Magnetic Flux Leakage Detection Research Articles

Magneto-acoustic Fusion Sensing for Surface and Subsurface Defect Differentiation in the Ferromagnetic Material

The conventional periodic permanent magnet (PPM) shear horizontal (SH0) guided wave electromagnetic acoustic transducer (EMAT) has a good detection capability for defects on both the surface and subsurface of the material. However, it is difficult to distinguish between surface and subsurface defects in the material using only the amplitude of the defect echo. In this paper, SH0 guided wave is excited using the SH0 guided wave EMAT with PPM configuration, which is used to achieve the detection and evaluation of defects on the surface and subsurface of ferromagnetic metal; on the other hand, the local magnetic field perturbation (MFP) effect of the PPM configuration on ferromagnetic material is fully exploited to achieve the identification of surface defects by picking up the MFP of the material at the defects. The proposed magneto-acoustic fusion effect sensor has an improved spatial magnetic field at both the mechanical construction and the magnetic sensor compared to the traditional magnetic flux leakage (MFL) detection method. Both simulation and experiment show that the proposed magneto- acoustic fusion effect sensing method based on the PPM configuration is capable of detecting the defects on the surface and subsurface of metal material, while the MFP effect in the fusion sensor can be used to help differentiate between the surface and subsurface defects in the material.

Accurate wire rope defect MFL detection using improved Hilbert transform and LSTM neural network

ABSTRACT Wire rope is playing a significant role in various engineering fields. Although a great number of non-destructive testing methods are applied to wire rope defect detection, the results are greatly influenced by the signal noises and the quantitative defect detection of wire rope is consequently limited. Therefore, a new accurate and quantitative wire rope defect magnetic flux leakage (MFL) recognition method based on improved Hilbert transform and long short-term memory (LSTM) neural network is proposed. The theoretical background of the proposed signal processing models and preliminary results, as well as the influence of the main model parameters are analyzed first. Then, different wire rope defect classification principles through LSTM neural network and performance evaluation based on the improved signal processing and machine learning combined method are presented. Finally, the defect inspection and classification comparison results between the proposed method and other deep learning neural networks and pattern recognition strategies are given, which manifest that the proposed method has higher classification accuracy and shorter running time for five different wire rope defects under various servicing conditions, and is promising in accurate defect inspection for multi-scenario wire rope. Additionally, the main conclusions, disadvantages of the proposed method are summarized.

A defect contour imaging method of small diameter pipeline based on self-made three-dimensional magnetic sensor

Purpose The purpose of this paper is to use the corresponding magnetic sensor and detection method to detect and image the defects of small diameter pipelines. Urban gas pipeline is an energy transportation tool for urban industrial production and social life, which is closely related to urban safety. Preventing the occurrence of urban gas pipeline transportation accidents and carrying out pipeline defect detection are of great significance for the urban economic and social stability. To perform pipeline defect detection, the magnetic flux leakage internal detection method is generally used in the detection of large-diameter long-distance oil and gas pipelines. However, in terms of the internal detection of small-diameter pipelines, due to the heavy weight, large structure of the detection device and small pipe diameter, the detection is more difficult. Design/methodology/approach In order to solve the above matters, self-made three-dimensional magnetic sensor and three-dimensional magnetic flux leakage imaging direct method are proposed for studying the defect identification. Firstly, for adapting to the diameter range of small-diameter pipelines, and containing the complete information of the defect, a self-made three-dimensional magnetic sensor is made in this paper to improve the accuracy of magnetic flux leakage detection. And on the basis of it, a small diameter pipeline defect detection system is built. Secondly, as detection signal may be affected by background magnetic field interference and the jitter interference, the complete ensemble empirical mode decomposition with adaptive noise method is utilized to screen the detected signal. As a result, the useful signal is reconstructed and the interference signal is removed. Finally, the defect contour inversion imaging of detection is realized based on the direct method of three-dimensional magnetic flux leakage imaging, which includes three-dimensional magnetic flux leakage detection data and data segmentation recognition. Findings The three-dimensional magnetic flux leakage imaging experimental results shown that, compared to the actual defects, the typical defects, irregular defects and crack groove defects can be analyzed by the magnetic flux leakage defect contour imaging method in qualitative and quantitative way respectively, which provides a new idea for the research of defect recognition. Originality/value A three-dimensional magnetic sensor is made to adapt the diameter range of small diameter pipeline, and based on it, a small-diameter pipeline defect detection system is built to collect and display the magnetic flux leakage signal.

A defect opening profile reconstruction method based on multidirectional magnetic flux leakage detection

Magnetic flux leakage (MFL) is a popular technique for detecting defects. The opening profile reconstruction is one of the most popular concerns. The commonly used unidirectional magnetization-based methods easily lead to incomplete MFL information to reconstruct the opening profile. The superimposition of MFL signals from different directions would cause information cancellation, making it difficult for the existing data processing methods to reconstruct. To solve the issues, this study constructs a model-based method to inverse defect opening profiles. It builds the forward equations from one magnetic permeability to multidirectional MFL fields. Then the pseudo-inversion with Tikhonov regularization is applied as the inversion algorithm. The algorithm associates the forward equations and finds the solution that can fulfill them simultaneously. Such a method can combine multidirectional MFL information without cancellation. It reduces the ill-posed problems that exist in unidirectional inversion and makes the reconstructed profile complete and accurate. The comparison experiments have been carried out, and the results have proved the high accuracy of the proposed method.

Magnetic Flux Leakage Defect Identification Method for Small-Diameter Pipeline Elbow Based on the Improved YOLOv5

Abstract The elbow is an important constituent of oil and gas pipeline systems and plays a key role in changing the direction of pipelines. Corrosion defects pose a significant risk to the safe operation of elbows. Magnetic flux leakage (MFL) detection has been developed as a suitable technique for identifying defects in pipelines. To address the distortion of elbow defect signals in the images arising from variations in the liftoff value of the leakage detector, this paper proposed an image identification method based on an improved YOLOv5 network. The differences in defect images are simulated by analyzing the liftoff value of the magnetization unit. A defect image enhancement method of multiscale retinex with color restoration fusion homomorphic filtering (MSRCR-HF) is employed to enhance the features of defective MFL signal images. To further improve the accuracy of the model, the YOLOv5 network is optimized by integrating the convolutional block attention module (CBAM) and the space-to-depth-nonstrided convolution (SPD-Conv) module. The results show that the proposed image enhancement method effectively accentuates the features of defect images. Moreover, the suggested image identification method exhibits superior accuracy in identification. The mean average precision (mAP) values for the original image set and the enhanced image set are 85.0% and 91.4%, respectively. Consequently, the proposed method is shown to be highly viable for the automatic identification of MFL defects in small-diameter pipe elbows.

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

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

Improving Pipeline Magnetic Flux Leakage (MFL) Detection Performance With Mixed Attention Mechanisms (AMs) and Deep Residual Shrinkage Networks (DRSNs)

Improving Pipeline Magnetic Flux Leakage (MFL) Detection Performance With Mixed Attention Mechanisms (AMs) and Deep Residual Shrinkage Networks (DRSNs)

An Improved Empirical Wavelet Transform Filtering Method for Rail-Head Surface-Defect Magnetic-Flux Leakage Signal

The rail is an important factor in railway traffic safety. Surface defects in the rail head comprise a common type of rail damage, and magnetic flux leakage (MFL) technology is applied for its detection. MFL detection is influenced by various factors, resulting in high noise and a low signal-to-noise ratio (SNR) in the collected MFL signal, which influence defect assessment. This article improves the empirical wavelet transform (EWT) to apply it to rail surface-defect MFL signal filtering. A boundary optimization method based on mutual information (MI) is proposed to reduce the boundary redundancy caused by adaptive spectrum division. A method for component selection based on MI and kurtosis is proposed to select the suitable components from the decomposed components for signal reconstruction. The experimental results show that the method can effectively filter out the interference in the MFL signal, and the effectiveness is superior to the traditional methods, such as complementary ensemble empirical mode decomposition (CEEMD) and wavelet transform (WT).

Fusion PCAM R-CNN of Automatic Segmentation for Magnetic Flux Leakage Defects.

Magnetic leakage detection technology plays an important role in the long-oil pipeline. Automatic segmentation of defecting images is crucial for the detection of magnetic flux leakage (MFL) works. At present, accurate segmentation for small defects has always been a difficult problem. In contrast to the state-of-the-art MFL detection methodologies based on convolution neural network (CNN), an optimization method is devised in our study by integrating mask region-based CNN (Mask R-CNN) and information entropy constraint (IEC). To be precise, the principal component analysis (PCA) is utilized to improve the feature learning and network segmentation ability of the convolution kernel. The similarity constraint rule of information entropy is proposed to be inserted into the convolution layer in the Mask R-CNN network. The Mask R-CNN optimizes the convolutional kernel with similar weights or higher similarity, meanwhile, the PCA network reduces the dimension of the feature image to reconstruct the original feature vector. As such, the feature extraction of MFL defects is optimized in the convolution check. The research results can be applied in the field of MFL detection.

Research on a Wire Rope Breakage Detection Device for High-Speed Operation Based on the Multistage Excitation Principle

Wire rope breakage, as damage easily produced during the service period of wire rope, is an important factor affecting the safe operation of elevators. Especially in the high-speed elevator operation process, the problem of magnetization unsaturation caused by speed effects can easily lead to deformation of the magnetic flux leakage detection signal, thereby affecting the accuracy and reliability of wire breakage quantitative detection. Therefore, this article focuses on the problem that existing wire rope detection methods cannot perform non-destructive testing on high-speed elevator wire ropes and conducts design and experimental research on a high-speed running wire rope breakage detection device based on the principle of multi-stage excitation. The main research content includes simulation research on the multistage excitation, structural design, and simulation optimization of open–close copper sheet magnetizers and the building of a detection device for wire rope breakage detection experimental research. The simulation and experimental results show that the multistage magnetization method can effectively solve the problem of magnetization unsaturation caused by the velocity effect. The multistage excitation device has a good wire breakage recognition effect for speeds less than or equal to 3 m/s. It can detect magnetic leakage signals with a minimum of four broken wires and has good detection accuracy. It is a new and effective wire breakage detection device for high-speed elevator wire rope, providing important technical support for the safe and reliable operation of high-speed elevators.

A cascaded deep learning approach for detecting pipeline defects via pretrained YOLOv5 and ViT models based on MFL data

Long-distance oil and gas pipeline has become the main mode of transporting oil and gas resources as the demand for fossil energy increases. The complex environment of long-distance pipeline makes the pipeline susceptible to surface defects, which pose a major safety threat. Magnetic flux leakage (MFL) detection is a non-destructive testing (NDT) method that can be used to map and monitor pipeline defects. However, the accuracy and reliability of MFL detection can be severely compromised by the data processing and analytics methods used. This paper presents a novel hybrid method for detecting pipeline defects by combining well-established computer vision algorithms, i.e., YOLOv5 and Vision Transformer (ViT), enabling accurate detection and classification of pipeline defects simultaneously. An in-house laboratory dataset generated by Pull Through Testing (PTT) was used for model training and verification, which contains the manipulated pipeline defects, e.g., metal corrosion, geometrical defect, metal material loss, and geometrical structures etc. The effect of different model architectures on “the defect identification performance was investigated. Comparisons show that the proposed cascaded approach surpasses the YOLOv5 algorithm alone in pipeline defect classification accuracy, while still preserving the versatile ability of defect detection with high precision.

Intelligent identification of girth welds defects in pipelines using neural networks with attention modules

Girth weld defects (crack, lack of penetration, lack of fusion, and edge nibbling) can cause pipeline cracking failure accidents. Internal magnetic flux leakage (MFL) detection can successfully identify pipeline defects, while the intelligent identification of MFL signals based on deep learning can promote the accurate determination of pipeline girth weld defects. Although the YOLOv5 model can effectively identify abnormal image objects, it exhibits no attention preference during the feature extraction process, proving insufficient for small objects. This study targeted minor defects in the girth weld of the pipeline and used the Convolutional Block Attention Module (CBAM) to optimize the YOLOv5 network model structure, increasing detection network attention preference toward extracting small-target defect signals. The CBAM+YOLOv5 model improved the detection accuracy of the MFL signal of the girth weld in the pipeline from 89.33% to 98.11% and correctly identified and classified the MFL signal of the pipeline girth weld with 85% confidence, with minor anomalies. The CBAM+YOLOv5 model effectively improved the identification accuracy of the girth weld defect signal in the pipeline, providing technical support for safety grade assessment and excavation verification.

A Signal Filtering Method for Magnetic Flux Leakage Detection of Rail Surface Defects Based on Minimum Entropy Deconvolution

Magnetic flux leakage (MFL) detection of rail surface defects is an important research field for railway traffic safety. Due to factors such as magnetization and material, it can generate background noise and reduce detection accuracy. To improve the detection signal strength and enhance the detection rate of more minor defects, a signal filtering method based on minimum entropy deconvolution is proposed to denoise. By using the objective function method, the optimal inverse filter parameters are calculated, which are applied to the filtering detection of MFL signals of the rail surface. The detection results show that the peak-to-peak ratio of the defect signal and noise signal detected by this algorithm is 2.01, which is about 1.5 times that of the wavelet transform method and median filtering method. The defect signal is significantly enhanced, and the detection rate of minor defects on the rail surface can be effectively improved.

Exploration and Application of Acoustic Emission Detection Technology in Leakage Detection of the Bottom Plate of Crude Oil Metal Storage Tanks

The use of acoustic emission detection technology can conduct online and non-stop detection and evaluation of crude oil metal storage tanks, achieving the integrity detection and evaluation of storage tanks. In order to explore the applicability of acoustic emission detection technology in the leakage detection of the bottom plate of crude oil metal storage tanks, this project carried out online acoustic emission detection of crude oil metal storage tanks, magnetic flux leakage detection after tank opening and cleaning, and compared and analyzed the results of two detection technologies. The adaptability of online acoustic emission detection technology was analyzed and explored. It was found that acoustic emission technology can qualitatively evaluate the corrosion of tank bottom plates, but cannot accurately locate corrosion defects. For tanks with severe corrosion that require maintenance, it is still necessary to open the tank and use magnetic flux leakage and other detection techniques to determine the location of defects. Through comparative analysis of acoustic emission testing technology and magnetic flux leakage testing technology, the results obtained from acoustic emission testing technology are conservative compared to the actual results. It is recommended to upgrade the evaluation of corrosion degree for the acoustic emission evaluation results of crude oil metal storage tanks.

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.

Design of magnetic flux leakage detector for mine wire rope

At present, the online detection technology of wire rope damage in coal mine hoisting needs to be improved. Given the large number of detection elements and high detection cost in the wire rope detection device, the magnetic flux leakage detection device of the wire rope damage detection system is designed. The magnetic sensor is selected. At the same time, in order to improve the strength of the magnetic leakage field at the defect and reduce the number of magnetic sensors, a magnetic focusing device is designed. The finite element simulation shows that the magnetic focusing device can effectively improve the sensitivity of defect detection. The above research provides the basis for the design of steel wire rope damage detection systems and is of great significance to ensure the safe and efficient operation of mine hoisting equipment.

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.

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.

Design of magnetic flux leakage detection device for three dimensional small diameter pipeline based on MEMS

Based on the common pipeline detection device faced with small diameter pipeline space, small turning radius is prone to the problem of stuck pipe phenomenon, this paper combined with the actual production needs, for small diameter pipeline leakage detection based on MEMS technology designed a detection device. COMSOL Multiphysics software was used to create a finite element model to simulate the magnetic flux leakage detection of pipeline defects. The influence of the type, size and location of pipeline defects on magnetic flux leakage signals was discussed. Based on MEMS triaxial magnetic force sensor, a magnetic flux leakage detection device with small volume, high precision and strong adaptive ability was designed. Through experiments, the magnetic flux leakage detection data consistent with the simulation results were obtained, which provided ideas for solving the detection problem of small-caliber pipelines.

The MFL detection method by the gradient hall sensors array for the cracks on the top surface of the rails

Cracks on the top surface of the rails due to contact fatigue are usually narrow and variable in direction. Thus, more spatial features need to be explored when using the magnetic flux leakage detection method. In this article, we proposed a way to predict the size and direction of the cracks by the gradient hall sensors array. First, the experimental equipment was designed for leakage field signal detection. Then, the peak time differences of the leakage field signal were analyzed to predict the crack angle, and the magnetic gradient modulus was calculated to predict the crack width. Furthermore, the principal component analysis was performed to reduce the number of eigenvalues. With that, BP neural network algorithm was used to predict the length and depth of the cracks. The experimental results indicate that the method can improve the accuracy of crack prediction, and the average relative errors of the prediction for crack angle, width, length, and depth were 5.7%, 15.3%, 9.2%, and 11.2%, respectively.