Magnetic Flux Leakage Inspection Research Articles

Development of an In-Pipe Inspection Robot for Large-Diameter Water Pipes.

This paper describes the development of an in-pipe inspection robot system designed for large-diameter water pipes. The robot is equipped with a Magnetic Flux Leakage (MFL) sensor module. The robot system is intended for pipes with diameters ranging from 900 mm to 1200 mm. The structure of the in-pipe inspection robot consists of the front and rear driving parts, with the inspection module located centrally. The robot is powered by 22 motors, including eight wheels with motors positioned at both the bottom and the top for propulsion. To ensure that the robot's center aligns with that of the pipeline during operation, lifting units have been incorporated. The robot is equipped with cameras and LiDAR sensors at the front and rear to monitor the internal environment of the pipeline. Pipeline inspection is conducted using the MFL inspection modules, and the robot's driving mechanism is designed to execute spiral maneuvers while maintaining contact with the pipeline surface during rotation. The in-pipe inspection robot is configured with wireless communication modules and batteries, allowing for wireless operation. Following its development, the inspection robot underwent driving experiments in actual pipelines to validate its performance. The field test bed used for these experiments is approximately 1 km in length. Results from the driving experiments on the field test bed confirmed the robot's ability to navigate various curvatures and obstacles within the pipeline. It is posited that the use of the developed in-pipe inspection robot can reduce economic costs and enhance the safety of inspectors when examining aging pipes.

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.

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.

Image recognition model of pipeline magnetic flux leakage detection based on deep learning

Abstract Deep learning algorithm has a wide range of applications and excellent performance in the field of engineering image recognition. At present, the detection and recognition of buried metal pipeline defects still mainly rely on manual work, which is inefficient. In order to realize the intelligent and efficient recognition of pipeline magnetic flux leakage (MFL) inspection images, based on the actual demand of MFL inspection, this paper proposes a new object detection framework based on YOLOv5 and CNN models in deep learning. The framework first uses object detection to classify the targets in MFL images and then inputs the features containing defects into a regression model based on CNN according to the classification results. The framework integrates object detection and image regression model to realize the target classification of MFL pseudo color map and the synchronous recognition of metal loss depth. The results show that the target recognition ability of the model is good, its precision reaches 0.96, and the mean absolute error of the metal loss depth recognition result is 1.14. The framework has more efficient identification ability and adaptability and makes up for the quantification of damage depth, which can be used for further monitoring and maintenance strategies.

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.

A new type of magnetising and magnetic flux leakage inspection system for the non-destructive testing of pit defects on the surface of a steel sheet

A new type of magnetising and magnetic flux leakage (MFL) inspection system is designed, in which an ARM microprocessor-based measuring circuit and a novel magnetising device developed by the authors are combined to detect the magnetic field around a pit defect in a steel sheet. Based on a series of careful detection experiments, the spatial distributions of the three-dimensional (3D) MFL components of some typical artificial defects are described and observation, comparison, feature extraction and analyses are then carried out. The results show that the 3D components of the MFL field produced by the newly proposed magnetising device present some distinctive properties compared to those made by the traditional magnetising device. They involve rich information that exhibits close correlations to the morphology and critical parameters of the pit defects on the near side of the steel sheet, demonstrating the feasibility, effectiveness and reliability of the newly proposed inspection system, as well as laying a good foundation for the testing, evaluation or reconstruction and inversion of the pit defect.

A novel method for defects marking and classifying in MFL inspection of pipeline

Magnetic Flux Leakage (MFL) testing is the most widely used non-destructive technique for the inner inspection of oil and gas pipelines. Accurate quantification of defects is a long-standing difficulty in the field of pipeline leak detection. Scientific marking and classification of defects is an important prerequisite for accurate quantification. A novel method of marking and classifying defects with MFL signals is proposed, which is aiming at the problem that it is difficult to mark and classify defects accurately due to the tremendous amount of oil and gas pipeline leakage magnetic detection data. An improved CLIQUE algorithm is used to mark the defect areas of segmented pipelines to estimate the number and location of defects. Then the 3D MFL characteristic signals of the marked areas are studied and extracted. The SSA_BP neural network is trained to classify the defects. The effectiveness and accuracy of the proposed method are tested and verified by using finite element simulation defects and actual defects. The results show that the method is more efficient in marking defects and more detailed in marking areas.

FMD-Framework: A Size Estimation Method for Pipeline Defects in Weld-Affected Zones

Defect size estimation plays a crucial role in pipeline magnetic flux leakage (MFL) inspection. For the defects in the weld-affected zones, size estimation is a challenge due to the interference of weld signals. In this paper, Feature-Modeling-Decoupling-Framework (FMD-Framework), a size estimation method, is proposed aiming at obtaining the accurate sizes of defects in the weld-affected zones based on MFL measurements. First, a feature extraction module is established to extract relatively pure defect features without weld information. In this part, the feature extraction module searches the intersection and difference between abstract features, and the weld-related information can be modeled and eliminated on the feature level. Second, an inversion module is constructed to decouple the information of different size dimensions on the feature level and achieve accurate estimation. In this part, the designed training suites and novel loss function guide the network to capture the feature information of a certain size dimension. The evaluation experiments are conducted based on the collected MFL measurements. FMD-Framework reduces the size estimation errors by around 2mm(length), 3mm(width), and 3%OD(depth). The results indicate the effectiveness of the proposed method.

Designing and Investigating a Nondestructive Magnetic Flux Leakage Detection System for Quantitatively Identifying Wire Defects

In-haul cables must be examined and maintained regularly to ensure the smooth operation of cable-stayed bridges. The magnetic flux leakage (MFL) detection method is being applied increasingly commonly to inspect defects. For the quantitative identification of the broken wire, we designed an MFL inspection equipment and investigated the performance. A trapezoidal magnetic dipole model is constructed to simulate the leakage distribution, and the axial and circumferential excitation schemes are used to obtain the intensity of magnetic induction. Then, based on the simulation of model parameters, an MFL detection system is configured for detecting the damage in the broken wire. To investigate the adaptability of the developed system, a rectangular defect is inspected in the vertical and spiral climbing modes. The signal acquired under different paths is processed by the median filtering and wavelet transform methods and analyzed. A random forest algorithm is used to quantitatively identify the number of broken wires, defect dimensions, and cross-sectional area loss to verify the proposed method. The results show that the axial excitation generates a single peak signal, which offers better detection of the wire defects. In the vertical climbing mode, the maximum detection errors in the width and cross-sectional area loss are 0.64 mm and 0.46%, respectively, while the values are 0.21 mm and 0.1% in the spiral climbing mode, indicating that the latter mode offers higher identification accuracy. Furthermore, in future work, the detection capability of irregular defects should be studied, expanding its application in health monitoring.

Online magnetic flux leakage detection of inclusions and inhomogeneities in cold rolled steel plate

Abstract Cold rolled steel plate is found in different applications. In manufacturing processes with high stretch ratios like bending or deep drawing, non-metallic inclusions, or segregations, which remain in the material during slab casting, can lead to ruptures of the plate. While not being visible optically, those inclusions are detectable by magnetic flux leakage inspection. Therefore, an online inspection system using giant magnetoresistance gradiometers was developed. The individual components were optimized by researching the magnetic properties of the cold rolled plate and natural as well as artificial defects on a laboratory setup for moving material simulation. Correlations between defect properties, inspection parameters and the resulting signals were determined. By optimizing components and configuration accordingly, a sensor module containing 48 sensors, signal conditioning, digitization, and an adjustable magnet was arrived at. Due to the necessarily small distance to the moving material, attention was paid to mechanical repeatability and ruggedness. An inclusion detection system, fully covering 1344 mm strip width, was installed in a coating line. Operating in production for over a year, the inspection system has shown high sensitivity and is, in addition to internal defects, able to detect various strip damages like roll marks and scratches.

A novel identification approach for corrosion and gouging of oil and gas pipelines based on low magnetisation level MFL inspection

The ability to characterise corrosion and gouging associated with metal loss and the identification of gouge type from metal loss defect types are two of the primary obstacles affecting magnetic flux leakage (MFL) internal inspection technology. Gouges in pipelines are not extraordinarily severe; however, the depth of corrosion increasing due to a certain corrosion rate can be quite serious. In this paper, a novel theoretical model combined with a new approach for the analysis of the signals that distinguish gouging and corrosion using a low magnetisation level is presented for MFL detection, since the traditional MFL internal detection tools are insensitive to stress characteristics in the case of saturation magnetisation. A two-stage finite element (FE) model for the prediction of magnetic flux leakage resulting from two types of defect is built. In the first stage, the stress distribution associated with gouging is obtained from a solid mechanics model and, in the second stage, the stress distribution is incorporated into a magnetic finite element model by mapping the stress levels to permeability. The possibility of detection and identification of corrosion and gouging using the MFL technique at low-level magnetisation was confirmed by experimentally comparing the characteristics of the MFL signals for each defect type.

Attention Module Magnetic Flux Leakage Linked Deep Residual Network for Pipeline In-Line Inspection.

Pipeline operational safety is the foundation of the pipeline industry. Inspection and evaluation of defects is an important means of ensuring the safe operation of pipelines. In-line inspection of Magnetic Flux Leakage (MFL) can be used to identify and analyze potential defects. For pipeline MFL identification with inspecting in long distance, there exists the issues of low identification efficiency, misjudgment and leakage judgment. To solve these problems, a pipeline MFL inspection signal identification method based on improved deep residual convolutional neural network and attention module is proposed. A improved deep residual network based on the VGG16 convolution neural network is constructed to automatically learn the features from the MFL image signals and perform the identification of pipeline features and defects. The attention modules are introduced to reduce the influence of noises and compound features on the identification results in the process of in-line inspection. The actual pipeline in-line inspection experimental results show that the proposed method can accurately classify the MFL in-line inspection image signals and effectively reduce the influence of noises on the feature identification results with an average classification accuracy of 97.7%. This method can effectively improve identification accuracy and efficiency of the pipeline MFL in-line inspection.

Magnetic-Charge Element Method for Magnetic Flux Leakage Inspection

Magnetic flux leakage (MFL) inspection is widely used in testing and evaluating of defects in ferromagnetic structures, providing important assurances for their safe operations. The calculation of MFL signals based on defects shape is critical for the accuracy of defect evaluation. The existing methods mainly include magnetic dipole model (MDM) and finite element method (FEM), while MDM is incapable of calculating the MFL fields of complex defects, and FEM is time-consuming for the MFL analysis. It is necessary to develop a method which can calculate MFL fields of arbitrary defects fast. In this paper, we propose the magnetic charge element method (MCEM) based on the microscopic mechanism of magnetization and the theory of MDM, which can quickly calculate the MFL fields of arbitrary defects. The effectiveness of the proposed method is verified by a series of simulations and MFL inspection experiments. The results show that the proposed method has the same accuracy as the existing methods. Compared with the FEM, the computation time is greatly reduced.

A Single-Stage Enhancement-Identification Framework for Pipeline MFL Inspection

Magnetic flux leakage (MFL) inspection, one of the nondestructive testing methods, has been widely applied in pipeline maintenance. In pipeline MFL data processing, defect identification is a crucial step, which aims at measuring the locations of defect MFL signals in MFL heat maps. MFL signals collected from the pipeline are not ideal, containing noise and interference. In this case, measuring the locations of defect MFL signals, especially the locations of weak defect MFL signals, is a challenge. To address this challenge, an enhancement process is required to coordinate with the identification process. In this manuscript, two separated processes, enhancement and identification, are integrated into a single-stage framework, aiming at improving the defect identification performance through strengthening the differences between defect signal areas and pipe wall signal areas. The proposed framework can enhance the defect areas purposefully, and ignore the noise and interference in non-defect areas, which promotes the measuring effect for locations of defect MFL signals. In the proposed method, an enhancement module is constructed to upsample the MFL heat maps, and the resolution of feature maps in the framework is increased to 288×600. A novel loss function is designed, and the gray value contrast between defect signal areas and pipe wall signal areas in MFL heat maps is enhanced from 10 to 48 approximately through task-oriented joint training. The proposed method achieves 0.967 AP, and the identification accuracy is improved to 97.3%. In addition, the average deviations of identified defect signals are reduced by around 2mm - 9mm, and the uncertainties are reduced to 0.27mm - 0.35mm. The experiment results validate the superiority of the proposed framework in industrial applications.

A Pipeline Defect Inversion Method With Erratic MFL Signals Based on Cascading Abstract Features

Defect inversion, as a key step in magnetic flux leakage (MFL) inspection widely used in nondestructive testing (NDT) systems, is critical to quantitative analysis of pipeline risk level. However, the conventional methods are difficult to achieve desirable results with complex and changeable MFL signals, mainly because the defect inversion procedure relies on prior knowledge and the features of MFL signals are not fully mined. To resolve the above problem, a novel direct inversion method based on cascading abstract features is proposed in this article, capable of handling feature extraction and defect size estimation problems effectively under complicated conditions. First, an adaptive abstract defect feature extraction network with stacked multipath denoising sparse autoencoder (sMPDS-AE) is constructed, and several extra feature transmitting channels are created, so that abstract features can be transferred across multiple layers. Second, multifeature fusion and sparsity rules are designed to strengthen the sMPDS-AE model and unify abstract feature dimensions. Third, a novel ensemble-backed predictor is developed to study the complex nonlinear relationship between abstract features and defect sizes. With above-mentioned structure, the proposed method can mining out abstract features of pipeline defects from MFL signals adequately and distinguish defects with different sizes more accurately. Finally, several comparison experiments are conducted on a pipeline network with MFL signals. Experimental results and comprehensive analysis with other mainstream inversion methods validate the superiority of this method.

Performance assessment of multi-MFL inspection using feature-based POD

Oil and gas pipelines, which transport large quantities of oil products and natural gas, are subject to pipeline failures caused by corrosion. Magnetic flux leakage (MFL) is one of the most popular non-destructive testing (NDT) techniques for the detection of pipeline corrosion. Since individual MFL is insensitive to the corrosion components that are parallel with its magnetic field, two types of MFL tools with perpendicular magnetic fields are usually employed in one inspection to detect all corrosion defects. This study applies probability of detection (POD) to quantitatively assess the detection capabilities of two individual MFL tools and their combination. Due to the characteristics of MFL inspection, this study proposes the construction of the POD model as a function of two geometric features, namely the volume and the orientation, which have a significant influence on the MFL signal response. Detection results from two MFL tools are integrated using logical OR operation to study the POD of their combination. With the proposed POD model, the minimum criteria that ensure a corrosion defect will be reliably detected by MFL tools are studied in this paper. The validity of the proposed POD model is justified on the data collected from an in-service pipeline.

FEM of Magnetic Flux Leakage Signal for Uncertainty Estimation in Crack Depth Classification using Bayesian Convolutional Neural Network and Deep Ensemble

This paper investigates the impact of uncertainties during dynamic magnetization due to relative motion between magnetic flux leakage (MFL) sensors and material under test. A 3D finite element method (FEM) based model for the MFL inspection system is developed to better understand the relationships between inline inspection (ILI) data and damage parameters in nonlinear ferromagnetic materials. In real-life field testing or inspection scenario, there exist lots of uncertainties associated with nondestructive evaluation (NDE), which will further affect damage condition-based decision making. Therefore, it is vital to quantitatively analyze the uncertainties in NDE. This paper investigates uncertainty quantification via numerical simulation by developing Bayesian based Convolutional Neural Network and Deep Ensemble methods, which are demonstrated in MFL based defect depth classification for NDE applications. Prediction accuracy and uncertainties are compared, which is valuable in better assessing the performance and quantifying uncertainties for generalized NDE problems.

Best Practices for Avoiding Erosion in Annular Fracturing

This article, written by JPT Technology Editor Judy Feder, contains highlights of paper SPE 204417, “Avoiding Erosion: Best Practices for Coiled Tubing Annular Fracturing Operations,” by Kaveh Yekta, SPE, and Jamie Fenwick, SPE, Essential Energy Services, and Kevin Elliott, SPE, NOV, et al., prepared for the 2021 SPE/ICoTA Virtual Well Intervention Conference, 22–25 March. The paper has not been peer reviewed. The onset of erosion of coiled tubing (CT) strings may be difficult to predict in annular fracturing operations. The complete paper describes a methodology of verifying that CT strings have not been subject to erosion caused by annular fracturing operations. An exploration of pumping rates used on these strings in operations also provides field-tested practical guidelines for avoiding erosion when performing annular fracturing jobs. Inspection A CT string may be exposed to erosion in the outer surface during CT annular fracturing operations. The critical parameters that may influence the magnitude of erosion include fracturing pump rate, sand concentration, fluid rheology, wellbore geometry, and the grade of CT string. One measurable parameter to examine the string’s suitability is the wall thickness for each section. The CT strings discussed in the complete paper were sent for inspection to learn about the effect of the fracturing treatment. A series of nondestructive tests was conducted pre- and post-operation to evaluate the readiness of strings for subsequent operations. The complete paper includes two graphs related to the results of these tests. Materials The API 5ST specification requires using eddy current (EC) inspection for CT. With respect to API 5ST, quenched and tempered tubing had not been addressed at the time of this paper’s submission, so it is not clear whether the CT manufacturers employ EC techniques after quenching and tempering. However, it is expected that all tubing will be subject to EC inspection at least before any quenching and tempering operations. Inspection methodology and equipment consistency is important to establish a baseline inspection necessary for subsequent comparisons. In the case of the CT strings used in this study, all were subject to EC inspection at tubing manufacturing and then subsequently inspected by an outside, third-party vendor using magnetic flux leakage (MFL) technology when spooling the strings on the unit. No known third-party EC vendors can inspect string lengths of CT, so MFL inspection is the only available solution. Photographs of CT inspections taken at the manufacturing facility and the service and distribution point are included in the paper. Several commercial third-party inspection companies operate in North America, where this work was performed. Provided that the inspection can begin by identifying features in the calibration standard (often a through-drilled hole), the selection of inspection methodology is primarily a matter of preference for the service company and operator.

A Pinning Actor-Critic Structure-Based Algorithm for Sizing Complex-Shaped Depth Profiles in MFL Inspection with High Degree of Freedom

One of the most efficient nondestructive methods for pipeline in-line inspection is magnetic flux leakage (MFL) inspection. Estimating the size of the defect from MFL signal is one of the key problems of MFL inspection. As the inspection signal is usually contaminated by noise, sizing the defect is an ill-posed inverse problem, especially when sizing the depth as a complex shape. An actor-critic structure-based algorithm is proposed in this paper for sizing complex depth profiles. By learning with more information from the depth profile without knowing the corresponding MFL signal, the algorithm proposed saves computational costs and is robust. A pinning strategy is embedded in the reconstruction process, which highly reduces the dimension of action space. The pinning actor-critic structure (PACS) helps to make the reward for critic network more efficient when reconstructing the depth profiles with high degrees of freedom. A nonlinear FEM model is used to test the effectiveness of algorithm proposed under 20 dB noise. The results show that the algorithm reconstructs the depth profile of defects with good accuracy and is robust against noise.

The Effect of Motion-Induced Eddy Currents on Three-Axis MFL Signals for High-Speed Rail Inspection

Nondestructive testing (NDT) methods are widely used in the rail industry to detect rolling contact damage (RCD) defects to ensure railway safety. With the need of monitoring millions of miles of rail tracks more effectively and efficiently that leads to improved rail safety, reliability, and optimized assets management, the research on high-speed and high-accuracy NDT methods that can obtain high signal-to-noise ratio (SNR) and high sensitivity defect signals at higher speeds is of vital importance. Magnetic flux leakage (MFL) is one of the NDT methods used for high-speed rail inspection, which is suitable for detecting surface and subsurface RCD defects on railhead, but the measured MFL signals are distorted severely by the effect of motion-induced eddy currents (MIEC). However, the fundamental understanding of the mechanism of MIEC generation, distribution, and magnitude is lacking, and the physical interaction between MIEC and MFL measurement during high-speed rail inspection has not been well studied. This article investigates the effect of MIEC on three-axis MFL signals through 3-D finite-element method (FEM) simulations. The 3-D distributions of MIEC and 2-D image signals of MFL measurements for high-speed rail MFL inspection are presented and analyzed. The simulations are conducted with an inspection speed range of 0-62.5 mi/h, and a surface hemisphere-type defect and subsurface hole-type defect are both considered. The results show that the motion between the magnetizer and the rail will generate two types of MIEC, which in turn decreases the peak values of three-axis MFL signals from the subsurface defect and affects the MFL signals spatially, especially the base values along rail transverse and vertical directions. The effect of MIEC on the three-axis MFL signals in terms of SNR, sensitivity, and asymmetry is discussed.