Pipeline Defects Research Articles

A genetic algorithm with tabu search method for 3 dimensional detect profile reconstruction using MFL measurement.

In nondestructive testing, magnetic flux leakage (MFL) inspection is extensively employed for the inversion of pipeline defects. Exact reconstruction of the defect plane with measurements is a pressing issue in the field of MFL detection. This article proposes a method that incorporates a layer-by-layer genetic algorithm with tabu search (GA-TS). During the iterative process, our objective is to utilize a tabu search based on evolutionary algorithms to avoid local optimal solutions, obtain global optimal solutions, and reconstruct defects with enhanced accuracy. In addition to enhancing the accuracy of pipeline defect categorization, our approach has two significant limitations: its limited global search capability and slow convergence rate. Finally, the method is evaluated via experiments in which MFL signals obtained from an experimental platform and simulation signals are used. Practical results and a thorough comparative study of alternative approaches confirm the model's superiority.

Location Detection and Numerical Simulation of Guided Wave Defects in Steel Pipes

At present, researchers in the field of pipeline inspection focus on pipe wall defects while neglecting pipeline defects in special situations such as welds. This poses a threat to the safe operation of projects. In this paper, a multi-node fusion and modal projection algorithm of steel pipes based on guided wave technology is proposed. Through an ANSYS numerical simulation, research is conducted to achieve the identification, localization, and quantification of axial cracks on the surface of straight pipelines and internal cracks in circumferential welds. The propagation characteristics and vibration law of ultrasonic guided waves are theoretically solved by the semi-analytical finite element method in the pipeline. The model section is discretized in one-dimensional polar coordinates to obtain the dispersion curve of the steel pipe. The T(0,1) mode, which is modulated by the Hanning window, is selected to simulate the axial crack of the pipeline and the L(0,2) mode to simulate the crack in the weld, and the correctness of the dispersion curve is verified. The results show that the T(0,1) and L(0,2) modes are successfully excited, and they are sensitive to axial and circumferential cracks. The time–frequency diagram of wavelet transform and the time domain diagram of the crack signal of Hilbert transform are used to identify the echo signal. The first wave packet peak point and group velocity are used to locate the crack. The pure signal of the crack is extracted from the simulation data, and the variation law between the reflection coefficient and the circumferential and radial dimensions of the defect is calculated to evaluate the size of the defect. This provides a new and feasible method for steel pipe defect detection.

Enhancing urban infrastructure: water supply and drainage pipeline defect detection with the GLIP pre-trained model and the YOLOv5 model

ABSTRACT Urban water supply and drainage systems are a crucial component of urban infrastructure, directly affecting residents' livelihoods and industrial production. The normal operation of the water supply and the drainage pipeline is of great significance for conserving water resources and preventing water pollution. However, due to characteristics such as deep burial, diverse materials, and extensive lengths, the detection of defects becomes exceptionally complex. Traditional detection methods used in practical applications, such as ground excavation and destructive testing, typically require the shutdown of water pipelines. This process is time-consuming and labor-intensive, often resulting in significant economic losses. This paper proposes an effective technique for detecting defects in the water supply and the drainage pipeline. The method involves capturing images of the inner walls of water supply conduits and subsequently utilizing an artificial intelligence large-scale model approach (grounded language-image pre-training, GLIP) and a You Only Look Once version 5 (YOLOv5) model to detect defects within them. The experimental results show that GLIP demonstrates impressive detection performance in zero-shot scenarios, while YOLOv5 performs well on existing datasets. By combining these two models, we were able to achieve a balance between fast, flexible detection and high precision, making our approach both practical and efficient for real-world applications.

Drainage Pipeline Defect Detection System Based on Semantic Segmentation

Since drainage pipes are widely used in daily life and are necessary for cities to function normally, intelligent fault detection in these pipes has emerged as both an urgent necessity and a major area of development. Compared to traditional target detection methods, the semantic segmentation-based pipeline defect detection method has some special qualities. It can effectively detect a variety of pipeline defect types and segment the defects using precise geometrical attributes to support subsequent defect assessment. This study replaces the backbone network and fuses bar pooling and cascade feature fusion modules in the encoding and decoding phases, respectively, to obtain richer semantic information. This approach strikes a balance between segmentation accuracy and model complexity, aiming to address the problems of the large computational volume of semantic segmentation and the loss of detailed semantic information of pipeline defects. In the pipe defect dataset, the proposed approach is compared with the baseline algorithm and the experimental findings indicate that it has a greater segmentation accuracy than the conventional algorithm. Additionally, the lightweight design of the algorithm lowers the model’s complexity.

Quantitative Evaluation of Deformation in High-Speed Magnetic Flux Leakage Signals for Weld Defects in Oil and Gas Pipelines

Complex multiphase flow in oil and gas pipelines raises safety risks. Magnetic flux leakage (MFL) detection effectively identifies pipeline defects. However, the high-speed movement of MFL inspection tools induces motion-induced eddy currents (MIECs), complicating defect recognition and quantification. Most prior research has primarily focused on rectangular defects, leaving a gap in understanding the impact of MIECs on weld defects. This paper proposes the amplitude and shape deformation coefficients to analyze the influence of velocity on various weld defects, including internal reinforcement, lack of penetration, crack, external corrosion, internal corrosion, porosity, and lack of fusion. Utilizing these coefficients, this study examines the influence of the defect size and magnetizer configuration on these velocity-induced effects. The results show that the shape deformation coefficients range from 2.75 to 3.57 for Bx and from −0.13 to −0.3 for By, indicating a significant change in the MFL signal shape at 10 m/s compared to 0 m/s. The amplitude deformation coefficients for lack of penetration, internal corrosion, and porosity range from −0.01 to 0.1 for Bx, and from 0.86 to 0.98 for By, suggesting a decrease in peak-to-peak values. In contrast, other defects exhibit an increase in peak-to-peak values, indicating that the velocity effect may enhance the MFL signal. Also, the defect size and magnetizer configuration can affect the velocity effect on signals. These findings provide essential guidance for quantifying defect sizes and a solid foundation for designing more effective magnetization devices.

Real-Time Pipeline Fault Detection in Water Distribution Networks Using You Only Look Once v8.

Detecting faulty pipelines in water management systems is crucial for ensuring a reliable supply of clean water. Traditional inspection methods are often time-consuming, costly, and prone to errors. This study introduces an AI-based model utilizing images to detect pipeline defects, focusing on leaks, cracks, and corrosion. The YOLOv8 model is employed for object detection due to its exceptional performance in detecting objects, segmentation, pose estimation, tracking, and classification. By training on a large dataset of labeled images, the model effectively learns to identify visual patterns associated with pipeline faults. Experiments conducted on a real-world dataset demonstrate that the AI-based model significantly outperforms traditional methods in detection accuracy. The model also exhibits robustness to various environmental conditions such as lighting changes, camera angles, and occlusions, ensuring reliable performance in diverse scenarios. The efficient processing time of the model enables real-time fault detection in large-scale water distribution networks implementing this AI-based model offers numerous advantages for water management systems. It reduces dependence on manual inspections, thereby saving costs and enhancing operational efficiency. Additionally, the model facilitates proactive maintenance through the early detection of faults, preventing water loss, contamination, and infrastructure damage. The results from the three conducted experiments indicate that the model from Experiment 1 achieves a commendable mAP50 of 90% in detecting faulty pipes, with an overall mAP50 of 74.7%. In contrast, the model from Experiment 3 exhibits superior overall performance, achieving a mAP50 of 76.1%. This research presents a promising approach to improving the reliability and sustainability of water management systems through AI-based fault detection using image analysis.

Experimental Study on Quick‐Locking Reinforcement Model for Local Defects in Pipelines in the Rich Water Area

ABSTRACTThe management of local defects in municipal pipelines in water‐rich areas remains a significant challenge, particularly under high‐pressure conditions. This study investigates the performance of quick‐lock steel sleeves as a trenchless repair method through full‐scale experiments on five different pipeline diameters. The experiments focus on the failure modes and critical buckling pressure of the sleeves under external pressure. A novel design model based on structural reliability theory was developed and validated against experimental results. The results show a close match between the calculated and experimental buckling pressures, with a ratio ranging from 0.87 to 1.15. These findings provide valuable insights for the design and application of quick‐lock sleeves in high‐pressure municipal networks. This study contributes to improving the reliability and effectiveness of pipeline repair technologies, offering practical solutions for addressing pipeline leakage and instability in challenging environments.

Modelling and experimental study on pipeline defect characterisations using a pulsed eddy current measurement

ABSTRACT Pipelines are widely used as the main transportation method in petrochemical and metallurgical industries. However, the special nature of the materials transported in pipelines means they are often subjected to high temperature, high pressure and corrosive conditions. This can lead to defects, such as thinning and cracking, resulting in pipeline failure and causing serious economic losses. Therefore, regular non-destructive testing of pipelines is particularly important. This paper uses the non-destructive testing method with pulsed eddy current technology to characterise pipeline thinning and defects. A preliminary study is conducted on the pulsed eddy current detection signal patterns for pipeline crack defects with varying orientations and quantities. A more portable pulsed eddy current detection device with an extended wall thickness detection range is developed. It can achieve measurements with a maximum thickness of 30 mm for detections of pipelines with rectangular defects and thickness-stepped reduced. The experimental results are consistent with modelling results, and the accuracy for pipelines with insulation layer is also investigated. On-site measurements using the PEC system are conducted on equipment sections with pipe thickness within 20 mm in a petrochemical plant. It is confirmed that the developed PEC inspection device can accurately characterises the thickness of pipelines with 0–50 mm cladding.

Internal inspection method for crack defects in ferromagnetic pipelines under remanent magnetization

Magnetic flux leakage (MFL) testing is effective in detecting corrosion in ferromagnetic pipelines. However, the MFL generated at the crack is highly weak and is easily drowned in the strong background magnetic field and difficult to detect. Therefore, this paper creatively proposes a remanent magnetic flux leakage (RMFL) detection method. This method utilizes the hysteresis state of the pipeline after MFL detection and identifies crack defects by collecting the remanent magnetic flux density near the inner wall surface of the pipeline. The remanent magnetic field distribution near the pipeline surface is examined at different crack sizes, magnetization speeds, and lift-off values via theoretical analysis, finite element simulation, and experimental verification. In addition, experiments have demonstrated that in terms of crack detection capability, RMFL is superior to MFL. Therefore, adding a RMFL measurement module to the conventional MFL detector has important practical value. It can realize the targeted collaborative application of these two technologies, namely the synchronous detection of corrosion and crack defects, which greatly improves the detection coverage and detection efficiency.

Dynamic Maintenance of Underground Pipelines via a Systematic Approach for Conservative Estimation of Pipeline Defect Probability Density Under Data Scarcity

Dynamic Maintenance of Underground Pipelines via a Systematic Approach for Conservative Estimation of Pipeline Defect Probability Density Under Data Scarcity

A technology of multiple-harmonic magnetic field excitation and its signal-processing method for noncontact steel pipeline defect detection

A technology of multiple-harmonic magnetic field excitation and its signal-processing method for noncontact steel pipeline defect detection

Analysis of the gas pipeline defects formation mechanism as a result of arc discharge impact on the pipe wall

The article presents an analysis of the formation possibility through defects in a gas pipeline as a result of short-term exposure to an arc discharge. The results of metallography, electron microscopy, elemental analysis, as well as the modeling results, confirming the formation possibility through defects in a 7 mm thick gas pipeline wall during an exposure to an arc discharge of less than 2 seconds are presented.

Research on Ultrasonic Guided Wave Pipeline Defect Detection Method Based on Modal Analysis

Abstract Pipelines are important transmission media in the petroleum industry, and their safe and stable operation is of great significance to industrial safety. The secure and reliable operation of oil and gas pipelines holds paramount importance for industrial safety. The significance of non-destructive testing (NDT) technology in this context is steadily growing. Presently, guided wave-based pipeline defect detection technology has garnered considerable attention. This method is noteworthy for its extensive coverage, ability to inspect the entire cross-section, and its capability to conduct inspections without disrupting ongoing production processes. Research focused on the displacement distribution of various modes of guided waves, exploring different excitation methods for each mode. The investigation also delved into the interaction mechanism between guided waves and pipeline defects; Conducted research on the detection of axial, circumferential and radial dimensions of defects using different modes of guided waves, and carried out experimental verification, studying the transformation law of the reflection coefficient of the defect reflection echo when the flaw size changes provide a reference for pipeline defect detection based on guided waves.

PE Gas Pipeline Defect Detection Algorithm based on Improved YOLO v5

In order to improve defects detection efficiency in polyethylene (PE) gas pipelines and decrease leakage or other pipelines abnormalities in operation, this research proposed an improved YOLO(You Only Look Once) v5 detection model. First, the collected pipeline defect images were processed in grey scale, which improved the computational efficiency of the computer; then, Gamma transform and double filtering algorithms were applied respectively for image enhancement and noise reduction filtering of defects, which enhanced image quality and reduced image noise. Finally, the improved Sobel algorithm was applied to detect defective image edges and the defects in the image were segmented by adaptive threshold segmentation method to obtain binary images. The obtained binary images were employed to train the improved YOLO v5 detection model. The obtained experimental results showed that, compared with the original algorithm, the improved detection algorithm had better detection efficiency and higher robustness as well as higher recognition for common defects,improved YOLOv5 mAP and recall were 97.18% and 98.03%, respectively, the mAP has increased by 1.33% and the recall has increased by 3.83%,which can achieve the detection and identification of defect types of effects in PE gas pipes.

Design and Optimization of Elliptical B-shaped Sleeve Configuration for Oil&Gas Pipeline

Abstract Affected by external loads or pipeline corrosion, oil & gas transmission pipelines may encounter safety hazards during service. In terms of defect repair, both domestic and foreign standards currently stipulate that the permanent repair method is B-type sleeve or tube replacement. Compared to the costly tube replacement, B-type sleeves are undoubtedly the preferred choice for economy and safety. However, due to installation size limitations, conventional sleeves are difficult to repair large deformation defects such as wrinkles and buckling in oil and gas pipelines. This study aims at developing, designing, and optimizing a B-type sleeve for welding repair of irregular defects in oil and gas pipelines. By designing irregular B-shaped sleeves with different curve configurations, the stress levels and concentration coefficients of irregular sleeves at different defect heights and sleeve lengths are calculated. The optimal pumpkin shaped and elliptical curve sleeve configurations are selected as the repair solutions for irregular sleeves. Under the existing limitations of machining and detection accuracy, pumpkin shaped irregular sleeves can be prioritized for application in engineering, while elliptical curve sleeves are considered as alternative solutions.

Pipeline defect detection system based on phased array excitation technology

Abstract To address the challenges of non-destructive testing of professional industrial pipeline conditions, this paper designs a pipeline defect detection system based on phased array excitation technology. Online/offline detection equipment can be selected according to different application scenarios, achieving various functions such as phase delay calculation, ultrasonic data collection, and graphical display of calculation results. This article mainly elaborates on the principle of phased array excitation technology, the circuit design of detection equipment, and the overall design of upper computer software, proposing a practical solution for non-destructive testing of industrial pipelines.

Experimental study of natural fiber Jute reinforced by metal wires and its using as patch on semi-elliptical defected of the API X52 pipelines corroded according to ratio a/t

The aim of this study is to evaluate the efficiency of a hybrid patch of Jute with/without Glass fiber reinforced with Steel wire for repairing defective API X52 pipeline. Five specimens were tested using Jute fiber, Glass fiber and Steel wire to enhance the mechanical properties according to ASTM 3039 and D790 standards. The experimental results showed that the effects of hybrid materials (synthetic and natural) for repairing API X52 pipeline defect depend on the nature of the patch and the properties of the adhesive (polyester resin) by increasing the tensile strength and bending load. In the first step, specimen S5 was selected due to its distinctive mechanical properties, which revealed maximum values of tensile strength, toughness and bending load respectively 47.42014 MPa, 2357.42 kJ/m3 and 488.3 N. In the second step, the selected sample (S5) was used as a patch to repair API X52 pipeline, which has semi-elliptical defects where the bending load is 31.4246 kN. According to the obtained results, the patch efficiency is 103% for defects with a/t < 0.3, which can be, restored 100% of the mechanical properties. There fore, repairing API X52 pipeline using hybrid patch (Jute reinforced with Steel wires) with shallower defects can be a successful technique.

A High-Precision Size Inversion Method for Pipeline Defects With the Influence of Velocity Effects

A High-Precision Size Inversion Method for Pipeline Defects With the Influence of Velocity Effects

Differentiating three distinct kinds of flaws in oil and gas pipelines derived from the spacing effect of capacitive imaging sensors

Purpose This paper aims to apply the spacing effect of capacitive imaging (CI) sensors to inspect and differentiate external flaws of the protective module, internal flaws of the protective module and external flaws of the metallic module in oil and gas pipelines simultaneously. Through experimental verification, a method for differentiating three distinct kinds of flaws derived from the spacing effect of CI sensors has been demonstrated. Design/methodology/approach A 3Dimensions (3D) model for simulating the inspection of these flaws was established by using COMSOL. A novel CI sensor with adjustable working electrode spacing was designed, and a modular CI system was developed to substantiate the theoretical findings with experimental evidence. A method for differentiating three distinct kinds of flaws derived from the spacing effect of CI sensors was established. Findings The results indicate that the method can successfully discriminate external flaws of the protective module, internal flaws of the protective module and external flaws of the metallic module using CI sensors. Originality/value The method for differentiating three distinct kinds of flaws derived from the spacing effect of CI sensors is vital for keeping the transportation safety of oil and gas pipelines.

Identification of coating layer pipeline defects based on the GA-SENet-ResNet18 model

Detecting damage in coated pipelines is a challenging and costly task. This study proposes a method for pipeline defect identification based on VMD-DWT noise reduction and GA-SENet-ResNet18. Combining wavelet transform to convert denoised defect signals into time-frequency representations enhances the model's ability to capture both time-domain and frequency-domain features of defect signals, thereby improving its recognition capability for different types of defects. The study analyzed the feature extraction capabilities of ALexNet, GooleNet, VGG16, ResNet18, SENet-ResNet18, and GA-SENet-ResNet18 models in pipeline defect recognition. Experimental results show that SENet-ResNet18 achieved an accuracy of 0.9591 on the training set in 9m38s, significantly outperforming the first four models. GA-SENet-ResNet18 achieved 96.83 % accuracy, 96.67 % precision, 96.73 % recall, and 96.68 % F1 score in pipeline defect signal recognition. Compared to ResNet18, it improved accuracy by 2.06 %, precision by 1.94 %, recall by 2.09 %, F1 score by 2.37 %, with a reduction in time by 1m1s. The study demonstrates that the combined improvement of GA and SENet enhances ResNet18 not only in feature selection and response enhancement but also significantly improves its performance compared to traditional ResNet18 networks, making it more effective in pipeline defect recognition tasks. This research is crucial for ensuring pipeline system integrity and preventing pipeline accidents.