Multi-sensor data fusion using geometric transformations for gas transmission pipeline inspection

Joseph A. Oagaro, Shreekanth Mandayam

Research output: Chapter in Book/Report/Conference proceedingConference contribution

10 Scopus citations

Abstract

This paper presents a technique that can be used to fuse data from multiple sensors that are employed in nondestructive evaluation (NDE) applications, specifically for 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 experimental images obtained from the NDE of a test specimen suite using magnetic flux leakage (MFL), ultrasonic (UT) and thermal imaging methods. The results presented in this paper indicate that neural network based geometric transformation algorithms show considerable promise in multi-sensor data fusion applications.

Original languageEnglish (US)
Title of host publication2008 IEEE International Instrumentation and Measurement Technology Conference Proceedings, I2MTC
Pages1734-1737
Number of pages4
DOIs
StatePublished - 2008
Event2008 IEEE International Instrumentation and Measurement Technology Conference, I2MTC - Victoria, BC, Canada
Duration: May 12 2008May 15 2008

Publication series

NameConference Record - IEEE Instrumentation and Measurement Technology Conference
ISSN (Print)1091-5281

Other

Other2008 IEEE International Instrumentation and Measurement Technology Conference, I2MTC
Country/TerritoryCanada
CityVictoria, BC
Period5/12/085/15/08

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering

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    Mandayam, S. (Manager) & Lecakes, G. D. (Manager)

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