Corrosion Defect Depth Research Articles

Pulse eddy current testing of corrosion defects in pipelines with insulation layer based on U-shaped sensors

Abstract In response to the insufficient detection ability of CUI in pipelines, based on U-shaped sensors, combined with the wide spectrum and non-contact characteristics of pulse eddy current detection technology, the area and depth of corrosion defects on the outer wall of pipelines can be effectively detected without removing the insulation. Simulation and experimental research are conducted on the outer wall defects of carbon steel test tubes with insulation layers. The simulation results show that the excitation magnetic field generated by the U-shaped sensor can effectively penetrate the inner wall of the pipeline, and the two signal-receiving coils of the U-shaped sensor will generate significant characteristic signals when passing through defects. The detection voltage position curve is obtained by applying the voltage distribution analysis method, and the characteristic signal length is the defect length. The experimental results show that when the detection sensor passes through and leaves the defect, the detection voltage distribution curve will generate a significant voltage drop characteristic signal, and the distance between the measurement points is consistent with the length of the defect. The experimental results are highly consistent with the simulation results.

Nondestructive differential eddy current testing for corrosion detection on coated aluminium alloys

PurposeThe objective of this study is to use non-destructive testing of corrosion on coated aluminium alloys using differential eddy current detection (DECD), with the aim of elucidating the relationship between the characteristics of corrosion defects and the detection signal.Design/methodology/approachPitting corrosion defects of varying geometrical dimensions were fabricated on the surface of aluminium alloy plates, and their impedance signals were detected using DECD to investigate the influence of defect diameter, depth, corrosion products and coating thickness on the detection signals. Furthermore, finite element analysis was used to ascertain the eddy current distributions and detection signals under different parameters.FindingsThe size of the defect is positively correlated with the strength of the detection signal, with the defect affecting the latter by modifying the distribution and magnitude of the eddy current. An increase in the diameter and depth of corrosion defects will enhance the eddy current detection (ECD) signal. The presence of corrosion products in the corrosion defects has no significant effect on the eddy current signal. The presence of a coating results in a decrease in the ECD signal, with the magnitude of this decrease increasing with the thickness of the coating.Originality/valueThe objective is to provide experimental and theoretical references for the design of eddy current non-destructive testing equipment and eddy current testing applications.

Machine learning-based Shapley additive explanations approach for corroded pipeline failure mode identification

Rapid failure mode identification of oil and gas pipelines can prevent catastrophic consequences, improve fast intervention and enhance the design safety of these critical systems. This paper proposes explainable-based machine learning models using to determine the failure mode of corroded pipelines as a function of geometric configurations, material properties, and corrosion defect details. To determine the best identification model, this study examined eight machine learning models, including Nave Bayes, K-Nearest Neighbors, Decision Tree, Random Forest, Adaptive Boosting, Extreme Gradient Boosting, Light Gradient Boosting Machine, and Category Boosting, based on a comprehensive experimental database for steel pipelines with various corrosion/crack defect configurations. Furthermore, the Shapley additive explanations approach is utilized to rank the input variables for failure mode identification and explains the machine learning model predicting a specific failure mode for a given sample. In identifying the failure mode of corroded pipelines, the proposed Extreme Gradient Boosting model indicated the highest accuracy in term of performance evaluation compared to all other proposed models. In addition, the model-explanation findings show that the important parameter influencing the failure mechanism of corroded pipelines is the depth of corrosion defects followed by the pipeline wall thickness. The proposed framework is adaptable enough to allow further use of experimental results for having new insights.

Failure assessment of corroded offshore pipelines using code-based approaches and a combination of numerical analysis and artificial neural network

The main purpose of the present paper is to investigate the failure pressure and the remaining life of the corroded offshore pipelines utilizing code-based methods such as ASME B31G, modified ASME B31G, DNV RP-F101 and FFS Level-1. Besides, the results of these approaches are compared with the responses of Finite Element Method (FEM) using Artificial Neural Network (ANN) model. To this purpose, the probability distribution of the corrosion defect length and depth as well as the corrosion rate values are obtained from an In-Line Inspection (ILI) survey of an offshore pipeline project in the Persian Gulf. In addition, a 3D verified numerical model of a corroded pipeline is constructed to conduct the numerical analyses. Using the numerical analysis data, a Multi-Layer Perceptron (MLP) model is created to estimate the failure pressure of the pipeline with arbitrary defect dimensions. Moreover, the remaining life of the corroded pipeline is quantified in order to prevent the catastrophic consequences of the pipeline failure. The results show that the MLP model is capable of estimating the failure pressure of the corroded offshore pipeline with quite acceptable accuracy compared with the mentioned code-based methods. Furthermore, the calculated failure pressure of the corroded pipeline can be sorted in ascending order as: (1) FFS-Level 1 (2) ASME modified B31G (3 & 4) ASME B31G & DNV RP-F101 (5) FEM + MLP. In fact, the failure pressure and the remaining life calculated by DNV RP-F101 are closer to the FEM + MLP results for the defects with small and medium dimensions. Diversely, the DNV RP-F101 method underestimates the failure pressure of the corroded pipeline when the defect dimensions become larger.

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.

Application of Improved Support Vector Machine in Predicting Failure Pressure of Oil and Gas Pipelines with Internal Corrosion Defects

In this study, the failure pressure of submarine oil and gas pipelines was predicted by using five commonly used machine learning models and derivative models. Firstly, an efficient local time-intensive finite element algorithm is constructed and used to generate a machine learning database. Secondly, in order to improve the accuracy of machine learning algorithm, the frontier optimization algorithm is improved to form a new predictive regression model IAEO-SVM model. To compare the accuracy of commonly used machine learning models and IAEO-SVM models, a comprehensive evaluation standard consisting of k-fold cross-validation and three statistical indicators was used. Finally, the influence of seawater depth and geometric factors of corrosion defects on the blasting pressure of submarine oil and gas pipelines was comprehensively analyzed through the high-dimensional surface built by the IAEO-SVM model. The IAEO-SVM model shows superior stability and accuracy compared to the comparison model, as demonstrated by its MSE’ of 0.0482, R2’ of 0.9982, MAE’ of 0.1295, and SD of 0.2198. The high-dimensional surface obtained through inversion shows a linear relationship between seawater depth and failure pressure. Meanwhile, the width of corrosion defects has a significant impact on failure pressure, accounting for up to 23% and thus cannot be overlooked.

Quantification of depth and morphology of internal corrosion defects by stepped eddy current thermography skewness under weak excitation conditions

In the nondestructive testing (NDT) of eddy current thermography of oil and gas tanks, the low and non-uniform temperature rise due to weak excitation conditions makes the quantitative estimation of the depth and area of defects still challenging. This work proposes a mathematical-physical model of skewness to characterize defect depth quantitatively. The skewness image is estimated in Gaussian scale space using the background difference method combined with the chunked threshold segmentation method to characterize the defect morphology. The experimental results show that the skewness is affected by the combination of defect depth and area. As the defect area decreases, the relationship between defect depth and skewness gradually degrades from nonlinear to linear, while the rate of change of skewness becomes insignificant. The skewness analysis model predicted the maximum MRE of 5.15%, the minimum R2 of 0.94 for defect depth, and the MRE of 8.39% for defect area. It shows that the skewness can effectively characterize the depth and morphology of internal corrosion defects.

An investigation on the effect of widespread internal corrosion defects on the collapse pressure of subsea pipelines

Deepwater subsea pipelines are an essential part of offshore oil and gas infrastructure, and it is a statutory requirement for operators to monitor the degradation of pipelines left-in-place at cessation of production. Collapse is one of the failure modes for subsea pipelines subjected to external pressure. Surface corrosion defects in subsea pipelines, either while still operational or after cessation of production, usually manifest as widespread or localized corrosion pits that are randomly spread on the steel surface. Extensive work has been done on predicting the collapse pressure of pipelines containing single corrosion defects however, the same cannot be said for randomly distributed corrosion defects. In this study, we investigate, for the first time, the effects of the geometry and coverage extent of widespread internal corrosion defects with random spatial distribution on the collapse pressure of pipelines using finite element analysis. The results show that corrosion defect depth and coverage surface area are the main defect geometric parameters that significantly influence the collapse strength of pipelines with randomly distributed internal corrosion. An empirical function for predicting the collapse pressure of a corroded pipeline is developed to reduce the need for recurrent computational effort.

Experimental investigation for multi-defect detection of industrial pipeline based on passive infrared thermal imaging analysis: A case study

Corrosion of pipe walls is a major hidden hazard for the safety of chemical enterprises; therefore, detection of the depth of pipe wall corrosion defects is extremely important. In this study, passive thermal imaging technology was used to detect and evaluate corrosion defects of different shapes in pipe walls and of different depths for the same shape. Three-dimensional finite element models of defects with different depths were established using COMSOL, and the thermal response mechanisms of defects with different depths and the effects of defect depth and shape on heat transfer were analyzed. The thermal response mechanism of defects with different shapes and depths (1–3 mm) was also analyzed experimentally and compared with that of defect-free parts to analyze the safety of the pipeline and verify the results of the numerical simulation analysis. The results indicated that the level of fluid temperature determined the accuracy of the test, and at the same temperature, the defects at a depth of 3 mm exhibited multiple peaks and a large peak area. However, defects with a depth of 2 mm exhibited the opposite behavior, and the heating rate of 3-mm deep defects during the temperature change was 1.08 times higher than that for 2 mm-deep defects. This study provides a reference for detecting leaks in chemical pipelines.

Magnetic Flux Leakage Testing Method for Pipelines with Stress Corrosion Defects Based on Improved Kernel Extreme Learning Machine

This study aims to study the safety of oil and gas pipelines under stress corrosion conditions and grasp the corrosion damage situation timely and accurately. Consequently, a non-destructive testing method combining magnetic flux leakage testing technology and a kernel function extreme learning machine improved by genetic algorithm (GA-KELM) is proposed. Firstly, the variation of the corrosion defect dimension and profile with time is obtained by numerical simulation. At the same time, the distribution of the magnetic flux leakage signal under different defect conditions is analyzed and studied. Finally, feature selection is carried out on the magnetic flux leakage signal distribution curve, and GA-KELM is used to predict the depth and length of corrosion defects so as to realize the non-destructive testing of the pipeline defects. The results show that different geometric features result in different magnetic flux leakage signal distributions. There is a corresponding relationship between the defect dimension and extreme value, area, and peak width of the magnetic flux leakage signal distribution curve. The GA-KELM prediction model can effectively predict the depth and length of corrosion defects, and the prediction accuracy is better than the traditional extreme learning machine prediction model.

Strength Analysis of Corroded Seawater Pipeline Based on Finite Element Method

The residual strength of corroded defective pipelines is an important manifestation of pipeline-bearing capacity. The change of residual strength of Q235 steel and B10 Cu-Ni alloy pipeline caused by the change of defect depth, length, and width in the case of grooved corrosion defect was analyzed and studied by the finite element method. The results show that the effect of corrosion defect depth on the residual strength is greater than the defect length and width. At the same time, Q235 steel has better pressure-bearing performance than B10 Cu-Ni alloy.

Quantitative Evaluation of Corrosion Defects on Structural Steel Plates via Metal Magnetic Memory Method

ABSTRACT The detection and evaluation of corrosion defects take on a critical significance to ensure the service safety of steel structures in civil engineering. The quantitative evaluation of corrosion defects has not been well addressed though metal magnetic memory (MMM) testing technology has been investigated in steel corrosion problems. In this study, the Q345qD steel plates were taken as the specimens of MMM testing. Specimens with different corrosion degrees were developed through electrochemical corrosion, and the change laws of the MMM signals and the characteristics of different corrosion specimens were analyzed. A three-dimensional (3D) magnetic charge model of the corrosion area was built based on the magnetic charge theory, such that the change laws of the MMM signal in the corrosion area from the mechanism were explained. The finite element simulation results of the corrosion specimens were well consistent with the experimental and theoretical results. A quantitative evaluation method for corrosion defect depth was proposed in combination with finite element simulation and experimental data. Comparing the experimental data and the inversion data, the relative errors of the determined defect depth h were within 20%, suggesting that the proposed evaluation method is feasible for the quantitative evaluation of steel corrosion depth.

Electromagnetic Ultrasonic Shear-Horizontal Wave to Detect Corrosion Defect of Flat Steel for Grounding Device of Transmission Pole Tower

Detecting defects in grounded flat steel is essential for ensuring the safety and reliability of transmission tower grounding devices. However, traditional inspection methods, such as physical excavation and verification, are costly and time-consuming. This paper proposes a corrosion defect detection method for flat steel transmission tower grounding devices based on electromagnetic ultrasonic SH waves. In addition, using commercial software, a three-dimensional finite element simulation model of grounded flat steel with simulated pitting corrosion defects is constructed. The specified displacements applied to multiple surface sources mimic the horizontal shear vibrations generated by the electromagnetic ultrasonic transducer on the surface of the grounded flat steel during actual inspection. A simulation was used to investigate the propagation and attenuation characteristics of shear-horizontal ultrasonic SH0guided waves for simulated corrosion defects with various geometric configurations in grounded flat steel. The simulation investigated the propagation and attenuation characteristics of the SH0 wave in grounded flat steel and the detection of various defects for linear analysis of the results. The simulation results show that the attenuation of the electromagnetic ultrasonic guided wave is small, at only 0.0016 dB/mm, and the displacement amplitude of the echo signal decreases with the increase of the SH0 wave propagation distance. Increasing the depth and length of corrosion defects increases the echo signal amplitude. At the same time, the width of corrosion defects has little effect on the echo amplitude. Finally, a flat steel defect detection experiment was conducted, and the experimental results fit with the simulation to verify the accuracy of the simulation model. This detection method introduces a new idea for the on-site detection and quantitative identification of corrosion defects in grounded flat steel, which has significant reference value and can provide a more effective and economical method for ensuring the safety and dependability of transmission tower grounding devices.

Impact of the correlation between random variables on the reliability assessment of the corroded pipelines

In the present work, an Algerian pipeline made of API X70 steel, used for transporting gas and subjected to corrosion attack for three decades, was investigated. Using an intelligent inspection tool, the sizes and orientations of the defects were determined to analyze and improve the accuracy of reliability and/or probability of failure estimates for the damaged pipelines. A probabilistic methodology has been developed to assess the reliability index and the sensitivity of each random variable, within the limit state function, of a gas-transporting steel pipeline that has been corroded for three decades. The reliability analysis utilizes the Second Order Reliability Method, which is based on the BFGS algorithm. In addition, the corrosion defect depth, pipe wall thickness, and corrosion defect length are the most dominant variables within the limit state function, followed by ultimate stress and operating pressure, respectively. Meanwhile, for a failure scenario, thus, it is essential to include a potential parameter such as the correlation coefficient between random variables within the objective function during reliability assessment. For instance, not taking into account the correlation between random variables can lead to an overestimation of the reliability index by 8.89% for a defect depth to wall thickness ratio d/t of 20%. This overestimation increases to 19.16% when the d/t ratio increases to 70%. If the correlation coefficient is significant, it can cause an increase of 8.86% in the error of reliability index assessment for a defect with a depth to wall thickness ratio of 20%, and an increase of 14.58% for a defect with a ratio of 50%. Besides, these avoid the conservative practice resulting from the negligence of the existing correlation between random variables within the limit state function. Hence, considering the correlation coefficients during reliability assessment contributes to setting optimal maintenance schedules for pipelines made of steel under localized corrosion attack.

Reliability assessment method for tank bottom plates based on hierarchical Bayesian corrosion growth model

For effectively predicting the tank failure time and analyzing the key elements influencing the reliability of corroded bottom plates, this article presents a model for calculating the reliability of corroded tank bottom plates based on a hierarchical Bayesian corrosion growth model. Firstly, the growth of corrosion defect depth is expressed by the gamma process, and the hierarchical Bayesian model is used to calculate the corrosion depth growth. After that, the reliability calculation model of the corroded tank base plate is established by combining the results of the hierarchical Bayesian model with the stress-strength interference theory, and the three uncertain factors of the base plate thickness, radius, and yield strength are considered in the model. Finally, the reliability assessment and sensitivity analysis of corroded bottom plate are carried out. The results show that the proposed reliability calculation model can provide more accurate failure state prediction results than the reliability calculation model which only considers the influence of corrosion depth, and can provide reference for reducing the failure rate of tank floor and reasonably formulating the maintenance plan of tank floor.

Sensitivity Analysis of Influencing Factors of Gas Pipelines with Corrosion Defects under the Action of Landslides

Sensitivity analysis aids in determining important factors affecting pipeline safety. Sensitivity analysis of stress inside gas pipelines with corrosion defects in a landslide region can provide a theoretical basis for the safe operation of pipelines. This study considered an X80 high-grade steel gas pipeline model with corrosion defects using finite element analysis (ABAQUS software) under lateral landslide conditions. Particularly, we studied the six major engineering elements of soil cohesion to understand the stress variations in buried gas pipelines and performed a sensitivity analysis of each influencing parameter. The calculation results revealed that all the factors influencing the stress in corroded gas pipelines during landslide conditions were positively correlated to the internal pipe stress, except for the axial position of corrosion defects. The factors in the descending order of influence on the sensitivity coefficient are stated as follows: landslide displacement, axial position of corrosion defect, soil cohesion, depth of corrosion defect, pressure, and length of corrosion defect. The results of this study will aid in the design and implementation of such pipelines in mountainous or other landslide-prone terrains.

Intergranular corrosion effect on fatigue behavior of dissimilar 6005A‐5083 aluminum alloys weld joints

AbstractThe effects of the intergranular corrosion (IGC) defect depth on the high circle fatigue performance of tungsten inert gas (TIG)‐welded joints of 6005A–5083 dissimilar aluminum alloys were investigated. After soaking in different IGC solutions for 72 h, corrosion defects of different depths were generated in various areas of the specimen surfaces. It was found that the 5083 base metal exhibited the poorest corrosion resistance, while the welded metal displayed the best corrosion resistance. Fatigue tests carried out after precorrosion revealed that when the IGC depth was shallow, the weld defects on the weld metal were the main source of cracking for fatigue extension. However, when the IGC depth in the 5083 base metal was >248 μm, the corrosion defect acted as the origin of the fatigue crack, and the fatigue life of the sample was significantly reduced. This was attributed mainly to the local high‐stress field at the tip of the corrosion defect. Finally, a corrosion fatigue crack expansion model was established to analyze the influence of the corrosion defect depth on the fatigue lives of welded joints.

Finite element analysis of the failure of high-strength steel pipelines containing group corrosion defects

Corrosion defects in steel pipes can lead to pipeline failure, and such failures can have severe impacts; thus, developing appropriate corrosion mitigation schemes to prevent them can be beneficial. To this end, the influences of variables such as the depth, length, and group spacing of corrosion defects on the failure of X80 steel pipes containing group corrosion defects were analysed in this study. Abaqus, a simulation software, was used for non-linear finite element analyses, and 52 groups corresponding to corrosions under different geometric factors were prepared. Furthermore, a pipeline failure pressure prediction model was established using 138 sets of simulations to determine the maximum von Mises stresses of pipelines subjected to internal pressures under different working conditions. Actual blast test data and error comparisons with various existing evaluation standards were conducted to verify the accuracy of the proposed model. The results revealed that an increase in the internal pressure and the changes in the depth, length, and group spacing of group corrosion defects impact the maximum von Mises stress in the pipe. Among the group corrosion defects considered, the depth of the deepest defect was found to be the most critical factor in determining the form of damage and the failure pressure of pipes containing corrosion defects. The length of corrosion defects and the axial spacing between groups of defects were found to be the secondary factors that affect the failure pressure. In an actual pipeline, group corrosion defects are expected to cause a reduction in the wall thickness and, thus, pose a threat to safety, an issue that warrants substantial attention. The results of this work can serve as a reference for assessing the failure of X80 steel pipelines containing group corrosion defects.

Application of HMC-SS Method in Pipeline Reliability Analysis and Residual Life Assessment

In this paper, the reliability analysis and residual life assessment model of gas pipelines with multiple corrosion pits are established. Aiming at the simulation evaluation of small failure probability of gas pipelines, a new method for reliability analysis and residual life assessment of gas pipelines with multiple internal corrosion pits is proposed, which is called the Hamiltonian Monte Carlo subset simulation (HMC-SS) method. Compared with the traditional MCS (Monte Carlo simulation) algorithm, the HMC-SS method has the advantages of less sampling, low cost, and high accuracy. And compared with the random walk SS method, the HMC-SS method can analyze the state space more efficiently and achieve faster convergence. In this paper, the HMC-SS method is applied to the reliability analysis and residual life assessment of gas pipeline engineering, and the sensitivity analysis of the random parameters affecting the failure probability of the pipeline is carried out. The results show that the corrosion rate, the depth of corrosion defects, and the wall thickness of the pipeline have great influence on the residual life of the pipeline, while the yield strength, working pressure, and the length of corrosion pits have no obvious influence on the failure probability and residual life of the pipeline. The analysis shows that the proposed HMC-SS method can be used as a reasonable tool for failure assessment of natural gas pipelines affected by corrosion to determine the remaining life of the pipeline system. This method provides a reliable theoretical basis for the integrity management of the gas pipeline.

Elastic-plastic buckling of pipes with asymmetric dual corrosion defects subject to external pressure

In the deep-sea environment, pipelines are prone to buckling instability under the coupling effect of hydrostatic pressure and corrosion defects. This instability phenomenon is affected by the pipe dimensions, material parameters and corrosion defects. Based on the shell buckling theory, a semi-analytical method is proposed to analyze the elastoplastic buckling response of the pipe with dual corrosion defects. The buckling pressure of the pipe can be solved by the characteristic equation numerically. Then, the results of semi-analytical approach are in good agreement with those of experimental tests and FE analysis, which verifies the accuracy of the approach. The effects of geometric dimensions, material parameters, corrosion defect depth and angle on the buckling pressure of the pipe are analyzed in details. In addition, based on the FE model, the collapse mode and the post-buckling response of the pipe with asymmetric dual corrosion defects are considered. The application scope of the semi-analytical approach for pipes with different defect lengths and initial ovality is analyzed. Finally, the semi-analytical approach is extended for pipes with multiple corrosion defects.