Magnetic Flux Leakage Research Articles

Dynamic Defect Detection in Fast, Robust NDE Methods by Transfer Learning Based Optimally Binned Hypothesis Tests

ABSTRACT The structural integrity of safety-critical infrastructures diminishes over time, necessitating periodic inspections. The gathering of low-noise, precise nondestructive evaluation (NDE) readings for defect detection can be both time-consuming and costly. In practical applications, NDE data can be highly noisy due to factors like liftoff/stand-off distances, probe drift, scanning speed, and variations in data acquisition rates. Given the broad spectrum of potential uncertainties that can contribute to noise accumulation, characterizing the noise in such NDE data presents significant difficulties. As a result, the extraction of defect signals through deconvolution becomes challenging. This article introduces an NDE-based dynamic defect tracking framework designed for robust conditions. It incorporates regular periodic monitoring of the material under test (MUT) using a cost-effective, easy-to-implement magnetic flux leakage (MFL) probe. The framework proposes a dynamic setup that includes occasional, highly accurate, expensive MFL scans among regular, low-cost, noisy MFL scans. The objective is to detect and precisely track defect formation in metallic pipes over time, thereby enabling the efficient alarming and localization of defects before they reach detrimental sizes. A key characteristic of the proposed dynamic monitoring method is its noise type agnosticism in the low-cost MFL scans. It remains effective even when the underlying noise-producing uncertainties fluctuate significantly over time, a feature achieved through the use of transfer learning. The method transfers accurate information regarding the locations and sizes of existing corrosions from the detailed MFL scan to the subsequent noisy MFL inspections. With this supportive information from defective scan-points, it estimates the degradation of the signal-to-noise ratio (SNR) in the noisy MFL scans. The framework suggests binned hypothesis tests in noisy MFL scans, setting the level of aggregation based on the SNR estimate in the scan data. This proposed binned hypothesis test optimizes defect coverage and maintains control over the false discovery rate (FDR). Experimental MFL data, gathered under a wide range of uncertainties and defect types, illustrates the highly heterogeneous nature of noise distributions in low-cost MFL scans and the significant variance in SNR deteriorations. The necessity of using binned hypothesis tests in these low-cost MFL data is highlighted by the poor performance of pointwise multiple hypothesis tests. Finally, the high effectiveness of the proposed transfer learning-based binned hypothesis testing method is demonstrated. Although the method was applied to MFL data in this instance, it is also suitable for other NDE applications. The applicability of the algorithms has also been confirmed through validation on experimentally generated Eddy Current (EC) data.

Optimization of magnetic coupling mechanism of dynamic wireless power transfer based on NSGA-II algorithm

Optimization of magnetic coupling mechanism is an important way to improve the performance of a dynamic wireless power transfer system. Inspired by the common radial magnetic core for circular coils, a new radial magnetic core for rectangular coils is adopt. Through simulation and experimental results comparison, which has higher coupling coefficient with the same core area. Combined with the magnetic circuit analysis, the magnetic flux leakage and conduction regions are divided into magnetic fluxes with different shapes, which magnetic resistances are calculated respectively. Based on the simulation results, parameter distributions of fluxes under different conditions are obtained. Therefore, the expressions of the coupling coefficient k of the adopt magnetic cores and coils and the design parameters of coils and cores are obtained. Taking the maximum k and the minimum rate of change of coupling coefficient with 100 mm displacement as the optimization objectives, a multi-objective optimization solution is carried out by using NSGA-II algorithm. The coil optimization scheme is obtained and verified by experiments. k and Δk are 0.442 and 6.8% respectively, and the errors are less than 5%. In the optimization process, there is no simulation model constructed. The optimization modeling combined of magnetic field segmentation method and parameter fitting has lower complexity and calculation time of optimization.

Evaluation of Pipe Thickness by Magnetic Hammer Test with a Tunnel Magnetoresistive Sensor.

A new nondestructive inspection method, the magnetic hammer test (MHT), which uses a compact and highly sensitive tunnel magnetoresistance (TMR) sensor, is proposed. This method complements the magnetic flux leakage method and eliminates the issues of the hammer test. It can therefore detect weak magnetic fields generated by the natural vibration of a pipe with a high signal-to-noise ratio. In this study, several steel pipes with different wall thicknesses were measured using a TMR sensor to demonstrate the superiority of MHT. The results of the measurement show that wall thickness can be evaluated with the accuracy of several tens of microns from the change in the natural vibration frequency of the specimen pipe. The pipes were also inspected underwater using a waterproofed TMR sensor, which demonstrated an accuracy of less than 100 μm. The validity of these results was by simulating the shielding of magnetic fields and vibration of the pipes with the finite element method (FEM) analysis. The proposed noncontact, fast, and accurate method for thickness testing of long-distance pipes will contribute to unmanned, manpower-saving nondestructive testing (NDT) in the future.

Application of magnetic flux leakage method in the crimping inspection of carbon fiber conductor

Compared with steel core aluminum strand wire, carbon fiber composite core wire has many excellent characteristics such as large flow capacity, good heat resistance, light weight, but its mechanical properties are poor, and the unqualified crimping quality is easy to lead to wire breakage accidents, which greatly affects its popularization. For this reason, a scheme for the detection of crimping defects of carbon fiber composite mandrel based on magnetic leakage method is proposed. Firstly, the finite element analysis model of the clamping fixture was established, and then the magnetic pole distance k and radial air gap n of the magnetic leakage testing device were explored. Based on the signal-to-noise ratio under the two typical defect conditions of fracture and low pressure, the most suitable excitation structure parameters of the magnetic leakage device were obtained. The results show that when k=55mm and n=3.5mm, the signal-to-noise ratio of MFL detection device reaches the best effect. The research results are of great significance to the quality inspection of compacts of carbon fiber composite mandrel.

Use of Magnetic Flux Leakage to Diagnose Damage to a Lift Guide Rails System with Reference to the Sustainability Aspect

The scientific objective of the conducted experimental research was to find an answer as to whether the application of magnetic flux leakage would be an effective tool for assessing the technical condition of lift guide rails in which the loss of thickness of the guide part is damage resulting from the brake whose operation destructively affects the surface shown. In particular, the scientific objective was to investigate the potential of this method in the context of quantitative assessment of the degree of damage featuring small increments in depth at the level of tenths of a millimetre. The conducted research was also aimed at determining the correlation of the effect of damage type with the nature of the signal recorded. The article presents the results of our own research, obtained from experiments on the use of magnetic flux leakage (MFL) to diagnose damage occurring on lift guide rails. During operation, lift guide rails are exposed to contact with the friction elements of brakes, resulting in the violation of their surfaces. Damage to the working surfaces of guide rails increases the vibration of the device, noise and wear of other components of the lift, such as guide rails. Currently, diagnostics of lift guide rails are not carried out, and their replacement depends on their technical condition. However, from an economic point of view, there are situations wherein their condition allows their use without their replacement with new ones. This was the main factor that guided the authors; we used a diagnostic head of our own design for the tests. The obtained measurement results showed that magnetic flux leakage can be used with great success to diagnose damage to guide rails. The results obtained in the laboratory shall be further developed in the form of research on correlating the signals obtained from magnetic sensors and the size of the damage, which shall eventually allow for a final quantitative assessment of guide rails regarding their technical condition. The conducted research fits into the scope of sustainable development by reducing the need for the consumption of electric energy and the emission of harmful substances into the atmosphere in the overall production balance. This will be made possible by implementing the developed head in industrial practice in the context of assessing the need to replace guide rails with new ones. The economic and environmental efficiency that is the basis of sustainable development in the context of lifts can be achieved at the modernisation stage by repeated (further) use of as many components as possible. An example of this is the guide rail system, the reuse of which is possible after a prior assessment of its wear and tear.

A global centralized magnetic flux leakage small defect detection network

Abstract To solve the problem that magnetic-flux-leakage (MFL) small defects are difficult to accurately detect by machine learning methods, a global centralized magnetic flux leakage small defect detection network (RCFPNet) is proposed. RCFPNet consists of simulation data enhancement, improved feature extraction (backbone), an improved centralized feature pyramid (CFP) and a detection head network. The MFL defect data of various scales and shapes are simulated by ANSYS simulation software and superimposed with the actual detected MFL defects to expand the dataset. The Repvgg module is used to replace the 3*3 convolution of the backbone to improve the detection speed. An improved spatially explicit vision center scheme (EVC) and a global centralized regulation rule (GCR) for feature fusion networks are proposed for feature fusion networks. RCFPNet is based on an improvement of the YOLOv5 network. Experiments have proven that RCFPNet has improved detection speed and accuracy and has achieved good results in the detection of magnetic leakage small defects. Experiments show that when the IOU = 0.5, the accuracy rate of this algorithm is 96.1%, and the reasoning time is 8.9 ms.

Investigation of the relationship between bending capacity and SMFL intensity of existing reinforced concrete hollow slab beams

This paper proposes a novel approach for monitoring the bearing capacity of RC beams using the spontaneous magnetic flux leakage (SMFL) effect. Three decommissioned reinforced concrete hollow slab (RCHS) beams underwent bending loading failure experiments to collect data on crack formation, deflection Y, concrete strain ε, steel bar strain ε and SMFL intensity Bx/y/z. The mechanical properties of concrete outside the structure and the magnetic properties of steel bars inside the structure during loading are investigated. The force-magnetic coupling effect of HRB400 steel bars is characterized. The SMFL parameter BC closely correlates with Y and ε variations in loading process, the quantitative relationship models between mechanical and magnetic parameters of the beam structures are established. A theoretical model predicted the ultimate bending capacity of RCHS beams. The relationship between beam bending capacity and SMFL intensity is clarified.

Improving Pipeline Magnetic Flux Leakage (MFL) Detection Performance With Mixed Attention Mechanisms (AMs) and Deep Residual Shrinkage Networks (DRSNs)

Improving Pipeline Magnetic Flux Leakage (MFL) Detection Performance With Mixed Attention Mechanisms (AMs) and Deep Residual Shrinkage Networks (DRSNs)

Comparative the effect of distribution transformer coil shape on electromagnetic forces and their distribution using the FEM

Abstract During the normal life of the transformers, they are subjected to different electromagnetic stresses. One of these stresses is the electromagnetic forces resulting from the passage of short-circuit current in the transformer coils due to internal or external faults and may lead to the failure of the transformer when this electromagnetic force exceeds the threshold level. This work deals with the computation and analysis of leakage magnetic flux and electromagnetic forces when the worst-case fault (symmetrical short-circuit current) occurs in distribution transformer (DT) coils using the finite element method (FEM). The three types of DTs that were adopted in this work are similar in capacity and voltage transformation ratio (250 kVA and 11,000/416 V), but they are different in the shape of coils (oval, cylindrical, and rectangular coils). ANSYS software was used to build two-dimensional models of the three transformers, which were different in coil shapes and in the type of iron core. The objective of this work is to compare the effect of coil shape on the distribution of electromagnetic forces and their value, in order to find out which coil shape is the best to withstand electromagnetic forces when short-circuit current is passed in the coils. The results of the simulation of the finite element models were approximately equivalent to the results of the design calculations that depend on the classical method (the analytical method), but the FEM is more accurate, due to the accuracy of calculating the magnetic flux and its distribution, which cannot be calculated using the classical method. One of the most important contributions of this research is the analysis and calculation of electromagnetic forces for three types of DTs with different coils by applying the same design parameters to all the transformers. The research also contributed to determining the best coil shape by comparing simulation results, and it was found that transformers with cylindrical coils are the best to withstand electromagnetic forces due to the homogeneity of the cylindrical coil structure from all sides.

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.

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).

Rotor optimization of axial-radial flux type synchronous machine based on magnetic flux leakage

Rotor optimization of axial-radial flux type synchronous machine based on magnetic flux leakage

Design of a rotor with a starter-generator integrated into an aero car

The Halbach array structure rotor of the aero motor can satisfy the requirements of high power density and high air-gap flux for aeronautical motors. The size parameters of the rotor are determined by the power rating of the motor based on an analytic method. Producing a Halbach array structure is difficult. Comparison and analysis of the structure of the aero motor show that the overall structure of the rotor adopts a three-axial-section classic Halbach-array hollow structure, and the rotor magnetic steel adopts a discrete structure of 4 blocks per pole and a single 45 degrees magnetisation mode, which reduces the processing difficulty of the rotor magnetic steel. The finite element method was used to analyse the magnetic flux density distribution of the aeronautical motor under various working conditions. The results show that the motor can produce uniform air-gap flux density at various working conditions and present good sinusoidal periodicity. Furthermore, the axial segment did not produce obvious magnetic flux leakage. Finally, considering the eddy current loss of the stator under the rated power-generation condition with high-frequency magnetic field, we conducted coupling analysis of electromagnetic and heat flows to verify that the thermal characteristics of the rotor magnetic steel material could meet the requirements for the aero motor.

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.

Machine Learning-Based Dipole Modeling of 3-D Magnetic Flux Leakage Caused by Outer-Surface Linear Cracks for Pipeline In-Line Inspection

Machine Learning-Based Dipole Modeling of 3-D Magnetic Flux Leakage Caused by Outer-Surface Linear Cracks for Pipeline In-Line Inspection

Torque Improvement and Magnetic Flux Leakage Reduction in Interior Permanent Magnet Axial Flux Motors With Flux Barrier Structure

Torque Improvement and Magnetic Flux Leakage Reduction in Interior Permanent Magnet Axial Flux Motors With Flux Barrier Structure

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

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

Online Pipeline Weld Defect Detection for Magnetic Flux Leakage Inspection System via Lightweight Rotated Network

Online Pipeline Weld Defect Detection for Magnetic Flux Leakage Inspection System via Lightweight Rotated Network