Magnetic Flux Leakage Testing Research Articles

A Physics-Guided MFL Deformed Defect Recovery Method

Magnetic flux leakage (MFL) testing and analysis is an effective non-destructive testing (NDT) method of pipeline health. The health status of pipelines is evaluated by analyzing MFL signals, especially the defect analysis. However, complex operating conditions and equipment deviations may cause deformation on the defects. Availability and integrity of the MFL defects are the keys to accurate signal processing. Due to the limited interpretability of abstract networks and insufficient feature extraction, it is hard to accurately recover the deformed defects. Considering the problems above, an MFL deformed defect recovery method with the guidance of physical features is proposed. The proposed method integrates a deep feature extraction model into a sparse autoencoder, whose parameters are guided by the combination of MSE loss and physics loss. First, the deep feature extraction model fully extracts the abstract features, which contains more information on deformed defects. Second, the sparse penalty factor in the sparse autoencoder reduces the feature redundancy of deformed defects. Third, the physical features are extracted to guide the MFL deformed defect recovery relying on the MFL mechanism for the first time, which enhances the robustness and accuracy of the network. Finally, several comparison experiments have been conducted on measured and simulated MFL defects. The results show that the proposed method is effective in deformed defect recovery. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —The motivation of this paper is a practical problem of MFL deformed defect recovery. The deformation on MFL defects affects defect inversion and evaluation seriously, which causes great potential harm to pipeline safety transmission. In this paper, compared with traditional neural network-based methods, physical features based on MFL mechanism are extracted to guide the parameter update. In addition, a pre-trained model replaces regular convolutional layers to extract features sufficiently, while a sparse factor is applied to reduce redundant features. The proposed method not only utilizes deep features from deep networks, but also increases the guidance of physical features, so that the algorithm is more robust and accurate. The experiment results show that our proposed method is more effective than the comparative methods on MFL simulated and measured defects. In a word, the proposed method has strong theoretical research and practical value.

Adaptive fast Walsh-Hadamard transform for magnetic flux leakage signal of broken wire damage extraction under noise background

ABSTRACT Steel wire rope usually works in harsh environments, making it prone to damage during frequent use. Magnetic flux leakage testing is an important way of non-destructive testing, preventing some major accidents of hoist equipment by identifying the damage to steel wire ropes. Whereas, the harsh environment usually generates noise and results in difficulty in extracting the damage features of the magnetic flux leakage signal, which makes it hard to identify the damage. The fast Walsh-Hadamard transform is widely used in signal processing. The denoising effect depends closely on the transform coefficients. If the transformation coefficients can be adaptively retained, better denoising effects can be achieved. Therefore, we propose the adaptive fast Walsh-Hadamard transform to extract the magnetic flux leakage signal of steel wire rope under noise background. Using the cross-correlation co efficient and the similarity of the greyscale histogram as the indexes, the Fast Walsh-Hadamard transform combines the adaptive particle swarm optimisation to adaptively retain the optimal transformation coefficient and then gets the optimal denoising signal. Meanwhile, compared to wavelet denoising methods, the adaptive fast Walsh-Hadamard transform can effectively improve denoising performance in peak-to-peak reductivity and can be successfully used in steel wire rope damage extraction.

Nondestructive testing method for internal defects in ferromagnetic materials under weak bias magnetization

Magnetic flux leakage (MFL) testing is effective in detecting internal defects in ferromagnetic materials. However, it requires the saturation magnetization of materials, which is not always practicable in complex inspection environments. Therefore, this study presents a novel dynamic permeability testing (DPT) method, in which internal defects will induce dynamic permeability variations on the surface of weakly magnetized ferromagnetic materials, and the variations are then detected by alternating current detection coils with magnetic cores, enabling the identification of internal defects. The principle of DPT is analyzed through simulations, which are subsequently verified by experiments. Furthermore, the effects of excitation frequency, probe lift-off, and defect size on the DPT signal are investigated experimentally. Finally, the comparison with conventional MFL demonstrates that the DPT method can effectively identify the internal defect using a weak magnetization current of 0.1 A (about 0.25 T in the steel plate), whereas MFL requires 0.5 A (about 1.32 T) to achieve a similar detection capability under the same probe lift-off values. This new method has significant practical value, especially in situations where saturation magnetization is not feasible.

Wavelet structuring element-based morphological filtering method in wire rope inspection signal denoising

Aiming at the multi-source interference signals in wire rope inspection, while common morphological filtering methods with traditional structuring elements are hard to distinguish, a new wavelet structuring element-based one-dimensional (1D) generalized morphological filtering method is proposed. First, principles and related theories of the generalized morphological filter are introduced and then the new wavelet structuring elements are analyzed. Thereafter, simulation and comparison regarding common structuring element morphological filter and the new wavelet function-based models are conducted through signal-to-noise ratio (SNR) and mean square error (MSE) analysis. Meanwhile, the influence of different noises and main structuring element parameters of length L, amplitude H to the signal processing results are revealed. Finally, experiments for different wire rope defects inspection by magnetic flux leakage testing are conducted, and characterizations of the wavelet structuring element-based morphological filtering methods are presented and compared through six case studies. Besides, the superior performance of the wavelet structuring morphological gradient models are compared and validated by short-time Fourier transform, SNR, MSE analysis, and quantitative defect recognition. The comparison results show that db4 and sym4 wavelet structuring elements-based 1D morphological filter features the highest wire rope defect detection and signal recognition accuracy under the gradient operation, which demonstrates the feasibility and effectiveness of the proposed methods. Additionally, advantages and disadvantages of the new models are summarized and discussed.

A Double Remote Magnetic Field Synthesis Method for Reducing High-Speed MFL Signal Distortion Caused by Velocity Effect

Magnetic flux leakage (MFL) testing is coming into prominence as an inspection tool applied to pipeline evaluation. The MFL detectors are challenging to accurately evaluate pipe defects at high-speed due to velocity effects. Current methods typically require detectors to operate at low speeds or compensate for detection data offline, but they reduce detection efficiency or require sophisticated algorithms. In this article, a novel double remote magnetic field synthesis (DRMFS) method is proposed to address the issue. First, we give a thorough analysis of the influence of velocity effects on high-speed MFL signals, reinterpreting its action mechanism. Secondly, the critical parameters are optimized to improve pipe magnetization. The relationships between the sensor layout and normalized signal similarity are studied at different speeds to reduce signal distortion. Finally, the usability of the DRMFS method in reducing signal distortion and its capability in detecting deep defects are validated by measuring defect depths. Results showed synthesis signals are highly similar to static MFL signals and approximately linear with the defect depth, which could improve the detection accuracy. Our results can enable the exploration of information utilization in limited space and the establishment of a balance between detection efficiency and signal accuracy.

The homogeneity effects of magnetic flux density distribution on the detection of railhead surface defects via the magnetic flux leakage method

Magnetic flux leakage (MFL) is a non-destructive testing method used to detect railhead surface defects. For effective MFL testing, the homogeneity of the magnetic flux density distribution (MFDD) within the railhead is crucial. Inhomogeneous formation of the MFDD within the railhead reduces the efficiency of the MFL testing. The homogeneity of the MFDD depends on the distance between poles (DBP) of the MFL testing system. According to the literature, as the DBP parameter increases, the MFDD becomes more homogeneous. In this study, four different homogeneity levels of the MFDD are introduced based on the DBP parameter. 3D finite element method (FEM) modelling simulation is conducted to obtain MFL testing analyses. The analyses are performed on a rail that contains rectangular surface defects of varying depth and length. The results of this study are evaluated using characteristic features of the MFL signal BX component, namely the slope of the baseline, the bottom value and the peak-to-peak value. The results show that if the homogeneity level of the MFDD within the railhead is higher, the bottom value and slope of the baseline decrease and the peak-to-peak value increases. This indicates that higher homogeneity of the MFDD enhances the detection efficiency of the MFL testing. Eventually, it is found that with the formation of nearly 100% homogeneous MFDD in the railhead, the slope of the baseline, the bottom value and the peak-to-peak value are enhanced by up to 83%, 77% and 12%, respectively.

An improved magnetic dipole model for MFL testing based on non-uniform magnetic charge distribution

ABSTRACT Magnetic flux leakage (MFL) signal prediction is important in ferromagnetic defect evaluation and reconstruction. At present, the magnetic dipole model (MDM) is the most widely applied method because of its high efficiency. However, it has low accuracy when applied to analysing complex defects (complex in profile and magnetic permeability) due to the original setting of uniform magnetic charge distribution. To solve the issue, this paper proposes an improved MDM method for MFL inspection of ferromagnetic materials by calculating the non-uniform magnetic charge distribution. It partly applies the finite volume method (FVM) to derive the magnetic charge distribution on the defect surface, which makes it able to take into account the defect’s profile and magnetic permeability distribution. The new magnetic charge distribution can increase the MDM’s accuracy for complex defect MFL field prediction with little extra cost. Experimental results show that, when compared with traditional MDM, the proposed one can achieve a maximum 69% decrease in root mean squared error when analysing complex defects. Compared with the numerical method, the computation time could reach a great reduction.

Magnetic flux leakage defect size estimation method based on physics-informed neural network.

Magnetic flux leakage (MFL) is a magnetic method of non-destructive testing for in-pipe defect detection and sizing. Despite the fact that recent developments in machine learning have revolutionized disciplines like MFL defect size estimation, the most current methods for quantifying pipeline defects are primarily data-driven, which may violate the underlying physical knowledge. This paper proposes a physics-informed neural network-based method for MFL defect size estimation. The training process of neural network is guided by the MFL data and the physical constraints that is mathematically represented by the magnetic dipole model. We use synthetic MFL data produced by a virtual MFL testing of pipeline defects to validate the proposed method through a comparison to purely data-driven neural networks and support vector machines. The findings imply that the physics-informed strategy can both improve predictive accuracy and mitigate physical violations in MFL testing, providing us with a better knowledge of how neural networks perform in defect size estimation. This article is part of the theme issue 'Physics-informed machine learning and its structural integrity applications (Part 2)'.

Influence of tensile stress on the magnetic flux leakage signal of the coated steel belt

ABSTRACT The safety of coated steel belts (CSBs) is crucial for reliable elevator operation, as they are critical components in elevator systems. Magnetic flux leakage (MFL) testing is the preferred non-destructive method for evaluating CSBs due to its high sensitivity. Previous studies overlooked tensile stress's impact on in-service MFL testing of CSBs, potentially resulting in inaccurate defect assessments. This research aims to investigate the impact of tensile stress on the MFL signal of CSBs by developing a theoretical model, conducting finite element simulations, and performing experimental verification. In this paper, the rectangular edge defects are considered as the primary defect type in CSBs, and the applied stress on the CSBs varies from 30 MPa to 160 MPa. Under the above conditions, a linear relationship between MFL signal of CSBs and stress is established based on the simplified Jiles-Atherton model and magnetic dipole model suitable for CSBs. The finite element simulation and experiments further indicate that the MFL signal of CSBs increases linearly with the increasing tensile stress. The primary contributions of this study are establishing an MFL model suitable for CSBs and uncovering the linear relationship between the MFL signal of CSBs and tensile stress.

Exploration and Application of Acoustic Emission Detection Technology in Leakage Detection of the Bottom Plate of Crude Oil Metal Storage Tanks

The use of acoustic emission detection technology can conduct online and non-stop detection and evaluation of crude oil metal storage tanks, achieving the integrity detection and evaluation of storage tanks. In order to explore the applicability of acoustic emission detection technology in the leakage detection of the bottom plate of crude oil metal storage tanks, this project carried out online acoustic emission detection of crude oil metal storage tanks, magnetic flux leakage detection after tank opening and cleaning, and compared and analyzed the results of two detection technologies. The adaptability of online acoustic emission detection technology was analyzed and explored. It was found that acoustic emission technology can qualitatively evaluate the corrosion of tank bottom plates, but cannot accurately locate corrosion defects. For tanks with severe corrosion that require maintenance, it is still necessary to open the tank and use magnetic flux leakage and other detection techniques to determine the location of defects. Through comparative analysis of acoustic emission testing technology and magnetic flux leakage testing technology, the results obtained from acoustic emission testing technology are conservative compared to the actual results. It is recommended to upgrade the evaluation of corrosion degree for the acoustic emission evaluation results of crude oil metal storage tanks.

Magnetic Flux Leakage Testing Method for Pipelines with Stress Corrosion Defects Based on Improved Kernel Extreme Learning Machine

This study aims to study the safety of oil and gas pipelines under stress corrosion conditions and grasp the corrosion damage situation timely and accurately. Consequently, a non-destructive testing method combining magnetic flux leakage testing technology and a kernel function extreme learning machine improved by genetic algorithm (GA-KELM) is proposed. Firstly, the variation of the corrosion defect dimension and profile with time is obtained by numerical simulation. At the same time, the distribution of the magnetic flux leakage signal under different defect conditions is analyzed and studied. Finally, feature selection is carried out on the magnetic flux leakage signal distribution curve, and GA-KELM is used to predict the depth and length of corrosion defects so as to realize the non-destructive testing of the pipeline defects. The results show that different geometric features result in different magnetic flux leakage signal distributions. There is a corresponding relationship between the defect dimension and extreme value, area, and peak width of the magnetic flux leakage signal distribution curve. The GA-KELM prediction model can effectively predict the depth and length of corrosion defects, and the prediction accuracy is better than the traditional extreme learning machine prediction model.

Defect-depth-field algorithm for simulating magnetic flux leakage signals based on discrete magnetic dipole model

Magnetic flux leakage (MFL) testing is widely used for non-destructive detection of defects in ferromagnetic materials, in which fast and accurate processing of MFL signals is critical for reconstructing and evaluating the defects. Of the detection technologies, the existing magnetic dipole model (MDM) can simulate the MFL signals with regular or limited types of defects, but not with complex defects, such as naturally occurring corrosion material losses. With the help of finite element method (FEM), MFL signals of complex defects can be analysed but demanding time-consuming calculation. In this paper, an improved MDM method, i.e., the discrete MDM (DMDM) is proposed, and an algorithm for automatic MFL signals processing is further developed based on the DMDM and the defect-depth-field concept. By comparison, the difference of the simulated MFL signals between the DMDM and the FEM is less than 3%, and the maximum discrepancy of the MFL signals between the simulated and the experimental is about 6%. In addition, the simulation with the DMDM can be thousands of times more efficient than the FEM, indicating significant advantage over the existing method in both improving the accuracy and enabling the fast signal processing of reconstruction for any types of defects in MFL testing.

The defect depth evaluation based on the dual-sensor strategy: Resisting the lift-off disturbance in magnetic flux leakage testing

The quantitative evaluation of defect information is the key issue in magnetic flux leakage (MFL) nondestructive testing on ferromagnetic materials. For the multi-sensor strategy with the same lift-off values, the arrayed layout of probes improves the testing efficiency, but the evaluation precision is still susceptible to the lift-off disturbance. This paper recommends a dual-sensor strategy with a given lift-off difference to resist the lift-off disturbance. Here, the reason for the possible overestimating and underestimating evaluation is clarified for the traditional single-sensor strategy using the magnetic dipole theory in MFL testing. In this paper, the effectiveness of the proposed dual-sensor strategy is demonstrated by comparison with the traditional single-sensor strategy. Analysis shows that the proposed dual-sensor strategy in MFL testing can overcome the lift-off disturbance and improve evaluation precision of the defect depth in cases with uncertain lift-off values. This study provides a path for the quantitative evaluation of MFL testing based on the dual-sensor strategy.

Research on the Forward Solving Method of Defect Leakage Signal Based on the Non-Uniform Magnetic Charge Model.

Pipeline magnetic flux leakage inspection is widely used in the evaluation of material defect detection due to its advantages of having no coupling agent and easy implementation. The quantification of defect size is an important part of magnetic flux leakage testing. Defects of different geometrical dimensions produce signal waveforms with different characteristics after excitation. The key to achieving defect quantification is an accurate description of the relationship between the magnetic leakage signal and the size. In this paper, a calculation model for solving the defect leakage field based on the non-uniform magnetic charge distribution of magnetic dipoles is developed. Based on the traditional uniformly distributed magnetic charge model, the magnetic charge density distribution model is improved. Considering the variation of magnetic charge density with different depth positions, the triaxial signal characteristics of the defect are obtained by vector synthesis calculation. Simultaneous design of excitation pulling experiment. The leakage field distribution of rectangular defects with different geometries is analyzed. The experimental results show that the change in defect size will have an impact on the area of the defect leakage field distribution, and the larger the length and wider the width of the defect, the more sensitive the impact on the leakage field distribution. The solution model is consistent with the experimentally obtained leakage signal distribution law, and the model is a practical guide by which to improve the quality of defect evaluation.

Detection of crack on a mild steel plate by using a magnetic probe incorporating an array of fluxgate sensors

Magnetic flux leakage (MFL) and eddy current testing (ECT) are two of the most commonly used techniques to detect cracks in ferromagnetic materials. The MFL technique excels in determining the locality of a crack, while the ECT technique can provide useful information regarding the dimension and orientation of cracks. The objective of this research is to propose a simultaneous application of both MFL and ECT techniques in a single probe and to improve the visual resolution of the defect by superimposing signals from a sensor array. A magnetic probe was developed with an array of fluxgate sensors and two excitation coils. Line scan measurements and 2D mapping of artificial slits with different depths were conducted on a mild steel SS400 plate with a thickness of 12 mm to evaluate the performance of the developed probe. The line scan measurement results demonstrated the ability of the probe to detect the presence of artificial slits on the sample and to predict the depth of the slits, especially at the 110-Hz excitation frequency. Then, from the 2D mapping results, the location of the slit was determined. Furthermore, by obtaining a superimposed 2D map generated by each sensor, an improved 2D map resolution with lower background noise was obtained.

Research on magnetic inversion in alternating current magnetic flux leakage testing

Defects bring significant security risks to pipelines, which are essential carriers in oil and gas transportation. However, when alternating current magnetic flux leakage (AC-MFL) testing is applied to pipeline defect detection, the morphological reversal of the detection signal under specific parameters has adverse effects on defect identification and quantification. This paper identifies and explains the phenomenon of detection signal morphological reversal in AC-MFL testing, which is defined as magnetic inversion (MI). Based on the AC-MFL testing mechanism, the causes of MI are analysed. The influence of the excitation current frequency, excitation current intensity and defect depth on MI is numerically analysed using the finite element method. The excitation current frequency and the defect depth are important factors causing MI, but the excitation current density is not. MI only occurs when deep defects are detected under low-frequency excitation. The boundary conditions of AC-MFL testing are further clarified.

A Review of NDT Methods for Wheel Burn Detection on Rails.

Wheel burn can affect the wheel-rail contact state and ride quality. With long-term operation, it can cause rail head spalling or transverse cracking, which will lead to rail breakage. By analyzing the relevant literature on wheel burn, this paper reviews the characteristics, mechanism of formation, crack extension, and NDT methods of wheel burn. The results are as follows: Thermal-induced, plastic-deformation-induced, and thermomechanical-induced mechanisms have been proposed by researchers; among them, the thermomechanical-induced wheel burn mechanism is more probable and convincing. Initially, the wheel burns appear as an elliptical or strip-shaped white etching layer with or without deformation on the running surface of the rails. In the latter stages of development, this may cause cracks, spalling, etc. Magnetic Flux Leakage Testing, Magnetic Barkhausen Noise Testing, Eddy Current Testing, Acoustic Emission Testing, and Infrared Thermography Testing can identify the white etching layer, and surface and near-surface cracks. Automatic Visual Testing can detect the white etching layer, surface cracks, spalling, and indentation, but cannot detect the depth of rail defects. Axle Box Acceleration Measurement can be used to detect severe wheel burn with deformation.

Optimal design of iron-cored coil sensor in magnetic flux leakage detection of thick-walled steel pipe

Thick-walled steel pipes, which bear high internal pressure, are widely applied in nuclear power and pressure pipelines. If there are defects in the inner wall, they are easy to expand and cause accidents. Therefore, the thick-walled steel pipe must be subject to non-destructive testing after production. For the magnetic flux leakage (MFL) testing method, the detection sensitivity gradually decreases with the increase of wall thickness. To solve this problem, a new structure of MFL probe is proposed in this paper. The influence of the iron core permeability on the MFL signal is analyzed theoretically, and the effect of the core length and diameter on the MFL signal is analyzed by simulation. The variation of the MFL signal with the change of the iron core and coil lift-off is studied respectively. The simulation results are verified by experiments. It is found that the lift-off of the iron-cored coil is determined by the iron core position. Based on this phenomenon, an MFL array probe is designed, which can be used for online detection of thick-walled steel pipes to improve the detection sensitivity of inner wall defects.

Detection of Rail Defects Using NDT Methods.

The rapid development of high-speed and heavy-haul railways caused rapid rail defects and sudden failure. This requires more advanced rail inspection, i.e., real-time accurate identification and evaluation for rail defects. However, existing applications cannot meet future demand. In this paper, different types of rail defects are introduced. Afterwards, methods that have the potential to achieve rapid accurate detection and evaluation of rail defects are summarized, including ultrasonic testing, electromagnetic testing, visual testing, and some integrated methods in the field. Finally, advice on rail inspection is given, such as synchronously utilizing the ultrasonic testing, magnetic flux leakage, and visual testing for multi-part detection. Specifically, synchronously using the magnetic flux leakage and visual testing technologies can detect and evaluate surface and subsurface defects, and UT is used to detect internal defects in the rail. This will obtain full rail information, to prevent sudden failure, then ensure train ride safety.

Extraction of weak signal based on wire rope detection in timing belt of wind turbines

As promising non-destructive testing (NDT) method, magnetic flux leakage (MFL) testing has been widely applied for steel structure detection. However, magnetic flux leakage (MFL) signal collected by the sensors is weak, especially for the small diameter wire rope. The methods of using the traditional circular coil sensors, or increasing the number of magnets is ineffective in some situations. This study proposes a new method of highly sensitive long-strip-shape coil sensors with an analogue signal processing circuit used to detect wire rope in the timing belt of wind turbines. It is investigated by theory analysis, circuit simulation, and experiment conduction. The circuit is used to amplify the weak signal and filter high-frequency noise to obtain a high signal-to-noise ratio (SNR). Experiments show that the resolution of the method can reach 3 broken wires of 0.1 mm wire diameter, and the method can not only obtain a high SNR signal but also have good repeatability. Meanwhile, this method will have great significance in practical applications and provide some solutions for other similar NDT projects.