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.

Adaptive Wavelets for Characterizing Magnetic Flux Leakage Signals From Pipeline Inspection

TAMBURRINO, Antonello
2006-01-01

Abstract

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.
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11580/9067
 Attenzione

Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo

Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 113
social impact