Defects In Pipes Research Articles

Smart structural health monitoring (SHM) system for on-board localization of defects in pipes using torsional ultrasonic guided waves

Most reported research for monitoring health of pipelines using ultrasonic guided waves (GW) typically utilize bulky piezoelectric transducer rings and laboratory-grade ultrasonic non-destructive testing (NDT) equipment. Consequently, the translation of these approaches from laboratory settings to field-deployable systems for real-time structural health monitoring (SHM) becomes challenging. In this work, we present an innovative algorithm for damage identification and localization in pipes, implemented on a compact FPGA-based smart GW-SHM system. The custom-designed board, featuring a Xilinx Artix-7 FPGA and front-end electronics, is capable of actuating the PZT thickness shear mode transducers, data acquisition and recording from PZT sensors and generating a damage index (DI) map for localizing the damage on the structure. The algorithm is a variation of the common source method adapted for cylindrical geometry. The utility of the algorithm is demonstrated for detection and localization of defects such as notch and mass loading on a steel pipe, through extensive finite element (FE) method simulations. Experimental results obtained using a C-clamp for applying mass loading on the pipe show good agreement with the FE simulations. The localization error values for experimental data analysed using C code on a processor implemented on the FPGA are consistent with algorithm results generated on a computer running Python code. The system presented in this study is suitable for a wide range of GW-SHM applications, especially in cost-sensitive scenarios that benefit from on-node signal processing over cloud-based solutions.

Research on Pipeline Stress Detection Method Based on Double Magnetic Coupling Technology.

Oil and gas pipelines are subject to soil corrosion and medium pressure factors, resulting in stress concentration and pipe rupture and explosion. Non-destructive testing technology can identify the stress concentration and defect corrosion area of the pipeline to ensure the safety of pipeline transportation. In view of the problem that the traditional pipeline inspection cannot identify the stress signal at the defect, this paper proposes a detection method using strong and weak magnetic coupling technology. Based on the traditional J-A (Jiles-Atherton) model, the pinning coefficient is optimized and the stress demagnetization factor is added to establish the defect of the ferromagnetic material. The force-magnetic relationship optimization model is used to calculate the best detection magnetic field strength. The force-magnetic coupling simulation of Q235 steel material is carried out by ANSYS 2019 R1 software based on the improved J-A force-magnetic model. The results show that the effect of the stress on the pipe on the magnetic induction increases first and then decreases with the increase in the excitation magnetic field strength, and the magnetic signal has the maximum proportion of the stress signal during the excitation process; the magnetic induction at the pipe defect increases linearly with the increase in the stress trend. Through the strong and weak magnetic scanning detection of cracked pipeline materials, the correctness of the theoretical analysis and the validity of the engineering application of the strong and weak magnetic detection method are verified.

Automatic Detection of Water Supply Pipe Defects Based on Underwater Image Enhancement and Improved YOLOX

Automatic Detection of Water Supply Pipe Defects Based on Underwater Image Enhancement and Improved YOLOX

Non-destructive testing based on Unet-CBAM network for pulsed thermography

Infrared thermography (IRT) is a non-destructive testing technique that can detect the internal defects of materials. In the detection of austenitic stainless-steel pipes with large curvature, image noise caused by uneven heating is difficult to avoid. Traditional image processing methods are less effective. According to previous works, a supervised neural network was proposed in this paper using Unet network and convolutional block attention module. Existing image processing method and networks were used to compare with the proposed method. The results show that the proposed method can remove the noise caused by uneven heating, and detect all subsurface defects in stainless-steel pipe.

Effects of uneven wall thickness of steel pipes on magnetic permeability perturbation testing for internal defects

ABSTRACT Magnetic Permeability Perturbation testing (MPPT) has the advantage of high sensitivity for deeply buried defects in steel pipes. However, in complex working conditions, variations in the partial wall thickness of the pipe can affect the defect detection results. The paper discusses the influence mechanism of uneven wall thickness on the MPPT of internal defects on steel pipes and analyzes the magnetic field distribution caused by uneven wall thickness which influences on the magnetic permeability perturbation (MPP) of internal defects. The MPP is enhanced at the thinning wall and weakened at the thickening wall. The effect of uneven wall thickness on MPPT signals is confirmed through simulation and experiment. The signal of defects in thinning wall is enhanced, while the signal of defects in thickening wall is weakened. The greater the wall thickness variation, the degree of signal distortion is more significant. Experimental results show that uneven wall thickness has an effect on the detection of the defects with different depths. Finally, a method using deep saturation magnetisation is proposed to mitigate the inconsistent signals caused by uneven wall thickness. The findings will contribute to quantitative evaluation of defects in MPPT.

Characteristics analysis and monitoring of friction stir welded dissimilar AA5083/AA6061-T6 using acoustic emission technique

The joining of aluminum alloys using fusion welding often leads to issues such as hot tears, porosity, distortion, and solidifying cracks. To address these challenges, solid-state welding processes like Friction Stir Welding (FSW) are preferable. This study investigates the FSW of dissimilar aluminum alloys AA 5083 and AA 6061-T6, employing Acoustic Emission (AE) techniques for real-time monitoring. FSW was conducted under conditions with no defects, pinhole defects, and piping defects. The resulting joints were evaluated for their mechanical properties and metallurgical characteristics. Microstructural analysis was performed using an optical microscope, revealing that the defect-free condition achieved the highest tensile strength of 256 MPa and superior hardness of 93 HV at the stir zone. Tensile failure commonly manifested within the heat-affected zone (HAZ) of AA 6061-T6, primarily due to grain enlargement and the impact of Mg2Si precipitates. Examination of the fractured regions via scanning electron microscopy revealed ductile-mode fractures featuring elongated dimples and microvoids. AE parameters such as hits, amplitude RMS, and energy were analyzed, demonstrating that defect-free welds had consistent AE signal patterns, while significant variations were observed in pinhole and piping defect conditions due to larger defect areas. The findings suggest that AE monitoring is effective in detecting and analyzing welding defects, providing valuable insights into the FSW process for dissimilar aluminum alloys.

Design of guided wave magnetostrictive patch transducer with vertical static bias magnetic field for pipe inspection

Compared with electromagnetic acoustic transducer (EMAT), magnetostrictive patch transducer (MPT) has higher energy conversion efficiency. MPT is often used to excite and receive long-distance ultrasonic guided waves for defect detection or structural health monitoring of large industrial equipment. In this work, a new design method of permanent magnet MPT using the vertical static bias magnetic field to excite shear horizontal (SH) guided waves is proposed. First, the mathematical model of exciting ultrasonic guided waves by a vertical static bias magnetic field MPT is established, and the equivalent surface stress in the magnetostrictive patch is calculated. It is shown that the vertical static bias magnetic field MPT can excite SH guided waves in plates. Then, the influence of static bias magnetic field intensity on the amplitude of guided wave signal is analyzed. Compared with other traditional MPTs, the amplitude of SH guided wave signal excited by the vertical static bias magnetic field MPT is increased. Finally, a periodic permanent magnet (PPM)-MPT with a central frequency of 120 kHz is designed. The proposed PPM-MPT improves the energy conversion efficiency compared to the circumferential alternating permanent magnet array type MPT. The PPM-MPT can detect defects in a steel pipe with a wall thickness of 3.5 mm and an inner diameter of 100 mm. The results show that the PPM-MPT designed in this paper successfully detects the through hole with a diameter of 2 mm, and the cross-section loss rate of the defect is about 0.6 %.

Inspection of Semi-Elliptical Defects in a Steel Pipe Using the Metal Magnetic Memory Method

Ferromagnetic pipes are widely used for fluid transportation in various industries. The failure of these ferromagnetic pipes due to surface defects can generate industrial accidents, economic losses, and environmental pollution. Non-destructive testing techniques are required to detect these surface defects. An alternative is the metal magnetic memory (MMM) method, which can be employed to detect surface flaws in ferromagnetic structures. Based on this method, we present an analysis of experimental results of the magnetic field variations around five different surface semi-elliptical defects of an ASTM A36 steel pipe. A measurement system of MMM signals is implemented with a rotatory mechanism, a magnetoresistive sensor, a data processing unit, and a control digital unit. The MMM method does not require expensive equipment or special treatment of the ferromagnetic structures. In order to research a potential relationship between the defect sample size and the measured MMM signals, variable defect dimensions are experimentally considered. According to these results, the shape and magnitude of the normal and tangential MMM signals are altered by the superficial semi-elliptical defects. In particular, the maximum and mean tangential components and the maximum and minimum normal components are related to the defect dimensions. The proposed measurement system can be used to study the behavior of magnetic field variations around surface defects of ferromagnetic pipes. This system can be adapted to measure the position and damage level of small defects on the surface of ferromagnetic pipes.

Analysis of Properties and Macroscopic Defects of Metallic Bars, Pipes, and Strands through the Spectrum of Low-Frequency Excitations.

It is demonstrated that the application of piezoelectric sensors to metallic bars and strands can enable determining the status of the integrity of these elements through the spectrum of their acoustic excitations. The voltage output of the piezo, secured to metal bars or strands, is fed to the input of a Fast Fourier Transform analyzer, which allows displaying the spectrum of the excitations from which information on the length, overall quality of the metal, and the presence of defects can be obtained. We show that the analysis, performed on several materials and strands of different lengths, could be useful for cases in which visible inspection and/or direct access to the entire body of the metallic elements is not possible. Applications of our study for testing metallic structures embedded in concrete foundations are reported for construction sites.

PipeTransUNet: CNN and Transformer fusion network for semantic segmentation and severity quantification of multiple sewer pipe defects

With the continuous development of urbanization, the service life of sewer pipes is gradually approaching a critical threshold. Defects within pipe networks can significantly affect the municipality operations and residents' quality of life. Towards efficient and automatic sewer pipeline inspection, an integrated framework was proposed for semantic segmentation and severity quantification of multiple sewer pipe defects using the PipeTransUNet model that fuses convolutional neural networks with Transformer. The capability of the fusion network to extract and localize sewer defects was further enhanced by incorporating the convolutional block attention module and improving the activation function. In extensive experiments, PipeTransUNet shows enough competitiveness after hyperparameter selection, architecture tweaks, and comparison with other state-of-the-art models. Specifically, PipeTransUNet outperformed other models in terms of both quantitative and visual evaluations, with mean intersection over union, the mean of pixel accuracy, mean pixel accuracy, mean recall, mean F1-score, mean specificity, mean Kappa, and frequency-weighted intersection over union values reaching 71.92%, 84.90%, 80.74%, 85.93%, 83.05%, 84.44%, 55.55%, and 91.32%, respectively. A severity level assessment method for different sewer defects was developed based on PipeTransUNet and compared with expert reviews' results to demonstrate its feasibility and effectiveness. Moreover, the in-depth image features extracted from the segmentation head of our proposed model were visually evaluated and interpreted using pixel-level gradient-weighted class activation mapping. In summary, PipeTransUNet canrecognize complex defects well and provide a solid foundation for inspecting and maintaining sewer pipe networks.

Electromagnetic Acoustic Detection of Pipe Defects Hidden above T-Type Support Structures with Circumferential Shear Horizontal Guided Wave.

When pipe defects are generated above the T-type support structure location, it is difficult to distinguish the reflection signals caused by the weld bead at the support structure from the reflection echoes of pipe defects. Therefore, in order to effectively detect pipe defects, a waveform subtraction method with a circumferential shear horizontal (CSH) guided wave is proposed, which is generated by an electromagnetic acoustic transducer (EMAT). First, a CSH0 guided wave mode with a center frequency of 500 kHz is selected to establish a three-dimensional model with and without pipe defects above the support structure. Following this, the influence of different widths of support structures on the echo signal is compared. Moreover, simulation and experimental results are used to compare the influence of different welding qualities on the detection results. Finally, the waveform subtraction method is used to process the simulation and experimental signals, and the influence of pipe defects with different lengths and depths is discussed. The results show that the non-through crack defect of 5 mm × 1 mm (length × depth) can be detected. The results show that this method can effectively detect the cracks by eliminating the influence of the weld echo, which provides a new concept for the detection of the defect above the support structure.

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.

Bayesian hierarchical hyper-Laplacian priors for high-resolution defect imaging in pipe structures

The sparse defect imaging using guided waves for pipe structures has attracted a lot of attention due to its intuitive way of defect localization. However, there exist few attempts to employ the lp norm (p < 1) in sparse imaging to improve imaging performance. In this study, a new sparse imaging method based on Bayesian hierarchical hyper-Laplacian prior is proposed for pipe defect detection. A hyper-Laplacian prior is considered to obtain a sparser resolution, which aims at improving the imaging performance including defect imaging resolution and defect detection accuracy. A solid theoretical framework is constructed and derived to represent the hyper-Laplacian prior and to adaptively estimate all model parameters. Meanwhile, a two-step discretizing strategy is designed to reduce the computational cost. The experimental results demonstrate the superiority of the proposed method.

EDDY CURRENT QUALITY CONTROL OF THIN-WALLED REFRACTORY ALLOY PIPES

The experience of non-destructive quality control of thin-walled pipes of small diameter from a refractory non-magnetic alloy based on eddy current and X-ray television methods is considered. An analysis of the signal of an eddy current differential transducer during scanning of defects of various types was performed. The possibility of detecting and classifying such pipe defects as cracks, pores, local wall thinning, and ferromagnetic inclusions is shown.

Visual inspection system for crack defects in metal pipes

Visual inspection system for crack defects in metal pipes

Discussion of “Image Processing–Based Framework for Determining Deterioration in Sewer Pipe Defects”

Discussion of “Image Processing–Based Framework for Determining Deterioration in Sewer Pipe Defects”

Numerical investigation of the instability of dry granular bed induced by water leakage

AbstractUnderground pipe defects or cracks under transport infrastructure can cause water leakage to upper soil layers (e.g. subgrade and capping), inducing local cavities or even failure of overlying road/railway formation. Although numerous studies on the instability of granular beds induced by injected water have been conducted, most of them focused on the behaviour of saturated granular beds, while research on dry granular beds is still limited. This paper aims to address this gap using a numerical model coupling volume of fluid method with discrete element method. We observed that dry granular beds go through three distinct regimes as water jet velocity increases including stationary, stable deformation with heave and fluidisation. However, the flow velocities required to deform and fluidise dry granular beds are significantly higher than those required for saturated beds. Increasing granular bed thickness can alter its failure mechanism from full depth to localised erosion, leading to cavity formation around pipe cracks prior to the bed fluidisation. The gravitational and frictional components of granular mass are identified as two main resisting forces of dry granular beds against water jet force, evidenced by the increase of critical jet velocities as particle density and friction coefficient increase. Nevertheless, the moblised zone of granular mass is practically independent of both the buried depth of dry granular beds.

Failure analysis of S30408 pipe cracking and preventive measures

BACKGROUND: The feed pipeline made from 30408 stainless steel of a new unit leaked during the air pressure test. OBJECTIVE: The present work aims to examine the specific cause of pipeline cracking, and providing effective approaches to avoid similar failures. METHODS: Macroscopic inspections of the cracked pipe defects were made on site immediately after leakage. Mechanical properties and hardness of specimens machined from the failed pipe were tested. In addition, microscopic analyses including material composition, microstructure observation and crack morphologies of the failed part were performed to get detail information. Composition of the feed raw material was also analyzed to identify whether it had been contaminated by corrosive elements or not. RESULTS: No impurity composition was found in the feed raw material. The element constituents, yield strength, tensile strength and hardness of the cracked pipe fulfill standard requirements. A number of scratches and defects with a size of several microns were found on the inner wall of the leaked pipe, and they were believed to be formed at the perforation step during pipeline processing. Liquation cracks were found at the pipeline butt weld joint, and they laid hidden dangers for the safety and steady operation of the pipeline. CONCLUSION: The overall analysis results indicated the pipeline leakage during air pressure test was caused by cracks initiated around inner wall defects, which sabotaged the bearing capacity of the pipe by wall thickness reduction and stress concentration. Therefore, improving the inner wall surface quality at the perforation step may help to avoid such failure. The metallurgical effect and weld stress caused during the welding process promoted the initiation and propagation of liquation cracks. The tendency of welding hot crack formation could be reduced by taking strict composition control of the welding rod and adopting reasonable welding parameters.

Effects of axial misalignment welding defects on the erosive wear of natural gas piping

When considering solid particles in the pipe and axial misalignment weld defects in natural gas transmission systems, changes in the flow field due to structural differences can lead to various erosion characteristics in the bends. To investigate the erosion process within the bend, the computational fluid dynamics (CFD) - discrete phase model (DPM) method to develop a bend model with axial misalignment weld seam defects was employed. The erosion distributions of a bend with an axial misalignment weld were investigated under various transport parameters, bend orientation, axial misalignment height, and particle incidence angle. The results indicated that the erosion rate of the outermost elbow increases with the increase of the height of the axial misalignment weld on the outside of the bend pipe. When the axial misalignment height ranges from 1 mm to more than 5 mm, the difference in the maximum erosion rate escalates from 7 % to approximately 30 %. When the length of the inlet straight pipe is shorter, an increased incident angle intensifies the erosion effect and vice versa. This research can provide a reference for field construction work involving defective natural gas pipe bends.

Microphone array analysis of the first non-axisymmetric mode for the detection of pipe conditions.

This paper reports on the use of a circular microphone array to analyze the reflections from a pipe defect with enhanced resolution. A Bayesian maximum a posteriori algorithm is combined with the mode decomposition approach to localize pipe defects with six or fewer microphones. Unlike all previous acoustic reflectometry techniques, which only estimate the location of a pipe defect along the pipe, the proposed method uses the phase information about the wave propagated in the form of the first non-axisymmetric mode to estimate its circumferential position as well as axial location. The method is validated against data obtained from a laboratory measurement in a 150 mm diameter polyvinyl chloride pipe with a 20% in-pipe blockage and 100 mm lateral connection. The accuracy of localization of the lateral connection and blockage attained in this measurement was better than 2% of the axial sensing distance and 9° error in terms of the circumferential position. The practical significance of this approach is that it can be implemented remotely on an autonomous inspection robot so that accurate axial location and circumferential position of lateral connections and small blockages can be estimated with a computationally efficient algorithm.