Magnetic Leakage Signals Research Articles

Detection method for structural defects of U-shaped hanging ring for power fittings based on magnetic leakage principle

The U-shaped hanging ring of electric power fittings is usually installed outdoors and operates in harsh environments. Under the combined effect of the weight of the conductor and wind load, the bending part at the bottom of the U-shaped hanging ring often experiences wear and cracks due to friction contact, which can easily lead to accidents such as the detachment of transmission lines. Traditional methods for detecting structural defects in ferromagnetic materials, such as ultrasonic testing, eddy current testing, and manual testing, are affected by environmental factors, have a shallow measurement depth, and are difficult to quantify. In order to solve the above problems, this paper proposes a measurement method for detecting structural defects in the U-shaped hanging ring of electric power fittings based on the principle of magnetic leakage. The magnetic leakage signal characteristics of U-shaped hanging ring defects under different excitation structural parameters and different defect types are analyzed. The method of arranging magnetic leakage signal measurement points is studied. The detection of defect parameters is achieved based on the characteristic quantity of the magnetic leakage signal under different defect conditions. Finally, the method is analyzed and verified through examples. The results show that this method can achieve quantitative detection of defect parameters in the U-shaped hanging ring under uniform structural defect and crack structural defect, and has the advantages of high measurement accuracy and small error. The research results of this paper can provide a scientific theoretical basis for the inspection and replacement of transmission line fittings in engineering, which is of great engineering significance for ensuring the safe and stable operation of the power grid.

Defect Recognition Method for Magnetic Leakage Detection in Oil and Gas Steel Pipes Based on Improved Neural Networks

The aging infrastructure of petroleum and natural gas pipelines poses a threat to national economies, necessitating precise defect detection for safety and efficiency. To enhance the accuracy of predicting pipeline defect sizes, this study introduces a magnetic leakage detection system, employing Backpropagation (BP) neural networks optimized with genetic algorithms. Traditional BP networks face challenges, including parameter determination and slow convergence, addressed through genetic algorithms' global search capabilities. Simulated data are generated using ANSYS software by using models of semi-circular defects in steel pipes, producing magnetic leakage signals of varying intensities. MATLAB was used to construct both standard BP and genetically optimized BP neural networks. Results show that the latter significantly reduces computational errors, demonstrating improved accuracy in defect dimension prediction. The approach contributes to overcoming non-uniqueness in the recognition process and the complex nonlinear relationship between magnetic signals and defect size parameters. The study offers a guided approach for selecting BP neural network parameters, enhancing practicality. Simulations validate the method's effectiveness, indicating low workload and high reliability. This research provides a meaningful advancement in the detection of defects in long-distance pipelines, impacting the safety and efficiency of petroleum and natural gas transportation.

Research on Magnetic Memory Detection of Weld Fatigue Damage Based on Two-Dimensional Spectral Entropy

Abstract In light of the intrinsic relationship between weld fatigue damage and the characteristics of magnetic leakage signals, fatigue experiments were conducted on butt welds to analyze the evolution of magnetic leakage signal as fatigue cycles increased. The introduction of two-dimensional spectral entropy allowed for the construction of a fatigue stage division model based on the two-dimensional spectral entropy diagram. The results revealed that distinctions between magnetic leakage signal under loaded and unloaded conditions were solely evident in their magnitude, with consistent curve distribution trends observed across various fatigue cycles. Furthermore, the distribution pattern of magnetic leakage signal displayed significant fluctuations, characterized by a sharp gradient increase following the emergence of fatigue cracks. These distribution characteristics of magnetic leakage signal differed markedly from the magnetic field distribution at other fatigue stages, with a higher growth rate observed as fatigue cycles increased. The two-dimensional spectral entropy spectrum analysis of magnetic leakage signals at different fatigue stages indicated the disorder in the magnetic leakage signal distribution within the high fatigue zone, signifying the expansion of fatigue microcracks. To some extent, two-dimensional spectral entropy can serve as an early warning system for detecting fatigue cracks in welds and achieving early identification of fatigue microcracks.

Research on a Wire Rope Breakage Detection Device for High-Speed Operation Based on the Multistage Excitation Principle

Wire rope breakage, as damage easily produced during the service period of wire rope, is an important factor affecting the safe operation of elevators. Especially in the high-speed elevator operation process, the problem of magnetization unsaturation caused by speed effects can easily lead to deformation of the magnetic flux leakage detection signal, thereby affecting the accuracy and reliability of wire breakage quantitative detection. Therefore, this article focuses on the problem that existing wire rope detection methods cannot perform non-destructive testing on high-speed elevator wire ropes and conducts design and experimental research on a high-speed running wire rope breakage detection device based on the principle of multi-stage excitation. The main research content includes simulation research on the multistage excitation, structural design, and simulation optimization of open–close copper sheet magnetizers and the building of a detection device for wire rope breakage detection experimental research. The simulation and experimental results show that the multistage magnetization method can effectively solve the problem of magnetization unsaturation caused by the velocity effect. The multistage excitation device has a good wire breakage recognition effect for speeds less than or equal to 3 m/s. It can detect magnetic leakage signals with a minimum of four broken wires and has good detection accuracy. It is a new and effective wire breakage detection device for high-speed elevator wire rope, providing important technical support for the safe and reliable operation of high-speed elevators.

Research on Geometric Parameter Prediction Algorithm for Oil and Gas Pipeline Defects

Aiming at the problem of insufficient feature extraction of magnetic leakage signals by traditional neural networks, this paper proposes an attention depth convolutional neural network model (ECA-VGG16), which adds an attention mechanism combined with convolutional neural network on the basis of deep convolutional neural network VGG16, so that the neural network can focus on the key information of the input data, further improve the ability of the grid to extract image data features, and realize the accurate expression of defect features.

Improved Leakage Detection and Recognition Algorithm for Residual Neural Networks Based on Transfer Learning

Due to the lack of other component information in traditional magnetic leakage signal defects and the low accuracy of prediction methods, this paper proposes an improved residual network for magnetic leakage detection defect recognition method that predicts defect size and different detection speeds. A new defect diagnosis method based on ResNet18 on the Convolutional Neural Network (CNN) is proposed in this study. This method transfers the pre-trained ResNet18 network and replaces the activation function in the transferred network structure. It extracts features from transformed two-dimensional images obtained by converting the original experimental signals and signals with added noise, removing the influence of manual features. The results demonstrated that the improved ResNet18 network model, after transfer learning, achieved 100% prediction accuracy for all 10,000 grayscale images generated with defect lengths of 50 mm; width of 2 mm; and depths of 2 mm, 4 mm, 6 mm, and 8 mm. Moreover, the prediction accuracies for the quasi-static, slow, compensated fast, and fast scanning speeds were 99.20%, 98.50%, 93.30%, and 94.00%, respectively, for defect depths of 2 mm, 4 mm, 6 mm, and 8 mm. These accuracies surpass those of other models, demonstrating the significant improvement in prediction accuracy achieved by this method.

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.

Influences of Magnetization Direction on the Flux Leakage Field of Weld Defects

The magnetic flux leakage (MFL) detection technique is applied to the detection of weld defects, such as cracks and pores. As the weld has a distinct structure, there are differences in the magnetization path and leakage field intensity under different magnetization directions. According to surveys, a suitable magnetization direction can significantly enhance detection rates of small-sized defects by stimulating a stronger field signal of the defect leakage. In this study, ANSYS finite element simulation software is used to calculate the weld defect leakage field based on the quantitative analysis. Specifically, the leakage field component strengths of circular hole defects and longitudinal rectangular groove defects are compared when the magnetization direction is perpendicular or parallel to the weld. Furthermore, the characteristic rules of the defect leakage field and its components under any magnetization direction are discussed, and a weld MFL detection platform is set up for validation. According to the experimental results, the amplitude of the magnetic leakage signal during vertical magnetization of circular hole defects is only 18.6% of that during parallel magnetization. Similarly, the amplitude of the magnetic leakage signal during parallel magnetization of longitudinal crack-type defects is only 9.2%~29.3% of that during vertical magnetization.

Demagnetization Fault Diagnosis of Permanent Magnet Synchronous Motors Using Magnetic Leakage Signals

In most industrial applications, it is difficult to obtain complete demagnetization fault signals of all conditions with labels for permanent magnet synchronous motor (PMSM), and motors are not allowed to be disassembled, so non-contact diagnostic methods are essential. A non-contact fault diagnosis method using magnetic leakage signal based on wavelet scattering convolution network (WSCN) and semi-supervised deep rule-based (SSDRB) classifier is proposed. Through magnetic equivalent circuit model analysis, the magnetic leakage signal on motor surface is selected as fault signal. To avoid complex signal processing, the symmetrized dot pattern method is introduced to convert fault signals into two-dimensional images. Then, WSCN is applied to extract features from images, and SSDRB classifier is adopted to diagnose demagnetization fault. Finally, faulty motor prototypes are manufactured for experiment. By comparing with other methods, the superiority and effectiveness of the proposed method using a small number of labeled samples under different conditions are verified.

Rail Magnetic Flux Leakage Detection and Data Analysis Based on Double-Track Flaw Detection Vehicle

The rapid development of the railway industry has brought convenience to people’s lives. However, with the high speed, high frequency and heavy load characteristics of rail use, the safety of rail is seriously threatened. In this paper, a magnetic flux leakage testing (MFL) detection technology of rail based on a double-track flaw detection vehicle is introduced in detail, which can effectively detect the damage of rail top surface, which is the blind area of ultrasonic detection. The magnetic dipole model is used to analyze that the leakage magnetic field in the direction of Bx and Bz above the damage is related to the depth and width of the damage. The relationship between the depth of the damage and the leakage magnetic field is quantitatively studied for the damage with fixed width but varying depth. The finite element simulation tool is used to model and simulate the damage at different depths. After analyzing the different characteristic values, it is found that the peak value of magnetic leakage signal has a certain correlation with the depth of damage, and the natural logarithm function is fitted out—VBx = 0.1451ln(b) + 0.2705, VBz = 2.7787ln(b) + 0.0087. In order to verify the prediction function of the injury depth fitted by the simulation data, the human injury with different depths was processed and the dual-track flaw detector was used to carry out the experiment of high-speed detection environment. The peak-to-peak fitting of the magnetic leakage signals in the direction of Bx and Bz of the experimental results shows that the peak-to-peak variation rule is roughly in line with the natural logarithm function in the simulation. The correlation between the fitting results of the experimental data and the simulation fitting function is analyzed using the Pearson coefficient. The Pearson coefficient in the direction of Bx is ρx = 0.91386. The Pearson coefficient of the Bz direction is ρz = 0.98597, the peak-to-peak value of Bx and Bz direction is positively correlated with the depth of damage and the fitting effect of the Bz direction is better than that of the Bx direction.

Stress and Corrosion Defect Identification in Weak Magnetic Leakage Signals Using Multi-Graph Splitting and Fusion Graph Convolution Networks

Weak magnetic flux leak detection is one of the most important non-destructive testing and measurement methods for pipelines. Since different defects cause different damage, it is necessary to classify the different types of defects. Traditional machine learning methods of defect type identification mainly use feature analysis methods and rely on expert a priori knowledge and the ability of designers. These methods have the following weaknesses: a priori knowledge needs to be designed iteratively, and a priori knowledge design relies on expert experience. In recent years, the rapid development of deep learning methods in the field of machine vision has led to the development of defect analysis in the industry. However, most deep learning methods lack the ability to analyze both detailed information and the overall structure. In this paper, we propose graph convolution networks for splitting and fusing multiple graphs of detail graphs and a root graph. Detail information (detail graphs) provides detailed information for the detection of WMFLs. The structure information (root graph) provides structural information for the detection of WMFLs. This paper uses simulation data and experimental data to verify that the proposed method can identify stress defects and corrosion defects well. The paper explains the experimental results in detail to demonstrate the superiority of the method in industrial methods.

Demagnetization Fault Diagnosis of Permanent Magnet Synchronous Motor Based on Empirical Mode Decomposition

The demagnetization failure of the permanent magnet synchronous motor (PMSM) will be accompanied by a magnetic leakage phenomenon. The degree of magnetic leakage will affect the utilization of the permanent magnet. If the magnetic leakage is serious, the motor can not run normally. To study the problem of the demagnetization fault degree of PMSM when partial demagnetization fault occurs, this paper employs the finite element method to simulate the radial magnetic leakage signal 1cm outside the 8-pole 48-slot surface-mounted PMSM. The amplitude of the harmonic wave of the radial magnetic leakage signal is analyzed when partial demagnetization occurs in the permanent magnet synchronous motor. The results show that the spatial order amplitude of the motor radial flux leakage signal decreases along with the increase of demagnetization, which can be used as a check for the demagnetization degree of the motor.

Recognition Method of Pipeline Weld Defects Based on Auxiliary Classifier Generative Adversarial Networks

This study aims to address the problem of poorly generated data quality and slow network training speed in data augmentation of 1-dimensional time domain signals. A data augmentation method for the generative adversarial network based on the time-frequency graph of magnetic flux leakage (MFL) signals is proposed. First, the original MFL is converted into a time-frequency signal graph, which is suitable for the input data of the conventional neural network. Second, to produce an adversarial network combined with auxiliary classification, the label of the original MFL signal is used as the input of this network for data augmentation. Compared with a 1-dimensional time domain signal data augmentation method, the proposed method effectively reduces the number of network training parameters and simultaneously solves the problem of slow training speed in traditional methods. Finally, the proposed method is applied to the identification of MFL signals collected by a pipeline company. The experimental results show that the proposed method can produce higher-quality data and effectively improve the accuracy of weld defect magnetic leakage signal recognition and the speed of network training, while the accuracy rate of magnetic leakage signal recognition is increased from 94.5% of the traditional method to 99.5%, which is feasible for practical engineering applications.

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.

Multi-loop magnetisation method for detecting coal mine wire rope defects

Existing methods for detecting magnetic leakage signals from damaged wire ropes require axial saturation magnetisation and the accuracy of the detection equipment depends on the degree of saturation of the magnetisation. Moreover, owing to the special characteristics of the structure, diameter and operating environment of the wire rope, it is difficult to achieve the expected magnetisation effect. Consequently, in addition to other issues, the detection accuracy and versatility of the equipment are low. This study proposes a method based on spatial multi-dimensional orthogonal array loop magnetisation to detect coal mine wire rope defects. First, a sensor excitation structure model is developed. Then, a method for detecting the radial magnetic flux using permanent magnet arrays is analysed and the influence of the number of permanent magnet radial magnetic blocks on the magnetisation effect is studied. In addition, a wire rope detection method based on the principle of clustering is investigated and used to discuss the influence of the shape, structure and size of the sensor on the magnetisation effect. Finally, through laboratory experiments, the applicability of the test model and the proposed method are verified. The results show that the magnetisation effect is better and more cost-effective when the number of permanent magnet radial magnetic blocks is eight and that the proposed detection method can effectively identify damaged wire rope joints. Furthermore, the proposed equipment achieves a signal-tonoise ratio of 28 dB, thereby improving the reliability of damage detection.

Study on the characteristics of metal magnetic memory signal of X70 pipeline steel

In view of the zero crossing phenomenon of the normal component of the magnetic memory signal on the surface of the ferromagnetic component in the crack or stress concentration, many scholars have disputes about it. In this paper, through the simulation of the crack by wire cutting, the normal component of the magnetic leakage signal on the surface of the pipeline component is stretched on the tensile testing machine, and the normal component of the magnetic memory signal on the surface of the pipeline component is analyzed. It is found that the zero crossing phenomenon does exist in the normal component of the crack, but the zero crossing occurs with the pulling when the load increases to a certain extent, it will not drift after passing the zero point. The results show that the normal component cannot be used to judge the crack or stress concentration simply.

Development of Robotic Nondestructive Testing of Steel Corrosion of Prestressed Concrete Bridge Girders using Magnetic Flux Leakage System

This paper describes the development and validation of a new magnetic-based corrosion detection device integrated in a robotic system. The system nondestructively scans the length of AASHTO-type prestressed concrete I-girders of bridges. The system includes two primary subsystems: an independent magnetic flux leakage (MFL) system for nondestructive testing, and a robotic rover to transport the MFL system along the girder’s length with navigation around transverse diaphragms. The MFL unit inspects prestressing steel strands embedded in concrete and detects cross-section losses caused by corrosion. The MFL is designed with two permanent magnets to magnetize embedded strands and multiple Hall-effect sensors to detect normal and axial magnetic flux leakage. The system is evaluated by testing a laboratory mockup girder and further applied on a field girder. Resulting magnetic signals from both normal and axial Hall-effect sensors are recorded using a newly developed and integrated data acquisition system. Finite element simulations through multi-physics 3D transient analysis aided the development of the new MFL system components, including appropriate magnets strength and dimensions, as well as the verification of the developed system. The effects of lateral reinforcements on nearby section loss are obtained from tests and analyzed with numerical models. Results indicate that the system can successfully disclose magnetic leakage signals at the corrosion zone.

Numerical Simulation of the Cable Breakage Based on Electromagnetic NDE Method for Nb3Sn Cable-In-Conduit Conductors

Superconducting cable is an important component of cable-in-conduit conductors (CICCs). The quality of the cable is the guarantee to ensure safe operation of the superconductor, and a key step to reduce the degradation of superconductor performance. The cables will sustain significant stress during the manufacturing process, which may damage the strands and lower conductor performance. The spiral structure caused by irregular cabling with multiple materials, cores and layers raise the complexity of cable-in-conduit conductors, which also brings more difficulties on R&D of non-destructive examination (NDE) of cable inspection. On the basis of electromagnetic NDE, this paper proposes a method based on an inversion of electromagnetic property and explores relative characteristics of cable damage signal and magnetic leakage signal, as well as the synchronous NDE quantitative evaluation method through the theoretical models.

Detection of a Rectangular Crack in Martensitic Stainless Steel Using a Magnetoreactance Sensing System

A magnetic flux leakage testing system based on the magnetoreactance effect was used to detect a rectangular crack in martensitic stainless steel. Detection of the transverse rectangular crack was achieved by probing the magnetic leakage fields from the crack using a magnetoreactance sensing system. The system was able to detect a small crack with a width of 1 mm and a depth of 1.5 mm even at a low frequency of 100 kHz. The influences of exciting-current intensity, sensing direction, and testing distance on the detection sensitivity were measured. As expected, the magnetic leakage signal strength increased with increasing exciting-current intensity. The magnetoreactance sensor was more sensitive to the magnetic leakage fields when it was perpendicular to the crack.

The Magnetic Leakage Inversion Method Based on Singular Value Decomposition of Magnetic Dipole Forward Model

Ferromagnetic materials are widely used in many fields of national economy. In actual engineering, under the influence of stress or environment, ferromagnetic materials can be defective and have serious consequences. Therefore, magnetic flux leakage inversion, which is speculating defects information according to the detected magnetic leakage signals, is of great practical significance. In allusion to the identification of irregular defects, this paper presented an inversion method based on singular value decomposition of magnetic dipole forward model, which is very effective in identifying irregular defects. This paper contrasted and analyzed the distribution characteristics of magnetic intensity horizontal component Mx when there was no defect and irregular defect, and the comparison verified that the magnetic intensity horizontal component Mx could be used as an inversion gist. Then this paper presented the magnetic dipole forward model B=LM. On account of the magnetic intensity component M containing defects information, this paper adopted the arithmetic of singular value decomposition of coefficient matrix L to solve the inversion equation LM=B and then acquired the distribution of magnetic intensity component M. In the end, this paper verified the validity of this method.