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

    8 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
    CountryCanada
    CityVictoria, BC
    Period5/12/085/15/08

    All Science Journal Classification (ASJC) codes

    • Electrical and Electronic Engineering

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