Pipelines play an important role in the national/international transportation of natural gas, petroleum products, and other energy resources. Pipelines are set up in different environments and ...consequently suffer various damage challenges, such as environmental electrochemical reaction, welding defects, and external force damage, etc. Defects like metal loss, pitting, and cracks destroy the pipeline’s integrity and cause serious safety issues. This should be prevented before it occurs to ensure the safe operation of the pipeline. In recent years, different non-destructive testing (NDT) methods have been developed for in-line pipeline inspection. These are magnetic flux leakage (MFL) testing, ultrasonic testing (UT), electromagnetic acoustic technology (EMAT), eddy current testing (EC). Single modality or different kinds of integrated NDT system named Pipeline Inspection Gauge (PIG) or un-piggable robotic inspection systems have been developed. Moreover, data management in conjunction with historic data for condition-based pipeline maintenance becomes important as well. In this study, various inspection methods in association with non-destructive testing are investigated. The state of the art of PIGs, un-piggable robots, as well as instrumental applications, are systematically compared. Furthermore, data models and management are utilized for defect quantification, classification, failure prediction and maintenance. Finally, the challenges, problems, and development trends of pipeline inspection as well as data management are derived and discussed.
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Simulation optimization of complex geological bodies is a necessary means to improve inversion accuracy and computational efficiency; thus, inversion of magnetotelluric (MT) based on unstructured ...grids has become a research hotspot in recent years. This article realizes the three-dimensional (3D) finite element forward modeling of MT based on the magnetic vector potential-electric scalar potential method, using unstructured grids as the forward modeling grid, which improves computational efficiency. The inversion uses the limited-memory Broyden–Fletcher–Goldfarb–Shanno (LBFGS) method, and in the process of calculating the objective function gradient, the quasi-forward method is used to avoid solving the Jacobian matrix, which has the advantages of requiring small storage space and fast computational efficiency. Finally, the 3D LBFGS inversion algorithm of MT based on unstructured grids was realized, and the inversion studies of classic and complex models verified the effectiveness and the reliability of the algorithm proposed in this article.
Carbon fiber-reinforced polymer (CFRP) is a widely-used composite material that is vulnerable to impact damage. Light impact damages destroy the inner structure but barely show obvious change on the ...surface. As a non-contact and high-resolution method to detect subsurface and inner defect, near-field radiofrequency imaging (NRI) suffers from high imaging times. Although some existing works use compressed sensing (CS) for a faster measurement, the corresponding CS reconstruction time remains high. This paper proposes a deep learning-based CS method for fast NRI, this plugin method decreases the measurement time by one order of magnitude without hardware modification and achieves real-time imaging during CS reconstruction. A special 0/1-Bernoulli measurement matrix is designed for sensor scanning firstly, and an interpretable neural network-based CS reconstruction method is proposed. Besides real-time reconstruction, the proposed learning-based reconstruction method can further reduce the required data thus reducing measurement time more than existing CS methods. Under the same imaging quality, experimental results in an NRI system show the proposed method is 20 times faster than traditional raster scan and existing CS reconstruction methods, and the required data is reduced by more than 90% than existing CS reconstruction methods.
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Compressed sensing (CS) is a popular data compression theory for many computer vision tasks, but the high reconstruction complexity for images prevents it from being used in many real-world ...applications. Existing end-to-end learning methods achieved real time sensing but lack theory guarantee for robust reconstruction results. This paper proposes a neural network called RootsNet, which integrates the CS mechanism into the network to prevent error propagation. So, RootsNet knows what will happen if some modules in the network go wrong. It also implements real-time and successfully reconstructed extremely low measurement rates that are impossible for traditional optimization-theory-based methods. For qualitative validation, RootsNet is implemented in two real-world measurement applications, i.e., a near-field microwave imaging system and a pipeline inspection system, where RootsNet easily saves 60% more measurement time and 95% more data compared with the state-of-the-art optimization-theory-based reconstruction methods. Without losing generality, comprehensive experiments are performed on general datasets, including evaluating the key components in RootsNet, the reconstruction uncertainty, quality, and efficiency. RootsNet has the best uncertainty performance and efficiency, and achieves the best reconstruction quality under super low-measurement rates.
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We have developed a three-dimensional (3-D) algorithm for computing the controlled-source audio-frequency magnetotelluric response which is capable of handling local inhomogeneities in the electrical ...conductivity and magnetic permeability distribution. It is a difference equation algorithm that is based on the integral forms of Maxwell’s equations. To avoid a spatial singularity being introduced by the source, we used a secondary electric field approach. We adopt a staggered-grid finite difference method to disperse the secondary electric field equation which is simple to realize and can calculate relatively complex models. The final sparse equation system after dispersion is solved using an ILU preconditioning quasi-minimal residual (QMR) method. The code is validated for nonmagnetic and permeable conductive structures by two synthetic models. One of them is a two-dimensional (2-D) nonmagnetic conductive geoelectric model; we validate our 3-D computation result by comparing it to the result of 2-D CSEM finite element forward program. The other one is a 3-D permeable geoelectric model; we validate our 3-D computation result by using the COMSOL Multiphysics software. We also designed two theoretical 3-D geoelectrical models to analyze the effects of magnetic permeability heterogeneity. Numerical simulation results well reveal the influence of magnetic permeability heterogeneity on high-resistance and low-resistance models.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
In the pigging operation of an oil pipeline, the traditional pigging localization method can only achieve fixed-point detection and cannot track continuously. To ensure the normal operation of the ...pigging operation and track and locate the pig in a timely manner, a tracking and localization method of an oil pipeline pig based on a noise reduction autoencoder is proposed. First, using the advantages of U-Net (U-shaped Convolutional Neural Network) multi-scale feature extraction, combined with coding and residual-dense blocks, U-Net combined with Residual Dense Block U-shaped Network (RDBU) was proposed for noise reduction, and then real-time localization was calculated out based on the negative pressure wave localization formula of pigs. The experimental results show that, compared with the traditional method, the SNR of the RDBU is increased by 0.9dB and the root mean square error is reduced by 14.92%. The denoising algorithm in this paper can effectively eliminate the noise of the negative pressure wave signal of pigs.
The elasticity of erythrocytes is an important criterion to evaluate the quality of blood. This paper presents a novel research on erythrocytes’ elasticity with the application of optical tweezers ...and the finite element method (FEM) during blood storage. In this work, the erythrocytes with different in vitro times were linearly stretched by trapping force using optical tweezers and the time dependent elasticity of erythrocytes was investigated. The experimental results indicate that the membrane shear moduli of erythrocytes increased with the increasing in vitro time, namely the elasticity was decreasing. Simultaneously, an erythrocyte shell model with two parameters (membrane thickness h and membrane shear modulus H) was built to simulate the linear stretching states of erythrocytes by the FEM, and the simulations conform to the results obtained in the experiment. The evolution process was found that the erythrocytes membrane thicknesses were decreasing. The analysis assumes that the partial proteins and lipid bilayer of erythrocyte membrane were decomposed during the in vitro preservation of blood, which results in thin thickness, weak bending resistance, and losing elasticity of erythrocyte membrane. This study implies that the FEM can be employed to investigate the inward mechanical property changes of erythrocyte in different environments, which also can be a guideline for studying the erythrocyte mechanical state suffered from different diseases.
Object positioning is a basic need for visual robotics in automatic assembly lines. An assembly line requires fast transfer to new object positioning tasks with few or no training data for deep ...learning algorithms, and the captured visual images usually suffer from partial missing and cropping and environmental lighting interference. These features call for efficient and robust arbitrary shape positioning algorithms under data-scarce and shape distortion cases. To this end, this paper proposes the Random Verify Generalised Hough Transform (RV-GHT). The RV-GHT builds a much more concise shape dictionary than traditional GHT methods with just a single training image. The location, orientation, and scaling of multiple target objects are given simultaneously during positioning. Experiments were carried out on a dataset in an automatic assembly line with real shape distortions, and the performance was improved greatly compared to the state-of-the art methods. Although the RV-GHT was initially designed for vision robotics in an automatic assembly line, it works for other object positioning mechatronics systems, which can be modelled as shape distortion on a standard reference object.
The prerequisite of inversion is the forward modeling study. In recent years, structured electromagnetic method for forward modeling has become mature. However, it has low computing efficiency and ...difficulty in simulating complex geological bodies. The electromagnetic field equations are represented by the coupled equations of magnetic vector potential and electric scalar potential. The node-based finite element method can efficiently calculate the electromagnetic field. The linear equations obtained are solved using a symmetric successive overrelaxation preconditioned conjugate gradient solver. This iterative method provides a faster solution speed. Unstructured grids used in numerical simulation can effectively simulate complex geological bodies. The use of adaptive finite elements optimizes the grid and improves calculation accuracy. The accuracy of the forward modeling code is verified through the establishment of layered models and three-dimensional models; the significant advantage of unstructured grids in simulating complex geological bodies is confirmed by the forward modeling simulation of complex ellipsoidal anomaly bodies; the effectiveness of the adaptive finite element method is verified by the forward modeling simulation of the prism model.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
In the production and service of pipelines, automated electromagnetic detection is widely employed to diagnose potential defects. However, the single inspection method can hardly multiposition detect ...detection simultaneously due to limited signal features. In this article, a novel composite approach of electromagnetic acoustic transducer (EMAT) and eddy current (EC) is proposed to detect different kinds of defects by combining their technical merits. A comprehensive theoretical analysis of the composite approach is given to derive the modified model of the send-receive mode. The influences of the critical parameters on hybrid signals are discussed and the probe performance is improved by over 65% in magnitude. Then, the time-division multiplexing strategy is utilized to separate hybrid signals. Finally, laboratory experiments are conducted to extract independent features and diagnose potential defects in the steel plate. Results indicated that the proposed approach could achieve a good performance in the detection task of inner and outer defects. Compared with existing detection methods, our proposed approach has significant merits in detectability and efficiency.
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