Defects In Pipes Research Articles

A Double Remote Magnetic Field Synthesis Method for Reducing High-Speed MFL Signal Distortion Caused by Velocity Effect

Magnetic flux leakage (MFL) testing is coming into prominence as an inspection tool applied to pipeline evaluation. The MFL detectors are challenging to accurately evaluate pipe defects at high-speed due to velocity effects. Current methods typically require detectors to operate at low speeds or compensate for detection data offline, but they reduce detection efficiency or require sophisticated algorithms. In this article, a novel double remote magnetic field synthesis (DRMFS) method is proposed to address the issue. First, we give a thorough analysis of the influence of velocity effects on high-speed MFL signals, reinterpreting its action mechanism. Secondly, the critical parameters are optimized to improve pipe magnetization. The relationships between the sensor layout and normalized signal similarity are studied at different speeds to reduce signal distortion. Finally, the usability of the DRMFS method in reducing signal distortion and its capability in detecting deep defects are validated by measuring defect depths. Results showed synthesis signals are highly similar to static MFL signals and approximately linear with the defect depth, which could improve the detection accuracy. Our results can enable the exploration of information utilization in limited space and the establishment of a balance between detection efficiency and signal accuracy.

Improving the Navigation System of Underground Pipe Defect Detection Device

This article addresses the issue of noise and drift in microelectromechanical gyroscopes and their impact on measurement accuracy in engineering applications. The use of a complementary filter is proposed to combine information from the accelerometer and gyroscope to reduce inaccuracies. Research shows that the accelerometer has better result repeatability, which is important for obtaining stable measurements. At the same time, the gyroscope may be more effective in measuring translational movements. The selection of sensor sensitivities and proper parameter tuning are crucial aspects. A developed system is capable of effectively filtering and measuring the angle of an object, and the use of a complementary filter improves measurement accuracy. The proposed approach can be successfully utilized for accurately detecting the angle of position of a measurement setup in defect inspection of underground pipelines.

Non-Destructive Characterization of Cured-in-Place Pipe Defects.

Sewage and water networks are crucial infrastructures of modern urban society. The uninterrupted functionality of these networks is paramount, necessitating regular maintenance and rehabilitation. In densely populated urban areas, trenchless methods, particularly those employing cured-in-place pipe technology, have emerged as the most cost-efficient approach for network rehabilitation. Common diagnostic methods for assessing pipe conditions, whether original or retrofitted with-cured-in-place pipes, typically include camera examination or laser scans, and are limited in material characterization. This study introduces three innovative methods for characterizing critical aspects of pipe conditions. The impact-echo method, ground-penetrating radar, and impedance spectroscopy address the challenges posed by polymer liners and offer enhanced accuracy in defect detection. These methods enable the characterization of delamination, identification of caverns behind cured-in-place pipes, and evaluation of overall pipe health. A machine learning algorithm using deep learning on images acquired from impact-echo signals using continuous wavelet transformation is presented to characterize defects. The aim is to compare traditional machine learning and deep learning methods to characterize selected pipe defects. The measurement conducted with ground-penetrating radar is depicted, employing a heuristic algorithm to estimate caverns behind the tested polymer composites. This study also presents results obtained through impedance spectroscopy, employed to characterize the delamination of polymer liners caused by uneven curing. A comparative analysis of these methods is conducted, assessing the accuracy by comparing the known positions of defects with their predicted characteristics based on laboratory measurements.

A Pipeline Defect Instance Segmentation System Based on SparseInst.

Deep learning algorithms have achieved encouraging results for pipeline defect segmentation. However, existing defect segmentation methods may encounter challenges in accurately segmenting the complex features of pipeline defects and suffer from low processing speeds. Therefore, in this study, we propose Pipe-Sparse-Net, a pipeline defect segmentation system that combines StyleGAN3 to segment the complex forms of underground drainage pipe defects. First, we introduce a data augmentation algorithm based on StyleGAN3 to enlarge the dataset. Next, we propose Pipe-Sparse-Net, a pipeline segmentation model based on SparseInst, to accurately predict the defect regions in drainage pipes. Experimental results demonstrate that the segmentation accuracy of this model can reach 91.4% with a processing speed of 56.7 frames per second (FPS). To validate the superiority of this method, comparative experiments were conducted against Yolact, Condinst, and Mask R-CNN, and the model achieved a speed improvement of 45% while increasing the accuracy by more than 4%.

Probabilistic burst pressure prediction model for pipelines with single crack-like defect

While several models have been developed to predict the failure pressure of pipelines with corrosion defects, relatively few models are available for pipelines with cracking defects. The goal of this study is to develop an unbiased and accurate probabilistic burst failure pressure model for thin-walled pipelines containing a single, longitudinally-oriented, crack-like defect. A comprehensive database is first established, consisting of experimental and numerical data collected from literature as well as numerical data obtained from the extended finite element method (XFEM) conducted in this study. The failure pressure prediction model is developed by adding a correction factor to the modified Ln-Secant method, a semi-empirical model previously developed. The correction factor is modeled using a multivariate linear regression that considers the pipe properties and defect characteristics as independent variables. The proposed model delivers unbiased and less variable failure pressure predictions. A case study of an example pipeline with a crack like defect shows that the failure pressure model plays a critical role in the risk management of pipelines through a life-cycle cost analysis.

Research on Traditional Image Segmentation Method Based on Oil Drilling Pipe Defects

This study explores the feasibility and efficacy of conventional image seg-mentation technology in diagnosing failures in oil drilling pipe images. Simultaneously, it envisions an intelligent approach to diagnose defects in oil drilling pipes. The present paper examines and scrutinizes traditional image segmentation methods in light of the characteristics of oil drilling pipe defect images. It devises experiments tailored for these defect images and employs various traditional image segmentation methods to facilitate comparison and evaluation. The experimental findings illustrate that the traditional image segmentation methods possess a discernible impact on detecting defects in oil drilling pipes, with the image segmentation effect based on the Canny operator method of edge detection proving to be the most effective. The experiments are specifically devised for defective images of oil drilling pipes, utilizing diverse traditional image segmentation methods for comparison and evaluation. The experimental results demonstrate that the traditional image segmentation methods exhibit a certain degree of efficacy in detecting defects in oil drilling pipes, with the image segmentation effect based on the Canny operator method of edge detection being the most optimal.

Internal and External Pipe Defect Characterization via High-Frequency Lamb Waves Generated by Unidirectional EMAT

Periodic permanent magnet(PPM) electromagnetic acoustic transducers (EMATs) are commonly employed for axial defect inspection in pipelines. However, the lowest-order shear horizontal waves (SH0) guided waves have difficulties in distinctly differentiating internal and external defects. To enhance the signal-to-noise ratio and resolution, a unidirectional electromagnetic acoustic transducer (EMAT) based on Circumferential Lamb waves (CLamb waves) is developed. Through structural parameter optimization and excitation frequency adjustment, high-amplitude and low-dispersion CLamb waves are successfully generated in the high-frequency-thickness product region of the dispersion curve. Finite element simulations and experimental validation confirm the capability of this EMAT in exciting CLamb waves for the detection of crack-like defects. Experimental results demonstrate that the excitation efficiency of the CLamb EMAT exceeds that of the periodic permanent magnet electromagnetic acoustic transducer by more than tenfold. The defect reflection signal of the CLamb EMAT exhibits higher resolution and more significant amplitude compared to the PPM EMAT. The integration of this method with SH0 mode detection allows for the inspection of both internal and external defects in pipelines, offering a new avenue for EMAT applications in pipeline inspection.

Circumferential Damage Monitoring of Steel Pipe Using a Radar Map Based on Torsional Guided Waves

Ultrasonic guided wave technology has been successfully applied to detect multiple types of defects in pipes. However, the circumferential location and coverage of a defect are less studied because it is difficult to determine. In this study, the fundamental torsional mode T (0, 1) is selected to conduct monitoring of the circumferential defect in pipelines because of its almost non-dispersive property. A radar map of the peak wave signals at 30 circumferential positions is proposed to detect the damage. The circumferential defect of a steel pipe is thoroughly investigated using numerical simulation. First, the circumferential positioning of defects in various areas of the pipe is studied. Second, the results are compared to those based on longitudinal guide waves. Finally, the circumferential coverage of a defect in the pipeline is determined. The waves are excited and received using the pitch–catch approach, and the collected monitoring signals are processed using the Hilbert transformation. According to the findings, the circumferential defect in the pipe can be effectively identified from a ‘T’ shape in the radar image, and the monitoring method by the torsional guided wave is superior to the longitudinal wave method. The results clearly demonstrate the advantages of torsional guided waves in defect monitoring. The proposed method is expected to provide a promising solution to circumferential damage identification in pipelines.

Effect of Tensile Stress on Self-Magnetic Leakage Field in Carbon Steel Pipe Material

Metal magnetic memory (MMM) technique is a promising non-destructive technology for determining the state of material deformation and structural defect in carbon steel pipe by measuring the self-magnetic leakage field (SMLF). The relationship between SMLF and stress concentration of cracked specimens has been extensively researched, but little is known about corrosion defects in carbon steel pipes, such as circular holes. A static tensile test and MMM measurement were performed on the ASTM A106 Gr. B steel to investigate its applicability in predicting material deformation state and structural defect. The tensile load makes the distribution of self-magnetic leakage field, SMLF intensities in the elastic stage regular. In the defect zone of defective specimens, a peak-trough in the SMLF intensity in the tangential component, Hp(x), and a peak in the normal component, Hp(y), were observed. As yield approached, the SMLF intensities remained unchanged. During plastic deformation, dislocation slip and dislocation interaction rarely change the SMLF intensities and its gradient. The SMLF intensities and its gradient rise as the tensile load approach its ultimate strength. The distribution of SMLF intensities show a peak feature in the Hp(x), and a peak-trough in the Hp(y) as the specimen fractures. Variations in the magnetic and stress fields of carbon steel pipe are caused by structural defect. As a result, the SMLF intensity varies. The method described in this study can be applied to MMM technique to detect material deformation states and defect formation in ASTM A106 Gr. B steel at low cost and with minimal equipment.

IMAGES PROCESSING TECHNIQUES FOR INNER SURFACE OF THE PIPES

During the conveyance of oil and gas, as a result of environmental factors, pressure fluctuations, and varying temperatures, the pipes sustain damage due to the occurrence of fractures, erosion, and internal corrosion. To maintain the functionality of any pipeline, the pipes must be diagnosed frequently. The article provides information about the current endoscopic diagnostic method. The steps of the automatic measurement method are shown. So, usually the obtained crack images are visually read by the operator. Currently, it is possible to detect pipe defects using computer vision methods obtained by the proposed method. Images obtained during in-pipe diagnostics are read using the selected algorithm and Python program, and the parameters of the cracks are determined. KeywordS: crack detection, computer vision, image processing, , pipeline, diagnostic of pipe, algorithm.

Detecting Background Leakages in Water Infrastructure With Fiber Optic Distributed Temperature Sensing: Insights From a Heat Transfer-Unsaturated Flow Model

The use of fiber optic distributed temperature sensing (DTS) to detect and locate leaks is still in its infancy in water infrastructure, despite its promising capabilities. Only few experiments tested this technology, and none of these studies focused on small but persistent leaks, like background leakages, which are ubiquitous and generally go undetected with the technology currently available, thus posing a serious threat to the available water resource. To test the feasibility of detecting and locating background leakages with fiber optic DTS, this study provides a detailed analysis on flow and temperature alterations around leaking water pipelines in presence of small leaks (5, 25, and 125 L/d) with small to moderate temperature differences with the surrounding soil, under 3 different pipe defect configurations, either in absence or in presence of pipe thermal insulation. Transient 3D heat transfer-unsaturated flow numerical simulations showed that there is potential to use temperature alterations to detect and locate incredibly small leaks with fiber optic DTS, like background leakages, despite the influence of pipe temperature on the surrounding soil. The analysis showed that extent, distribution, and magnitude of these alterations are convection dominated at a given temperature difference between leaked water and undisturbed soil, and that it may not be strictly necessary to place the optical fiber directly below the pipe. Indeed, optical fibers located within the utility trench at the sides of the pipe and below its bottom showed comparable or even better performance, thus giving new opportunities to retrofit existing pipelines as well.

Passive Guided Wave Tomography for Erosion Monitoring in an Air/Sand Test Loop

The detection and quantification of defects in pipes by non-destructive testing techniques plays a crucial role in several industries (petrochemical and nuclear in particular). Once a defect is detected, assessing its severity enables to schedule maintenance appropriately, hence reducing maintenance cost and rupture or leakage risks. Structural Health Monitoring (SHM) is an approach that consists in instrumenting a structure with permanent sensors to monitor its health status throughout its life. Guided elastic waves are particularly suitable for SHM applications on thin structures (such as pipes) thanks to their ability to propagate over long distances and their high sensitivity. An SHM approach called "passive guided waves tomography" has emerged in recent years to detect and quantify corrosion in pipes (or plate-like structures). This technology is based on the combination of guided elastic wave tomography algorithms and a passive method such as the so-called ambient noise cross-correlation. It allows to obtain absolute and precise maps of the thickness of an area surrounded by a distribution of sensors without emitting waves, simply by analyzing the elastic noise that exists naturally in the pipe (due to vibrations, fluid-structure interactions with a moving fluid, etc.). In this paper passive guided waves tomography is tested in representative conditions thanks to an erosion loop that circulates air and sand. By impacting the tube wall, sand grains erode the structure, creating thickness losses that we monitor with passive tomography.

유도파 모드분리 및 반사계수에 의한 배관 결함검출

유도파 모드분리 및 반사계수에 의한 배관 결함검출

Wastewater pipe defect rating model for pipe maintenance using natural language processing

IntroductionClosed-circuit video (CCTV) inspection has been the most popular technique for visually evaluating the interior status of pipelines in recent decades. Certified inspectors prepare the pipe repair document based on the CCTV inspection. The traditional manual method of assessing structural wastewater conditions from pipe repair documents takes a long time and is prone to human mistakes. The automatic identification of necessary texts has received little attention. Computer Vision based Machine Learning models failed to estimate structural damage because they are not entirely understood and have difficulty providing high data needs. Hence, they have problems providing physically consistent findings due to their high data needs. Currently, a very small curated annotated image and video data set with well-defined, precisely labeled categories to test Computer Vision based Machine Learning models.MethodsThis study provides a valuable method to determine the pipe defect rating of the pipe repair documents by developing an automated framework using Natural Language Processing (NLP) on very small, curated annotated images, video data, and more text data. The text used in this study is broken into grammatical units using NLP technologies. The next step in the analysis entails using words to find the frequency of pipe defects and then classify them into respective defect ratings for pipe maintenance.Results and discussionsThe proposed model achieved 95.0% accuracy, 94.9% recall, 95% specificity, 95.9% precision score, and 95.7% F1 score, showing the potential of the proposed model to be used in large-scale pipe repair documents for accurate and efficient pipeline failure detection to improve the quality of the pipeline.

CNN-LSTM Hybrid Model to Promote Signal Processing of Ultrasonic Guided Lamb Waves for Damage Detection in Metallic Pipelines.

The ultrasonic guided lamb wave approach is an effective non-destructive testing (NDT) method used for detecting localized mechanical damage, corrosion, and welding defects in metallic pipelines. The signal processing of guided waves is often challenging due to the complexity of the operational conditions and environment in the pipelines. Machine learning approaches in recent years, including convolutional neural networks (CNN) and long short-term memory (LSTM), have exhibited their advantages to overcome these challenges for the signal processing and data classification of complex systems, thus showing great potential for damage detection in critical oil/gas pipeline structures. In this study, a CNN-LSTM hybrid model was utilized for decoding ultrasonic guided waves for damage detection in metallic pipelines, and twenty-nine features were extracted as input to classify different types of defects in metallic pipes. The prediction capacity of the CNN-LSTM model was assessed by comparing it to those of CNN and LSTM. The results demonstrated that the CNN-LSTM hybrid model exhibited much higher accuracy, reaching 94.8%, as compared to CNN and LSTM. Interestingly, the results also revealed that predetermined features, including the time, frequency, and time-frequency domains, could significantly improve the robustness of deep learning approaches, even though deep learning approaches are often believed to include automated feature extraction, without hand-crafted steps as in shallow learning. Furthermore, the CNN-LSTM model displayed higher performance when the noise level was relatively low (e.g., SNR = 9 or higher), as compared to the other two models, but its prediction dropped gradually with the increase of the noise.

Detection of defects in polyethylene pipes using open microwave coaxial line resonator sensor

A new method using a microwave coaxial line resonator sensor for the non-destructive evaluation of defects in polyethylene pipes is presented. The sensor consists of a coaxial line section and an extended section of circular waveguide. One end is short-circuited and the other end is open to free space. The sensor aperture is designed to conform to the curvature of the pipe outer surface, so that the common problem of the standoff distance effect by open-ended waveguide scanning is well circumvented. The presence of defects causes local permittivity and volume changes, leading to variations of the resonance frequency and quality factor due to material perturbation. The detection principle is confirmed by electromagnetic simulation. From the experiments, it is shown that the proposed sensor can perform both detection and classification of flat-bottom holes. The minimum diameter of the hole that can be detected is 1 mm, which is lower than that of a hole required in the standard practice. This method can also effectively detect axial defects and determine the lengths. In addition, an open-ended waveguide is used to detect axial holes, and the results were compared with those of coaxial line resonator. The sensor system presented here has the advantages of low cost, convenience for on-site detection and quantitative analysis.

Defect Severity Assessment Model for Sewer Pipeline Based on Automated Pipe Calibration

To address the low-efficiency issue of the manual assessment method for sewer pipe defects, we propose a defect severity assessment model based on automated pipe calibration (DSA-APC), which can provide automated and quantitative assessments. First, the cross-section feature is extracted by automated pipe calibration. A pipe cross-section feature extraction algorithm based on restricted Hough gradient transform (RHGT) is proposed. Then, a fine-defect feature extraction method based on edge detection is proposed to extract the features of pipe defects more finely. Finally, according to the assessment standards of the sewer pipe defect, a defect severity assessment table is constructed, and the area ratio of the defect feature and cross-section feature is used to evaluate the severity. Experiments are carried out on the Songbai data set and Level-sewer10 data set. The average absolute deviation of the DSA-APC model is 2.008%, and the average accuracy is 86.73%. The experimental results show that the DSA-APC model can correctly evaluate the severity level of sewer pipe defects, which has a good practical application value.

Cause Analysis of Leakage Failure of Seamless Steel Pipe during Pipeline Pressure Test

By means of chemical analysis, tensile test, metallographic analysis, and energy spectrum analysis, the leakage causes of seamless steel pipe in the process of pipeline pressure test were analyzed. The analysis shows that the crack of steel pipe appears before rolling, and the non-metallic inclusions near the crack are the crack source or preferred path of steel pipe cracking. The leakage of steel pipe defects in the subsequent eddy current and ultrasonic inspection is one of the main reasons the unqualified steel pipe flows into the construction site and leads to the leakage of the pressure test.

Application of Terahertz Nondestructive Testing Technology in the Detection of Polyethylene Pipe Defects.

At present, polyethylene pipeline is widely used in urban gas projects, but a relatively mature and reliable nondestructive testing technology has not been formed. Therefore, it is urgent to develop a new nondestructive testing technology to meet the increasing demand for inspection of non-metallic pipes. The terahertz testing technology and related equipment have played an increasingly important role in the nondestructive testing of many nonmetallic structures, but they have not been applied to polyethylene (PE) pipes. In this work, terahertz time-domain spectroscopy was used to detect prefabricated defects inside the PE pipe specimens. The results show that the terahertz nondestructive testing technology can be used to detect common defects in nonblack PE pipes with a detection error of less than 10%. Higher-power terahertz devices can detect defects in black PE pipe, while lower-power terahertz devices cannot. Because the black PE pipe contains carbon and has a strong absorption of terahertz waves. The penetration of lower-power terahertz devices to the black PE pipe is not enough, resulting in a low resolution of the imaging. The results of this work may promote the progress of the nondestructive testing technology of nonmetallic pipelines.

Ultrasonic defect detection of high-density polyethylene pipe materials using FIR filtering and block-wise singular value decomposition

Condition monitoring of high-density polyethylene (HDPE) pipes used for fluid and gas transfer is important for the safety of energy conservation and the environment. Ultrasonic phased array imaging methods provide a solution to detect and assess defects in HDPE pipes. However, ultrasonic bulk waves propagating in these viscoelastic media are strongly attenuated, resulting in reduced signal amplitude. In this study, a linear-phase Finite Impulse Response (FIR) filter is used to remove unwanted frequency components from the measured ultrasonic signals to improve the signal-to-noise ratio before applying the imaging algorithm of the total focusing method (TFM). Building upon this, a block-wise singular value decomposition (SVD) technique, which can adaptively determine the singular value cutoff threshold based on each block in the whole TFM image, is used to enhance the obtained TFM image quality. The performance of the combination of FIR filtering and block-wise SVD technique is validated by experimental data of HDPE pipe materials. Results demonstrate that the proposed approach generates good images to provide the detection and characterization of side-drilled holes in HDPE pipe materials.