis presents a technique that can be used to fuse data from multiple sensors that are employed in modem NDE applications, specifically in the in-line inspection of gas transmission pipelines. A radial basis function artificial neural network is used to perform geometric transformations on data obtained from multiple sources. The technique allows the user to define the redundant and complementary information present in the data sets. The efficacy of the algorithm is demonstrated using simulated canonical images and experimental images obtained from the NDE of a test specimen suite using magnetic flux leakage, ultrasonic and thermal imaging methods. The results presented in this thesis indicate that neural network based geometric transformation algorithms show considerable promise in multi-sensor data fusion applications.
"]},{"key":"dc:title","label":"Title","values":["Multi-sensor data fusion using geometric transformations for the nondestructive evaluation of gas transmission pipelines"]}]}],"canonical_facts":{"dc:contributor":["Mandayam, Shreekanth"],"dc:creator":["Kulick, Philip James"],"dc:date.available":["2016-05-09T07:00:00Z"],"dc:description.abstract":["Nondestructive evaluation (NDE) plays a vital component in the operation and maintenance of large infrastructure such as gas transmission pipelines, nuclear power plants, aircraft, bridges and highways, etc. As this infrastructure continues to age it is essential that the inspection techniques reliably and accurately predict the integrity of these systems. No single NDE method is capable of inspecting all types of anomalies and extracting all required information – a combination of methods must be used and the resulting data fused. Moreover, newer systems that are developed are often made of composite materials that include metals and dielectrics. One interrogation modality cannot be used to inspect such components for reliability – multiple tests are always needed.
This thesis presents a technique that can be used to fuse data from multiple sensors that are employed in modem NDE applications, specifically in the in-line inspection of gas transmission pipelines. A radial basis function artificial neural network is used to perform geometric transformations on data obtained from multiple sources. The technique allows the user to define the redundant and complementary information present in the data sets. The efficacy of the algorithm is demonstrated using simulated canonical images and experimental images obtained from the NDE of a test specimen suite using magnetic flux leakage, ultrasonic and thermal imaging methods. The results presented in this thesis indicate that neural network based geometric transformation algorithms show considerable promise in multi-sensor data fusion applications.
"],"dc:identifier":["https://rdw.rowan.edu/etd/1333"],"dc:subject":["Gas pipelines; Nondestructive testing","Electrical and Computer Engineering"],"dc:title":["Multi-sensor data fusion using geometric transformations 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":"2026-07-24T04:14:25Z"}