Pipeline Defects Research Articles

Design of hexapod robot equipped with omnidirectional vision sensor for defect inspection of pipeline’s inner surface

Abstract Defect detection of inner surface of precision pipes is a crucial aspect of ensuring production safety. Currently, pipeline defect detection primarily relies on recording video for manual recognition, with urgent need to improve automation, quantification and accuracy. This paper presents a hexapod in-pipe robot with carrying capacity designed to transport the omnidirectional vision sensor to specified location within unreachable pipelines. The feasibility of the robot’s mechanical design and sensor load-carrying module is analyzed using theory calculations, motion simulations and finite element method. To address the challenges of small pixel ratio and weak background changes in panoramic images, a tiny defect segmentor (TDS) based on ResNet is proposed for detecting tiny defects on the inner surface of pipelines. The hardware and software systems are implemented, and the motion performance of the pipeline robot is validated through experiments. The results demonstrate that the robot achieves stable movement at a speed of over 0.1m/s and can adapt to pipe diameter ranging from of 110 to 130 mm. The novelty of the robot lies in providing stable control of the loaded vision sensor, with control precision of the rotation angle and the displacement recorded at 1.84% and 0.87%, respectively. Furthermore, the proposed method achieves a detection accuracy of 95.67% for tiny defects with a diameter less than 3 mm and provides defect location information. This pipeline robot serves as an essential reference for development of in-pipe 3D vision inspection system.

Multi-modality hierarchical attention networks for defect identification in pipeline MFL detection

Abstract Magnetic flux leakage (MFL) testing is widely used for acquiring MFL signals to detect pipeline defects, and data-driven approaches have been effectively investigated for MFL defect identification. However, with the increasing complexity of pipeline defects, current methods are constrained by the incomplete information from single modal data, which fail to meet detection requirements. Moreover, the incorporation of multimodal MFL data results in feature redundancy. Therefore, the multi-modality hierarchical attention networks (MMHAN) are proposed for defect identification. Firstly, stacked residual blocks with cross-level attention module (CLAM) and multiscale 1D-CNNs with multiscale attention module are utilized to extract multiscale defect features. Secondly, the multi-modality feature enhancement attention module (MMFEAM) is developed to enhance critical defect features by leveraging correlations among multimodal features. Lastly, the multi-modality feature fusion attention module (MMFFAM) is designed to dynamically integrate multimodal features deeply, utilizing the consistency and complementarity of multimodal information. Extensive experiments were conducted on multimodal pipeline datasets to assess the proposed MMHAN. The experimental results demonstrate that MMHAN achieves a higher identification accuracy, validating its exceptional performance.

Quantification of Corrosion-Like Defects in Pipelines Using Multifrequency Identification of Nondispersive Torsional Guided Waves

Quantification of Corrosion-Like Defects in Pipelines Using Multifrequency Identification of Nondispersive Torsional Guided Waves

Acoustic emission source localization in complex pipe structure using multi-task deep learning models

Localizing defects in long-range pipelines is essential to reduce the inspection time and develop timely repair strategies. The acoustic emission (AE) method is employed to pinpoint the position of defects in pipelines. The conventional 1-D localization algorithm requires time of arrival differences between two sensors, which may not be accurately captured due to the dispersive nature of the pipe structures. The geometric variations such as elbows and welds can influence the propagating elastic waves and, consequently, arrival time. In this study, an AE source localization approach using a deep learning model is developed to tackle the influences of sensor-source distance and geometric variables. The multi-task learning model first identifies the impact of the elbow and subsequently integrates this information when predicting the source location. The proposed model is evaluated on a complex piping system, which features welded elbows in its connections. Incorporating the elbow effect into the model shows a notable improvement in overall accuracy, rising from 53% (conventional method) to 94% (proposed multi-task learning method).

Correction Method for Perspective Distortions of Pipeline Images

It is common to find severe perspective distortion in a pipeline’s image in medium-diameter pipeline defect detection by the panoramic image unwrapping method, resulting in low-quality image unwrapping and stitching, which is caused by the camera’s optical axis being completely deviated from the pipeline’s center. To solve this problem, a novel correction method for reducing perspective distortion in pipeline images was proposed for pipeline defect detection. Firstly, the method enhances the edges of unevenly illuminated regions within a pipeline to facilitate image segmentation and identify key points necessary for correcting perspective distortion. Then, a six-feature-point extraction method was proposed for a circle target to establish the projection relationship between the extracted feature and mapped points on the reference circle. Finally, a perspective matrix was constructed to complete the perspective transformation correction of the distorted images. The results show that the average correction rate and the average relative error of the proposed correction method can reach 90.85% and 1.31%, respectively. The study innovatively used the enhancement of uneven illumination to find distorted edge information. It proposed an extraction method using a reference circle and six key feature points to build a mapping model. It can provide a novel method which can be used to obtain a superior image for pipeline detection and lay a solid foundation for subsequent high-quality pipeline image stitching.

Non-destructive testing technology for corrosion wall thickness reduction defects in pipelines based on electromagnetic ultrasound

Non-destructive testing technology for corrosion wall thickness reduction defects in pipelines based on electromagnetic ultrasound

Mathematical Modeling for Robot 3D Laser Scanning in Complete Darkness Environments to Advance Pipeline Inspection

This paper introduces an autonomous robot designed for in-pipe structural health monitoring of oil/gas pipelines. This system employs a 3D Optical Laser Scanning Technical Vision System (TVS) to continuously scan the internal surface of the pipeline. This paper elaborates on the mathematical methodology of 3D laser surface scanning based on dynamic triangulation. This paper presents the mathematical framework governing the combined kinematics of the Mobile Robot (MR) and TVS. It discusses the custom design of the MR, adjusting it to use of robustized mathematics, and incorporating a laser scanner produced using a 3D printer. Both experimental and theoretical approaches are utilized to illustrate the formation of point clouds during surface scanning. This paper details the application of the simple and robust mathematical algorithm RANSAC for the preliminary processing of the measured point clouds. Furthermore, it contributes two distinct and simplified criteria for detecting defects in pipelines, specifically tailored for computer processing. In conclusion, this paper assesses the effectiveness of the proposed mathematical and physical method through experimental tests conducted under varying light conditions.

Research on damage assessment of buried pipelines with circular dent defects subjected to blast loading

In order to evaluate the damage characteristics of buried natural gas pipelines with circular dent defects subjected to blast loading, based on pressure–impulse damage theory, explosion experiment and numerical simulations were implemented to evaluate the damage of a natural gas with circular dent defects buried in soil under blast loading in this research. The ALE method was used to develop a coupled pipeline-soil simulation model using LS-DYNA software, and the validity of the established model was verified correctly compared with experimental results and empirical theory equations calculations. Furthermore, according to regulations in the Assessment and Management of Pipeline Dents (American Petroleum Institute Recommended Practice 1183), the effect with different circular dent defect diameter on the mechanical properties of natural gas was also investigated and analysed. The results showed that, under the blast loading, plastic deformation happened on the surfaces of the pipeline facing the explosive, and the high-stress zone appeared in the circumferential direction with 32.27°. The range of the high-stress zone firstly expanded along the axial direction of the pipelines and then along the circumferential direction. The larger diameter of the circular dent defects had, which resulted in a failure at the defects under the condition of the depth of the defects keeping constant. The effect of the size of defect diameter on the amount of pipeline deformation was further investigated, and the mathematical formula described the maximum plastic deformation with different defect diameter was established. Meanwhile, a finite element model of pipeline with a diameter of 457 mm was established for numerical analysis, which implied that the larger diameter of pipeline with the same defect had, the lower risk of pipeline in failure subjected to blast loading had. And through mathematical analysis and considering the feasibility of the actual situation, the curve described the maximum plastic deformation of the natural gas pipeline with three different defects varying in diameter were established respectively. In addition, a formula to express the relationship between pipeline defect diameter and maximum plastic deformation was established, which can effectively predict the critical plastic dent deformation of pipelines with different defect diameter subjected to blast loading. Besides, based on the pressure-pulse damage theory and with the damage assessment criterion of dent depth–dent length ratio of 0.072, the pressure–impulse diagrams of buried natural gas pipelines with defect diameters of 30 mm, 40 mm and 50 mm were defined, which can be used to predict the damage of pipelines with different defect diameters. Moreover, with the pressure–impulse damage evaluation curves, mathematical formula for the buried natural gas pipeline with the circular defects were established. Furthermore, the critical circular defect diameters with 30 mm was confirmed and the unique formula was also established, which could be effectively used to evaluate the safety of buried natural gas pipelines with the critical circular defects.

Feature extraction method for ultrasonic pipeline defects based on fractional-order VMD

ABSTRACT Ensuring the safety of pipeline transportation is vital for societal well-being. Traditional methods for identifying defects in steel pipelines through ultrasonic echo signal pattern recognition often fail to extract comprehensive and effective features crucial for accurate defect detection. This study introduces an innovative feature extraction method employing a fractional Fourier transform variational modal decomposition (FRFT-VMD), hereafter referred to as the fractional-order VMD algorithm. This method utilises the fourth-order central moment and envelope entropy to optimise several key parameters: the fractional order in the Fourier transform, the number of decomposition layers, and the penalty factor in variational modal decomposition. To evaluate the effectiveness of the proposed method, ultrasonic echo signals from both finite element simulations and experimental platforms were analysed using the FRFT-VMD technique. The features extracted were then classified using a Least Squares Support Vector Machine (LSSVM) to determine defect depths. The results show a recognition accuracy of 95.2% in simulated signals and 89.1% in experimentally measured signals across various defect depths, indicating a significant improvement over existing feature extraction methodologies. The fractional-order VMD algorithm proves to be superior in extracting features that enhance the accuracy of defect identification in steel pipelines.

Probabilistic Fatigue Crack Growth Prediction for Pipelines with Initial Flaws

This paper presents a probabilistic method to predict fatigue crack growth for surface flaws in pipelines using a particle filtering method based on Bayes theorem. The random response of the fatigue behavior is updated continuously as measured data are accumulated by the particle filtering method. Fatigue crack growth is then predicted through an iterative process in which particles with a high probability are reproduced more during the update process, and particles with a lower probability are removed through a resampling procedure. The effectiveness of the particle filtering method was confirmed by controlling the depth and length direction of the cracks in the pipeline and predicting crack growth in one- and two-dimensional cases. In addition, the fatigue crack growth and remaining service life with a 90% confidence interval were predicted based on the findings of previous studies, and the relationship between the fatigue crack growth rate and the crack size was explained through the Paris’ law, which represents fatigue crack growth. Finally, the applicability of the particle filtering method under different diameters, aspect ratios, and materials was investigated by considering the negative correlation between the Paris’ law parameters.

MFL detection of adjacent pipeline defects: a finite element simulation of signal characteristics

MFL detection of adjacent pipeline defects: a finite element simulation of signal characteristics

A Novel Magnetoelectric Composite Probe for Dynamic and Rapid Inspection of Minor Metal Loss Defects in Pipelines

A Novel Magnetoelectric Composite Probe for Dynamic and Rapid Inspection of Minor Metal Loss Defects in Pipelines

Guided wave localization of small defects based on stochastic resonance characteristics of Duffing systems

To improve the location accuracy of small defects in anti-corrosion pipelines by using ultrasonic guided wave, this study developed a guided wave location method for small defects based on stochastic resonance (SR) characteristics of a time-moving Duffing system. Firstly, it solved the shortcomings of the selection of the key parameters of the system, which depended on human subjectivity and lacked interaction correlation in the previous research. By using the stochastic resonance characteristics of the Duffing system, the best fit parameter group of the system was determined synchronously and coordinately. Secondly, with the conjoint analysis of the key parameters of the system and the numerical simulation verification method, found that part of the energy of the noise signal would be transferred to the system output response, leading to the system output response was strengthened. Finally, A time-moving window function was introduced in the experimental verification, and the enhanced system output response in the window was analyzed using power spectral density (PSD), which could realize the location of guided waves with small defects. The experimental result showed that the small defect guided wave with a cross-section loss rate of at least 6 % could be effectively located.

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

PurposeThe 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/approachIn 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.FindingsThe 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/valueA 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.

Intelligent Diagnosis of Urban Underground Drainage Network: From Detection to Evaluation

During the process of urban development, there is large-scale laying of underground pipeline networks and coordinated operation of both new and old networks. The underground concrete drainage pipes have become a focus of operation and maintenance due to their strong concealment and serious corrosion. The current manual inspections for subterranean concrete drainage pipelines involve high workloads and risks, which makes meeting the diagnostic needs of intricate urban pipeline networks challenging. Through advanced information technology, it has reached a consensus to intelligently perceive, accurately identify, and precise prediction of the condition of urban subterranean drainage networks. The development process of detection and evaluation methods for underground concrete drainage pipe networks is the focus of this study. The study discusses common algorithms for classifying, locating, and quantifying pipeline defects by combining the principles of deep learning with typical application examples. The intelligent progression of information collection methods, image processing techniques, damage prediction models, and pipeline diagnostic systems is systematically elaborated upon. Lastly, prospects for future research of intelligent pipeline diagnosis are provided.

Systematic Evaluation of Ultrasonic In-Line Inspection Techniques for Oil and Gas Pipeline Defects Based on Bibliometric Analysis.

The global reliance on oil and gas pipelines for energy transportation is increasing. As the pioneering review in the field of ultrasonic defect detection for oil and gas pipelines based on bibliometric methods, this study employs visual analysis to identify the most influential countries, academic institutions, and journals in this domain. Through cluster analysis, it determines the primary trends, research hotspots, and future directions in this critical field. Starting from the current global industrial ultrasonic in-line inspection (ILI) detection level, this paper provides a flowchart for selecting detection methods and a table for defect comparison, detailing the comparative performance limits of different detection devices. It offers a comprehensive perspective on the latest ultrasonic pipeline detection technology from laboratory experiments to industrial practice.

Defect Recognition Method for Magnetic Leakage Detection in Oil and Gas Steel Pipes Based on Improved Neural Networks

The aging infrastructure of petroleum and natural gas pipelines poses a threat to national economies, necessitating precise defect detection for safety and efficiency. To enhance the accuracy of predicting pipeline defect sizes, this study introduces a magnetic leakage detection system, employing Backpropagation (BP) neural networks optimized with genetic algorithms. Traditional BP networks face challenges, including parameter determination and slow convergence, addressed through genetic algorithms' global search capabilities. Simulated data are generated using ANSYS software by using models of semi-circular defects in steel pipes, producing magnetic leakage signals of varying intensities. MATLAB was used to construct both standard BP and genetically optimized BP neural networks. Results show that the latter significantly reduces computational errors, demonstrating improved accuracy in defect dimension prediction. The approach contributes to overcoming non-uniqueness in the recognition process and the complex nonlinear relationship between magnetic signals and defect size parameters. The study offers a guided approach for selecting BP neural network parameters, enhancing practicality. Simulations validate the method's effectiveness, indicating low workload and high reliability. This research provides a meaningful advancement in the detection of defects in long-distance pipelines, impacting the safety and efficiency of petroleum and natural gas transportation.

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.

Statistical study of localized internal corrosion defects in oil and gas pipelines through sampling inspection

Historical failure data of corroded oil and gas pipelines helps estimate risk and reliability using statistical models if the distribution of the characteristics of corrosion defects is known. This research presents a field survey of corrosion defect characteristics in Mexican oil and gas pipelines. Because of the different conditions that pipelines typically undergo and the different fluids that they transmit, it was found that corrosion defect depth has a bimodal behavior. This unusual characteristic is a reason to study the cause of this conduct in more detail. The fluid properties are of great importance to characterize statistically the internal corrosion damage. A Bayesian methodology allows the statistical estimation of the distribution for the depth of internal corrosion defects using limited information obtained from “unpiggable” pipelines.

A classification and quantitative assessment method for internal and external surface defects in pipelines based on ASTC-Net

Classification and size quantification of defects on both the internal and external surfaces of pipelines are critical to pipeline integrity assessment. However, defect classification is challenging because of the similarities of defect signals on the internal and external surfaces. In addition, most existing size quantification methods are not sufficiently accurate. To solve these problems, this paper proposes a classification and quantitative evaluation method based on an asymmetric student–teacher with a classifier network (ASTC-Net). First, a novel approach for expanding defect magnetic flux leakage (MFL) data is validated through experiments and simulations. Second, ASTC-Net is built to address the problem of defect classification and quantification. Finally, the superiority of the method is verified by experiments. The results show that this approach pioneers the accurate classification of defects on both internal and external surfaces by achieving an accuracy of 99.41%. Furthermore, a high-precision quantitative assessment of defect size is realized, with length, width, and depth errors of only 0.35 mm, 0.34 mm, and 0.41% of the wall thickness, respectively. These experimental results clearly demonstrate that this method has exceptionally high accuracy in defect classification and quantification, offering vast prospects for its application in pipeline MFL evaluation.