Investigation of the Lift-off Effect on the Corrosion Detection Sensitivity of Three-axis MFL Technique

Azizzadeh, T.; Safizadeh, M S

doi:10.4283/jmag.2018.23.2.152

Cited by 8 publications

(3 citation statements)

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“…During MFL in-line inspections, surface irregularities such as changes in coating thickness, welds, or hardness deposits can cause fluctuations in the liftoff distance, adding complexity to the inspection process. These fluctuations affect the amplitude of MFL signals, which can alter the detection sensitivity [44]. Thus, investigating the liftoff distance is a crucial uncertainty factor for MFL inspection.…”

Section: Uncertainty Sources Of Mflmentioning

confidence: 99%

Quantifying predictive uncertainty in damage classification for nondestructive evaluation using Bayesian approximation and deep learning

Li,

Deng

2024

Inverse Problems

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Magnetic flux leakage (MFL), a widely used Nondestructive Evaluation (NDE) method, for inspecting pipelines to prevent potential long-term failures. During field testing, uncertainties can affect the accuracy of the inspection and the decision-making process regarding damage conditions. Therefore, it is essential to identify and quantify these uncertainties to ensure the reliability of the inspection. This study focuses on the uncertainties that arise during the inverse NDE process due to the dynamic magnetization process, which is affected by the relative motion of the MFL sensor and the material being tested. Specifically, the study investigates the uncertainties caused by sensing liftoff, which can affect the output signal of the sensing system. Due to the complexity of describing the forward uncertainty propagation process, this study compared two typical machine learning-based approximate Bayesian inference methods, Convolutional Neural Network (CNN) and Deep Ensemble (DE), to address the input uncertainty from the MFL response data. Besides, an Autoencoder method is applied to tackle the lack of experimental data for the training model by augmenting the dataset, which is constructed with the pre-trained model based on transfer learning. Prior knowledge learned from large simulated MFL signals can fine-tune the Autoencoder model which enhances the subsequent learning process on experimental MFL data with faster generalization. The augmented data from the fine-tuned Autoencoder is further applied for machine learning-based defect size classification. This study conducted prediction accuracy and uncertainty analysis with calibration, which can evaluate the prediction performance and reveal the relation between the liftoff uncertainty and prediction accuracy. Further, to strengthen the trustworthiness of the prediction results, the decision-making process guided by uncertainty is applied to provide valuable insights into the reliability of the final prediction results. Overall, the proposed framework for uncertainty quantification offers valuable insights into the assessment of reliability in MFL-based decision-making and inverse problems.

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Section: Uncertainty Sources Of Mflmentioning

confidence: 99%

Quantifying predictive uncertainty in damage classification for nondestructive evaluation using Bayesian approximation and deep learning

Li,

Deng

2024

Inverse Problems

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“…The MFL signal is dependent on the liftoff distance between the probe and the specimen. The MFL signal reduces as the increase in liftoff distance [ 33 , 34 , 35 ], an example of liftoff effect is shown in Figure 8 .…”

Section: Factors Influencing Mfl Signalmentioning

confidence: 99%

A Review of Magnetic Flux Leakage Nondestructive Testing

Feng

et al. 2022

Materials

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Magnetic flux leakage (MFL) testing is a widely used nondestructive testing (NDT) method for the inspection of ferromagnetic materials. This review paper presents the basic principles of MFL testing and summarizes the recent advances in MFL. An analytical expression for the leakage magnetic field based on the 3D magnetic dipole model is provided. Based on the model, the effects of defect size, defect orientation, and liftoff distance have been analyzed. Other influencing factors, such as magnetization strength, testing speed, surface roughness, and stress, have also been introduced. As the most important steps of MFL, the excitation method (a permanent magnet, DC, AC, pulsed) and sensing methods (Hall element, GMR, TMR, etc.), have been introduced in detail. Finally, the algorithms for the quantification of defects and the applications of MFL have been introduced.

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“…A change in a probe's lift-off value will change the testing signals. This phenomenon is called the lift-off effect of MFL testing [8]. It is known that MFL testing is sensitive to the lift-off effect.…”

Section: Introductionmentioning

confidence: 99%

Linearization of the lift-off effect for magnetic flux leakage based on Fourier transform

Wang

et al. 2021

Meas. Sci. Technol.

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In magnetic flux leakage testing, the lift-off effect shows that testing signal voltages vary with lift-off values. This has many adverse effects on detection and evaluation. This paper proposes a novel method for linearization of the lift-off effect in the spatial domain. The spatial analytical expression is obtained through magnetic dipole theory and the Fourier transform method. An analytical method to linearizethe lift-off effect is proposed. Further, the finite-element method and discrete Fourier transform method are applied to linearize the lift-off effect. Experimental studies are also conducted to verify the analytical results. The linear fitting function of the lift-off effect takes the form of Y = AX + B. When the spatial frequency ω is 35 mm−1, the linear fitting results of the simulation and experiment are Y = −0.23596X−2.0268 and Y = −0.23511X + 4.8152. The goodness of fit R2 is 0.9997 and 0.9975. The results show that the lift-off effect can be linearized in the spatial domain. The coefficient A is slightly affected by the defect size. The coefficient B is strongly related to the defect size. We consider these results to be quite significant.

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Investigation of the Lift-off Effect on the Corrosion Detection Sensitivity of Three-axis MFL Technique

Cited by 8 publications

References 0 publications

Quantifying predictive uncertainty in damage classification for nondestructive evaluation using Bayesian approximation and deep learning

Quantifying predictive uncertainty in damage classification for nondestructive evaluation using Bayesian approximation and deep learning

A Review of Magnetic Flux Leakage Nondestructive Testing

Linearization of the lift-off effect for magnetic flux leakage based on Fourier transform

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