Magnetic Flux Leakage Images Research Articles

Adaptive Local Flaw Detection Based on Magnetic Flux Leakage Images With a Noise Distortion Effect for Steel Wire Ropes

Steel wire rope (SWR) is widely used in industrial scenarios because of its high strength and toughness. To avoid accidents that local flaws (LFs) can cause, SWRs should be inspected regularly. Magnetic flux leakage (MFL) image detection, which is a common SWR inspection method, is inevitably influenced by shaking noise and strand noise. Although noise-reduction methods for these two types of noise have been proposed, the phenomenon of noise distortion has not received enough attention. This paper investigates the noise distortion phenomenon on the MFL image and finds that the distorted noise severely affects the performance of existing noise-feature-oriented (NFO) denoising methods. To solve this problem, we adopt a target-feature-oriented (TFO) denoising method. Specifically, the removal of noise is avoided, instead, an LF-enhancement-based denoising process is proposed. Moreover, to localize LFs in denoised images, a three-stage adaptive localization method mainly based on disjoint region analysis is proposed. The experiment results show that the proposed TFO denoising method significantly improves the denoising performance for images that distorted noise influences. In addition, the proposed adaptive localization method improves the intelligence of the localization process and has better localization performance for images with distorted noise.

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.

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.

Image Registration for Visualizing Magnetic Flux Leakage Testing under Different Orientations of Magnetization

The Magnetic Flux Leakage (MFL) visualization technique is widely used in the surface defect inspection of ferromagnetic materials. However, the information of the images detected through the MFL method is incomplete when the defect (especially for the cracks) is complex, and some information would be lost when magnetized unidirectionally. Then, the multidirectional magnetization method is proposed to fuse the images detected under different magnetization orientations. It causes a critical problem: the existing image registration methods cannot be applied to align the images because the images are different when detected under different magnetization orientations. This study presents a novel image registration method for MFL visualization to solve this problem. In order to evaluate the registration, and to fuse the information detected in different directions, the mutual information between the reference image and the MFL image calculated by the forward model is designed as a measure. Furthermore, Particle Swarm Optimization (PSO) is used to optimize the registration process. The comparative experimental results demonstrate that this method has a higher registration accuracy for the MFL images of complex cracks than the existing methods.

Image Classification Method Based on Improved Deep Convolutional Neural Networks for the Magnetic Flux Leakage (MFL) Signal of Girth Welds in Long-Distance Pipelines

Girth weld defects in long-distance oil and gas pipelines are one of the main causes of pipeline leakage failure and serious accidents. Magnetic flux leakage (MFL) is one of the most widely used inline inspection methods for long-distance pipelines. However, it is impossible to determine the type of girth weld defect via traditional manual analysis due to the complexity of the MFL signal. Therefore, an automatic image classification method based on deep convolutional neural networks was proposed to effectively classify girth weld defects via MFL signals. Firstly, the image data set of girth welds MFL signal was established with the radiographic testing results as labels. Then, the deep convolutional generative adversarial network (DCGAN) data enhancement algorithm was proposed to enhance the data set, and the residual network (ResNet-50) was proposed to address the challenge presented by the automatic classification of the image sets. The data set after data enhancement was randomly selected to train and test the improved residual network (ResNet-50), with the ten validation results exhibiting an accuracy of over 80%. The results indicated that the improved network model displayed a strong generalization ability and robustness and could achieve a more accurate MFL image classification of the pipeline girth welds.

Research on 3D MFL testing of wire rope based on empirical wavelet transform and SRCNN

Magnetic flux leakage (MFL) testing is one of the most effective methods in nondestructive testing of wire rope. However, traditional MFL testing devices have problems such as low recognition rate, single detection dimension and fuzzy magnetic leakage image. Based on the non-saturated magnetic excitation 3D MFL testing device, this paper proposes a wavelet denoising method based on empirical wavelet transform (EWT) to denoise the collected 3D MFL signal. After noise reduction, the Signal-to-Noise Ratio (SNR) and Root Mean Squared Error (RMSE) of the three dimensions have improved. Color imaging technology is used to fuse defect grayscale images into color images, and Super-Resolution Convolutional Neural Network (SRCNN) is applied to MFL images of broken wires. After SRCNN reconstruction, the resolution of defect color images is improved. The color moment feature of the defect color image is extracted as the input of the Elman neural network to quantitatively identify broken wires. Experimental results show that the noise reduction algorithm can effectively suppress the noise in three dimensions, and the broken wires recognition rate after reconstruction has been significantly improved, which verifies the effectiveness of SRCNN in wire rope MFL images.

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

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

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.

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.

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.

Magnetic Flux Leakage Image Enhancement using Bidimensional Empirical Mode Decomposition with Wavelet Transform Method in Oil Pipeline Nondestructive Evaluation

The magnetic flux leakage (MFL) method is the most widely used and cost-effective inspection technique for oil pipeline. However, noise is inevitable in the process of data collection; thus, the image enhancement method can be more intuitive to identify the defects in an oil pipeline MFL inspection. In this paper, the bidimensional empirical mode decomposition (BEMD) method has been used to study oil pipeline MFL images. The MFL image is decomposed into a finite number of two-dimensional intrinsic mode functions (BIMF) and a residual component by the BEMD. The wavelet transform (WT) was used to remove the noise in the BIMF. The image was then reconstructed by retaining the basic information and removing the noise. The results show that the noise is effectively removed and the detail of the MFL image is well preserved. Thus, the BEMD with WT method for oil pipeline MFL image enhancement is better than the mean filtering method and the WT method.

Magnetic flux leakage image classification method for pipeline weld based on optimized convolution kernel

In order to intelligently classify magnetic flux leakage signals, this study proposes a method of magnetic flux leakage image classification based on sparse self-coding. With inputting with the magnetic flux leakage image of the pipe weld, it extracts features automatically from the Convolutional Neural Network (CNN) rather than the artificial extraction process. The network classification ability can be improved through pre-training of the convolution kernel and introducing the sparse constraints and the image entropy similarity constraint rules. The experiment uses 500 images of magnetic flux leakage signals to classify the girth welds and spiral welds. The accuracy of classification is 95.1%, which is superior to the traditional convolution neural network model. Experimental results show that the improved model has good feature extraction ability and generalization ability.

Pipeline Magnetic Flux Leakage Image Detection Algorithm Based on Multiscale SSD Network

In order to solve the problem of low detection accuracy of small targets in the SSD detection algorithm, a pipeline magnetic flux leakage image detection algorithm based on multiscale SSD network is proposed in this paper. The dilated convolution and attention residual module are introduced into the SSD algorithm to fuse the low-resolution high-semantic information feature map with the high-resolution low-semantic information feature map so as to improve the resolution of the low-resolution feature map and provide detailed features for small targets. Finally, the target location and category are obtained by regression algorithm. The experimental results show that the proposed algorithm can automatically identify the location of circumferential weld, spiral weld, and defect of magnetic flux leakage data. Compared with the original SSD algorithm framework, the improved algorithm has higher detection accuracy, 97.62%, 18.01% lower false detection rate, 18.36% lower false detection rate, better robustness, and obvious effect on small target detection.

Tsallis Entropy Segmentation and Shape Feature-based Classification of Defects in the Simulated Magnetic Flux Leakage Images of Steam Generator Tubes

Early detection of water or steam leaks into sodium in the steam generator units of nuclear reactors is an important requirement from safety and economic considerations. Automated defect detection and classification algorithm for categorizing the defects in the steam generator tube (SGT) of nuclear power plants using magnetic flux leakage (MFL) technique has been developed. MFL detection is one of the most prevalent methods of pipeline inspection. Comsol 4.3a, a multiphysics modeling software has been used to obtain the simulated MFL defect images. Different thresholding methods are applied to segment the defect images. Performance metrics have been computed to identify the better segmentation technique. Shape-based feature sets such as area, perimeter, equivalent diameter, roundness, bounding box, circularity ratio and eccentricity for defect have been extracted as features for defect detection and classification. A feed forward neural network has been constructed and trained using a back-propagation algorithm. The shape features extracted from Tsallis entropy-based segmented MFL images have been used as inputs for training and recognizing shapes. The proposed method with Tsallis entropy segmentation and shape-based feature set has yielded the promising results with detection accuracy of 100% and average classification accuracy of 96.11%.

Quantitative Nondestructive Testing of Wire Ropes Based on Features Fusion of Magnetic Image and Infrared Image

Magnetic flux leakage (MFL) detection is one of the most widely used and best performing wire rope nondestructive testing (NDT) methods for more than a decade. However, the traditional MFL detection has the disadvantages of single source of information, low precision, easy to miss detection, and false detection. To solve these problems, we propose a method of fusion recognition of magnetic image features and infrared image features. A denoising algorithm based on Hilbert vibration decomposition (HVD) and wavelet transform is proposed to denoise the MFL signal, and the modulus maxima method is used to locate and segment the defect. An infrared image acquisition system was designed to collect the infrared image of the surface of the wire rope. Digital image processing techniques are used to segment infrared defect images. The features of the MFL image and the infrared image are extracted separately for fusion. The fusion feature is input into the nearest neighbor (NN) algorithm for quantitative identification, and the same data are input into the backpropagation (BP) neural network for comparison verification. The experimental results show that the fusion of MFL features and infrared features effectively improves the recognition rate of wire rope defects and reduces the recognition error.

Modeling and Experimental Studies on 3D-Magnetic Flux Leakage Testing for Enhanced Flaw Detection in Carbon Steel Plates

ABSTRACTFor enhanced detection of flaws in engineering components using magnetic flux leakage (MFL) technique, measurement of the leakage magnetic field components along the three perpendicular directions is beneficial. This article presents the three dimensional-magnetic flux leakage (3D-MFL) modeling and experimental studies carried out on carbon steel plates. Magnetic dipole model has been used for the prediction of MFL signals and images. Sensitivity of the MFL signals peak amplitudes of tangential (HX), circumferential (HY), and normal (HZ) components with respect to flaw length, width, depth and lift-off have been studied. A 3D-GMR sensor has been used for simultaneous measurement of all the three components of leakage magnetic fields from surface flaws in 12 mm thick carbon steel plates. The experimental MFL images have been compared with the model predicted MFL images. The sensor has shown the capability to detect and image 0.9 mm deep surface flaws with a signal to noise ratio of 8 dB. Principal component analysis (PCA)-based image fusion has been performed for fusion of the 3D-MFL images to obtain a geometrical profile of the flaws. Study reveals that 3D-GMR enhances the capability for detection of flaws having irregular geometries.

Evaluation of Composite Wire Ropes Using Unsaturated Magnetic Excitation and Reconstruction Image with Super-Resolution

Estimating the exact residual lifetime of wire rope involves the security of industry manufacturing, mining, tourism, and so on. In this paper, a novel testing technology was developed based on unsaturated magnetic excitation, and a fabricating prototype overcame the shortcomings of traditional detection equipment in terms of volume, sensibility, reliability, and weight. Massive artificial discontinuities were applied to examine the effectiveness of this new technology with a giant magneto resistance(GMR) sensor array, which included types of small gaps, curling wires, wide fractures, and abrasion. A resolution enhancement method, which was adopted for multiframe images, was proposed for promoting magnetic flux leakage images of a few sensors. Characteristic vectors of statistics and geometry were extracted, then we applied a radial basis function neural network to achieve a quantitative recognition rate of 91.43% with one wire-limiting error. Experimental results showed that the new device can detect defects in various types of wire rope and prolong the service life with high lift-off distance and high reliability, and the system could provide useful options to evaluate the lifetime of wire rope.

Injurious or Noninjurious Defect Identification From MFL Images in Pipeline Inspection Using Convolutional Neural Network

This paper proposes an injurious or noninjurious defect identification method from magnetic flux leakage (MFL) images based on convolutional neural network. Different from previous approaches, this method is fed by the MFL images instead of the features of the MFL measurements, and thus it can skip the procedure of feature extraction. Moreover, for convenience, a normalization layer is added to the front of model. In the convolution layers, the rectified linear units are employed as the activation functions to shorten the training period and improve the performance. In addition, two local response normalization layers are also embedded into the proposed structure. We demonstrate the performance of the proposed model using real MFL data collected from experimental pipelines. Benefited from the special structure of the proposed model, this method is robust for shift, scale, and distortion variances of input MFL images. We also present a comparative result of the proposed model and other methods. The results prove that the proposed method can achieve higher accuracy than the traditional approaches.

Quantitative Inspection of Remanence of Broken Wire Rope Based on Compressed Sensing.

Most traditional strong magnetic inspection equipment has disadvantages such as big excitation devices, high weight, low detection precision, and inconvenient operation. This paper presents the design of a giant magneto-resistance (GMR) sensor array collection system. The remanence signal is collected to acquire two-dimensional magnetic flux leakage (MFL) data on the surface of wire ropes. Through the use of compressed sensing wavelet filtering (CSWF), the image expression of wire ropes MFL on the surface was obtained. Then this was taken as the input of the designed back propagation (BP) neural network to extract three kinds of MFL image geometry features and seven invariant moments of defect images. Good results were obtained. The experimental results show that nondestructive inspection through the use of remanence has higher accuracy and reliability compared with traditional inspection devices, along with smaller volume, lighter weight and higher precision.

Characterization of Wire Rope Defects with Gray Level Co-occurrence Matrix of Magnetic Flux Leakage Images

Magnetic flux leakage (MFL) techniques are used extensively for non-intrusively detecting and characterizing wire rope defects. Traditionally, MFL signals are captured with induction coil sensors. However, the output of coil sensors is related to the wire rope speed, and they can only provide the axial distribution along the wire rope. Hall sensors array are designed due to the limitation of coil sensors. In this paper, a Hall sensors array was designed to capture the MFL signals both axially and circumferentially. 30-channel data from Hall sensors are processed to compose a MFL image. A digital image process technique is introduced to preprocess the MFL image, the MFL images from different types of defects show different texture characteristics. Gray level co-occurrence matrix is utilized for feature extraction of the texture in the MFL image. Five typical eigenvalues (contrast, correlation, energy, homogeneity and entropy) are used as the inputs of back propagation (BP) networks. After training with typical samples, the BP networks show good performance in the quantitative recognition of different defects. The result of this work shows that texture analysis method for MFL image is suitable for feature extraction and quantitative detection of wire rope defects.