Pipeline Crack Research Articles

Pipeline reliability assessment and predictive maintenance considering multi-crack dependent degradation

Cracks due to corrosion are one of the main reasons for natural-gas pipeline leaks. Making the reliability assessment, prediction, and maintenance decision of pipelines based on measurable crack data is a central issue at present. The failure of pipelines is usually a result of the cumulative impact of multiple cracks. The interaction between adjacent cracks accelerates crack propagation, and greatly affects the degradation mechanism of pipelines. In this study, the reliability prediction and maintenance decisions were studied by considering the dependent degradation between multiple cracks in pipelines. Firstly, the initiation and propagation of pipeline cracks were modeled using a non-homogeneous Poisson process and a Gamma process, respectively. The interaction between cracks was defined to be a function of the random crack distance, which could be reflected by the change of shape parameters in the Gamma process. Secondly, the pipeline’s failure was defined as the competitive failure of the number of cracks, the maximum crack depth, and the total crack depth. The reliability prediction model of a pipeline under this failure mode was determined. A non-periodic combined maintenance policy considering both the pipeline condition and its predictive reliability was then proposed, and an optimal predictive maintenance decision model was constructed to minimize the long-term average cost rate. Finally, the effectiveness of the proposed model and policy was verified by a numerical experiment and a crack dataset of a transnational pipeline.

Use of NEWS and MMM technologies for diagnostics of NPP components

The paper provides information on the results of the R&D projects of the Research Centre Rez carried out between 2019 and 2022 for the Technological Agency of the Czech Republic as part of the "Perspective Diagnostic Methods" projects. In the first part of this paper, the development of the use of NEWS technology for the detection of cracks in pipelines is described. Results from development on half-pipe and pipe test pieces are presented, as well as results from measurements on the main circulation pipeline and connection pipes up to the first shut-off fitting at NPP Temelín. The method of certification and verification of the methodology is also described. The second part of this paper describes the development of the use of MMM technology to detect the state of tightening of bolts of flange joints. The development focused on determining the dependence of the tension in the bolt and the values obtained by the MMM method, determining the influence of the measurement location for the MMM method on the assessment of the condition of the flange joint, and determining the sensitivity of the method to unsatisfactory flange conditions such as undertightened bolts, overtightened bolts etc. is described. At the end, implementation plans are presented, either for specific applications at the NPP or in a subsequent R&D project.

The efficiency of advanced polymeric composite sleeves in the rehabilitation of cracked pipelines under combined loadings

The main objective of the present work was to numerically study the effect of loading conditions on the progressive damage of initial defects in steel pipelines. The adhesively-bonded composite sleeve repair technique was utilized to extend the lifetime of such cracked pipelines with different inclination angles (θ) under combined loads (i.e., internal and axial pressure) with different values. Three different glass fiber-reinforced polymer sleeves (GFRPs; [0°]8s, [90°]8s, and [0°/90°]4s) were simulated to study the effect of fiber orientation on the efficiency of composite sleeves in reducing the crack driving force. A three-dimensional (3D) elastic-plastic finite element method was utilized in the present study. The numerical results showed that the loading sequence has a great effect on the development of the crack-tip plastic zone size when the internal pressure is applied first. In the following two cases, if the internal pressure and the tensile stress were applied simultaneously or the tensile stress was applied in the first step followed by internal pressure, the loading sequence has little effect. In the case of the presence of internal pressure, the crack path is mainly dependent on it, and axial pressure has a marginal effect on its value. In such cases, applying [0°]8s sleeves is the best way to arrest cracks in steel pipelines. However, in the absence of pressure within the pipelines, [90°]8s sleeves are the appropriate ones to use to arrest the cracks due to axial pressure. Therefore, [0°/90°]4s sleeves may be recommended for repairing cracked steel pipelines to prevent crack growth in both situations.

Improvement of collapsing problematic soils on the sabzevar-mashhad railway route (northeast of Iran) using traditional additives

Collapsing soils are among the problematic soils in geotechnical engineering. These soils have a relatively good resistance in their natural state with low humidity, but in case of saturation without change in the incoming load or subjected to vibration force, they settle a lot and in many cases, it will cause cracking or destruction of foundations, roads, railway lines, dams, pipelines and other existing structures. Collapsing soils are scattered in many regions of the world, in Iran these soils are mostly observed in the central and eastern regions. In this research, according to the samples taken from Khorasan-Razavi province in the area of Sabzevar-Mashhad, these samples have been shown to be in the category of collapsing soils based on the tests performed according to ASTM standards. In order to improve this soil, cement and lime additives with percentages of 5 and 10 percent have been used, which has improved the shear properties of the soil due to the addition of lime and cement, so that it causes an increase in the friction angle in the soil, which indicates an improvement in the soil parameters. The results obtained with regard to the addition of cement and lime to the compacted soil show that the index of compaction in the addition of lime is better than that of cement and the results show that with the increase of lime, the soil has a 68% and 78% decrease in the soil compaction index, respectively, but when cement is added to the soil, it causes the percentage of raking index to improve by 34 and 37%.

Defect signal intelligent recognition of weld radiographs based on YOLO V5-IMPROVEMENT

Internal pipeline weld defects cause pipeline cracking accidents, whereas X-ray detection can detect these defects. The deep learning-based intelligent defect identification model of weld radiographs extracted weld defects automatically through a convolutional neural network, thereby eliminating the subjective interference of human factors and improving the quality and speed of film evaluation. By proposing the YOLO V5-IMPROVEMENT model and adding the CA attention mechanism, SIOU loss function, and FReLU activation function, this paper improved the ability to detect small targets, capture low-sensitivity spatial information, and perform global optimization. A total of 7500 radiographs containing weld defects of a Chinese oil and gas long-distance pipeline were selected for training, verifying, and testing the model developed in the paper. Precision and recall of the YOLO V5-improvement presented in this paper reached 92.2 % and 92.3 %, which were 10.7 % and 12.5 % higher than YOLO V4, and 9 % and 11.2 % higher than the unimproved YOLO V5 model, respectively. It is confirmed that YOLO V5-IMPROVEMENT has high accuracy and high robustness and that applying this model to the intelligent defect identification of weld ray images can significantly improve detection efficiency and reduce the misjudgment rate.

Differential Electromagnetic Acoustic Probes for Quantitative Detection of Pipeline Cracks

The circumferential 0-mode shear horizontal (CSH <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> ) guided waves have great advantages for the detection of cracks in pipelines due to the small attenuation of acoustic signals. However, the CSH <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> guided wave detection methods are confused by the signal mixing, low signal-to-noise ratio and poor quantitative accuracy of cracks. In this paper, the novel detection method which includes three circumferential uniformly distributed probes are proposed for quantitative detection of pipeline cracks. The signals received by two receiving probes constitute a couple of differential signals. A full physical field coupled finite element model is developed to study the effect of CSH guided wave-defect interactions. The differential signal enhancement algorithm can separate the crack signal from the overlapped signal, which greatly improves the accuracy of crack location. The characteristic coefficient method is introduced to improve the quantitative accuracy of the crack sizes. The localization and quantification methods are verified through experiments. The results show that the defect localization method proposed in this paper can improve the signal-to-noise ratio of the crack signal from -15 dB to 2.4 dB. The localization error of crack is less than 6.75%. The quantitative error of the crack sizes is less than 8.75% based on the characteristic coefficient method.

Non-Destructive Detection of Pipe Line Cracks Using Ultra Wide Band Antenna with Machine Learning Algorithm

In this article, an Ultra-Wide Band (UWB) antenna for the pipeline crack detection process is proposed. A UWB antenna has been designed with the dimension of 32 x 32 mm2 and it resonates from 3 GHz to 10.8 GHz. The designed antenna produces a peak gain of 4.36 dB. A pair of UWB antennas are employed in various pipeline scenarios and the received pulse from antenna 1 to antenna 2 is used for further processing and detection of pipeline cracks. Through the suitable machine learning data classifier algorithm the dimension of the crack has been detected. The various features such as mean, standard deviation (σ), mean average deviation (mad), skewness, and kurtosis have been extracted from the received pulse. Then the three different machine learning algorithms namely Support Vector Machine (SVM), k-Nearest Neighbor (kNN), and Naïve Bayse (NB) were trained and tested using extracted features, and the dimension of the void has been identified. Out of these three machine learning algorithms, kNN provides better accuracy and precision. It predicts the small cracks with 100% accuracy having a dimension as small as 1 mm width.

Processing methods of the pipeline crack detection signal by a balanced field electromagnetic technique based on phase characteristics

The balanced field electromagnetic technique is an effective way of in-line inspection to detect cracks in pipelines. A signal demodulation method based on phase characteristics is proposed for the problem of interference signals generated by the sensor tilt shaking during the detection, which affects the judgment of the cracks. The method uses a reference signal whose phase is orthogonal to the signal generated by the sensor shaking to demodulate the detection signal to eliminate the shake interference. The generation principles of crack detection signals and interference signals generated by sensor shaking are analyzed, and the influence of sensor lift-off on detection is compared. A demodulation model is established based on the characteristic of that same frequency and different phases of crack and shake signals. The feasible conditions of the method are analyzed by simulation, and the phase value of the reference signal in the demodulation method is determined. The platform detection experiment and pulling tests at different speeds are carried out, respectively, to verify the effectiveness of the proposed method. The results show that there is a significant phase difference between the signals generated by the sensor shaking and the crack. For carbon steel pipelines, the signal phase of different shake angles is −4°. When the sensor structure and excitation frequency in this study are used, the reference signal phase is chosen to be 86°. The method preserves the detection signal characteristics before processing and enables the linear output responses to be obtained for different depths of cracks.

Microclimate stability on the critical zone of a karst hillslope in southwest China: Insights from continuous temperature observations at the air–soil–epikarst interface

Temperature is an important near-surface microclimate parameter that plays a key role in hydrological, ecological, and biogeochemical functions. However, the spatio-temporal distribution of temperature on the invisible and inaccessible soil–weathered bedrock continuum, wherein hydrothermal processes are most active, remains poorly understood. Temperature dynamics were monitored at 5 min intervals in the air–soil–epikarst (∼3 m) system at different topographical positions of the karst peak-cluster depression in southwest China. The weathering intensity was characterized based on the physicochemical properties of samples collected through drilling. No significant difference was observed in air temperature across slope positions, which was related to the limited distance and elevation resulting in roughly consistent energy input. The control effect of air temperature on the soil–epikarst was weakened with the decrease in elevation (±0.36 to ±0.25 °C). It is attributed to the enhanced temperature regulation capacity of vegetation cover from the up slope (shrub dominant) to down slope (tree dominant) in a relatively uniform energy environment. Temperature stability is clearly distinguished in two adjacent hillslopes that were differentiated by weathering intensity. For every 1 °C change in the ambient temperature, the amplitude of soil-epikasrt temperature variation on the strongly and weakly weathered hillslopes were ±0.28 and ± 0.32 °C, respectively. The response of soil-epikarst temperature to ambient temperature was more sensitive in the wet season (±0.40 °C) than in the dry season (±0.20 °C), which was related to the cooling effect caused by abundant rainfall. The cooling effect was particularly prominent in the preferential flow development area composed of pipeline cracks, which appear in the hillslope with relatively weak weathering intensity. These demonstrate that soil–epikarst temperature responds more gently to the variability of rainfall and ambient temperature on a relatively strong weathered hillslope. Accordingly, this study highlights that the sensitivity of soil–epikarst temperature to climate change is regulated by vegetation and weathering intensity on karst hillslopes in southwest China.

A crack characterization model for subsea pipeline based on spatial magnetic signals features

The inspection and quantification of minor defects for subsea pipelines is an important topic for ensuring structural stability and safety. This study proposes an in-line inspection and characterization model for the subsea pipeline cracks based on spatial magnetic signals extraction technology and machine learning algorithms. First, the crack inspection mechanism is elaborated by the magnetic domain deflection from the micro level, and the effect of the crack width, depth, and lift-off on the signal features are evaluated by numerical simulations. Second, a custom-built experimental system adopted to scan the tiny cracks is incorporated to validate the reliability of the simulation results. Finally, 11 statistic features are extracted from the time series signals and defined as the input of the regression models based on various machine learning algorithms to realize the quantitative characterization of the crack dimensions. Consequently, the probes manufactured in this research can accurately identify cracks with a minimum width of 0.3 mm. Moreover, the random forest algorithm achieves state-of-the-art inversion performance with mean errors of the prediction accuracy to the crack width, depth, and lift-off at 0.102 mm, 0.040 mm, and 0.004 mm, respectively.

Study on crack tip opening angle evolutions during dynamic crack propagation of pipelines

AbstractCrack tip opening angle (CTOA) has been used to describe the arrest toughness of high‐grade pipelines. However, its evolutions and influencing factors during pipeline crack propagation are unclear. In view of this, this paper reproduces the full‐scale test using a numerical simulation method that is proven to be reliable. On this basis, the evolutions of CTOA during the dynamic crack propagation of the pipeline are systematically studied, and the effects on CTOA values are clarified. The results show that the critical CTOA of a pipeline is independent of the design factor, the pressure decompression, and the large change in the crack velocity and is also not sensitive to the pipe strength, pipe wall thickness, and the inertial effect of soil, but only related to the pipe diameter and pipe toughness. The above conclusions have laid a foundation for the subsequent establishment of the arrest control method for natural pipelines based on CTOA.

Causes of corrosion cracking of pipe metal and methods for their protection

The article is devoted to studying the problem of corrosion cracking of pipelines and methods of their protection. An analysis of the main factors that determine the phenomenon of corrosion cracking is given. The article deals with the issue of identification of corrosion cracking based on the “carbonate theory”. The main methods of protection of metal structures from corrosion cracking are given.

Numerical simulation of pipeline crack detection probe with poly-magnetic structure

The detection and characterization of cracks is one of the key issues in oil and gas transmission pipeline inspection. The weak degree of spatial magnetic field distortion caused by the small size of the crack creates great difficulties for signal extraction. In this paper, we propose an ACM crack detection probe with poly-magnetic structure, and investigate the influence of the core, shield on the detection signal through numerical simulations. The results show that the crack detection probe with the core and shield can effectively improve the distorted magnetic field component in space acquired by the magnetic element, increasing the distorted magnetic field signals in the vertical and horizontal directions by a factor of 2.10 and 1.76 respectively. Verification that ACM probe with poly-magnetic structure can effectively increases the crack-induced magnetic field distortion signal and reduce the probability of misses.

Discriminative Method for Crack Detection Signals in Balanced-Field Electromagnetic Technique Based on Amplitude-Phase Composite Figure.

The balanced-field electromagnetic technique is an effective in-line inspection method for pipeline cracks. To address the problem that the interference signal generated by the tilt jitter of the sensor during the detection process affects the judgment of cracks, this paper proposes a method to differentiate the crack detection signal from the sensor jitter signal by using an amplitude-phase composite figure. The generation principle of the detection signal was analyzed by using the mutual inductance model, and the amplitude-phase composite figure was constructed by using the components of the detection signal after quadrature demodulation. The feasibility of using the phase as a signal discrimination feature was illustrated by finite element simulations, and the characteristics of the amplitude-phase composite figure were determined. The validity of the proposed method was verified experimentally. The results show that the crack detection signal and the signal generated by the sensor jitter are of the same frequency with similar waveforms and significantly different phases. The phase base value of the crack detection signal ranges from 35° to 55°, and the phase base value of the jitter signal is −4°. In terms of the characteristics of the amplitude-phase composite figure, the crack detection signal distribution is symmetrical about the origin in the first and third quadrants, and the axial crack is closer to the Y-axis than the circumferential crack; the jitter signal is distributed in the second and fourth quadrants and has a very small angle to the X-axis. In addition, the proposed method effectively weakens the observation of the phase noise region in the detection signal of the balanced-field electromagnetic technique.

Multi-mode fusion BP neural network model with vibration and acoustic emission signals for process pipeline crack location

Vibration fatigue occurs easily in offshore platform pipelines that have been in service for several years. Timely damage detection and the detection of fatigue cracks are of great significance to ensure safe production. To accurately locate pipe cracks, a multi-mode fusion crack location model is proposed in this paper. The model combines two location methods based on vibration signals and acoustic emission (AE) signals through a backpropagation (BP) neural network. The natural frequencies of a large number of crack models at different locations were obtained by combining vibration modal test and finite element simulation results. A large number of acoustic emission signal time differences of cracks at different positions were obtained by simulating the acoustic emission signals at the initiation and propagation stages of cracks with the pencil-lead break method. The multi-mode fusion model was trained by experimental and simulation data, and the positioning effect of the model was verified. The results showed that training the neural network based on multi-mode data could effectively avoid the over-fitting of the network and improve the training effect. Compared with the method based on single information or only a weighted average method, this model could effectively improve the accuracy of crack location and reduce the fluctuations of the location errors, and the location results were more reliable.

A Novel Crack Quantification Method for Ultra-High-Definition Magnetic Flux Leakage Detection in Pipeline Inspection

Cracks that may cause pipeline cracking and leakage become the main risk of in-service pipelines after conventional metal loss defects have been detected. Therefore, it is imperative to develop ultra-high-definition magnetic flux leakage (MFL) detection for cracks. Due to the larger spatial sampling interval of conventional MFL inspections than the crack defect width, sparse sampling will result in problems such as missed detection of crack defects or insufficient signal sampling of crack defect MFL signals. To address the problem, this paper presents a detection topology in which the magnetic field signal is integrated first and then sampled, and it can effectively sample the leakage magnetic field of cracks. Furthermore, a magnetic field spatial integration (MFSI) method is proposed based on the magnetic dipole model, which develops an analytical solution for the depth quantification of crack defects. Using the multi-MFL spatial integral values under different lift offs, iterative equations are constructed, and the numerical solution for the width quantification of crack defects is provided. The finite element simulation and physical experiment are constructed. And, the results show that the proposed MFSI method can realize the quantification of crack size. When the crack defect aspect ratio is greater than 4, the quantization error of the crack depth and width does not exceed 0.7 mm and 0.1 mm, respectively. The robustness of the MFSI method for crack quantification is also discussed and verified. Moreover, based on theoretical analysis and simulation data, this paper concludes that the distribution of cracks MFL signal is width-insensitive, which would cause that classic MFL opening profile recognition methods for metal loss defects are no longer applicable to crack defects. Therefore, the proposed MFSI method is useful and meaningful for crack defect quantification in ultra-high-definition MFL detection.

Simulation and Design of a Balanced-Field Electromagnetic Technique Sensor for Crack Detection in Long-Distance Oil and Gas Pipelines

Due to the extremely small size and arbitrary orientation of the cracks, a highly sensitive sensor based on the balanced-field electromagnetic technique was designed for in-line inspection of oil and gas pipeline cracks. A balanced-field electromagnetic technique sensor mutual inductance model was established and used to theoretically analyze the parameters affecting sensitivity. Finite element simulation was used to analyze the specific effects of the magnetically conductive medium, the number of coil turns, and the sensor lift-off height on the sensor output, respectively, and the sensor parameters of high sensitivity were determined. The detection effect of the sensor on the pipeline crack was tested by the single-sensor experiment and the pulling test. The results show that the designed balanced-field electromagnetic technique sensor is effective in detecting both circumferential and axial cracks of 0.5 to 6 mm in depth. As the crack depth increases, the sensitivity decreases and the detection voltage amplitude increases linearly. The sensitivity of the sensor is highest when detecting circumferential and axial cracks of 1 mm in depth at 1.76 and 0.87 mV/mm, respectively. In addition, the amplitude of the circumferential crack signal at the same depth is approximately twice that of the axial crack signal.

Prediction model of natural gas pipeline crack evolution based on optimized DCNN-LSTM

Pipelines are one of the most important tools for natural gas transportation. To avoid accidents caused by local cracks in the pipeline, it is necessary to develop a model that can predict crack evolution. A time series prediction method for the evolution of natural gas pipeline crack based on acoustic emission signals is proposed in this paper based on the convolutional neural network (CNN), and long-short-term memory (LSTM) models (CNN-LSTM), on attention mechanism (AM), and optimized noise reduction. The model includes three structures. First, based on the original CEEMD algorithm combined with wavelet threshold denoising, the crack evolution signal features are effectively enhanced by improving the denoising threshold function. Then, the crack evolution features are extracted and predicted for the noise-reduced acoustic emission signal through an AM-based bidirectional CNN-LSTM network. Lastly, the final prediction result of the model is output by the fully connected layer. The experimental results show that the method effectively improves the prediction accuracy of crack evolution and achieves end-to-end prediction. Compared with other prediction models, the results obtained in this paper demonstrate that the proposed model is characterized by higher effectiveness and superiority in predicting time series failures. The aforementioned is significant for improving the long-term safe operation of natural gas projects.

Stochastic Simulation of Flow Rate and Power Consumption Considering the Uncertainty of Pipeline Cracking Rate and Time-Dependent Topology of a Natural Gas Transmission Network

Various gas pipeline networks used for the transit of energy sources are some of the most important infrastructures. However, carrying gas from one point to another is not the only concern when planning the construction of a new network or expanding an already existing one. The reliability and environmental impact of the system are crucial when evaluating the network and risks posed by potential gas leaks, fires, explosions, etc. Even though everyone admits that reliability is a key aspect of any system, its constraints will still be most likely neglected in the assessment of the pipeline project. How much energy is wasted by pushing an additional amount of gas through the pipeline network, which will eventually gush out of the pipeline because of one crack or another? Moreover, if this additional power or fuel consumption and related environmental impact are significant, how could it be reduced? In this paper, an approach is introduced for the simulation and quantification of how much more power would be required if the pipelines are regarded as unreliable (i.e., by leaking, rupturing, or even exploding). By employing stochastic simulations and time-dependent topology (topology determined by the value of a variable representing time) of the pipeline network as a case study for the selected representative gas transmission network, the amount of additional power consumption in gas compressor stations due to uncertain cracking and the leaking rate was evaluated. Although the analysis of power consumption was performed for a hypothetical network, the estimates of the cracking rates, detection effectiveness, and leaking rates used were as close to the real cases as possible.

Microstructural Effects in the Development of Near-Neutral pH Stress Corrosion Cracks in Pipelines.

The corrosion and stress corrosion cracking (SCC) behaviors of 20#, X60, and X80 pipeline steels in a near-neutral pH environment were investigated by means of electrochemical measurement, immersion test, and interrupted slow strain rate tensile (SSRT) test. The propensity for SCC, as indicated by the stress threshold value for crack initiation, was found to be dependent on the type of steel microstructure. Cracks were initiated in the high-strength steel X80 at a stress less than its yield strength, whereas in the other lower-grade steels, the initiation of cracks occurred after the yielding point. The threshold stress of SCC initiation in the near-neutral pH environment for 20#, X60, and X80 steels were 130.64% σys, 106.79% σys, and 86.92% σys, respectively. The SCC of 20# and X60 were characterized by the formation of transgranular and intergranular cracks, while X80 steel was only by transgranular cracking. The occurrence of corrosion had a great effect on crack initiation and the growth at the later stage. The latter involved hydrogen effects. A correlation between SCC sensitivity and the yield strength of the steel has been identified.