Finite Element Modeling of Magnetic Flux Leakage from Metal Loss Defects in Steel Pipeline

Sorabh,; Gupta, Arvind; Chandrasekaran, K.

doi:10.1007/s11668-016-0073-6

Cited by 15 publications

(4 citation statements)

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“…Usually, however, the signal from the far-side defect is weaker than the signal from the near-side defect of identical shape and size. Results of a numerical analysis carried out by Sorabh et al [1] confirm this statement for the same 50% metal losses located on the opposite sides of the wall. Therefore, one can claim that there is a risk of underestimation of defect dimensions, especially its depth, in situation when a far-side defect is classified as a near-side one.…”

Section: Terms Describing Defectsmentioning

confidence: 59%

“…internal [1][2][3][4][5][6][7] inner/inside [8,9], near-side/surface [10][11][12][13], top [14][15][16], front [17], front-side [18,19] external [1][2][3][4][5][6][7], outer/outside [8,20], far-side/surface [10][11][12][13]21], bottom [14][15][16]22], back [17], back-side [18,19,[23][24][25][26], opposite-side [27], sub-surface [28] side' are probably most unequivocal and universal, so these two terms are generally used in the rest of this article.…”

Section: Terms Describing Defectsmentioning

confidence: 99%

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A Comparative Study on Methods of Distinction Between Near- and Far-Side Defects as Techniques Used Alongside with the Magnetic Flux Leakage Testing

2022

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Results of the finite element analysis show that a far-side defect in a steel plate, with the depth greater by 10% of the plate thickness than a near-side defect, can produce a very similar magnetic flux leakage (MFL) signal. Due to the fact that a measurement of MFL itself can lead to misclassification of a far-side defect as a near-side one, and thus to underestimation of its depth, a comparative study of three complementary magnetic techniques was performed. The following techniques were studied: surface topology air-gap reluctance system (STARS), residual magnetic flux leakage (RMFL) and stray magnetic flux leakage (SMFL). Numerical results showed that in the case of the STARS and SMFL, defect signatures in signals were observed for investigated near-side defects, but not for far-side defects. The signature of the far-side defect in the RMFL was observed, however its peak-to-peak value was only about 8% of the value corresponding to the near-side defect.

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Section: Terms Describing Defectsmentioning

confidence: 59%

Section: Terms Describing Defectsmentioning

confidence: 99%

A Comparative Study on Methods of Distinction Between Near- and Far-Side Defects as Techniques Used Alongside with the Magnetic Flux Leakage Testing

2022

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“…MR Kandroodi [ 13 ] proposed an axial flux detection algorithm for defect detection based on image processing approaches and morphological methods, which was validated through examinations of simulated defects and real experimental MFL data. Sorabh [ 14 ] proposed a three-dimensional finite element model and static simulation that studied the dependency of the characteristic defect dimensions and the leakage flux signal. In recent years, with the development of artificial intelligence, deep-learning methods have been applied in the field of pipeline MFL inspection.…”

Section: Introductionmentioning

confidence: 99%

Attention Module Magnetic Flux Leakage Linked Deep Residual Network for Pipeline In-Line Inspection

Liu

Wang

Li³

2022

Sensors

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Pipeline operational safety is the foundation of the pipeline industry. Inspection and evaluation of defects is an important means of ensuring the safe operation of pipelines. In-line inspection of Magnetic Flux Leakage (MFL) can be used to identify and analyze potential defects. For pipeline MFL identification with inspecting in long distance, there exists the issues of low identification efficiency, misjudgment and leakage judgment. To solve these problems, a pipeline MFL inspection signal identification method based on improved deep residual convolutional neural network and attention module is proposed. A improved deep residual network based on the VGG16 convolution neural network is constructed to automatically learn the features from the MFL image signals and perform the identification of pipeline features and defects. The attention modules are introduced to reduce the influence of noises and compound features on the identification results in the process of in-line inspection. The actual pipeline in-line inspection experimental results show that the proposed method can accurately classify the MFL in-line inspection image signals and effectively reduce the influence of noises on the feature identification results with an average classification accuracy of 97.7%. This method can effectively improve identification accuracy and efficiency of the pipeline MFL in-line inspection.

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“…It is a noncontact nondestructive testing method for stress concentration and plastic deformation [3][4][5]. However, the current research on the mechanism of magnetic memory effect of stress is far from conclusive [6][7][8][9]. The magnetic memory mechanism and signal characteristics are explained from different perspectives by a lot of researches, but there is no definite conclusion on them [10][11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

The Magnetic Memory Effect of Ferromagnetic Materials in the Process of Stress-Magnetism Coupling

Wang

Peng

Wei

et al. 2017

Advances in Materials Science and Engineering

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Ferromagnetic materials can produce the magnetic memory effect under stress. This provides a practical method to measure stress concentration. The relation between stress and magnetic characteristic is analyzed through energy balance theory. Forcemagnetism coupling process of Fe-C crystal system is simulated by CASTEP software which is based on first principle. Electron band structure, electron density of states, and atomic magnetic moment in the process of force-magnetism coupling process are calculated. Experimental investigation of the magnetic memory effect of ferromagnetic material under different stresses has been undertaken in X52 pipeline. The results show that the magnetic characteristic of ferromagnetic material weakens under stress, and the magnetic memory signals intensity linearly decreases with the increasing stress. When material yields, the variation character of magnetic memory signals suddenly changes and the inflection points of the stress-B curves emerge. Experimental investigation is in agreement with the theoretical analysis.

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Finite Element Modeling of Magnetic Flux Leakage from Metal Loss Defects in Steel Pipeline

Cited by 15 publications

References 0 publications

A Comparative Study on Methods of Distinction Between Near- and Far-Side Defects as Techniques Used Alongside with the Magnetic Flux Leakage Testing

A Comparative Study on Methods of Distinction Between Near- and Far-Side Defects as Techniques Used Alongside with the Magnetic Flux Leakage Testing

Attention Module Magnetic Flux Leakage Linked Deep Residual Network for Pipeline In-Line Inspection

The Magnetic Memory Effect of Ferromagnetic Materials in the Process of Stress-Magnetism Coupling

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