Sizing limits of metal loss anomalies using tri-axial MFL measurements: A model study

Kopp, Gundolf; Willems, H.

doi:10.1016/j.ndteint.2013.01.011

Cited by 47 publications

(13 citation statements)

References 3 publications

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“…It has been shown that the radial component of the MFL signal is more representative, so it is more suitable for studying defects' characteristics [15] . In our work, through comparison of the tested results of the radial component and the tangential components of …”

Section: Experiments Description and Mfl Signal Acquisitionmentioning

confidence: 99%

Internal and external defect identification of pipelines using the PSO-SVM method

Jian¹,

Ai²

2015

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Internal and external defect identification of pipelines using the PSO-SVM method Defects due to corrosion can occur on both the internal and external surfaces of a pipeline. Magnetic flux leakage (MFL) pipeline inspection gauges can detect and locate defects on both the internal and external wall surfaces of pipelines but are generally unable to discriminate between internal and external defects. Therefore, this paper presents a classification approach to achieve defect discrimination based on support vector machines (SVM). First, time-frequency analysis is employed to acquire the feature vectors of the time domain and frequency domain from the MFL signal. Then, a distance evaluation technique is used to remove the redundant and irrelevant information and select the salient features for the classification process. Next, the selected feature vectors are used as the inputs to the proposed SVM model of defect classification. The parameters of this model are optimised using the particle swarm optimisation (PSO) algorithm. Finally, with the optimised model parameters and training sample data, the classification model of the location of the defect based on SVM is established. The experimental results indicate that the proposed method is an effective approach for internal and external defect classification based on the MFL signal.

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Section: Experiments Description and Mfl Signal Acquisitionmentioning

confidence: 99%

Internal and external defect identification of pipelines using the PSO-SVM method

Jian¹,

Ai²

2015

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“…Former approach has minimum computation time when compared with the latter [2]. Analytical approach has to provide the information to develop the MFL tools [3]. Three dimension of the MFL field due to the defect on ferromagnetic specimen has been derived from the concept of dipolar theory [4].…”

Section: Introductionmentioning

confidence: 99%

Dipole Model to Predict the Rectangular Defect on Ferromagnetic Pipe

Suresh¹,

Abudhair²

2016

Journal of Magnetics

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Dipole model based analytical expression is proposed to estimate the length and depth of the rectangular defect on ferromagnetic pipe. Among the three leakage profiles of Magnetic Flux Leakage (MFL), radial and axial leakage profiles are considered in this work. Permeability variation of the specimen is ignored by considering the flux density as close to saturation level of the inspected specimen. Comparing the profile of both the components, radial leakage profile furnishes the better estimation of defect parameter. This is evident from the results of error percentage of length and depth of the defect. Normalized pattern of the proposed analytical model radial leakage profile is good agreement with the experimentally obtained profile support the performance of proposed expression.

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“…Several key creative MFL techniques, such as multi-magnetising testing for longitudinal and circumferential flaws using circumferential and axial magnetisation, have been developed [2] ; thus, much attention has been paid to MFL technology that directly focuses on particular aspects, such as the velocity effects, the utility of magnetic sensing units, the optimisation of apparatus design and signal processing [3][4][5][6][7][8][9][10][11][12] . Meanwhile, a great deal of theoretical research has been conducted on MFL distribution calculations [13] , the lift-off distance effect [14] and the inversion of defects [15] . Accurate evaluations are based on a satisfactory consistency, ie defects with the same size should generate the same testing MFL amplitude.…”

Section: Introductionmentioning

confidence: 99%

The influence of non-uniform wall thickness on MFL testing for a steel pipe

Wu¹,

Fang²,

Wang³

et al. 2015

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A non-uniform wall thickness inevitably forms during the production and use of a steel pipe, leading to a sensitivity difference in the magnetic flux leakage (MFL) during non-destructive testing of the steel pipe. To promote the MFL non-destructive testing technology, the magnetic characteristics of pipe walls with non-uniform thicknesses were analysed by applying magnetic refraction theory. Magnetic field lines in the thin wall leak into the air; however, magnetic field lines in the air are absorbed into the thick wall, producing background magnetic fields with different distributions. The induced flux intensity of the thin wall is larger than that of the thick wall. As a result, different magnetic fields are produced by identically-sized defects during testing, but in pipe walls with different thickness features their relative intensities descend from large to small in the thin to the thick walls, respectively, causing sensitivity differences. These results were verified by relevant MFL experiments using drill pipes.Keywords: steel pipe, magnetic flux leakage (MFL), non-uniform wall thickness, magnetic field line, induced flux density, sensitivity difference.

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Sizing limits of metal loss anomalies using tri-axial MFL measurements: A model study

Cited by 47 publications

References 3 publications

Internal and external defect identification of pipelines using the PSO-SVM method

Internal and external defect identification of pipelines using the PSO-SVM method

Dipole Model to Predict the Rectangular Defect on Ferromagnetic Pipe

The influence of non-uniform wall thickness on MFL testing for a steel pipe

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