Magnetic Flux Leakage Signals Research Articles

A defect contour imaging method of small diameter pipeline based on self-made three-dimensional magnetic sensor

Purpose The purpose of this paper is to use the corresponding magnetic sensor and detection method to detect and image the defects of small diameter pipelines. Urban gas pipeline is an energy transportation tool for urban industrial production and social life, which is closely related to urban safety. Preventing the occurrence of urban gas pipeline transportation accidents and carrying out pipeline defect detection are of great significance for the urban economic and social stability. To perform pipeline defect detection, the magnetic flux leakage internal detection method is generally used in the detection of large-diameter long-distance oil and gas pipelines. However, in terms of the internal detection of small-diameter pipelines, due to the heavy weight, large structure of the detection device and small pipe diameter, the detection is more difficult. Design/methodology/approach In order to solve the above matters, self-made three-dimensional magnetic sensor and three-dimensional magnetic flux leakage imaging direct method are proposed for studying the defect identification. Firstly, for adapting to the diameter range of small-diameter pipelines, and containing the complete information of the defect, a self-made three-dimensional magnetic sensor is made in this paper to improve the accuracy of magnetic flux leakage detection. And on the basis of it, a small diameter pipeline defect detection system is built. Secondly, as detection signal may be affected by background magnetic field interference and the jitter interference, the complete ensemble empirical mode decomposition with adaptive noise method is utilized to screen the detected signal. As a result, the useful signal is reconstructed and the interference signal is removed. Finally, the defect contour inversion imaging of detection is realized based on the direct method of three-dimensional magnetic flux leakage imaging, which includes three-dimensional magnetic flux leakage detection data and data segmentation recognition. Findings The three-dimensional magnetic flux leakage imaging experimental results shown that, compared to the actual defects, the typical defects, irregular defects and crack groove defects can be analyzed by the magnetic flux leakage defect contour imaging method in qualitative and quantitative way respectively, which provides a new idea for the research of defect recognition. Originality/value A three-dimensional magnetic sensor is made to adapt the diameter range of small diameter pipeline, and based on it, a small-diameter pipeline defect detection system is built to collect and display the magnetic flux leakage signal.

Magnetic leakage behavior assessment in pipeline stress-concentrated areas using improved force-magnetic coupling model.

Magnetic flux leakage (MFL) technology is remarkable for its capability to detect pipeline geometric deformation and general corrosion defects. However, it cannot characterize the MFL behavior in stress-concentrated areas, thereby greatly challenging the subsequent pipeline maintenance. This study suggests that the MFL characteristics of pipeline in stress-concentrated areas are caused by the combined effect of the face magnetic charge on the deformed end-face and the body magnetic charge of the dislocation stack. In addition, an improved force-magnetic coupling model of the pipeline in stress-concentrated areas is established based on the magnetic dipole model and Jiles-Atherton (J-A) theory. In the verification experiment, the Q235 steel plate is magnetized along the extension direction (axis of the pipeline) through the solenoid coil to obtain the distribution law of the MFL signal in the stress-concentrated area under different excitation intensities. The results show that with the increase in excitation intensity, the deformation of the MFL field signal caused by the end-face of the stress-concentrated area gradually increases to a stable state. Moreover, the internal stress of the MFL field signal generated by the pipe dislocation rapidly increases to a peak value and then decays exponentially to a certain base value. The overall change trend is in good agreement with the calculation results of the established force-magnetic coupling model. Meanwhile, the differentiation research between deformation and internal stress MFL field signals under different magnetic field intensities can provide a reliable theoretical basis for the subsequent accurate identification and quantification of pipeline stress-concentrated areas.

Research on Intelligent Identification Algorithm for Steel Wire Rope Damage Based on Residual Network

As a load-bearing tool, steel wire rope plays an important role in industrial production. Therefore, diagnosing the fracture and damage of steel wire ropes is of great significance for ensuring their safe operation. However, the detection and identification of wire rope breakage damage mainly focus on identifying external damage characteristics, while research on inspecting internal breakage damage is still relatively limited. To address the challenge, an intelligent detecting method is proposed in this paper for diagnosing internal wire breakage damage, and it introduces residual modules to enhance the network’s feature extraction ability. Firstly, time–frequency analysis techniques are used to convert the extracted one-dimensional magnetic flux leakage (MFL) signal into a two-dimensional time–frequency map. Secondly, the focus of this article is on constructing a residual network to identify the internal damage accurately with the features of the time–frequency map of the MFL signal being automatically extracted. Finally, the effectiveness of the proposed method in identifying broken wires is verified through comparative experiments on detecting broken wires in steel wire ropes. Three common recognition methods, the backpropagation (BP) neural network, the support vector machine (SVM), and the convolutional neural network (CNN), are used as comparisons. The experimental results show that the residual network recognition method can effectively identify internal and external wire breakage faults in steel wire ropes, which is of great significance for achieving quantitative detection of steel wire ropes.

A defect opening profile reconstruction method based on multidirectional magnetic flux leakage detection

Magnetic flux leakage (MFL) is a popular technique for detecting defects. The opening profile reconstruction is one of the most popular concerns. The commonly used unidirectional magnetization-based methods easily lead to incomplete MFL information to reconstruct the opening profile. The superimposition of MFL signals from different directions would cause information cancellation, making it difficult for the existing data processing methods to reconstruct. To solve the issues, this study constructs a model-based method to inverse defect opening profiles. It builds the forward equations from one magnetic permeability to multidirectional MFL fields. Then the pseudo-inversion with Tikhonov regularization is applied as the inversion algorithm. The algorithm associates the forward equations and finds the solution that can fulfill them simultaneously. Such a method can combine multidirectional MFL information without cancellation. It reduces the ill-posed problems that exist in unidirectional inversion and makes the reconstructed profile complete and accurate. The comparison experiments have been carried out, and the results have proved the high accuracy of the proposed method.

Defect Width Estimation of Magnetic Flux Leakage Signal with Wavelet Scattering Transform

Defect Width Estimation of Magnetic Flux Leakage Signal with Wavelet Scattering Transform

Magnetic Flux Leakage Defect Identification Method for Small-Diameter Pipeline Elbow Based on the Improved YOLOv5

Abstract The elbow is an important constituent of oil and gas pipeline systems and plays a key role in changing the direction of pipelines. Corrosion defects pose a significant risk to the safe operation of elbows. Magnetic flux leakage (MFL) detection has been developed as a suitable technique for identifying defects in pipelines. To address the distortion of elbow defect signals in the images arising from variations in the liftoff value of the leakage detector, this paper proposed an image identification method based on an improved YOLOv5 network. The differences in defect images are simulated by analyzing the liftoff value of the magnetization unit. A defect image enhancement method of multiscale retinex with color restoration fusion homomorphic filtering (MSRCR-HF) is employed to enhance the features of defective MFL signal images. To further improve the accuracy of the model, the YOLOv5 network is optimized by integrating the convolutional block attention module (CBAM) and the space-to-depth-nonstrided convolution (SPD-Conv) module. The results show that the proposed image enhancement method effectively accentuates the features of defect images. Moreover, the suggested image identification method exhibits superior accuracy in identification. The mean average precision (mAP) values for the original image set and the enhanced image set are 85.0% and 91.4%, respectively. Consequently, the proposed method is shown to be highly viable for the automatic identification of MFL defects in small-diameter pipe elbows.

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.

Classification and evaluation for nearside/backside defect via magnetic flux leakage: A dual probe design with SVM and PSO intelligence algorithms

In automated magnetic flux leakage (MFL) applications, the defect location on the nearside/backside of the tested specimen is often unknown, posing challenges for evaluating defect parameters due to varying calibration relationships. To address this, we propose a combined scheme of defect classification followed by parameter evaluation for testing steel plates with nearside/backside defects. The innovative design of dual probes, featuring lift-off difference, is used for signal acquisition and analysis. An efficient defect classification is achieved through a PSO-SVM neural network, and defect parameter evaluation involves solving the inverse problem using a particle swarm intelligence algorithm. MFL signal from steel plates with circular hole defects of different sizes were tested using a self-developed system and dual probes. Finite element analysis, alongside experimental data, explores the change in MFL signal. Defect classification and parameter evaluation, based on experimental signals and simulations, confirm the effectiveness of scheme in quantitatively assessing defect parameters with an evaluation accuracy within 0.3 mm. This scheme serves as a reference for intelligent testing and evaluation using MFL signals.

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

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

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.

An Improved Empirical Wavelet Transform Filtering Method for Rail-Head Surface-Defect Magnetic-Flux Leakage Signal

The rail is an important factor in railway traffic safety. Surface defects in the rail head comprise a common type of rail damage, and magnetic flux leakage (MFL) technology is applied for its detection. MFL detection is influenced by various factors, resulting in high noise and a low signal-to-noise ratio (SNR) in the collected MFL signal, which influence defect assessment. This article improves the empirical wavelet transform (EWT) to apply it to rail surface-defect MFL signal filtering. A boundary optimization method based on mutual information (MI) is proposed to reduce the boundary redundancy caused by adaptive spectrum division. A method for component selection based on MI and kurtosis is proposed to select the suitable components from the decomposed components for signal reconstruction. The experimental results show that the method can effectively filter out the interference in the MFL signal, and the effectiveness is superior to the traditional methods, such as complementary ensemble empirical mode decomposition (CEEMD) and wavelet transform (WT).

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.

A Masked Multi-View Bidirectional Network for Class Extremely Imbalanced Object Detection Under Magnetic Flux Leakage Signals

A Masked Multi-View Bidirectional Network for Class Extremely Imbalanced Object Detection Under Magnetic Flux Leakage Signals

Surface Crack Size Estimation based on Quantification and Decoupling of Magnetic Flux Leakage (MFL) Signals of Circular Array Sensors

Surface Crack Size Estimation based on Quantification and Decoupling of Magnetic Flux Leakage (MFL) Signals of Circular Array Sensors

Improved reconstruction of magnetic flux leakage signal from wire rope damage using novel smooth function and feature peak compensation

Improved reconstruction of magnetic flux leakage signal from wire rope damage using novel smooth function and feature peak compensation

Development of a convolutional neural network model to predict the size and location of corrosion defects on pipelines based on magnetic flux leakage signals

This study employs the convolutional neural network (CNN) to predict the dimensions and locations of corrosion defects on steel pipelines based on magnetic flux leakage (MFL) signals. Extensive three-dimensional parametric finite element analyses are carried out to generate MFL signals corresponding to semi-ellipsoidal-shaped corrosion defects with different sizes and locations on a pipe model. The white noises characterized by different signal-to-noise ratios are considered in the analysis to represent the measurement errors in the real MFL inspection tool. The numerically generated MFL signals are used to train and validate the CNN model to predict the dimensions and locations of the corrosion defects. The results indicate that the developed CNN model achieves a high predictive accuracy. The study demonstrates the application of CNN model to improve the pipeline integrity management practice.

A Novel Defect Detection Method for Overhead Ground Wire.

Overhead ground wires typically have strong axial tension and are prone to structural defects caused by corrosion and lightning strikes, which could lead to serious safety hazards. Therefore, it is important to detect defects accurately and quickly to avoid those problems. Existing defect detection methods for overhead ground wires are mainly traditional metal defect detection methods, including eddy current detection, ultrasonic detection, and manual visual inspection. However, those methods have problems of low detection efficiency, high environmental requirements, and insufficient reliability. To solve the above problems, this paper studies a novel type of defect detection technology for overhead ground wire. Firstly, the magnetic leakage characteristics around the defects of overhead ground wires are analyzed, and the defect detection device is designed. Then, the influence of air gap, lift-off distance, defect width, and cross-sectional loss rate on the magnetic flux leakage signal is studied, a novel defect detection method for overhead ground wire is proposed, and experimental verification is carried out. The results show that the proposed method can accurately locate and quantify the defect, which has the advantages of good reliability and high efficiency and lays the foundation for preventing accidents caused by defective overhead ground wires.

Semi-supervised learning for steel surface inspection using magnetic flux leakage signal

Semi-supervised learning for steel surface inspection using magnetic flux leakage signal

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.