A texture analysis approach for automatic flaw detection in pipelines

S. V.Bharath Kumar, Sivaramanivas Ramaswamy

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

3 Citations (Scopus)

Abstract

Oil and natural gas are normally transported through a vast network of pipelines, a major segment of which are seamless pipes. The manufacturing processes associated with the production of seamless pipes introduces an artifact known as seamless pipe noise (SPN), in the data obtained from magnetic flux leakage (MFL) inspection of these pipelines. SPN poses a major challenge in the flaw detection and characterization as it can overwhelm the flaw response and can therefore, mask flaw signature in MFL data. In this paper, we present a texture analysis approach to automatically detect flaws while characterizing the textures of SPN and flaw response using gray level co-occurrence matrix (GLCM). The motivation to propose texture analysis approach is to investigate the statistical properties of these signals. The proposed method provides a recognition rate of 97.29% for flaws which are deeper than 20% of the wall thickness of the pipe, thus confirming the existence of different textural characteristics for SPN and flaw response.

Original languageEnglish (US)
Title of host publication2004 International Conference on Signal Processing and Communications, SPCOM
Pages320-323
Number of pages4
StatePublished - Dec 1 2004
Event2004 International Conference on Signal Processing and Communications, SPCOM - Banalore, India
Duration: Dec 11 2004Dec 14 2004

Publication series

Name2004 International Conference on Signal Processing and Communications, SPCOM

Other

Other2004 International Conference on Signal Processing and Communications, SPCOM
CountryIndia
CityBanalore
Period12/11/0412/14/04

Fingerprint

Pipelines
Textures
Pipe
Defects
Magnetic flux
Leakage (fluid)
Masks
Natural gas
Inspection

All Science Journal Classification (ASJC) codes

  • Engineering(all)

Cite this

Kumar, S. V. B., & Ramaswamy, S. (2004). A texture analysis approach for automatic flaw detection in pipelines. In 2004 International Conference on Signal Processing and Communications, SPCOM (pp. 320-323). (2004 International Conference on Signal Processing and Communications, SPCOM).
Kumar, S. V.Bharath ; Ramaswamy, Sivaramanivas. / A texture analysis approach for automatic flaw detection in pipelines. 2004 International Conference on Signal Processing and Communications, SPCOM. 2004. pp. 320-323 (2004 International Conference on Signal Processing and Communications, SPCOM).
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abstract = "Oil and natural gas are normally transported through a vast network of pipelines, a major segment of which are seamless pipes. The manufacturing processes associated with the production of seamless pipes introduces an artifact known as seamless pipe noise (SPN), in the data obtained from magnetic flux leakage (MFL) inspection of these pipelines. SPN poses a major challenge in the flaw detection and characterization as it can overwhelm the flaw response and can therefore, mask flaw signature in MFL data. In this paper, we present a texture analysis approach to automatically detect flaws while characterizing the textures of SPN and flaw response using gray level co-occurrence matrix (GLCM). The motivation to propose texture analysis approach is to investigate the statistical properties of these signals. The proposed method provides a recognition rate of 97.29{\%} for flaws which are deeper than 20{\%} of the wall thickness of the pipe, thus confirming the existence of different textural characteristics for SPN and flaw response.",
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Kumar, SVB & Ramaswamy, S 2004, A texture analysis approach for automatic flaw detection in pipelines. in 2004 International Conference on Signal Processing and Communications, SPCOM. 2004 International Conference on Signal Processing and Communications, SPCOM, pp. 320-323, 2004 International Conference on Signal Processing and Communications, SPCOM, Banalore, India, 12/11/04.

A texture analysis approach for automatic flaw detection in pipelines. / Kumar, S. V.Bharath; Ramaswamy, Sivaramanivas.

2004 International Conference on Signal Processing and Communications, SPCOM. 2004. p. 320-323 (2004 International Conference on Signal Processing and Communications, SPCOM).

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

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N2 - Oil and natural gas are normally transported through a vast network of pipelines, a major segment of which are seamless pipes. The manufacturing processes associated with the production of seamless pipes introduces an artifact known as seamless pipe noise (SPN), in the data obtained from magnetic flux leakage (MFL) inspection of these pipelines. SPN poses a major challenge in the flaw detection and characterization as it can overwhelm the flaw response and can therefore, mask flaw signature in MFL data. In this paper, we present a texture analysis approach to automatically detect flaws while characterizing the textures of SPN and flaw response using gray level co-occurrence matrix (GLCM). The motivation to propose texture analysis approach is to investigate the statistical properties of these signals. The proposed method provides a recognition rate of 97.29% for flaws which are deeper than 20% of the wall thickness of the pipe, thus confirming the existence of different textural characteristics for SPN and flaw response.

AB - Oil and natural gas are normally transported through a vast network of pipelines, a major segment of which are seamless pipes. The manufacturing processes associated with the production of seamless pipes introduces an artifact known as seamless pipe noise (SPN), in the data obtained from magnetic flux leakage (MFL) inspection of these pipelines. SPN poses a major challenge in the flaw detection and characterization as it can overwhelm the flaw response and can therefore, mask flaw signature in MFL data. In this paper, we present a texture analysis approach to automatically detect flaws while characterizing the textures of SPN and flaw response using gray level co-occurrence matrix (GLCM). The motivation to propose texture analysis approach is to investigate the statistical properties of these signals. The proposed method provides a recognition rate of 97.29% for flaws which are deeper than 20% of the wall thickness of the pipe, thus confirming the existence of different textural characteristics for SPN and flaw response.

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M3 - Conference contribution

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Kumar SVB, Ramaswamy S. A texture analysis approach for automatic flaw detection in pipelines. In 2004 International Conference on Signal Processing and Communications, SPCOM. 2004. p. 320-323. (2004 International Conference on Signal Processing and Communications, SPCOM).