Magnetic Flux Leakage Measurements Research Articles

Evaluation of the Size of a Defect in Reinforcing Steel Using Magnetic Flux Leakage (MFL) Measurements.

This study aimed to evaluate 2D magnetic flux leakage (MFL) signals (Bx, By) in D19-size reinforcing steel with several defect conditions. The magnetic flux leakage data were collected from the defected and new specimens using an economically designed test setup incorporating permanent magnets. A two-dimensional finite element model was numerically simulated using COMSOL Multiphysics to validate the experimental tests. Based on the MFL signals (Bx, By), this study also intended to improve the ability to analyze defect features such as width, depth, and area. Both the numerical and experimental results indicated a high cross-correlation with a median coefficient of 0.920 and a mean coefficient of 0.860. Using signal information to evaluate defect width, the x-component (Bx) bandwidth was found to increase with increasing defect width and the y-component (By) amplitude rise with increasing depth. In this two-dimensional MFL signal study, both parameters of the two-dimensional defects (width and depth) affected each other and could not be evaluated individually. The defect area was estimated from the overall variation in the signal amplitude of the magnetic flux leakage signals with the x-component (Bx). The defect areas showed a higher regression coefficient (R2 = 0.9079) for the x-component (Bx) amplitude from the 3-axis sensor signal. It was determined that defect features are positively correlated with sensor signals.

A Novel Method of Transformer Overloading Detection by Magnetic Flux Leakage Measurements

A Novel Method of Transformer Overloading Detection by Magnetic Flux Leakage Measurements

FMD-Framework: A Size Estimation Method for Pipeline Defects in Weld-Affected Zones

Defect size estimation plays a crucial role in pipeline magnetic flux leakage (MFL) inspection. For the defects in the weld-affected zones, size estimation is a challenge due to the interference of weld signals. In this paper, Feature-Modeling-Decoupling-Framework (FMD-Framework), a size estimation method, is proposed aiming at obtaining the accurate sizes of defects in the weld-affected zones based on MFL measurements. First, a feature extraction module is established to extract relatively pure defect features without weld information. In this part, the feature extraction module searches the intersection and difference between abstract features, and the weld-related information can be modeled and eliminated on the feature level. Second, an inversion module is constructed to decouple the information of different size dimensions on the feature level and achieve accurate estimation. In this part, the designed training suites and novel loss function guide the network to capture the feature information of a certain size dimension. The evaluation experiments are conducted based on the collected MFL measurements. FMD-Framework reduces the size estimation errors by around 2mm(length), 3mm(width), and 3%OD(depth). The results indicate the effectiveness of the proposed method.

Wall thickness image construction in wellbore casings using data-driven inversion of magnetic flux leakage

We describe the response of magnetic flux leakage measurements obtained in wellbore casings and detail an analytical approach for inverting the data to construct a 3D image of metal loss for the general case in which the magnetic permeability of the casing is unknown. This approach contrasts with current practice which assumes a defect shape and attempts to deduce metal loss from one or more response metrics matched to a library of defects generated under laboratory conditions for that shape. The traditional approach breaks down where defects do not conform to the assumed shape and when the properties of the laboratory casing are not the same as the properties of the installed casing. Moreover, there is no direct way to know whether magnetic flux in the casing is at saturation level; thus, the measurement could be operating in a nonlinear part of the magnetization curve whose shape also is unknown. The new method makes no assumptions about defect shape and approximates the relevant part of the magnetization curve within an inversion process that transforms the flux scan into a metal loss scan showing the shape and depth of metal loss. The approach is validated using data from simulations, laboratory tests, and field operations. Unlike images of flux distribution, thickness profiles reconstructed from the results of the inversion reflect the actual distribution of metal loss to a useful level of accuracy and thereby enable the casing’s mechanical integrity to be assessed with greater confidence.

Crack identification system on MOH cold rolled grain oriented sheets: Application of K-fold cross validated BRANN

To increase efficiency of transformers, iron (hysteresis and Fuko losses), copper and abnormal losses must be prevented. Hysteresis loss may be significantly reduced by adding silicon to iron, and Fuko loss may be significantly reduced by using cold-rolled grain-oriented steel (CRGO) in the form of thin sheets with high resistance. To reduce abnormal losses, it is necessary not to use CRGO with ferromagnetic features including cracks in the core sequence. In line with this, currently, the magnetic flux leakage (MFL) method is commonly used to research cracks in ferromagnetic materials. Generally, literature studies researched the variation characteristics of MFL signals collected with magnetic sensors and size of amplitude values according to the physical properties (crack width, crack depth, etc.) of the crack in the ferromagnetic material. Different to the literature, our primary aim in this study is to determine this variation according to the type and geometric features of an artificial full crack with known physical properties created in MOH CRGO steel. The secondary aim of our study is to develop an artificial neural network predicting the type and geometric features of a full crack with unknown geometric features using the data obtained. In line with this, our study first produced an MFL measurement system. Then, artificial full crack samples were prepared with different types and geometric features using MOH CRGO steel. These artificial full crack samples were magnetized by being placed in the core of an electromagnet with 50 kHz AC signal and the surface of the material was scanned in a single dimension with a location-controlled fluxgate sensor. Harmonics of signals obtained from the fluxgate sensor were investigated with DSP lock-in amplifier and the amplitude values for the harmonics with greatest variation were uploaded to the computer. Finally, 3 different Bayesian regularized artificial neural networks (BRANN) were developed and trained using the harmonic amplitude values for full crack samples with known geometric features and used to predict the type and geometric features of full cracks with unknown geometric features. The first of these BRANN was used to identify the fixed or variable width of the full crack type, while the others were used to find the geometric features according to the full crack type. For the BRANNs trained with the K-fold cross-validation method, accuracy degrees of R = 0.99934, R = 0.99999 and R = 0.91654 were obtained, respectively. The trained BRANNs provided results compatible with reality for artificial full crack models with unknown crack type and geometric properties.

Defect reconstruction of magnetic flux leakage measurements based on single dimension PSO algorithm

Magnetic flux leakage (MFL) detection is widely used in the technology of oil and gas pipeline intelligent detection at present. For the defect reconstruction problem in MFL detection, a new improved particle swarm optimization (PSO) algorithm, called SDPSO, is introduced to enhance the accuracy of defect profile reconstruction in this paper. In order to reduce the interaction between multidimensional information, dimensional coupling will deteriorate the search speed and convergence accuracy of the algorithm, a single dimension evolution strategy is complemented to optimize the tradition PSO algorithm. Each generation of evolution only updates a single dimension of the objective function. The dimension with the maximum error between the candidate signal and reference signal is selected as the one which need to be updated in next generation. In order to testify the performance of the proposed method, the simulated signals are utilized to compare with PSO algorithm. The experiment results verify that the proposed method has a higher adequacy of the performance in terms the quality of the solution and robustness for noise.

Accurate 3D reconstruction of complex defects based on combined method of MFL and MFDs

The two-step strategy for 3D reconstruction of complex defects was updated by combining the magnetic field disturbance (MFD) with magnetic flux leakage (MFL) measurement methods. Firstly, the plane-scanned MFD imaging was employed to achieve high-accuracy reconstruction of the opening outlines of complex defects. Secondly, the finite element simulation model of the defect with the recognized opening outline was established as the forward model for depth profile inversion with the particle swarm optimization algorithm. The performances of the conventional MFL-based and updated strategies were carefully compared through finite element simulation. The results proved that the updated method combining MFD signals with MFL signals had the better performance in accurate 3D reconstruction of complex defects than the conventional MFL-based inversion strategy, displaying smaller errors in opening outline, peak depth and the profile. Experimental results confirmed that the updated strategy could accurately reconstruct the 3D shape of 8-shaped defect and rectangular defect with rounded corners.

The Effect of Motion-Induced Eddy Currents on Three-Axis MFL Signals for High-Speed Rail Inspection

Nondestructive testing (NDT) methods are widely used in the rail industry to detect rolling contact damage (RCD) defects to ensure railway safety. With the need of monitoring millions of miles of rail tracks more effectively and efficiently that leads to improved rail safety, reliability, and optimized assets management, the research on high-speed and high-accuracy NDT methods that can obtain high signal-to-noise ratio (SNR) and high sensitivity defect signals at higher speeds is of vital importance. Magnetic flux leakage (MFL) is one of the NDT methods used for high-speed rail inspection, which is suitable for detecting surface and subsurface RCD defects on railhead, but the measured MFL signals are distorted severely by the effect of motion-induced eddy currents (MIEC). However, the fundamental understanding of the mechanism of MIEC generation, distribution, and magnitude is lacking, and the physical interaction between MIEC and MFL measurement during high-speed rail inspection has not been well studied. This article investigates the effect of MIEC on three-axis MFL signals through 3-D finite-element method (FEM) simulations. The 3-D distributions of MIEC and 2-D image signals of MFL measurements for high-speed rail MFL inspection are presented and analyzed. The simulations are conducted with an inspection speed range of 0-62.5 mi/h, and a surface hemisphere-type defect and subsurface hole-type defect are both considered. The results show that the motion between the magnetizer and the rail will generate two types of MIEC, which in turn decreases the peak values of three-axis MFL signals from the subsurface defect and affects the MFL signals spatially, especially the base values along rail transverse and vertical directions. The effect of MIEC on the three-axis MFL signals in terms of SNR, sensitivity, and asymmetry is discussed.

A Novel Compensation Method of Probe Gesture for Magnetic Flux Leakage Testing

For in-line inspection of ferromagnetic materials, magnetic flux leakage (MFL) detection is one of the most common nondestructive testing methods. In practical applications of conventional MFL inspection, random mechanical vibration can cause the detection gesture of the probe, in particular, the lift-off and tilt detection angle, to continuously fluctuate throughout the inspection process, which introduces measurement errors to MFL signals. To address the problem and realize ultra-high-definition MFL detection, this paper proposed a new type of probe with dual diagonal distributed magnetic sensors for MFL detection. Based on the dual magnetic sensor probe structure and magnetic dipole model, this paper further developed analytical solutions of the real-time sensor lift-off, tilt detection angle, and defect depth. Additionally, this paper presented a compensation algorithm for MFL measurement errors caused by random lift-off and tilt detection angles. A practical implementation for the dual magnetic sensor probe in the actual MFL testing tool was given. The finite element simulation and physical experiments were designed to verify the feasibility of probe gesture compensation algorithm. Even under abnormal probe detection gesture case, MFL signals measured and compensated by the dual magnetic sensor probe were also in line with the engineering application, and measurement errors of MFL signals were reduced from more than 30 % to less than 10 %, which is conducive to the realization of ultra-high-definition MFL testing.

Defect reconstruction from magnetic flux leakage measurements employing modified cuckoo search algorithm.

Accurate and efficient estimation for defect profile of magnetic flux leakage (MFL) signals is important for nondestructive evaluation in industry. To improve the accuracy of defect profile reconstruction, an improved reconstruction method based on modified cuckoo search (CS), called MCS, is proposed in this paper. Firstly, a novel single-dimension updating evolution strategy is proposed to avoid the interference between multiple dimensions, which can make full use of the appropriate nest position in the historical search. Secondly, an adaptive multi-strategy difference evolution is introduced into the evolution process to improve the diversity and efficiency of CS algorithm. The proportion factor of each strategy in multi-strategy difference evolution is adjusted dynamically according to the value of the objective fitness. Finally, various MFL signals are selected to verify the effectiveness of the proposed MCS algorithm. The experiment results illustrate that the proposed method has high performance on the quality of the solution and robustness for noise.

Analysis of Magnetic-Flux Leakage (MFL) Data for Pipeline Corrosion Assessment

Oil and gas pipelines transport and distribute large quantities of oil products and natural gas to industrial and residential customers over a long distance. However, pipeline failures could lead to enormous hazards and safety issues. Metal loss, due to corrosion, is a significant cause for pipeline failures. To detect and quantify metal loss, in-line inspection (ILI) is carried out periodically to assess the integrity of the pipeline. Among all the ILI techniques, magnetic-flux leakage (MFL) is the most popular one due to its high efficiency, robustness, and good applicability to both oil and gas pipelines. This article provides a comprehensive review of the pipeline corrosion assessment with the MFL technique from the data analytic perspective. The analyses of the MFL signal and data contribute to both corrosion quantification and prediction. In this article, the state of the art for the MFL measurement technique is briefly reviewed first. For corrosion quantification, the signal processing methods together with the characterization models, which aim to enhance the measurement and characterize the corrosion, are described and discussed. For corrosion prediction, this article investigates multiple MFL data matching methods, which align defects from successive ILI runs. Subsequently, corrosion growth models, which aim to predict the future corrosion status, are presented. Besides, the reliability analysis of the corroded pipeline is reviewed. The potential of fusing MFL with other non-destructive testing (NDT) techniques are explored as well. At the end of this article, we summarize the existing issues and describe the trends for future research on pipeline corrosion assessment.

Anomaly Detection of Complex MFL Measurements Using Low-Rank Recovery in Pipeline Transportation Inspection

Pipeline transportation is the most economic and reasonable transport means of petroleum and natural gas. Influenced by complex corrosion environment, it is challenging to detect anomalies accurately with precise boundaries. Inspired by the idea of low-rank (LR) recovery, a novel anomaly detection model with a spatial–temporal regularization (STLR) is proposed to distinguish anomalies and background from magnetic flux leakage (MFL) measurements. By adding the STLR, the model enlarges the differences between the two parts and improves the effectiveness of anomaly detection. Then, an anomaly detection framework is proposed, where the raw MFL measurements are first transformed including profile transformation and filter transformation to prepare the measurements with high saliency. Second, a measurement fusion method is introduced to divide the transformed measurements into multiple blocks and fuse all detection results together. In experiments, the simulated MFL measurements generated by the magnetic dipole model are used to verify the method and obtain optimal parameters, and the real MFL measurements collected from experimental pipelines and in-service pipelines are evaluated for comparisons with other baseline methods.

Magnetic Properties of Structural Steels for Simulation of Crack Monitoring by Finite Element Method

The metal magnetic memory method is a novel technique for monitoring fatigue cracks in steel structures, which can reduce operational expenses and increase safety by minimizing inspections. The crack geometry can be identified by measuring the self magnetic flux leakage, which is induced by the Earth’s magnetic field and the permanent magnetization. The finite element method can be used to simulate the induced magnetic field around cracks to help interpret the self magnetic flux leakage measurements, but it is unclear what material properties to use. This study aims to determine the magnetic permeability of structural steel for accurate simulation of the induced magnetic field around cracks by the finite element method. The induced magnetic field was extracted from measurements above two square steel plates, one without defect and one with a straight slit, and compared with finite element results in function of the relative permeability. For both plates, a uniform relative permeability could be found for which experimental and numerical results were in good agreement. For the plate without defect and a relative permeability of 350, errors were within 20% and were concentrated around the plate’s edges. For the plate with the slit and a relative permeability of 225, errors were within 5%.

Identification and Sizing of Neighboring Pits Using Three-Axis Magnetic Flux Leakage Technique

Neighboring corrosion pits are one of the most frequently found defects in gas pipelines. Because of the interaction with each other, these defects accelerate the corrosion growth rates in the wall of the pipelines. Hence, it is meaningful to discriminate multiple neighboring pits from single isolated pits by analyzing their characteristics. This article presents the results of a study on the use of a three-axis magnetic flux leakage (MFL) technique for the identification and sizing of neighboring corrosion pits. Various patterns of two neighboring pits are considered by modifying the separation distances between them. Finite element modeling as well as experimental measurements is used to investigate the spatial distributions of the three MFL components (axial, radial, and circumferential) generated by the neighboring pits. The results indicate that the axial as well as the radial MFL components of the neighboring pits merge to one signature the closer the two defects are, and thus, no width sizing of the individual pits is possible anymore. In contrast, both pits are still discernible in the circumferential MFL signatures. The described method can help to identify neighboring corrosion pits and thus improve the sizing accuracy of MFL measurements.

An Opening Profile Recognition Method for Magnetic Flux Leakage Signals of Defect

The defect opening profile recognition is of great concern in the magnetic flux leakage (MFL) measurement technique. The detected spatial MFL signal has three components: horizontal, vertical, and normal components. Horizontal and normal component signals are commonly used to estimate the defect profile, while the vertical component has always been neglected. With the development of the high resolution and the 3-D MFL testing techniques, the vertical component signal is becoming more available. This paper analyzes the essential right-angle features of the vertical component signal, which is useful for the defect opening profile recognition. After obtaining the initial profile from the horizontal or normal component, the types of the right angle is identified from the vertical component, and the opening profile is further optimized based on these right-angle features. The opening profile recognition method is put forward in this paper to improve the accuracy of the recognition result of the defect. Both simulation and experimental tests are conducted to verify the good performance of the proposed method. Compared with the opening profiles recognized merely by the horizontal component signal, the proposed method shows better recognition results, which also validates that the vertical component signal can also be a useful information for the defect estimation.

An Estimation Method of Defect Size From MFL Image Using Visual Transformation Convolutional Neural Network

In most current nondestructive testing systems, a magnetic flux leakage (MFL) method is widely used in various industry fields, where the structural integrity of specimens is of vital importance. The estimation of defect size in specimen from the MFL measurements is a key and difficult problem. The traditional methods have low precision because feature extraction procedure relies on prior knowledge and the ability of designer. Inspired by the idea of convolutional neural network (CNN), a novel visual transformation CNN (VT-CNN) is proposed in this paper to overcome the limitation of traditional method in a feature extraction procedure. By adding a visual transformation layer according to the characteristics of the MFL measurements, the VT-CNN can distinguish the defect feature with different sizes more accurately. Moreover, since the VT-CNN method is designed based on the deep learning theory, more industrial big data with accurate label should be used to train the network. Due to the difficulty of making real industrial big data, a novel mesher magnetic dipole model is designed to simulate this industrial process. A large simulated MFL measurement of irregular defects produced by this model can increase the number of training samples and improve the robustness of the network. Experiments to estimate natural corrosion defects on real industrial pipelines are performed to validate the proposed framework. The experimental results are illustrated in detail, which highlights the superiority of the proposed method in industrial applications.

Online Magnetic Flux Leakage Detection System for Sucker Rod Defects Based on LabVIEW Programming

Aiming at the detection of defects in sucker rod surface, a real-time detection system is designed using the non-destructive testing technology of magnetic flux leakage (MFL). An MFL measurement system contains many components, and this study focuses on the signal acquisition and processing system. First of all, this paper introduces the hardware part of the acquisition system in detail, including the selection and principle of the Hall-effect plural, the design and calculation of the signal conditioning circuit, and the working process of the single chip computer (SCM) control serial port. Based on LabVIEW, a graphical programming software, the software part of the acquisition system is written, including configuration of serial port parameters, identification of detected signals, filtering of original signals, real-time display, storage and playback of data. Finally, an experimental platform for MFL detection is set up, and the MFL measurement is carried out on the transverse and longitudinal defects of the sucker rod surface. The experimental result shows that the designed acquisition and processing system has good detection performance, simple design and high flexibility.

Nondestructive Testing for Shallow Defect of Ferromagnetic Objects Based on Magnetic Probe Structure

The Electromagnetic inspection technique plays an important role in the nondestructive testing (NDT) for many decades. Today, this NDT area is rather wide and significant. The magnetic flux leakage (MFL) technology is one of the most widely used electromagnetic nondestructive testing (NDT) techniques. MFL tools use permanent magnets to magnetize the detected object near to saturation flux density [1], [2]. Generally, the magnetizer mechanism of MFL are bulky and heavy. Although the shape of the opening and the depth profile of an arbitrary three-dimensional (3-D) defect from MFL measurements can be estimated [3], the inversion method is complicated and susceptible by the magnetization factors. For different shapes and different sizes of detected object, the magnetizer mechanism of MFL should be designed individually, and this requires a lot of time and experimentation. In this paper, a simple and portable magnetic detection device is designed with permanent magnets, magnetic probe structure and the Hall sensors. Compared with the magnetizer mechanism of MFL, the magnetic detection device is very light, low cost and easy to design and manufacture. The magnetic detection device can make qualitative, and quantitative evaluation for shallow defect of ferromagnetic objects.

Quick Reconstruction of Arbitrary Pipeline Defect Profiles From MFL Measurements Employing Modified Harmony Search Algorithm

This paper proposes a quick and accurate method for reconstructing defect profiles in pipelines from magnetic flux leakage (MFL) signals employing a modified harmony search (MHS) algorithm. To improve the quality of the initial harmony memory, a multiple selection opposition-based learning strategy is presented in the MHS algorithm. Moreover, the pitch adjusting rate is set to increase linearly, and the distance bandwidth ( bw ) is designed to be dynamically adjusted to shorten the calculation time. A specially designed modified sum-squared error with weights is proposed and employed as the fitness function to improve the fitting degree between the reference and predicted signals in the defect response segment. The weights are determined by the characteristics of reference signals. We demonstrate the adequacy of the performance of the proposed model using both simulated and real MFL signals. We also present comparative results of the proposed model and other methods. The results prove that the proposed method can achieve higher efficiency while maintaining excellent accuracy.

Importance of Magnetizing Field on Magnetic Flux Leakage Signal of Defects

The measurement of magnetic flux leakage (MFL) is widely used as a non-destructive technique for inspecting oil and gas pipelines. In this method, many factors can affect the MFL signal, but the magnetization of the pipeline is a fundamental issue that bears consideration. We investigated the dependence of the MFL signal on the magnetizing state of pipeline samples with respect to both near-side and far-side defects by varying the number of permanent magnets in each pole of the U-core system, varying the distance between the two poles, varying the gap between the U-core system and the surface of the steel plates engineered from a gas pipeline, and varying the depth of the rectangular defects. By systematically controlling the magnetization of the samples, we observed novel behaviors of the MFL signals as a function of defect depth. For samples having above-saturated magnetization, the dependence of the MFL signal can be expressed as a quadratic function for all defect depths. However, for samples having below-saturated magnetization, the MFL signal increases linearly at low-defect depths, but exhibits a quadratic response at high-defect depths. These findings have relevance for both fundamental research and practical applications, and they provide a novel and precise cartography method for accurately quantifying the depth of defects in MFL measurement.