Magnetic Flux Leakage Testing Research Articles

Internal inspection method for crack defects in ferromagnetic pipelines under remanent magnetization

Magnetic flux leakage (MFL) testing is effective in detecting corrosion in ferromagnetic pipelines. However, the MFL generated at the crack is highly weak and is easily drowned in the strong background magnetic field and difficult to detect. Therefore, this paper creatively proposes a remanent magnetic flux leakage (RMFL) detection method. This method utilizes the hysteresis state of the pipeline after MFL detection and identifies crack defects by collecting the remanent magnetic flux density near the inner wall surface of the pipeline. The remanent magnetic field distribution near the pipeline surface is examined at different crack sizes, magnetization speeds, and lift-off values via theoretical analysis, finite element simulation, and experimental verification. In addition, experiments have demonstrated that in terms of crack detection capability, RMFL is superior to MFL. Therefore, adding a RMFL measurement module to the conventional MFL detector has important practical value. It can realize the targeted collaborative application of these two technologies, namely the synchronous detection of corrosion and crack defects, which greatly improves the detection coverage and detection efficiency.

Translation of MFL and UT data by using generative adversarial networks: A comparative study

Magnetic flux leakage (MFL) and ultrasonic testing (UT) are widely used in-line inspection technologies to detect corrosion defects along pipelines. The integration of MFL and UT data has the potential to provide complementary insights that facilitate a comprehensive assessment of pipeline integrity. However, due to the inherent dissimilarity with their underlying physical principles, these techniques yield notable disparities in signal characteristics, posing challenges in integrating these multimodal data. This study aims to establish a translation mapping between MFL and UT signals to achieve consistent physical interpretations across the two modalities. Thus, this study explored the feasibility of generative adversarial network (GAN) based models encompassing both supervised and unsupervised translation approaches contingent on the availability of aligned data. Furthermore, two translation modes, MFL-UT and UT-MFL, were analyzed, respectively, to understand the effectiveness of the translation direction. The experimental results demonstrate satisfactory performance for both aligned and unaligned data translation, with the UT-MFL translation direction yielding superior results. Overall, the translation approaches pave the way for future applications, especially in subsequent data analysis tasks such as registration, comparison, and fusion of multimodal data.

Defect classification and quantification method based on AC magnetic flux leakage time domain signal characteristics

Pipelines play a crucial role in industries such as petroleum and nuclear power, where non-destructive testing is essential. Magnetic flux leakage testing methods are widely used for pipeline inspection due to their ability to detect both internal and external defects. However, accurately classifying and evaluating the size of such defects poses challenges due to the complex coupling relationship between ferromagnetic materials and defect magnetic fields. This paper proposes a method for defect classification and quantification based on the time domain characteristics of AC magnetic flux leakage signals. Firstly, the paper explores the shielding effects caused by transient high magnetic permeability within the material on signals from internal defects. It analyzes the differences in signals between internal and external defects. Then, based on the nonlinear attributes of defect signals, the paper proposes a defect classification method based on derivatives analysis of the windowed time-domain signal. Moreover, the study finds that rising time and zero-crossing time can be used to assess the depth of internal and external defects separately, which can decouple the width and depth. Finally, experimental validation confirms the effectiveness of defects classification and quantification. This paper provides a feasible method for evaluating the defect of ferromagnetic materials.

A composite testing method of homologous magnetic flux leakage and motion-induced eddy current for inner and outer defects

Magnetic flux leakage (MFL) testing, widely utilized for pipeline integrity assessments due to its efficiency and accuracy, is often challenged by motion-induced eddy currents (MIEC) that can distort MFL signals. Contrary to traditional perspectives that consider MIEC as detrimental, this paper introduces a novel MFL-MIEC composite testing method. This method leverages MIEC as a complementary technique while sharing the same excitation source. By exploring the generation and influence of MIEC on MFL signals, we simulate optimal probe positions and analyze the effects of defect position, angle, and detection speed on signal quality. Adjustments in the detection coils orientation are shown to enhance signal strength, demonstrating the methods effectiveness in identifying pipeline defects through practical experiments. This approach offers significant potential for widespread application in pipeline assessments.

Parametric design methodology for yoke-magnetization in magnetic flux leakage detection systems

Magnetic flux leakage (MFL) testing is a widely employed non-destructive testing (NDT) method, particularly for ferromagnetic materials. This paper proposes a systematic design procedure for the yoke-magnetization component in MFL systems utilizing permanent magnets. The dimensions of the yoke-magnetization are parameterized, and the design methodology is formulated in the form of analytical equations based on fundamental engineering principles and optimization techniques. The proposed design procedure outlines all the required parameters and metrics to establish a yoke-magnetization configuration tailored for a specified application, thereby facilitating scalability. The theoretical framework identifies the optimal operating point that maximizes leakage flux while minimizing the magnetic field amplitude. A genetic algorithm is employed to minimize the dimensions of the permanent magnets while maintaining a sufficient magnetic field strength for specimen magnetization. An experimental setup is constructed to validate the accuracy of the proposed design procedure. The experimental results exhibit consistency with finite element method (FEM) simulations conducted using a case study. Specifically, the findings reveal that the thickness of the magnetic bridge significantly impacts the leakage fluxes at defect sites within the specimen. When the thickness of the magnetic bridge is 10 mm, slightly smaller than the optimal thickness of approximately 11 mm, the leakage flux at the defect site leaks out towards the surrounding air insignificantly. In contrast, a thickness of 15 mm is observed to strongly improve the leakage flux at the defect site.

Multi-modality hierarchical attention networks for defect identification in pipeline MFL detection

Abstract Magnetic flux leakage (MFL) testing is widely used for acquiring MFL signals to detect pipeline defects, and data-driven approaches have been effectively investigated for MFL defect identification. However, with the increasing complexity of pipeline defects, current methods are constrained by the incomplete information from single modal data, which fail to meet detection requirements. Moreover, the incorporation of multimodal MFL data results in feature redundancy. Therefore, the multi-modality hierarchical attention networks (MMHAN) are proposed for defect identification. Firstly, stacked residual blocks with cross-level attention module (CLAM) and multiscale 1D-CNNs with multiscale attention module are utilized to extract multiscale defect features. Secondly, the multi-modality feature enhancement attention module (MMFEAM) is developed to enhance critical defect features by leveraging correlations among multimodal features. Lastly, the multi-modality feature fusion attention module (MMFFAM) is designed to dynamically integrate multimodal features deeply, utilizing the consistency and complementarity of multimodal information. Extensive experiments were conducted on multimodal pipeline datasets to assess the proposed MMHAN. The experimental results demonstrate that MMHAN achieves a higher identification accuracy, validating its exceptional performance.

Flexible anisotropic magnetoresistive sensors for novel magnetic flux leakage testing capabilities

Rigid magnetic field sensors such as anisotropic magnetoresistive (AMR), giant magnetoresistive (GMR) and Hall sensors have been used for years and have become industry standard for electromagnetic non-destructive testing (NDT). Recent technological developments in the field of flexible electronics allow for the fabrication of reshapeable magnetic field sensors on flexible substrates via thin-film deposition or printing. The magnetic properties of these sensors have comparable characteristics to industry-standard rigid magnetic field sensors, with the added ability of adapting to the surface of complex components and scanning in contact with the sample surface. This improves defect detectability and magnetic signal strength by minimizing the scanning lift-off (LO) distance. In this article flexible AMR sensors mounted on a rotative mechanical holder were used to scan a semi-circular ferromagnetic sample with 3 reference defects via magnetic flux leakage (MFL) testing, thus demonstrating the applicability of this type of sensors for the scanning of curved samples. In order to benchmark the performance of these sensors in comparison to industry standard rigid magnetic field sensors, a ferromagnetic sample with 10 reference defects of different depths was scanned employing flexible AMR and rigid GMR sensors. Defects with depths ranging from 110μm up to 2240μm were detected with an signal-to-noise ratio (SNR) of 2.7 up to 27.9 (for flexible AMR sensors) and 6.2 up to 72.3 (for rigid GMR sensors), respectively. A 2D magnetometer mapping of the sample with a spatial scanning step of 10×50μm2 (flexible AMR) and 16×100μm2 (rigid GMR) was obtained. The results show that this type of sensor can be used for high-resolution and high-detail mapping of defects on the surface of planar and non-planar ferromagnetic samples since the scanning lift-off distance is equal to the substrate thickness of 20μm for in-contact scanning. The SNR comparison between flexible and rigid sensors shows that the performance of the flexible AMR sensors employed is not very far behind the performance of the rigid GMR sensors used.

Development of multi-channel magnetic flux leakage testing system for bridge cables

Magnetic flux leakage (MFL) is one of the most popular techniques for detecting broken wire in bridge cables. MFL sensor array has attracted a lot of attention in recent years due to its capacity to measure the spatial distribution of the magnetic field of bridge cable. More sensors in the array lead to increased useful information extraction from the MFL field, allowing for more precise quantitative evaluation of bridge cable damage. This study reports the development of a cable MFL testing system capable of synchronous acquiring up to 512 channels of MFL sensors signal. Radial sensor arrays (RSA) composed of vertically placed magnetic sensors spaced at equal intervals are developed to be arranged around the bridge cable. They record the spatial distribution of the magnetic leakage field of the broken wire by collecting the MFL signal at various lift-offs. To solve the problem of multi- channel MFL signal synchronous acquisition, this study develops a MFL signal acquisition card based on multiplexing technology, capable of synchronous acquisition of up to 512 channels of MFL signal. Multiplexers are used to switch sensor channels, followed by multi-channel A/D conversion chips to convert the transmitted data. The converted data are temporally stored in the First-In-First-Out (FIFO) buffer of the Field Programmable Gate Array (FPGA) and transferred to the computer through the network port for display, storage, and signal processing. Finally, the motion control circuit, mechanical structure, and control software of the bridge cable MFL testing system are fully customized. Experiment results reveal that the system can be used in multi-channel MFL testing, laying the foundation for further multi-channel signal processing and bridge cable health evaluation.

Principle of the electromagnetic testing based on the varied frequency domain and its realization

With the development of modern industry, the types and quantities of industrial equipment are increasing. In the long-term production process, various failure modes such as corrosion and cracks will occur, therefore the workload of inspection will increase dramatically. It is extremely urgent to study the combined non-destructive testing (NDT) technology and develop corresponding multi-functional instruments to achieve rapid inspection. In this paper, an electromagnetic testing principle based on the varied frequency domain is proposed. In the first place, spatial uniformity and spatial periodic static fields are generated by the arrangement of the permanent magnetizers, these static fields are fields with different spatial frequencies. In the second place, dynamic fields with different frequencies are generated by different frequencies excitations. Under the time-space electromagnetic field coupling, a new combined electromagnetic testing principle of magnetic flux leakage (MFL), guided wave (GW) and electromagnetic acoustic transducer (EMAT) is proposed. The MFL testing method can be realized based on the uniform static field, GW based on the interaction of spatial periodic static field and low frequency dynamic field, while EMAT based on the combination of uniform static field and high frequency dynamic field. According to the above principle, a multi-functional instrument is developed, which includes a medium-low frequency excitation unit for GW, a high frequency excitation unit for EMAT, corresponding receiving amplifiers for GW/EMAT/MFL, and USB-based data transmitters. Finally, the relevant performance tests are carried out, which laid the foundation for field application.

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.