Magnetic Flux Leakage Research Articles

Defect Size Quantification for Pipeline Magnetic Flux Leakage Detection System via Multilevel Knowledge-Guided Neural Network

Defect size quantification plays a vital role in pipeline magnetic flux leakage (MFL) detection system. However, most existing methods suffer from poor applicability and low precision due to complex industrial process. Moreover, they often overlooks the valuable knowledge that may be beneficial for defect size quantification. To solve this problem, a defect size quantification method based on multi-level knowledge-guided neural network (MLKG-NN) is proposed, which effectively integrates prior knowledge and specific data. First, at the feature level, a recursive residual subnet is proposed to inject the mechanism features into the network, so that the network performance can be enhanced. Second, an experience-aided subnet is proposed to incorporate expert experience at the decision level, which supervises the network training with labels, so that the network stability can be improved. Third, at the modeling level, a cascade expression subnet based on two-point representation is first proposed in the defect size quantification area, where both the value and distribution of labels are considered to boost the network precision. These three parts are jointly trained and promote each other. Finally, extensive experiments are conducted with defects from a pipeline network in northern China and the experimental results highlight the superiority of our method.

Finite element analysis of magnetomechanical coupling behaviour of perforated steel plate

In this paper, finite element analysis is carried out for the stress of a 45# steel specimen with a round hole and its correlation with a magnetic signal. The leakage magnetic field signals of the specimen under different loads are obtained. The results show that the greater the tensile stress is, the greater the stress is at 2 mm above the round hole, and the permeability first increases and then decreases with the increase in stress. The leakage magnetic field signal is correlated with permeability and the tangential component of the magnetic flux leakage signal has a trend of increasing first and then decreasing. The phenomenon of zero crossing of the normal component of the leakage magnetic field signal appears.

Research on Improving the Shielding Effect of Stacked Superconducting Bulks Based on Increasing the Gap Reluctance

Superconducting bulk has great advantages in shielding magnetic fields owing to its high critical current density. Since the shielded area is usually larger than the size of a single bulk, superconducting bulks need to be stacked together to form a shielding layer. However, bulks stacked together can cause the magnetic flux to concentrate at the gaps between bulks. In order to increase the shielding effect, multiple shielding layers are usually required. This increases the shielding cost. In this paper, a shielding structure that makes an angle between gap and external field non-parallel is explored. This structure can increase the gap reluctance and reduce the magnetic flux leakage, which provides a method to get a uniform shielding area in a relatively economical way. The shielding effects are studied by experiment and finite element calculation. The established simulation model can be used to further explore and optimize new shielding structures.

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.

Defect-depth-field algorithm for simulating magnetic flux leakage signals based on discrete magnetic dipole model

Magnetic flux leakage (MFL) testing is widely used for non-destructive detection of defects in ferromagnetic materials, in which fast and accurate processing of MFL signals is critical for reconstructing and evaluating the defects. Of the detection technologies, the existing magnetic dipole model (MDM) can simulate the MFL signals with regular or limited types of defects, but not with complex defects, such as naturally occurring corrosion material losses. With the help of finite element method (FEM), MFL signals of complex defects can be analysed but demanding time-consuming calculation. In this paper, an improved MDM method, i.e., the discrete MDM (DMDM) is proposed, and an algorithm for automatic MFL signals processing is further developed based on the DMDM and the defect-depth-field concept. By comparison, the difference of the simulated MFL signals between the DMDM and the FEM is less than 3%, and the maximum discrepancy of the MFL signals between the simulated and the experimental is about 6%. In addition, the simulation with the DMDM can be thousands of times more efficient than the FEM, indicating significant advantage over the existing method in both improving the accuracy and enabling the fast signal processing of reconstruction for any types of defects in MFL testing.

Methodology for ranking internal corrosion-induced leakage susceptibility in oilfield pipelines

ABSTRACT This investigation focuses on the ranking of pipeline leakage susceptibility of two categories of pipeline systems. One consists of three oilfield water-handling pipeline systems and the other consists of five dry crude systems. Online corrosion monitoring (OCM) method was used for both systems, forthermore, in-line inspection (ILI) method, with Magnetic Flux leakage (MFL) tool, was also used for the dry crude systems. The OCM leakage susceptibility potential is derived from the combined severity of general corrosion, pitting corrosion, fluid corrosivity and sessile bacteria population density in the process streams. The ILI leakage susceptibility potential is derived from the average number of corrosion anomalies greater than 10% of the wall thickness and the maximum depth of the anomalies (as percentage of the wall thickness). In spite of such distinctions, the ranking of pipeline leakage susceptibility for the five dry crude pipeline segments turned out to be identical for the two methodologies.

Low-cost inspection tool for identifying illegal tapping

Pipelines for fuel transportation range in length from hundreds to thousands of kilometers, with part of their length underground. Due to the high cost of the fluids and products transported an increasing number of criminal activities are taking place all over the world. In addition to causing great economic and environmental losses the illegal activity brings risks to the surrounding population. For example, the accident in Tlahuelilpan, Mexico, due to illegal tapping, resulted in the tragedy of hundreds of deaths. In order to combat such activities, many efforts have been employed to early detect and locate illegal tapping in pipelines. The great challenge of such inspections is the unpredictable activity of the clandestine taps, requiring frequent inspections. Usual pipeline inspection tools, such as ultrasound or MFL (Magnetic Flux Leakage) PIG’s, are sensitive for detecting and positioning the anomalies, however, the high operating costs make the recurring application of such tools unfeasible. Focusing on these challenges, a low-cost inspection tool embedded with a minimalist eddy current instrumentation was developed. The results achieved show the capability to detect and differentiate the components existing in the pipeline and illegal taps of different diameters as well as, in a complementary way, the detection of corrosion on the internal surface of the pipelines.

Development of Non-destructive Testing Method for Tube Inspection in Fin-Fan Coolers in Kazakhstan’s Oil/Gas, Chemical and Power Industries.

Many oil/gas fields in Kazakhstan contain high levels of highly corrosive H2S and CO2, sometimes at very high pressures. The management of corrosion is essential in maintaining plant safety and integrity of the processing facility. This paper describes the development of a non-destructive testing (NDT) method that improves the reliability of air-cooled heat exchangers by reducing down-time related to corrosive and erosive failure of fin-fan tubes. The project goal was to maximize the output of oil and gas plants and refineries while reducing the plant operating cost. The work first identified those NDT requirements for air-cooled heat exchangers damage assessment that would provide the greatest economic benefit for Kazakhstan industry. The main objective was to develop a state-of-an-art NDT method for air-cooled heat exchanger tubes, capable to: a) detect any damage mechanism while testing from tube internal diameter, b) accurately determine the damage in terms of wall loss, c) perform inspection quickly and expediently, d) requires minimum tube cleaning. Consequently, the method specially adapted for Kazakhstan conditions was developed based on a combination of Magnetic Flux Leakage (MFL) technique for flaw detection and with Hall effect measurement of wall thickness and gradual corrosion in tubes. The method has been tested in both laboratory and field conditions and the results were compared with accurate but slow ultrasonic IRIS method. High correlation was obtained, which proved that the developed technology is capable to deliver similar results at the speed almost 10 times faster and less than half the cost.

Methodology for Electromagnetic Optimization of a Partially Superconducting 1.4 MW Electric Machine for Electrified Aircraft Propulsion

Large transport aircraft with electrified propulsion systems require MW-class electric machines that have higher efficiency and power-to-weight ratio than existing machines. Superconducting machines are being evaluated as potential solutions. This paper presents a method to optimize the electromagnetic design of a partially superconducting machine in terms of these metrics and the approximate fuel burn of the aircraft. The optimization is based on 2D electromagnetic finite element simulations, a prediction of the superconducting coils' critical current, and geometry relations that design the superconducting coils based on dependent variables while constraining the total cost of superconductor and ensuring adequate space for structure. The design process is completed by (1) a 3D electromagnetic finite element simulation to improve the prediction of torque and assess magnetic flux leakage, (2) refinement of the superconductor width for each individual racetrack coil, and (3) evaluating inverter-produced stator current harmonics on eddy current losses in the rotor.

Multi-Objective Optimization Analysis of Magnetic Gear With HTS Bulks and Uneven Halbach Arrays

To improve the output torque of coaxial magnetic gears, a novel coaxial magnetic gear (CMG) with high temperature superconducting (HTS) bulks and uneven Halbach arrays is proposed in this paper. The convex structure is used in the middle of inner rotor permanent magnets (PMs). The PMs of the outer rotor are arranged in a low-high-low arrangement, epoxy resin is added between the PMs of outer rotor and the outer iron yoke. At the same time, the HTS bulks replace the epoxy resin to reduce magnetic flux leakage. In addition, according to the sensitivity analysis, the structural parameters can be divided into different levels, the multi-objective optimization analysis is carried out. Finally, the optimal CMG topology with 4 inner and 17 outer pole pairs is established. The results show that the amount of PM is reduced and the output torque is effectively increased by finite element analysis (FEA).

AC-MFL testing method for surface crack on reground billets using flexible parallel-cable-based probe

Square billet is an important industrial raw material. Due to the large amount of oxidation skin on the surface after continuous casting process, it needs surface grinding treatment. Meanwhile, the billet surface is prone to stress concentrations and cracks in the crystallization zone, so it is easy to break and cause accidents in the subsequent process. However, the poor surface condition of the reground billet, such as the large roughness and fluctuations in the shape, makes it difficult to be tested by traditional methods. In this paper, the relationship between the excitation frequency of alternative current magnetic flux leakage (AC-MFL) and the fluctuation of magnetic field generated by uneven surface is studied, and the high-frequency AC leakage field formation is restated. Furthermore, a flexible probe integrating parallel cables (PC) magnetizer and receiving coils is proposed, which can realize the profiling testing and reduce the influence of the undulating surface. Finally, the wavelet transform is used to reconstruct signals, which increases the average signal-to noise ratio (SNR) from 7.25 dB to 11.89 dB.

Theoretical and experimental study on the effect of stress corrosion on magnetic flux leakage signals

Stress corrosion is widespread in engineering materials, which seriously threatens the bearing capacity of the structures. The current study aims to establish a Corrosion-Stress-Magnetic coupling model based on weak magnetic detection technology. This paper proposes a uniform plane current model under stress corrosion by analyzing the corrosion mechanism, and the magnetic field generated by the corrosion current is obtained. The Corrosion-Stress-Magnetic coupling model is established based on the magnetic memory method. Then the Magnetic Flux Leakage (MFL) signals of whole uniform stress corrosion specimens are studied experimentally and theoretically. Particularly, the experimental values of the changes in the slope of the normal MFL component and the amplitude of the tangential MFL component are consistent with the computational values, which indicates that the proposed model is correct and effective in predicting the effect of stress corrosion on the magnetization of a specimen.

Physical Coupling Fusion Sensing of MFL-EMAT for Synchronous Surface and Internal Defects Inspection

Non-destructive testing (NDT) techniques are widely used for inspection and evaluation of conductive materials. However, a single physical mechanism sensing cannot simultaneously satisfy multiple inspection requirements. In this paper, we propose a hybrid transducer based on magnetic flux leakage (MFL) and electromagnetic acoustic transducer (EMAT) for synchronous detection of surface and internal defects. In the proposed hybrid transducer, both MFL and EMAT share a common magnetic field. The magnetic circuit characteristics of the MFL and EMAT principles are fully exploited, and the magnetic field provided by a single permanent magnet is used to excite ultrasonic waves that can detect internal defects within the sample. Iron is used to introduce magnetic field into the sample and provide a horizontal magnetic field to detect discontinuities on the near-surface of the sample. The huge frequency difference between the MFL signal and the EMAT signal effectively suppresses interference between the two signals. Simulations and experiments have been undertaken to show that the proposed transducer can overcome the detection limitations associated with the MFL and the dead detection zone of the EMAT, while simultaneously detecting the surface defects, internal defects and bottom thinning defects in the specimen.

DPGWO Based Feature Selection Machine Learning Model for Prediction of Crack Dimensions in Steam Generator Tubes

The selection of an appropriate number of features and their combinations will play a major role in improving the learning accuracy, computation cost, and understanding of machine learning models. In this present work, 22 gray-level co-occurrence matrix features extracted from magnetic flux leakage images captured in steam generator tubes’ cracks are considered for developing a machine learning model to predict and analyze crack dimensions in terms of their length, depth, and width. The performance of the models is examined by considering R2 and RMSE values calculated using both training and testing data sets. The F Score and Mutual Information Score methods have been applied to prioritize the features. To analyze the effect of different machine learning models, their number of features, and their selection methods, a Taguchi experimental design has been implemented and an analysis of variance test has been conducted. The dynamic population gray wolf algorithm (DPGWO) has been adopted to select the best features and their combinations. Due to the two contradictory natures of performance metrics, Pareto optimal solutions are considered, and the best one is obtained using Deng’s method. The effectiveness of DPGWO is proved by comparing its performance with Grey Wolf Optimization and Moth Flame Optimization algorithms using the Friedman test and performance indicators, namely inverted generational distance and spacing.

The defect depth evaluation based on the dual-sensor strategy: Resisting the lift-off disturbance in magnetic flux leakage testing

The quantitative evaluation of defect information is the key issue in magnetic flux leakage (MFL) nondestructive testing on ferromagnetic materials. For the multi-sensor strategy with the same lift-off values, the arrayed layout of probes improves the testing efficiency, but the evaluation precision is still susceptible to the lift-off disturbance. This paper recommends a dual-sensor strategy with a given lift-off difference to resist the lift-off disturbance. Here, the reason for the possible overestimating and underestimating evaluation is clarified for the traditional single-sensor strategy using the magnetic dipole theory in MFL testing. In this paper, the effectiveness of the proposed dual-sensor strategy is demonstrated by comparison with the traditional single-sensor strategy. Analysis shows that the proposed dual-sensor strategy in MFL testing can overcome the lift-off disturbance and improve evaluation precision of the defect depth in cases with uncertain lift-off values. This study provides a path for the quantitative evaluation of MFL testing based on the dual-sensor strategy.

Corrosion Detection and Surface Repair with Coatings on Condensate Storage Tanks Internal Surfaces

In the Nuclear power plant Krško, there are two single hull condensate storage tanks with floating diaphragm each containing up to 757 m3 of demineralized water. The main purpose of these two storage tanks is to provide a capacity of cooling water for cooling the reactor coolant system via steam generators with the use of auxiliary feedwater pumps. This is a very important function from the safety point of view and that is the reason that both storage tanks are listed as safety class 3 components. It is also possible to fill up the condensate system if other means are not available. Condensate storage tanks are subject to periodic testing and periodic inspections to determine the condition of the components. Both tanks are in operation since the start-up of the powerplant, are located outside, and are exposed to different degradation processes. There was a concern that the tanks are leaking because there were often small puddles of water near the tanks. There were no changes in the levels of tanks. The design of tanks is a single hull, so there is no indication if a small leak is present.In the outage 2018, both tanks were emptied and examined with NDE methods to find any corrosion damage of bottom plates and any untight spots on adjacent welds.The article is about the NDE methods that were used (Magnetic Flux Leakage, Ultrasonic and Vacuum Box inspection) to determine the condition of the floor plates and adjacent welds as well as the process of internal surface reparation with coatings. Process of coatings qualification for use in safety class components is also explained: dedication process for material up-grade from non-safety related to safety-related because CY tank linings are classified as safety-related according to RG 1.54, rev 2 and corresponding ASTM standards and NEK technical specification SP-A5001. All activities for surface repair with coatings shall comply with safety-related requirements. Also, extensive immersion tests with selected and specially defined parameters were performed in NEK chemical laboratory in order to select the most suitable coating system for surface repair of CY tank floor lining. Further details concerning immersion tests are presented below.

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.

Numerical analysis and experimental research on detection of welding defects in pipelines based on magnetic flux leakage

Regular inspection of long-distance oil and gas pipelines plays an important role in ensuring the safe transportation of oil and gas, and inspection on welding defects is an important part of the inspection process. Magnetic flux leakage (MFL) is an electromagnetic non-destructive testing technique which has been commonly utilized to detect welding defects in pipelines. In the present study, Maxwell electromagnetic simulation software was used to carry out numerical study on the welding defects in pipelines, including incomplete penetration and undercut. The Ф406 pipeline with a wall thickness of 7 mm was selected as the study case to establish the numerical model. Setting the life-off value at 1 mm, the distribution of magnetic leakage field was investigated for pipeline without defect, pipeline with incomplete penetration defect and pipeline with undercut defect respectively, the characteristic values describing the depth and width of defects were found. Furthermore, quantified equations which can be used to describe the defect depth were proposed. Finally, experimental research was carried out to validate the effectiveness of the numerical model, and the experimental results showed good consistence with the numerical calculation results. The research results indicate that, it is technically feasible and reliable to diagnose the incomplete penetration and undercut welding defects in pipelines using MFL.

Detection of crack on a mild steel plate by using a magnetic probe incorporating an array of fluxgate sensors

Magnetic flux leakage (MFL) and eddy current testing (ECT) are two of the most commonly used techniques to detect cracks in ferromagnetic materials. The MFL technique excels in determining the locality of a crack, while the ECT technique can provide useful information regarding the dimension and orientation of cracks. The objective of this research is to propose a simultaneous application of both MFL and ECT techniques in a single probe and to improve the visual resolution of the defect by superimposing signals from a sensor array. A magnetic probe was developed with an array of fluxgate sensors and two excitation coils. Line scan measurements and 2D mapping of artificial slits with different depths were conducted on a mild steel SS400 plate with a thickness of 12 mm to evaluate the performance of the developed probe. The line scan measurement results demonstrated the ability of the probe to detect the presence of artificial slits on the sample and to predict the depth of the slits, especially at the 110-Hz excitation frequency. Then, from the 2D mapping results, the location of the slit was determined. Furthermore, by obtaining a superimposed 2D map generated by each sensor, an improved 2D map resolution with lower background noise was obtained.

An Intelligent Defect Detection Approach Based on Cascade Attention Network Under Complex Magnetic Flux Leakage Signals

Magnetic flux leakage (MFL) detection robots are broadly employed in acquiring MFL signals to detect pipeline defects. However, influenced by the complex pipeline environments, the accuracy of defect detection under complex MFL signals is undesirable. To resolve this problem, a cascaded attention feature fusion network (CAFF-Net) is proposed. First, a novel feature enhancement method named bidirectional compression is proposed to enhance the feature expression of defects. Second, a multi-level attention feature extraction module is presented, which consists of two cascade networks with different attention mechanisms, so that the explicit and implicit features can be fully extracted. Third, a feature aggregation module is put forward to fuse adjacent feature maps from the feature extraction module, which enhances effective dissemination between features. Finally, a feature loss function based on the mechanism feature is designed, and the CAFF-Net model is trained with the proposed loss function. The proposed method can focus on the multi-level explicit and implicit features of complex defects more effectively, and introduces mechanism feature to guide the training of the network for the first time. The proposed method is evaluated by real-world pipelines. The results show that the detection accuracy of the proposed method for complex defects can reach 92% on average, which is 6.4% higher than the best result of SOTA methods.