Magnetic Flux Leakage Signals Research Articles

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.

Evaluation of defect depth in ferromagnetic materials via magnetic flux leakage method with a double Hall sensor

In the magnetic flux leakage (MFL) testing method, a linear mapping is used for the direct evaluation of defects by applying a single Hall sensor to measure the signal under a certain lift-off value. Inevitably, the uncertainty of the lift-off value in practical testing affects the accuracy of the estimated defect size. To overcome this uncertainty in the lift-off value, this paper proposes a double Hall sensor configuration with a fixed lift-off interval, and establishes an evaluation method for the defect depth through the MFL signals measured by this sensor. The effectiveness of the proposed evaluation method is verified by theoretical analysis, with the double Hall sensor providing two different lift-off values above the surface of the specimen that characterize the tested defect. Based on the error between the measured signal characteristics induced by the tested defect and the predicted values from a fast calculation model, a particle swarm optimization algorithm is employed to realize accurate evaluation of the defect depth. Theoretical research shows that the characteristic values measured by the double Hall sensor enable the defect depth to be accurately estimated. The use of a double Hall sensor with a fixed lift-off interval allows the proposed evaluation method to compensate for the unknown lift-off value in practical testing.

Crack identification system on MOH cold rolled grain oriented sheets: Application of K-fold cross validated BRANN

To increase efficiency of transformers, iron (hysteresis and Fuko losses), copper and abnormal losses must be prevented. Hysteresis loss may be significantly reduced by adding silicon to iron, and Fuko loss may be significantly reduced by using cold-rolled grain-oriented steel (CRGO) in the form of thin sheets with high resistance. To reduce abnormal losses, it is necessary not to use CRGO with ferromagnetic features including cracks in the core sequence. In line with this, currently, the magnetic flux leakage (MFL) method is commonly used to research cracks in ferromagnetic materials. Generally, literature studies researched the variation characteristics of MFL signals collected with magnetic sensors and size of amplitude values according to the physical properties (crack width, crack depth, etc.) of the crack in the ferromagnetic material. Different to the literature, our primary aim in this study is to determine this variation according to the type and geometric features of an artificial full crack with known physical properties created in MOH CRGO steel. The secondary aim of our study is to develop an artificial neural network predicting the type and geometric features of a full crack with unknown geometric features using the data obtained. In line with this, our study first produced an MFL measurement system. Then, artificial full crack samples were prepared with different types and geometric features using MOH CRGO steel. These artificial full crack samples were magnetized by being placed in the core of an electromagnet with 50 kHz AC signal and the surface of the material was scanned in a single dimension with a location-controlled fluxgate sensor. Harmonics of signals obtained from the fluxgate sensor were investigated with DSP lock-in amplifier and the amplitude values for the harmonics with greatest variation were uploaded to the computer. Finally, 3 different Bayesian regularized artificial neural networks (BRANN) were developed and trained using the harmonic amplitude values for full crack samples with known geometric features and used to predict the type and geometric features of full cracks with unknown geometric features. The first of these BRANN was used to identify the fixed or variable width of the full crack type, while the others were used to find the geometric features according to the full crack type. For the BRANNs trained with the K-fold cross-validation method, accuracy degrees of R = 0.99934, R = 0.99999 and R = 0.91654 were obtained, respectively. The trained BRANNs provided results compatible with reality for artificial full crack models with unknown crack type and geometric properties.

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.

Instantaneous lift-off distance estimation in magnetic flux leakage testing of steel wire ropes

Abstract Steel wire rope (SWR) defects plagued its application in many important fields, such as cranes, ports, etc. However, the accuracy of SWRs’ local flaws detection based on magnetic flux leakage (MFL) method is susceptible to the lift-off distance. This paper proposes an instantaneous lift-off distance estimation method to study the effect of lift-off distance on MFL detection for SWR. We firstly derive the relation between the lift-off distance and the MFL field strength based on the magnetic dipole model. After obtaining the time-frequency representation of the MFL signal by using multi-synchrosqueezing transform, we extract the instantaneous amplitude (IA) of the strand signal without other noise interference. Finally, the nonlinear least-squares method is used to fit the relation between the IAs of the strand signals and the lift-off distances to achieve the estimation of the instantaneous lift-off distance. A SWR with three local flaws is tested to validate the proposed method. This work provides instantaneous monitoring of lift-off distances, and it can be used in future noise suppressing methods development or quantitative analysis of local flaws.

A Filtering Method for Suppressing the Lift-Off Interference in Magnetic Flux Leakage Detection of Rail Head Surface Defect

Magnetic flux leakage (MFL) detection is a common nondestructive detection method which is usually used to detect the surface defects of steel pipes and rails. To suppress the interference of lift-off on the detection signal of the defects in rail head surfaces, a filtering method is proposed according to the distribution characteristics of the defect leakage magnetic field (LMF) in different directions. The sensor array is used to confirm the reference signal according to the difference between the signals in x and z directions. The installation mode of the sensors is deduced according to the distribution of the defect LMF. The experimental results show that this method can effectively suppress the lift-off interference in the MFL signal of the defects in the rail head surfaces.

Research on the Analytical Model of Improved Magnetic Flux Leakage Signal for the Local Stress Concentration Zone of Pipelines.

Local stress concentrations pose a significant hazard to the safe operation of pipelines. However, the classical analytical model of the magnetic flux leakage (MFL) signal is still unable to effectively quantitatively analyze and accurately evaluate the local stress concentration zone of a pipeline. In this paper, based on the Jiles–Atherton model of the magnetomechanical effect, the mathematical relationship between stress and the magnetization of ferromagnetic material under hysteresis conditions is introduced, and an improved analytical model of the MFL signal based on the magnetomechanical model is established. The influence law of stress intensity on the MFL signal in the local stress concentration zone of the pipeline is calculated and analyzed, and the theoretical calculation results are verified through experiments. Simulation and experimental results show that, considering the hysteresis condition, the stress causes a change in the hysteresis loop of the ferromagnetic material, and the magnetization strength of the material decreases with increasing stress; the effect of stress on the magnetization strength of ferromagnetic materials is most obvious when the external magnetic field is approximately 5 KA/m. The MFL signal on the surface of the local stress concentration zone of the pipe changes abruptly, and the amount of change in the axial amplitude and radial peak-to-peak value of the leakage signal of the pipe tends to increase with the increase in the stress intensity of the local stress concentration zone. A comparison of the analysis with the classical analytical model of the MFL signal shows that the improved analytical model of the MFL signal is more suitable for the quantification study of the local stress concentration zone of the pipeline.

A Comparative Study on Methods of Distinction Between Near- and Far-Side Defects as Techniques Used Alongside with the Magnetic Flux Leakage Testing

Results of the finite element analysis show that a far-side defect in a steel plate, with the depth greater by 10% of the plate thickness than a near-side defect, can produce a very similar magnetic flux leakage (MFL) signal. Due to the fact that a measurement of MFL itself can lead to misclassification of a far-side defect as a near-side one, and thus to underestimation of its depth, a comparative study of three complementary magnetic techniques was performed. The following techniques were studied: surface topology air-gap reluctance system (STARS), residual magnetic flux leakage (RMFL) and stray magnetic flux leakage (SMFL). Numerical results showed that in the case of the STARS and SMFL, defect signatures in signals were observed for investigated near-side defects, but not for far-side defects. The signature of the far-side defect in the RMFL was observed, however its peak-to-peak value was only about 8% of the value corresponding to the near-side defect.

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.

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.

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.

Defect Detection and Quantification from Magnetic Flux Leakage Signals Based on Deep Learning

Defect Detection and Quantification from Magnetic Flux Leakage Signals Based on Deep Learning

Assessing the magnetic flux leakage contraction parameters for the fatigue life prediction of SAE1045 steel specimens

The aim of this paper is to assess the stress concentration zone (SCZ) of a circular smooth specimen at an early stage. Following the detection of the stress concentration zone, the specimen residual life was calculated and compared to the experimental result. Uniaxial loading on the servo-hydraulic testing machine was used for the laboratory testing. The stress concentration tester was used to detect the SCZ on the specimen, based on spontaneous surface micro magnetic signals on the ferromagnetic surface. In addition, cyclic testing for fatigue characteristics was conducted at 70% and 75% of ultimate tensile stress (UTS). The results indicated that the estimated residual life for the 70% UTS was nearly identical to the experimental value, based on two magnetic parameters, i.e, Kmax and the contraction value. Meanwhile, the estimated residual life for the 75% UTS was equivalent to the experimental findings. The minimum and maximum error values for life estimation using both parameters were 0.47% and 24.35%, respectively, for the contraction value. The correlation value, R2, between the estimated life using MMM and experimental life was 0.95, showing good estimation. All the estimated life data using this method was distributed within the range of a 95% to 90% confidence interval. Hence, the proposed method can be used to predict the fatigue life of ferromagnetic components through magnetic flux leakage signals, especially in the stress concentration region.

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.

A novel magnetic flux leakage method based on the ferromagnetic lift-off layer with through groove

In conventional magnetic flux leakage (MFL) detection, a lift-off layer based on air or non-ferromagnetic wear-resistant material is employed to prevent magnetic sensors from wearing. However, the increase of the lift-off will lower the detection sensitivity and reduce the MFL signal amplitude. To address the issue, theoretical analysis was used to investigate the effect of the ferromagnetic lift-off layer on MFL detection in this study. Then, we put forward a new MFL detection method based on the ferromagnetic lift-off layer. A through groove is machined in the ferromagnetic lift-off layer, which creates an air gap below the sensor. The leakage magnetic field (LMF) generated by the through groove increases the local magnetization intensity around the crack on the sample, while the LMF generated by the crack increases the magnetization intensity surrounding the through groove of the lift-off layer. As a result, the magnetic sensor above the through groove detects a greater MFL signal. In addition, the effect of applied magnetization intensity, groove width, and lift-off layer thickness on the detection signal were explored by experiments. The practicality of the proposed method of detecting different size cracks of nearside and farside were studied. In summary, experiments show that the ferromagnetic steel sheet with a through groove strengthens the MFL signal of the nearside and farside cracks.

Signal acquisition system for broken wire of elevator wire rope based on TMR sensor

Aiming at the problems of low detection accuracy, large size and quality of the detection equipment, and difficulty in online monitoring in the magnetic flux leakage detection of elevator wire ropes, a broken wire signal acquisition system was built based on the tunnel magnetoresistance sensor TMR2703, and a dual-loop excitation device was designed and simulated to verify. The dipole model determines the detection direction of the sensor, acquires the magnetic flux leakage signal with the signal preprocessing circuit and the differential detection ring, develops the host computer in LabVIEW, displays the broken wire signal waveform in real time, and stores the collected data in the form of TDMS files, completed the feature extraction of broken wire signals. Experiments show that the system can collect the magnetic flux leakage signal of the wire rope in real time, and can accurately extract the characteristic value of the magnetic flux leakage signal with different numbers of broken wires. The peak value, valley value, peak-to-peak value and signal wave width of the signal have an increase with the number of broken wires. The law of increase can be used to judge the damage degree of the wire rope according to the detection waveform.

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.

A new MFL imaging and quantitative nondestructive evaluation method in wire rope defect detection

As a reliable and flexible loading tool, wire rope has been widely applied in various of industries and practical applications. Nondestructive testing and evaluation for wire rope defect inspection have become necessary methods in safety guaranteeing. However, traditional one-dimensional (1D) and two-dimensional (2D) magnetic flux leakage (MFL) signal processing methods for wire rope nondestructive evaluation (NDE) may lead to miss and false detection, especially under complex operation conditions with strong noise interference. Thus, a new MFL imaging and quantitative defect recognition method is proposed. Based on the reshaped sine function, wavelet transformation and grid entropy matrix reconstruction, five different MFL imaging algorithms are presented and compared. Besides, three MFL image processing methods through the Gabor filtering, morphological filtering, and small object feature extraction as well as the quantitative centroid distance calculation are also investigated. Finally, the quantitative evaluation model for wire rope defect inspection is built and tested in the case study and performance comparison, where six groups of wire rope testing signal recognition results not only verify the feasibility and validity of the proposed quantitative methods, but also demonstrate its great application promise in wire rope defect evaluation under sophisticated conditions. Additionally, advantages and disadvantages of the proposed method as well as the future wok are discussed.

Simulation and experimental study on MFL-based damage detection capability considering velocity condition for railroad NDE

This paper used a magnetic flux leakage (MFL) method compatible with steel structures to analyze quantitative change in a leakage signal due to defects on the surface of a railroad. A numerical simulation using a two-dimensional finite element method (2D-FEM) was used to analyze MFL signals from defects on the railroad. An experiment was then carried out to investigate the capability of the MFL-based non-destructive evaluation (NDE). We also focused on the velocity effect of the MFL signals by analyzing the magnetic hysteresis phenomenon. The quantitative change in leakage signals was determined by selecting depth of the defect and inspection velocity as parameters in a simulation and in an experiment. The MFL signals obtained showed variations that were simultaneously affected by inspection velocity and defect depth. MFL-based damage detection in a railroad is conclusively confirmed to be sufficiently feasible within the range of operational speeds of an inspection train.

Quantitative Study on MFL Signal of Pipeline Composite Defect Based on Improved Magnetic Charge Model.

Pipeline magnetic flux leakage (MFL) internal detection technology is the most widely used and effective method in the field of long-distance oil and gas pipeline online detection. With the improvement of data quantization precision, the influence of stress on MFL signal has been paid more and more attention. In this paper, the relationship between stress and saturation magnetization is introduced based on J-A theory. The analytical model of MFL detection signal for pipeline composite defects is established. The MFL signal characteristics of composite defects are quantitatively calculated. The effect of stress on MFL signal is studied. The theoretical analysis is verified by experimental data and excavation results. The researches show that the saturation magnetization of ferromagnets decreases exponentially with the increase of stress in strong magnetic field. The MFL signal of composite defect is weaker than that of volumetric defects of the same dimension. The axial amplitude and radial peak-to-peak value of MFL signal decrease with the increase of stress around the defect. The axial amplitude and radial peak-to-peak value of MFL signal increase non-linearly with the increase of width and depth of defects. When using MFL signal to judge the defect depth, it is necessary to make clear whether there is stress concentration phenomenon around the defect because the stress will lead to underestimation of the defect depth.