Flux Leakage Research Articles

Uplifting the study of the inline inspection technique on the buckling pipelines in pipeline integrity management strategy

This work reports the development of inline inspection (ILI) methodology to enhance the pigging activity for the dented pipeline, facilitate the pigging process to prevent Pipeline Inspection Gauge (PIG) from getting stuck and improve the safety passage for buckled pipelines. The recent report unveils the condition of the UNPIGGABLE pipelines, which reduce the inner diameter of pipelines to 257.51 mm, equivalent to 27.58 % of the initial diameter and restricts the pigging activity. In this report, the pull-through test coupled with the collapsibility test was conducted. The success of the test above allows the ILI equipment based on the magnetic flux leakage (MFL) technique to record the internal and external wall loss inwardly and geometric defect on diameter of the pipelines. The prepared artificial dented pipeline was made before it underwent several tests. Based on the pull-through test, the maximum force of 27000 N is more significant than the pipeline operating pressure to enable the MFL tool to pass through the pipelines despite exhibiting the geometry anomaly. Compressing the opposite magnetic yoke of the collapsibility test is critical, showing that the ILI MFL tool reaches its maximum compression of 242 mm. The value is lower than the minimum internal diameter of 257 mm. The ILI results show that the highest metal loss was achieved at 73 % at 15504 m at the bottom of the inspected pipelines. At the same time, the dented area reduces to more than 6 % of the pipelines’ nominal outer diameter and imposes the pipe’s integrity status to red. The distinctive result of the research can be used to model the future unprecedented pigging process when buckles appear in pipelines

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.

Magnetic focusing sensor and its characterizations of defect non-destructive testing for ferromagnetic steel plate

The fast development in precision manufacturing and product quality inspection of ferromagnetic material desperately demands that the defect determining the performance, service life and reliability of the mechanical part should be accurately sensed, detected and evaluated. However, conventional defect detection methods and related magnetic induction sensors are usually characterized with low accuracy in small defect detection, which makes the non-destructive testing (NDT) for ferromagnetic material full of challenges. Thus, a new magnetic focusing sensor is proposed and designed for the precision defect detection of ferromagnetic steel plate here. First, the theoretical principle analysis of the magnetic focusing sensor is introduced, and the preliminary finite element modeling (FEM) is conducted to demonstrate the feasibility of the magnetic focusing sensor in tiny defect inspection. Then, both the static and coupling dynamic characterizations of the magnetic focusing sensor are investigated using the FEM method. The optimal structural parameters of the magnetic focusing (MF) sensor in static conditions are summarized. Furthermore, the effects of the dynamic movement of the MF sensor on the multi-physical fields of the magnetization field, electric field, electromagnetic attraction force field and thermal field are all revealed. Finally, experiments regarding the performance evaluation and comparison between the MF sensor and other two magnetic flux leakage (MFL) sensors are conducted, and the prominent capabilities of accurate and precise defect detection for the MF sensor is demonstrated through the defect detection with different sizes. The effectiveness and feasibility of the MF sensor in detecting tiny defects on ferromagnetic object, as well as its great potential for practical application is therefore validated. Additionally, the main conclusions, advantages of the MF sensor are summarized and the future work is discussed.

Study on Performance Improvement through Reducing Axial Force of Ferrite Double-Layer Spoke-Type Permanent Magnet Synchronous Motor with Core Skew

Recently, due to the price fluctuation and supply instability of rare earth mineral resources, there has been a lot of development of electric motors using non-rare-earth permanent magnets. As a result, motors using Dy-free permanent magnets and ferrite permanent magnets are being researched, and, in particular, ferrite permanent magnets often utilize spoke-type structures, which are magnetic flux concentrators, to compensate for their low coercivity and residual flux density. However, in general, spoke-type PMSMs do not use much reluctance torque, so double-layer spoke-type PMSMs have been studied for their more efficient design. Unlike general spoke-type PMSMs, double-layer spoke-type PMSMs can utilize high reluctance torque by increasing the difference between d-axis and q-axis reluctance. However, as the difference in magnetic resistance increases, vibration and noise are generated, which adversely affects the mechanical part and shortens the life of the motor. Although this problem seemed to be solved by applying core skew in the previous study, it was confirmed that the axial force caused by the axial leakage flux occurred in the maximum torque per ampere (MTPA) control section and the torque ripple was increased. Therefore, in this paper, a model that can apply symmetrical core skew and reduce axial force is proposed. First, the causes of the axial force generated in previous studies were analyzed. Based on the analysis of these causes, a new symmetrical core skew structure was proposed, and its justification was verified through FEA.

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.

Failure analysis of local effusion corrosion in small diameter gas pipeline: Experiment and numerical

In the later stage of gas well production, water–gas ratio increases, liquid carrying capacity decreases, gas field water accumulates in pipes for a long time, and the corrosion environment of pipelines becomes more and more severe. On the basis of carbon dioxide corrosion, microbial corrosion in gas field water accelerates catalytic pitting and finally leads to serious internal corrosion of pipelines. Therefore, accurate prediction of fluid distribution and flow characteristics in pipes is of great significance for engineering applications. In this work, the location and morphology of the corrosion pit were determined by physicochemical analysis. A computational fluid dynamics (CFD) model was established by using VOF and RNG k-ε models to predict fluid distribution characteristics and fluid accumulation locations in pipelines. The results showed that: 1) Corrosion and perforation occurred in the position of pipeline fluid accumulation. 2) The main elements of the corrosion pit on the inner surface of the pipe sample are O, Fe, C, S and Cl. The main corrosive substances in the pipeline corrosion pit are FeCO3 and Fe2O3. 3) The low-lying areas and uphill sections are prone to corrosion. A decrease in the fluid velocity in the pipe and a corresponding increase in the gas–liquid ratio will result in a greater accumulation of liquid in the pipe. 4) Magnetic flux leakage (MFL) detection method can be used to detect the characteristics and distribution of pipeline corrosion defects and determine the corrosion area. The research results are of great significance to the safety evaluation of small diameter pipelines for gathering and transporting in oil fields.

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 Local Flaw Detection Based on Magnetic Flux Leakage Images With a Noise Distortion Effect for Steel Wire Ropes

Steel wire rope (SWR) is widely used in industrial scenarios because of its high strength and toughness. To avoid accidents that local flaws (LFs) can cause, SWRs should be inspected regularly. Magnetic flux leakage (MFL) image detection, which is a common SWR inspection method, is inevitably influenced by shaking noise and strand noise. Although noise-reduction methods for these two types of noise have been proposed, the phenomenon of noise distortion has not received enough attention. This paper investigates the noise distortion phenomenon on the MFL image and finds that the distorted noise severely affects the performance of existing noise-feature-oriented (NFO) denoising methods. To solve this problem, we adopt a target-feature-oriented (TFO) denoising method. Specifically, the removal of noise is avoided, instead, an LF-enhancement-based denoising process is proposed. Moreover, to localize LFs in denoised images, a three-stage adaptive localization method mainly based on disjoint region analysis is proposed. The experiment results show that the proposed TFO denoising method significantly improves the denoising performance for images that distorted noise influences. In addition, the proposed adaptive localization method improves the intelligence of the localization process and has better localization performance for images with distorted noise.

Research on Three-Phase Wireless Power Transfer System

Aiming at the problems of low power, low energy transmission efficiency, and high stress in the circuit of a single-phase wireless power transfer system, this paper proposes a wireless power transfer (WPT) system with a three-phase angle difference of 120 degrees and establishes a COMSOL multi-physics simulation model for analysis. In this simulation model, the topology of the three-phase resonant compensation network is studied in detail, and the structure of the coupling coil is designed and adjusted. Compared with the single-phase system with the same environmental conditions, air gap, and operating frequency, the simulation results show that the proposed three-phase system can effectively reduce the magnetic flux leakage, reduce the stress in the circuit, and significantly improve the energy transmission efficiency. In order to verify the reliability of the simulation results, an experimental platform was built. The experimental results show that the efficiency and coupling degree of the new system are significantly improved at the resonant frequency of 47.5 kHz, and the stress in the circuit is also significantly reduced.

Electromagnetic performance analysis of axially magnetized magnetic gear with H-type demagnetization rings for flux leakage suppression

Electromagnetic performance analysis of axially magnetized magnetic gear with H-type demagnetization rings for flux leakage suppression

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.

Enhancing electric vehicle charging performance through series-series topology resonance-coupled wireless power transfer.

The current electric vehicles (EVs) market is experiencing significant expansion, underscoring the need to address challenges associated with the limited driving range of EVs. A primary focus in this context is the improvement of the wireless charging process. To contribute to this research area, this study introduces a circular spiral coil design that incorporates transceiver coils. First, an in-depth analysis is conducted using Ansys Maxwell software to assess the effectiveness of the proposed solution through the magnetic field distribution, inductance properties, and mutual inductance between receiver and transmitter coils. In the next step, a direct shielding technique is applied, integrating a ferrite core bar to reduce power leakage and enhance power transmission efficiency. The ferrite magnetic shielding guides magnetic field lines, resulting in a significant reduction in flux leakage and improved power transmission. Lastly, a magnetic resonance series (SS) compensation wireless system is developed to achieve high coupling efficiency and superior performance. The system's effectiveness is evaluated through co-simulation using Ansys Simplorer software. The results confirm the effectiveness of the proposed solution, showing its ability to transmit 3.6 kilowatts with a success rate approaching 99%. This contribution significantly advances the development of wireless charging systems for electric vehicles, addressing concerns and promoting global adoption.

A classification and quantitative assessment method for internal and external surface defects in pipelines based on ASTC-Net

Classification and size quantification of defects on both the internal and external surfaces of pipelines are critical to pipeline integrity assessment. However, defect classification is challenging because of the similarities of defect signals on the internal and external surfaces. In addition, most existing size quantification methods are not sufficiently accurate. To solve these problems, this paper proposes a classification and quantitative evaluation method based on an asymmetric student–teacher with a classifier network (ASTC-Net). First, a novel approach for expanding defect magnetic flux leakage (MFL) data is validated through experiments and simulations. Second, ASTC-Net is built to address the problem of defect classification and quantification. Finally, the superiority of the method is verified by experiments. The results show that this approach pioneers the accurate classification of defects on both internal and external surfaces by achieving an accuracy of 99.41%. Furthermore, a high-precision quantitative assessment of defect size is realized, with length, width, and depth errors of only 0.35 mm, 0.34 mm, and 0.41% of the wall thickness, respectively. These experimental results clearly demonstrate that this method has exceptionally high accuracy in defect classification and quantification, offering vast prospects for its application in pipeline MFL evaluation.

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.

Why state-of-the-art analytical models for eddy current losses in PM of PMSM are insufficient for variable speed motors

This is the third paper in a series on novel methods for calculating eddy current losses in permanent magnets (PM) and the shortcomings of previously conducted analyses. In the first paper, Ruoho’s work on homogeneous materials was discussed. The distribution of fields has been expanded to include the reaction field of the eddy currents within the permanent magnets. This approach was based on the methods of the harmonic complex AC calculation. In the second paper, the models were expanded to enable a harmonic calculation of eddy current losses in permanent magnets for homogeneous fields including the effects of eddy current losses in an adjacent ferromagnetic material, leakage flux factors, and source behavior. In this third and final publication, a look at state-of-the-art analytical models will be taken. With regard to the results of the second publication of this series, it can be shown that established analytical models are insufficient when calculating inverter-related eddy current losses in permanent magnets of permanent magnet synchronous machines (PMSM). Furthermore, a short introduction to eddy current loss mechanics in permanent magnets of PMSM will be given.

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.

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.

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.