Magnetic Flux Leakage Testing Research Articles

A Novel Magnetic Flux Leakage Testing Method Based on AC and DC Composite Magnetization

The flexible printed coil array (FPC) is employed extensively because of its flexibility in non-destructive testing (NDT). To make the inductive coil detect both external and internal defects under static conditions in magnetic flux leakage testing (MFL), this paper proposes a method based on the AC and DC composite magnetization. The AC magnetization changes the magnetic field strength in the vicinity of external defects. The DC magnetization makes the defects generate static leakage magnetic field. The permeability in the skin layer is also altered by the AC magnetization which affects the transmission of leakage magnetic field caused by internal defects. Therefore, external and internal defects can generate a time-varying leakage magnetic field. Simulations and experiments are carried out to validate the feasibility of this method. The results indicate that the method based on the AC and DC composite magnetization can be applied to detect both external and internal defects.

Imaging of Defect Signal of Reinforcing Steel Bar at High Lift-Off Using a Magnetic Sensor Array by Unsaturated AC Magnetic Flux Leakage Testing

Reinforcing steel bars (rebars) in the concrete need to be inspected to prevent the structure from collapsing. Magnetic nondestructive testing methods can inspect the rebar without the influence of concrete. However, since the inspection is performed at a high lift-off on concrete, a strong applied magnetic field is required in order to measure a sufficient magnetic signal generated from the rebar. In addition, if a single sensor is used to measure the rebar, this requires the scanning of extensive area along the invisible rebar covered with the concrete. To overcome these problems, we have developed a method to detect and image the defect signal using unsaturated ac magnetic flux leakage (USAC-MFL) testing combining a magnetic sensor array probe. The USAC-MFL is a method that uses an alternating magnetic field in the initial magnetization characteristics of steel, which does not require a strong applied magnetic field compared to the conventional MFL method. By measuring the magnetic flux from the rebar in a wide area using the magnetic sensor array probe, the defect signal was able to be detected and imaged. The defect signal was able to be confirmed up to the lift-off of 100 mm even when the defect was half length of the rebar or located on the back side.

Application of Pseudo-color Image Feature-Level Fusion in Nondestructive Testing of Wire Ropes

Among the many nondestructive testing methods of ferromagnetic materials, magnetic flux leakage (MFL) testing is a very mature technology. Due to the single source of target information in traditional MFL detection technology, the recognition accuracy is low. Infrared image is a color artificially given, so it is also a pseudo-color image. We use the feature-level image fusion technology to fuse color infrared images and pseudo-color MFL images and quantitatively identify the defect feature vectors after fusion. A wavelet noise reduction algorithm is used to perform noise reduction processing on the MFL signal and used the pseudo-color transform method to convert the noise-reduced MFL signal into a pseudo-color image. For the collected infrared images of the wire rope, we use an image segmentation algorithm to extract the defective parts. The color moments and texture features of the pseudo-color MFL defect image and the color infrared defect image are extracted for feature-level fusion and trained as the input of the nearest neighbor algorithm. The final experimental results prove that the fusion of the two images information has achieved good results and the defect recognition rate has been improved.

Electromagnetic Testing of Moisture Separation Reheater Tube based on Multivariate Singular Spectral Analysis

Moisture separator reheater (MSR) tubing systems are an important part of a pressurized-water power plant to increase the efficiency of the heat transfer rate. The MSR tubes are finned tubes which are made of ferritic stainless steel (SS439) with a high strength and corrosion resistance characteristics. However, corrosion can appear along with the fins after a long period of operation of the MSR tubes that requires nondestructive testing (NDT) of the MSR tubes’ periodically. Electromagnetic testing (ET) is an efficient NDT method for the inspection of far-side corrosion in the MSR tubes. However, the ET sensor signal is affected by signal noise from the fins. Material degradation that make it challenging to inspect and evaluate the corrosion. In this study, we proposed three ET methods, including magnetic flux leakage testing, eddy current testing and partial saturation eddy current testing, and incorporated with a multivariate singular spectral analysis (MSSA) filter to improve the detectability of the corrosion in the MSR tubes. The proposed MSSA filter was compared with the multivariate wavelet transform filter and Gabor transform filter, and the results showed more efficient and stable results of the MSSA filter in the extraction of the corrosion signal.

Data Recovery of Magnetic Flux Leakage Data Gaps Using Multifeature Conditional Risk

Safe transmission of oil pipelines is one of the guarantees of national defense and environmental protection. Magnetic flux leakage (MFL) testing is critical to the safety inspection of in-service pipelines. In the detection process, the incompleteness of MFL data affects defect location and inversion severely. This article proposes an MFL data recovery method based on multifeature condition risk, which can effectively handle the block data gap problem. First, a preprocessing mechanism is proposed to determine defect boundaries and uniformly interpolate the raw data automatically. Second, a multifeature extraction method of MFL defect data is proposed, which takes full advantage of the complete data and reduces the impact of data gaps. Third, a novel data reconstruction method based on feature conditional risk is proposed, where prior information of MFL data is regressed, relying on the regression coefficient calculated by dynamic programming. Finally, comparison experiments on different sizes of data gaps, varying robustness, and average running time are conducted, respectively. The MFL data are derived from actual measurements. The results indicate that the proposed method is more robust, more efficient, and faster. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This article is motivated by the problem of data gaps in magnetic flux leakage (MFL) data in actual pipeline safety transmission measurements, which seriously affects defect inversion and assessment. In this article, in terms of the intrinsic characteristics for MFL data, we propose a new and effective approach by extracting the multifeature of defects and using conditional risk to recovery data gaps. Our method takes advantage of the no-missing part of MFL data and prior samples, which are obtained from practical measurements. The experiment results show that our proposed method is more advantageous than the comparison methods, and therefore, our proposed method has strong practical value. Our future work will aim at improving the versatility of the proposed method.

A Heuristic Algorithm for the Reconstruction and Extraction of Defect Shape Features in Magnetic Flux Leakage Testing

The first step to quantifying complex defects is knowing its geometry, shape, and orientation. This is evidently challenging in electromagnetic nondestructive testing (ENDT), especially for subsurface complex defect detection, in cases where no information about defect or material is known or given. In this article, we propose a heuristic approach for the visualization, verification, and validation of complex defects in magnetic flux leakage (MFL) testing. This method is based on MFL experiment using magneto-optical images (MOIs) that are obtained from four different magnetization patterns. Using the proposed magnetization patterns, images of complex defect are captured with the aid of a charge-coupled device (CCD) camera, based on the interaction of magnetic field distribution and detected defects, from different angles and directions. An enhanced ant colony algorithm (EACA) is then used to reconstruct complex defect shapes from captured images using a mean image approach. The reconstructed image (mean image) reveals the defect shape with high precision for which the EACA is able to extract important defect features such as specific edges that might be hidden or blurred. This approach based on results has proved to provide promising solution to visually verifying complex defects in MFL, leading to a more simplified and faster way to characterize these defects as compared with conventional methods, using their shapes and orientation.

A new signal processing method for the nondestructive testing of a steel wire rope using a small device

Problems arise in the detection of defects in wire rope by magnetic flux leakage testing. The most obvious problems are that the excitation devices are cumbersome and inconvenient and the signal-to-noise ratio of damage signals is often low. This paper proposes a small detector. Compared with traditional detection devices, the proposed device has the advantages of a simple structure, easy installation, low weight and good portability. The weight of the device is only 508 g. The surface of the wire rope is characterized by wires and strands. The amplitude of the strand-waveform magnetic signal generated by the special helical structure of wire rope is larger than that of the defect signal. The magnetic image is processed using an adaptive spatial notch filter and the gradient method, and the detection results are analyzed. The strand-waveform magnetic signal cannot be completely eliminated, and not all defects can be detected. A new method is proposed to eliminate strand-waveform noise using the instantaneous phase solution in Hilbert transform and wavelet analysis. Experiments show that the proposed method suppresses noise well and detects and accurately locates all defects.

Skin depth and detection ability of magneto-optical imaging for weld defects in alternating magnetic field

To detect and evaluate weld defects, the skin depth and detection ability of magneto-optical imaging (MOI) for weld defects in alternating magnetic field were studied, and the application scope of MOI in alternating magnetic field was determined. A model of magnetic flux leakage testing (MFL) for weld defects is established by finite element method, and the reliability of the model is determined by law analysis and MOI experimental verification. Comparing MFL and eddy current detection with simulation method, the skin effect mechanism and rules of MOI for weld defects in alternating magnetic field are analyzed and obtained. Considering the effect of skin effect, different MFL detection models are established, and the detection ability of MOI for weld defects with different frequencies under corresponding models is studied and obtained. Finally, the MOI experiment is used to verify the results, which are consistent with the simulations. The skin effect rule and detection ability of MOI for weld defects obtained by simulation method can be used as the theoretical basis for practical experiments.

Defect size estimation method for magnetic flux leakage signals using convolutional neural networks

A method for defect size estimation of magnetic flux leakage (MFL) signals using convolutional neural networks (CNNs) is proposed to overcome the problem of quantitative identification of pipeline MFL testing. The model mainly includes two modules: defect classification and defect size regression. The former is used to realise data fusion, feature extraction and tasks of three components (axial, circumferential and radial) of the MFL defect signal. The defect size regression module includes seven CNNs, which realise the size estimation of different types of defect. The input is a different type of defect in the defect classification module and the output is the length, width and depth information of the defect. Finally, the training and prediction are conducted using a defect dataset. The results show that the proposed method can effectively identify the MFL defect size of the pipeline.

A new magnetic structural algorithm based on virtual crack closure technique and magnetic flux leakage testing for circumferential symmetric double-crack propagation of X80 oil and gas pipeline weld

Based on the virtual crack closure technique for finite element numerical simulation, a new magnetic structural algorithm is proposed to analyze fracture signals from nondestructive testing. As an example of solving engineering problems, this algorithm is employed to investigate the circumferential symmetric double-crack propagation in an X80 oil and gas pipeline welding zone. The material property of the welding zone is treated as bilinear kinematic hardening elastic–plastic, and the fluid pressure load on the inner wall of the pipeline weld is dynamically applied. In our magnetic structural multi-physics field model, every time when the incremental crack propagation is completed, the mesh is reconstructed. As a result, the crack propagation and magnetic field is analyzed cyclically. Six characteristic quantities (P, $$G_{ I}$$, $$L_\mathrm{g}$$, CTOA, $${Bx}^p$$, $${\hbox {Ms}}^\mathrm{p}$$) in the process of crack propagation are computed, forming the magnetic structural algorithm for circumferential symmetric double-crack propagation. The results show that using the algorithm can judge the damage location and damage degree of the pipeline weld by calculating the crack growth process. The algorithm has high sensitivity which can distinguish the double cracks whose circumferential spacing is greater than or equal to 0.05 times of the circumferential arc length of the weld. When the circumferential spacing of double cracks is less than or equal to 0.4 times of the circumferential arc length of the weld seam, the single crack grows faster than the double crack due to the interference effect of double cracks, and the existence of double cracks inhibits the crack growth. Through this practical example, it is proved that the implementation of the proposed algorithm can provide a theoretical basis for guiding the actual safety assessment of engineering materials and structures containing microcracks.

Miniaturized magneto-optical imaging sensor for crack and micro-crack detection

A highly-sensitive magneto-optical Faraday sensor was miniaturized, including Bi-substituted yttrium iron garnet (Bi: YIG) thin films. The magnetic field over the surface of the cracked steel was measured by using this sensor. The results demonstrated that this sensor could illustrate the micro-cracks on the surface and subsurface of the sample and was a proper candidate for Magnetic Flux Leakage method. In addition, the possibility of the crack and micro-crack detection was evaluated by utilizing a magneto-optical miniaturized sensor. The experimental setup encompassed miniaturized optical setup, Bi: YIG thin films, and some samples with different cracks and micro-cracks. The results of the present study revealed that the sensor can focus on the sample up to 1 μm and its output represented good sensitivity to the crack parameters. This technique can be proficient to high resolution and miniaturized magneto-optical sensor since it can find the cracks and micro-cracks on the sample surface for application in magnetic flux leakage test industry.

Investigation of a Magnetic Tunnel Junction Based Sensor for the Detection of Defects in Reinforced Concrete at High Lift-Off.

Magnetic flux leakage (MFL) testing is a method of non-destructive testing (NDT), whereby the material is magnetized, and when a defect is present, the magnetic flux lines break out of the material. The magnitude of the leaked magnetic flux decreases as the lift-off (distance from the material) increases. Therefore, for detection at high lift-off, a sensitive magnetic sensor is required. To increase the output sensitivity, this paper proposes the application of magnetic tunnel junction (MTJ) sensors in a bridge circuit for the NDT of reinforced concrete at high lift-off. MTJ sensors were connected to a full-bridge circuit, where one side of the arm has two MTJ sensors connected in series, and the other contains a resistor and a variable resistor. Their responses towards a bias magnetic field were measured, and, based on the results, the sensor circuit sensitivity was 0.135 mV/mT. Finally, a reinforced concrete specimen with a 1 cm gap in the center was detected. The sensor module (with an amplifier and low pass filter circuits) could determine the gap even at 50 cm, suggesting that MTJ sensors have the potential to detect defects at high lift-off values and have a promising future in the field of NDT.

Gradient Feature Extract for the Quantification of Complex Defects Using Topographic Primal Sketch in Magnetic Flux Leakage

Abstract Defect quantification is a very important aspect in nondestructive testing (NDT) as it helps in the analysis and prediction of a structure's integrity and lifespan. In this paper, we propose a gradient feature extraction for the quantification of complex defect using topographic primal sketch (TPS) in magnetic flux leakage (MFL) testing. This method uses four excitation patterns so as to obtain MFL images from experiment; a mean image is then produced, assuming it has 80–90% the properties of all four images. A gradient manipulation is then performed on the mean image using a novel least-squares minimization (LSM) approach, for which, pixels with large gradient values (considered as possible defect pixels) are extracted. These pixels are then mapped so as to get the actual defect geometry/shape within the sample. This map is now traced using a TPS for a precise quantification. Results have shown the ability of the method to extract and quantify defects with high precision given its perimeter, area, and depth. This significantly eliminates errors associated with output analysis as results can be clearly seen, interpreted, and understood.

New Discoveries on Electromagnetic Action and Signal Presentation in Magnetic Flux Leakage Testing

Based on the authors’ previous work of magnetic flux leakage (MFL) quantitative description and the research plan especially for the magnetic component of eddy current effect factor $$ B_{other} $$, further explorations for electromagnetic actions, magnetic and signal presentations in the defect in electromagnetic nondestructive testing (ENDT, such as direct current field measurement (DCFM), MFL and eddy current testing (ECT)) are continued here. Primarily, new discoveries indicate that the magnetic component of $$ B_{other} $$ is actually composed of the secondary magnetic field component of $$ \Delta B_{mfl} $$ which manifests as raised signal waves and volume current perturbation component of $$ \Delta B_{\Delta I} $$ which features as double peaked signals due to the departure or bypassing of the “volume current” in the defect. What’s more, the concrete expressions of magnetic components are deduced and given through Ampere’s (or the Ampere–Maxwell) law. Afterwards, the theory is well demonstrated and verified by means of finite element analysis for current density and magnetic flux density or MFL signals in typical quasi-static testing by DC magnetization for different specimens with defects. Additionally, suggestions for installation site of sensor elements in ENDT are also given. Finally, a unified evaluation standard for magnetic flux density is formed, which provides the basic theory or analysis principles for the ENDT techniques as well as research directions for precision defect detection and related applications.

Analysis of MFL Model for Sucker Rod Defects and Its MFL Signal Processing

Abstract Magnetic flux leakage (MFL) testing is a nondestructive testing technique widely used in the petroleum industry and pipeline transportation. In this article, the double-coil magnetization method was used to analyze and solve the MFL model of a sucker rod defect, and the collected MFL signals were processed and analyzed. The model of the sucker rod defect was established using the magnetic dipole model, and the leakage magnetic field intensity in the tangential and normal directions above the defect was solved. Then, the influences of the defect width, defect depth, and lift-off value on the leakage magnetic field were obtained. The magnetic field gradients of the MFL signals were deduced and discussed, and the trend of the magnetic field gradients in different directions were obtained. The cubic spline interpolation method (CSIM) was introduced and was applied to the interpolation of MFL data. Finally, the MFL testing platform of the sucker rod defect was constructed, and the collected MFL data were denoised by the wavelet filtering (WF) method; subsequently, the magnetic gradients of the MFL signals were analyzed. The experimental result shows that the continuous MFL signals can be achieved by using the CSIM, and the WF not only maintains the characteristic information of the effective signals but also has a good denoising effect and makes the signals smooth. Simultaneously, the result also indicates that the magnetic field gradients are good tools for analyzing the MFL signals, which reflect the characteristic information of the defect.

헬륨홀츠 코일로 자화시킨 자속누설탐상의 수치모델링 연구

헬륨홀츠 코일로 자화시킨 자속누설탐상의 수치모델링 연구

An Opening Profile Recognition Method for Magnetic Flux Leakage Signals of Defect

The defect opening profile recognition is of great concern in the magnetic flux leakage (MFL) measurement technique. The detected spatial MFL signal has three components: horizontal, vertical, and normal components. Horizontal and normal component signals are commonly used to estimate the defect profile, while the vertical component has always been neglected. With the development of the high resolution and the 3-D MFL testing techniques, the vertical component signal is becoming more available. This paper analyzes the essential right-angle features of the vertical component signal, which is useful for the defect opening profile recognition. After obtaining the initial profile from the horizontal or normal component, the types of the right angle is identified from the vertical component, and the opening profile is further optimized based on these right-angle features. The opening profile recognition method is put forward in this paper to improve the accuracy of the recognition result of the defect. Both simulation and experimental tests are conducted to verify the good performance of the proposed method. Compared with the opening profiles recognized merely by the horizontal component signal, the proposed method shows better recognition results, which also validates that the vertical component signal can also be a useful information for the defect estimation.

Pipe Crack Recognition Based on Eddy Current NDT and 2D Impedance Characteristics

Girth weld cracking of long-distance oil and gas pipelines yields substantial harm to pipeline safety and may cause serious accidents. As of today, non-destructive testing has been one of the most common methods for predicting potential faults and ensuring safe operation. Classical pipeline non-destructive testing methods include magnetic flux leakage testing and the use of ultrasonic testing by electromagnetic acoustic transducers. However, they are incapable of identifying the defects in complex surfaces like girth welds. Magnetic flux leakage testing exhibits poor anti-interference abilities and low space resolution. Ultrasonic testing by electromagnetic acoustic transducers suffer from low conversion efficiency and poor signal quality. In order to overcome the disadvantages of conventional pipeline non-destructive testing methods, we propose an embedded eddy current testing system by leveraging image processing and neural networks. Hough transform and the contour extraction technique are employed to extract the characteristic features from the two-dimensional (2D) eddy current impedance image. Experiment results show that the system can effectively identify the girth weld defects, featuring an accuracy of up to 92%. The low power consumption and compactness of the proposed system makes it a great candidate for pipeline inner inspection.

Quantitative identification of microcracks through magnetic flux leakage testing based on improved back-propagation neural network

Quantitative identification of microcracks through magnetic flux leakage testing based on improved back-propagation neural network

Development of Magnetic Flux Leakage Testing Using Residual Magnetism of Pulsed Magnetization

Development of Magnetic Flux Leakage Testing Using Residual Magnetism of Pulsed Magnetization