Magnetic Flux Leakage Testing Research Articles

Defect Width Assessment Based on the Near-Field Magnetic Flux Leakage Method.

Magnetic flux leakage (MFL) testing has been widely used as a non-destructive testing method for various materials. However, it is difficult to separate the influences of the defect geometrical parameters such as depth, width, and length on the received leakage signals. In this paper, a “near-field” MFL method is proposed to quantify defect widths. Both the finite element modelling (FEM) and experimental studies are carried out to investigate the performance of the proposed method. It is found that that the distance between two peaks of the “near-field” MFL is strongly related to the defect width and lift-off value, whereas it is slightly affected by the defect depth. Based on this phenomenon, a defect width assessment relying on the “near-field” MFL method is proposed. Results show that relative judging errors are less than 5%. In addition, the analytical expression of the “near-field” MFL is also developed.

Magnetic tunnel junction based gradiometer for detection of cracks in cement

Magnetic flux leakage (MFL) testing is a widely used technology for detecting structural defects in industry, where new magnetic sensors can provide an enhanced detectability. We have fabricated and developed vortex magnetic tunnel junctions (MTJs) into a new MFL probe, by designing a well-balanced magnetic gradiometer with a base length of 4 cm. Such gradiometer has exhibited a field detectability of 1.6 nT/Hz at 1 kHz, and a superior common-mode rejection ratio (CMRR) of 82 dB. We have studied multiple configurations of crack detection in weakly magnetic cement by simulations, and provided corresponding experimental confirmations by this MFL probe. It has achieved a large standoff distance of 22 mm, which finds its applications in energy industry and other infrastructural systems.

Location Detection Method of Detector in Pipeline Using VMD Algorithm and Machine Learning Classifier

The internal detector in a pipeline needs to use the ground marker to record the elapsed time for accurate positioning. Most existing ground markers use the magnetic flux leakage testing principle to detect whether the internal detector passes. However, this paper uses the method of detecting vibration signals to track and locate the internal detector. The Variational Mode Decomposition (VMD) algorithm is used to extract features, which solves the defect of large noise and many disturbances of vibration signals. In this way, the detection range is expanded, and some non-magnetic flux leakage internal detectors can also be located. Firstly, the extracted vibration signals are denoised by the VMD algorithm, then kurtosis value and power value are extracted from the intrinsic mode functions (IMFs) to form feature vectors, and finally the feature vectors are input into random forest and Multilayer Perceptron (MLP) for classification. Experimental research shows that the method designed in this paper, which combines VMD with a machine learning classifier, can effectively use vibration signals to locate the internal detector and has the characteristics of high accuracy and good adaptability.

Magnetic Flux Leakage Course of Inner Defects and Its Detectable Depth

As a promising non-destructive testing (NDT) method, magnetic flux leakage (MFL) testing has been widely used for steel structure inspection. However, MFL testing still faces a great challenge to detect inner defects. Existing MFL course researches mainly focus on surface-breaking defects while that of inner defects is overlooked. In the paper, MFL course of inner defects is investigated by building magnetic circuit models, performing numerical simulations, and conducting MFL experiments. It is found that the near-surface wall has an enhancing effect on the MFL course due to higher permeability of steel than that of air. Further, a high-sensitivity MFL testing method consisting of Helmholtz coil magnetization and induction coil with a high permeability core is proposed to increase the detectable depth of inner defects. Experimental results show that inner defects with buried depth up to 80.0 mm can be detected, suggesting that the proposed MFL method has the potential to detect deeply-buried defects and has a promising future in the field of NDT.

A Kriging-Based Magnetic Flux Leakage Method for Fast Defect Detection in Massive Pipelines

Abstract Systems in service continue to degrade with the passage of time. Pipelines are among the most common systems that wear away with usage. For public safety, it is of utmost importance to monitor pipelines. Magnetic flux leakage (MFL) testing is a widely used nondestructive evaluation (NDE) technique for defect detections within the pipelines, particularly those composed of ferromagnetic materials. Pipeline inspection gauge (PIG) procedure based on line scans can collect accurate MFL readings for defect detection. However, in real world, applications involving large pipe sectors such as extensive scanning techniques are extremely time consuming and costly. In this article, we develop a fast and cheap methodology that does not need MFL readings at all the points used in traditional PIG procedures but conducts defect detection with similar accuracy. We consider an under-sampling based scheme that collects MFL at uniformly chosen random scan points over large lattices instead of extensive PIG scans over all lattice points. On the basis of readings from the chosen random scan points, we use kriging to reconstruct MFL readings. Thereafter, we use thresholding-based segmentation on the reconstructed data for detecting defective areas. We demonstrate the applicability of our methodology on synthetic data generated using finite element models and on MFL data collected via laboratory experiments. In these experiments, spanning a wide range of defect types, our proposed novel MFL-based NDE methodology is witnessed to have operating characteristics within the acceptable threshold of PIG-based traditional methods and thus provide an extremely cost-effective, fast procedure with competing error rates.

Pipeline In-Line Inspection Method, Instrumentation and Data Management.

Pipelines play an important role in the national/international transportation of natural gas, petroleum products, and other energy resources. Pipelines are set up in different environments and consequently suffer various damage challenges, such as environmental electrochemical reaction, welding defects, and external force damage, etc. Defects like metal loss, pitting, and cracks destroy the pipeline’s integrity and cause serious safety issues. This should be prevented before it occurs to ensure the safe operation of the pipeline. In recent years, different non-destructive testing (NDT) methods have been developed for in-line pipeline inspection. These are magnetic flux leakage (MFL) testing, ultrasonic testing (UT), electromagnetic acoustic technology (EMAT), eddy current testing (EC). Single modality or different kinds of integrated NDT system named Pipeline Inspection Gauge (PIG) or un-piggable robotic inspection systems have been developed. Moreover, data management in conjunction with historic data for condition-based pipeline maintenance becomes important as well. In this study, various inspection methods in association with non-destructive testing are investigated. The state of the art of PIGs, un-piggable robots, as well as instrumental applications, are systematically compared. Furthermore, data models and management are utilized for defect quantification, classification, failure prediction and maintenance. Finally, the challenges, problems, and development trends of pipeline inspection as well as data management are derived and discussed.

Steel Rope Diagnostics by Magnetic NDT: From Defect Detection to Automated Condition Monitoring

Today it is hard to imagine an industry or an application that uses steel wire ropes in which the application of a magnetic flaw detector would be unsuitable. The ropes of loading cranes, mine hoists, ropeways, cable-stayed bridges, flare stacks, antenna masts, overhead power transmission lines, and other installations are not only subject to regular inspections (Sukhorukov and Mironenko 2003; Kotelnikov and Sukhorukov 2006; Sukhorukov et al. 2014), but the appropriate guidelines for these rope types are now described in industrial standards such as the International Organization for Standardization (ISO) standard for crane ropes, ISO 4309 (ISO 2017); the European Standards (EN) standard for ropeways, EN 12927 (BSI 2019); and the International Marine Contractors Association (IMCA) standards for offshore ropes, IMCA LR 004, IMCA HSSE 023, and IMCA M 197 Rev.1 (IMCA 2018). This paper examines how magnetic flux leakage testing (MFL) is used as the basis for an automated wire rope condition monitoring system, in which the human factor is completely removed.

Development of an MFL Coil Sensor for Testing Pipes in Extreme Temperature Conditions.

This paper aims to design a coil sensor for corrosion monitoring of industrial pipes that could detect variations in thickness using the MFL (Magnetic Flux Leakage) technique. An MFL coil sensor is designed and tested with pipe sample thicknesses of 2, 4, 6, and 8 mm based on the magnetic field effect of ferrite cores. Moreover, a measurement setup for analysing pipe samples up to a temperature of 200° Celsius is suggested. Experimental results reveal that the MFL coil sensor can fulfil the requirements for MFL testing of pipes in high temperature conditions, and that the precision of MFL monitoring of pipes to detect corrosion at high temperatures can be improved significantly.

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

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.

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.

A numerical simulation method of nonlinear magnetic flux leakage testing signals for nondestructive evaluation of plastic deformation in a ferromagnetic material

Nonlinear Magnetic Flux Leakage Testing (MFLT) method is a relative new non-destructive testing (NDT) technique for inspection defect or micro damage in structure of ferromagnetic material by measuring the leakage magnetic field during an alternative magnetization process. To enhance the nonlinear MFLT technique, a numerical tool for simulating the inspection signal is very important in views to clarify the detection mechanism and to improve the detection efficiency. However, most of the simulation methods on magnetic NDT problems solve problem without considering the hysteresis effects of ferromagnetic materials and is difficult to deal with the influence of mechanical damage. In this paper, a numerical scheme is proposed at first to simulate the nonlinear MFLT signals considering the nonlinearity and hysteresis characteristics of ferromagnetic materials based on the equivalent magnetic polarization approach and FEM-BEM hybrid method. The validities of the proposed method and the corresponding numerical code are proved by comparing the simulation results of a typical nonlinear MFLT problem with the experimental measured signals. Secondly, in order to cope with the influence of plastic deformation in material on the nonlinear MFLT signals, a magneto-mechanical coupling constitutive relation is proposed and applied to the developed numerical simulation code. Finally, the nonlinear MFLT signals of specimen with different plastic deformation are simulated with the developed numerical code and the influence mechanism of plastic deformation on nonlinear MFLT signals is discussed based on the numerical results. The results reveal that the distortion of nonlinear MFLT signals mainly due to the magnetic remanence coefficient of material which can be significantly influenced by micro damage such as the plastic deformation.

A Simplified Lift-Off Correction for Three Components of the Magnetic Flux Leakage Signal for Defect Detection

Magnetic flux leakage (MFL) testing is widely used to detect and estimate defect in ferromagnetic materials. The lift-off of sensors caused the weakening of the detected signal inevitably. Therefore, it is necessary to correct lift-off for the detected MFL signal. In this article, we analyze the approximate negative exponential relationship between the three components of MFL signal of the defect and the lift-off based on magnetic dipole analytical method and establish a simplified numerical calculation model of MFL signal based on the lift-off. A lift-off correction method is also proposed to revise the three components of the MFL signal at a suitable range of lift-off efficiently. In this method, an exponential function compensation is introduced and only the lift-off correction value is concerned. The validity of the correction method is verified by the 3-D MFL testing experiment for the ferromagnetic pipeline defects. The results demonstrate that the corrected MFL signals match the experimental signals well.

Feasibility Study of Pinhole Inspection via Magnetic Flux Leakage and Hydrostatic Testing in Oil & Gas Pipelines

Oil & Gas pipeline pinhole leaks are more likely to lead to serious consequences than larger leaks because they are difficult to discover through conventional monitoring and patrolling. An undetected pinhole leak can lead to significant soil and groundwater pollution over time. The effectiveness and applicability of magnetic flux leakage (MFL) in characterizing and sizing pinhole defects is studied by pull test in a 10-inch pipe string with manufactured defects. MFL technologies from five vendors were tested in blind scenarios. In addition, the feasibility of using hydrostatic testing to detect pinhole defects is investigated. The results show that the MFL signal may be affected by pinhole diameter, depth, position, and so on. An optimal practice was developed by comparing the gap between MFL tracks, sampling frequency, intensity of magnetic field, etc. And there was no significant reduction in pressure capacity within the test pipe segment with pinhole. So it is speculated that hydrostatic testing can only identify pinholes that have fully penetrated the pipe wall.

Extraction of Flux Leakage and Eddy Current Signals Induced by Submillimeter Backside Slits on Carbon Steel Plate Using a Low-Field AMR Differential Magnetic Probe

Magnetic Flux Leakage (MFL) and Eddy Current Testing (ECT) are commonly employed as the non-destructive evaluation (NDE) techniques used to detect defects within the steel. The MFL technique is advantageous in terms of deep defects detection, while the ECT technique excels in providing dense information regarding defects. In this work, artificial MFL and eddy current (EC) signals in ferromagnetic materials are studied, and an experimental magnetic probe that utilizes both techniques is developed for signal verification. The separation between MFL and EC signals is achieved by utilizing the phase-sensitive detection technique, implementing a dynamic referencing method as opposed to the conventional static phase referencing. A finite element method (FEM) based simulation is employed to study and verify the MFL and EC signals measured by the proposed magnetic probe. The proposed magnetic probe features highly sensitive anisotropic magneto-resistive sensors capable of measuring the MFL and EC signals induced by artificial slits of varying depths engraved onto a 2-mm carbon steel plate. Finite element simulations indicate different flux leakage patterns and eddy current signals detected in the vicinity of the back-side slits. A good agreement is observed between the simulated and the measured MFL and EC signals for the optimized frequency range of 110-210 Hz with the corresponding Lissajous curve for the detection of submillimeter back-side slits. The study has shown that the combination of MFL and EC signals can be successfully captured by an appropriate magnetic probe for an enhanced detection performance of back-side defects in ferromagnetic materials.

Improving the reliability of metal structures of transport and technological machines during operation in the Arctic

In the course of the study, we analyzed the main factors affecting the brittle fracture of the elements of welded structures made of structural steels, associated with its unexpectedness and the absence of noticeable plastic deformations at metal stresses much lower than the yield point. During experiments, the relationship between the magnetic parameter Hp and the structure of structural steels during thermal cycling was revealed. This became possible due to the high sensitivity of passive magnetic flux leakage testing when monitoring the structural transformations of metal during cyclic heating and cooling. We provide examples of the formation of ultrafine-grain structures in industrial structural steels based on the results of thermal cycling and analyze factors affecting the final grain size. It is shown that the degree of steel microstructure refinement depends on the chemical composition and initial structure of steels as well as the number of thermal cycling cycles. It was established that an increase in the number of thermal cycling cycles and the degree of steel alloying, the presence of a finer-grain initial structure, and preliminary cold plastic deformation during stage-by-stage monitoring using passive magnetic flux leakage testing contribute to the formation of a finer-grain structure. A decrease in the average grain size shifts the temperature of metal transition from ductile to brittle fracture to the region of lower temperatures, which increases the reliability of welded metal structures of transport and technological machines used in the Arctic.

STRUCTURAL CHANGES IN STRUCTURAL STEELS DURING THERMAL CYCLING

Introduction: During experimental studies of structural steels responsible for the reliability of metal structures used, for example, in hoisting and transporting machinery, we established a correlation between the magnetic parameter Hp and characteristic changes in the metal microstructure during thermal cycling. It is shown that the Нр parameter depends on the initial structural state of steel, the amount and mass fraction of alloying elements, and the number of heating/cooling cycles. Methods: We found that an increase in the number of heating/cooling cycles and the amount of alloying elements, as well as preliminary cold plastic deformation with stage-by-stage control of structural changes using passive magnetic flux leakage testing results in a finer-grained structure, which was confirmed by metallographic analysis. Results: The paper considers specifics of structural changes in steels with different initial structures during thermal cycling. It is shown that the final grain size depends on the number of treatment cycles, the amount of alloying elements, and the initial microstructure. It is the fine-grain structure that improves the most important performance characteristics of steels: strength, cyclic strength, and cold brittleness.

Magnetic charge model of defect in magnetic flux leakage testing

The defect in structures is the major risk to the structural integrity, thus to perform the defect detections and evaluations efficiently is critical in assuring the structural safety. Magnetic flux leakage testing (MFLT) is an important non-destructive testing (NDT) method. Due to its high testing sensitivity and simple operating procedure, it has been widely used in detecting surface and near-surface defects in ferromagnetic components. To improve the accuracy of defect detection, it is necessary to find a suitable source magnetization distribution around a defect, and furthermore, to correlate the defect with the magnetic leakage signals. In this study, a magnetic charge model is proposed, in which both volume- and surface- densities of magnetic charges around a defect are considered. Then, this model is used for the calculation of the magnetic leakage signals caused by a known complex V-shape defect for the verification purpose. The results from the simulation match very well with that from the experiment. It indicates potentials that the magnetic charge model and the associated approach can be applied in MFLT with improved accuracy.

Motion induced eddy current based testing method for the detection of circumferential defects under circumferential magnetization

Magnetic flux leakage (MFL) testing is widely applied in the online detection of steel pipes. Different magnetizing directions are required for the detection of defects in different directions. As the speed of online MFL testing increases, the motion induced eddy current (MIEC) effect becomes significant, and the direction of the MIEC is perpendicular to defects in the same direction as the magnetization. Therefore, the magnetic field signal generated by the MIEC perturbation is analyzed by simulation and compared with MFL signal. It is found that the amplitude of the magnetic field signal generated by the MIEC perturbation increases with the rise of the rotational speed and magnetization. In high rotational speed and strong magnetization, the amplitude of the magnetic field signal caused by MIEC perturbation is greater relative to the amplitude of the MFL signal, providing a possibility for detecting defects that are parallel to the direction of magnetization.

Comparison and analysis of multiple signal processing methods in steel wire rope defect detection by hall sensor

Steel wire rope (SWR) defect signal processing techniques and methods are the key to exact defect detection and identification, which is important in guaranteeing human lives and property. But the inspection signals sensed by the commonly used inductive coil sensor are often susceptible to the detector’s scanning speed, wire rope shaking and vibration, whereas they are unaffected when induced by hall element sensor. Thus, the experiments for wire rope local fault (LF) of broken wire inspection by hall sensors and magnetic flux leakage (MFL) testing method are conducted, and different groups of original signals are obtained. Then, performances of multiple signal processing methods are compared and evaluated in the perspective of baseline drift elimination and signal denoising. Besides, the main impact parameters in these signal processing methods are investigated and revealed. Finally, comparisons and discussions for different filtering methods are analyzed and presented.

Deep neural network for simulation of magnetic flux leakage testing

Magnetic flux leakage testing (MFLT) is an important nondestructive testing method for the detection and evaluation of defects in magnetic materials. Magnetic field distribution in an MFLT system is usually simulated by the finite element method (FEM), which required large memory, high computation, and complication of the meshing process. In this paper, an alternative simulation method will be proposed using a deep neural network (DNN). The DNN method provides an easy way of simulation by feeding only the distribution of supplied current and the physical properties such as magnetic permeability without the need for the meshing process. Defects with arbitrary sizes were simulated under different configurations of the MFLT systems. The DNN was trained on the simulation results of the FEM and provided an accurate prediction of the magnetic field distribution of the unseen data. This study paves the way for designing optimized MFLT systems in a bigdata-driven method.