Adaptive Wavelets for Characterizing Magnetic Flux Leakage Signals From Pipeline Inspection

Natural gas transmission pipelines are commonly inspected using magnetic flux leakage (MFL) method for detecting cracks and corrosion in the pipewall. Traditionally the MFL data obtained is processed to estimate an equivalent length (L), width (W), and depth (D) of defects. This information is then...

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Bibliographic Details
Published in:IEEE transactions on magnetics 2006-10, Vol.42 (10), p.3168-3170
Main Authors: Joshi, A., Udpa, L., Udpa, S., Tamburrino, A.
Format: Article
Language:English
Subjects:
Adaptive wavelets
Computer simulation
Corrosion
Cross-disciplinary physics: materials science
rheology
Defects
Discrete wavelet transforms
Estimates
Exact sciences and technology
Gas pipelines
Inspection
iterative inversion
Leakage
Magnetic flux
Magnetic flux leakage
Magnetism
Materials science
MFL inspection
Neural networks
Other topics in materials science
Physics
Pipelines
Probes
Radial basis function networks
RBFNN
Sensor arrays
Signal resolution
Wavelet
Wavelet transforms
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Summary:Natural gas transmission pipelines are commonly inspected using magnetic flux leakage (MFL) method for detecting cracks and corrosion in the pipewall. Traditionally the MFL data obtained is processed to estimate an equivalent length (L), width (W), and depth (D) of defects. This information is then used to predict the maximum safe operating pressure (MAOP). In order to obtain a more accurate estimate for the MAOP, it is necessary to invert the MFL signal in terms of the full three-dimensional (3-D) depth profile of defects. This paper proposes a novel iterative method of inversion using adaptive wavelets and radial basis function neural network (RBFNN) that can efficiently reduce the data dimensionality and predict the full 3-D depth profile. Initials results obtained using simulated data are presented
ISSN:0018-9464
1941-0069
DOI:10.1109/TMAG.2006.880091