Pulsed magnetic flux leakage method for hairline crack detection and characterization

Okolo, Chukwunonso and Meydan, Turgut ORCID: https://orcid.org/0000-0002-4608-0507 2018. Pulsed magnetic flux leakage method for hairline crack detection and characterization. AIP Advances 8 , 047207. 10.1063/1.4994187

Preview

PDF - Accepted Post-Print Version Available under License Creative Commons Attribution. Download (3MB) | Preview

Abstract

The Magnetic Flux leakage (MFL) method is a well-established branch of electromagnetic Non-Destructive Testing (NDT), extensively used for evaluating defects both on the surface and far-surface of pipeline structures. However the conventional techniques are not capable of estimating their approximate size, location and orientation, hence an additional transducer is required to provide the extra information needed. This research is aimed at solving the inevitable problem of granular bond separation which occurs during manufacturing, leaving pipeline structures with miniature cracks. It reports on a quantitative approach based on the Pulsed Magnetic Flux Leakage (PMFL) method, for the detection and characterization of the signals produced by tangentially oriented rectangular surface and far-surface hairline cracks. This was achieved through visualization and 3D imaging of the leakage field. The investigation compared finite element numerical simulation with experimental data. Experiments were carried out using a 10mm thick low carbon steel plate containing artificial hairline cracks with various depth sizes, and different features were extracted from the transient signal. The influence of sensor lift-off and pulse width variation on the magnetic field distribution which affects the detection capability of various hairline cracks located at different depths in the specimen is explored. The findings show that the proposed technique can be used to classify both surface and far-surface hairline cracks and can form the basis for an enhanced hairline crack detection and characterization for pipeline health monitoring.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Engineering
Publisher:	AIP Publishing
ISSN:	2158-3226
Date of First Compliant Deposit:	16 October 2017
Date of Acceptance:	12 October 2017
Last Modified:	07 Nov 2023 05:42
URI:	https://orca.cardiff.ac.uk/id/eprint/105600

Citation Data

Cited 15 times in Scopus. View in Scopus. Powered By Scopus® Data

Actions (repository staff only)

Edit Item