Pipeline Detection Research Articles

Water hammer detection based on FIV online analysis using a distributed fiber optic sensor.

In long-distance oil pipelines, the abnormal flow state of the fluid in the pipeline can cause continuous water hammer effects, which can damage the pipeline and even cause leakage or collapse. Based on this phenomenon, this paper proposes a new, to the best of our knowledge, detection method based on sensing optical fiber and phase-sensitive optical time-domain reflectometry (φ-OTDR) system to solve the problem of continuous water hammer detection in long-distance pipelines. The method uses a non-invasive, low-cost, real-time approach to monitor pipeline wall deformation characteristics and assess flow-induced vibration (FIV) intensity by statistically distinguishing water hammer signals from normal vibration signals and calculating the peak-to-average ratio (PAR) of these signals. Experimental results show that the FIV status of different types of pipelines can be effectively monitored by calculating the PAR of water hammer signals and vibration signals. The measured PAR based on the φ-OTDR system has high consistency with the PAR variation trend of simulation results, the distributed fiber optic sensor is less affected by environmental factors, and its detection distance and anti-interference ability are better than those of high-precision commercial triaxial acceleration sensors.

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.

Computational fluid dynamic study on design and modification of underwater remotely operated vehicle

Remotely Operated Vehicle (ROV) has been widely used in numerous underwater exploration applications such as exploration of complex deep sea environment, pipeline detection, installation, maintenance, and repair. Thus, it is worthy to design, optimize, and performance evaluation of ROV systems for underwater exploration purposes. Computational Fluid Dynamic (CFD) is a computational approach that is very helpful and widely used to examine the flow characteristics of ROV systems. In this research work, CFD analyses of ROV bodies with different thruster blades were carried out in order to generate thrust forces by the thruster blades. For this purpose, ANSYS FLUENT was used to examine the flow characteristics over the ROV body. The analyses were performed at different flow velocities, such as 1 m/s, 1.5 m/s, 2 m/s, 2.5 m/s, 3 m/s and 3.5 m/s. The outcomes were in the form of thrust force, drag force, pressure distribution, and velocity distribution. The result shows that four-blade thruster generates maximum thrust force as compare to other ROV thruster models. The main reason behind this higher thrust force is due to the larger motion of the fluid. Similarly, drag force was also investigated and observed that the drag force is greater for thruster model 3, having four blades due to the large frontal area of the ROV body and thruster blades. The results obtained in this study are very helpful for engineers and researchers to design and optimize ROV models using numerical methods.

Optimization for Pipeline Corrosion Sensor Placement in Oil-Water Two-Phase Flow Using CFD Simulations and Genetic Algorithm.

Internal corrosion is a major concern in ensuring the safety of transmission and gathering pipelines in Structural Health Monitoring (SHM). It usually requires numerous sensors deployed inside the piping system to comprehensively cover the locations with high corrosion rates. This study presents a hybrid modeling strategy using Computational Fluid Dynamics (CFD) and Genetic Algorithm (GA) to improve the sensor placement scheme for corrosion detection and monitoring. The essence of the proposed strategy harnesses the well-validated physical modeling capability of the CFD to simulate the oil-water two-phase flow and the stochastic searching ability of the GA to explore better solutions on a global level. The CFD-based corrosion rate prediction was validated through experimental results and further used to form the initial population for GA optimization. Importantly, fitness was defined by considering both sensing effectiveness and cost of sensor coverage. The hybrid modeling strategy was implemented through case studies, where three typical pipe fittings were used to demonstrate the applicability of the sensor layout design for corrosion detection in pipelines. The GA optimization results show high accuracy for sensor placement inside the pipelines. The best fitness of the U-shaped, upward-inclined, and downward-inclined pipes were 0.9415, 0.9064, and 0.9183, respectively. Upon this, the hybrid modeling strategy can provide a promising tool for the pipeline industry to design the practical placement.

A Sewer Pipeline Defect Detection Method Based on Improved YOLOv5

To address the issues of strong subjectivity, low efficiency, and difficulty in on-site model deployment encountered in existing CCTV defect detection of pipelines, this article proposes an object detection model based on an improved YOLOv5s algorithm. Firstly, involution modules and GSConv simplified models are introduced into the backbone network and feature fusion network, respectively, to enhance the detection accuracy. Secondly, a CBAM attention mechanism is integrated to improve the detection accuracy of overlapping targets in complex backgrounds. Finally, knowledge distillation is performed on the improved model to further enhance its accuracy. Experimental results demonstrate that the improved YOLOv5s achieved an mAP@0.5 of 80.5%, which is a 2.4% increase over the baseline, and reduces the parameter and computation volume by 30.1% and 29.4%, respectively, with a detection speed of 75 FPS. This method offers good detection accuracy and robustness while ensuring real-time detection and can be employed in the on-site detection process of sewer pipeline defects.

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.

Hybrid method for enhancing acoustic leak detection in water distribution systems: Integration of handcrafted features and deep learning approaches

Leak detection of water pipelines is of great significance to ensure water supply safety and conserve water resources. In the context of the prevalence of deep learning which can automatically extract features, deep learning-based acoustic leak detection methods are flourishing. The study compares the deep learning methods and traditional machine learning methods which need human-involved feature engineering, from aspects of detection performance, data requirement, and computational complexity. Furthermore, a hybrid leak detection method that integrates handcrafted features into deep learning networks is proposed. In this study, there are 70 features extracted from acoustic signals, constructing an extreme gradient boosting (XGBoost) classifier. Then three commonly used deep learning classifiers and the hybrid classifier are developed to carry out comparisons. Results show that handcrafted features, especially linear prediction features, still play an important role in acoustic leak detection at the current stage. The XGBoost classifier with key features can outperform deep learning classifiers and achieve acceptable computational complexity. Limited by insufficient data, deep learning classifiers cannot fully exploit their ability to directly extract useful features from model inputs (e.g., acoustic signals’ waveforms in the time or frequency domain). The hybrid classifier, requiring neither careful selection of handcrafted features nor complex deep learning network structures, obtains the best detection performance using the same amount of data, showing great promise in practical applications.

Acoustic injection method based on weak echo signals for leak detection and localization in gas pipelines

Underground gas pipelines are facing health and safety issues due to the material corrosion and equipment aging. Conventional acoustic methods may fail to detect and localize leaks in low and medium pressure gas pipelines because the sound of gas escaping from a leak source is usually too weak to be detected. In this paper, an active acoustic injection method is presented to stimulate specific frequency sound waves into the pipeline for leak detection based on weak echoes from the leak location. Further analysis is conducted on the relationships between the reflection coefficient and the size of the leak hole, the pipe diameter, the wall thickness of the pipe and the sound frequency. A signal reconstruction method is subsequently proposed to effectively detect and localize the echo signals. Finally, some experiments are carried out in a specially constructed gas pipe. Field test results indicate that a leak localization accuracy of 0.1 m can be achieved by the proposed method, which may be beneficial to practical leakage detection in gas pipelines.

A Method for Correcting Endoscopic Images to Measure the Size of Defects on the Inner Surface of a Hole

A method is proposed for correcting endoscopic images to enable the measurement of inner surface defects in holes. The distortion is decomposed into circumferential and axial components based on imaging principles, and geometric constraints are added to simplify the correction model parameters to a central coordinate and nonlinear parameter, improving accuracy at the edge of endoscopic images. Experimental results demonstrate good universality with an average error rate of 5.35% in defect measurements, making it applicable for automatic and intelligent detection of hole parts and pipelines.

An automated nondestructive testing system for the surface of pressure pipeline welds

AbstractPressure pipelines are widely used in hydropower generation, oil and gas transmission, and other fields. After years of operation, a pressure pipeline needs regular maintenance to ensure its safety. At present, manual detection methods are unable to meet this demand. An automatic pressure pipeline detection technology is urgently needed to achieve improved efficiency and accuracy. On the basis of the above requirements, a wall‐climbing robot is designed for automatic pressure pipe inspection and maintenance tasks. Moreover, rapid nondestructive testing of welds on the inner surface of pressure pipelines was performed, and a weld tracking function was developed for wall‐climbing robots. We propose an algorithm framework for weld recognition and centerline extraction by combining computer vision technology with traditional image processing technology using visual images. The experimental verification of the wall‐climbing robot designed in this paper and the algorithm framework for weld recognition and centerline extraction were performed based on actual pressure pipelines. The results show that the algorithm framework developed based on the wall‐climbing robot equipped with an industrial camera for pressure pipeline weld detection can achieve greatly improved efficiency, and the actual weld identification accuracy can exceed 90%, which is very meaningful for practical applications.

Underground infrastructure detection and localization using deep learning enabled radargram inversion and vision based mapping

Underground pipeline strikes, a pressing problem due to inaccurate subsurface data, are addressed in this paper with a pipeline detection and localization framework. First, abundant radargrams are generated to relieve radargram data shortage by simulating Ground Penetrating Radar (GPR) scans along the urban roadway and enhancing their realism with Generative Adversarial Network (GAN) technique. Second, a deep learning network is designed to directly reconstruct permittivity maps from radargrams for accurate pipeline detection and characterization, instead of detecting pipeline features within the radargram. Third, Simultaneous Localization and Mapping (SLAM) is employed for GPR position estimation, enabling precise georegistration of pipelines. The proposed method attains an R-squared (R2 15) value of 0.957 in permittivity map reconstruction and 96.2% precision in pipeline detection. Additionally, it provides satisfactory performance with a deviation of 1.71% in depth and 20.44% in diameter for the detected pipelines. Real-world experiments validate the effectiveness of the proposed framework, highlighting its potential to prevent excavation accidents, reduce project delays, and offer significant benefits to utility companies, contractors, and urban planners

Diagnostics of oil product leakage detection in main oil pipelines

Diagnostics of oil product leakage detection in main oil pipelines

Design of magnetic flux leakage detection device for three dimensional small diameter pipeline based on MEMS

Based on the common pipeline detection device faced with small diameter pipeline space, small turning radius is prone to the problem of stuck pipe phenomenon, this paper combined with the actual production needs, for small diameter pipeline leakage detection based on MEMS technology designed a detection device. COMSOL Multiphysics software was used to create a finite element model to simulate the magnetic flux leakage detection of pipeline defects. The influence of the type, size and location of pipeline defects on magnetic flux leakage signals was discussed. Based on MEMS triaxial magnetic force sensor, a magnetic flux leakage detection device with small volume, high precision and strong adaptive ability was designed. Through experiments, the magnetic flux leakage detection data consistent with the simulation results were obtained, which provided ideas for solving the detection problem of small-caliber pipelines.

MFCC-LSTM framework for leak detection and leak size identification in gas-liquid two-phase flow pipelines based on acoustic emission

Two-phase gas–liquid flows are crucial to the pipeline system. Due to their complicated flow state, existing leak detection techniques are unsuitable for two-phase flow pipelines. To avoid accidents caused by a leak of pipelines, we present a framework for combining the Mel-frequency cepstral coefficient and long short-term memory (MFCC-LSTM) based on acoustic emission (AE). A series of experiments are performed considering 1152 operating conditions, including flow pattern, leak size, direction, and location. In addition, the detection capability of the different features of AE signals combined with LSTM is discussed. The results show that the recognition accuracy of the MFCC-LSTM framework reaches 98.4%. Then, we further perform leak size identification under different flow patterns and found that the MFCC-LSTM framework still exhibits excellent performance. The proposed MFCC-LSTM framework provides a promising solution to identify the leak state and size in two-phase flow pipelines based on the AE technique.

Data-driven digital twin method for leak detection in natural gas pipelines

Leak detection in natural gas pipelines is an extremely important and persistent problem in the oil and gas industry. The construction of accurate physical models of pipelines is limited by the high complexity, unavailable closed-form solution, and strict experienced personnel requirements. Moreover, industrial automation has the common problems of large amount of data with little information. This paper proposes a data-driven digital twin (DT) method for leak detection as a new paradigm solution to these challenges. From the perspective of knowledge-based data-driven, a DT pipeline learning and updating scheme based on normal data directly from operational data during the entity pipelines life-cycle is proposed to enhance DT adaptability. A DT-driven leak detection method is proposed, making effective use of data interaction and fusion of DT. The effectiveness and performance of the proposed approach is illustrated by deploying the DT pipeline in a simulated leak scenario of a real running natural gas pipeline. © 2012 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of Global Science and Technology Forum Pte Ltd

3D-GPR-RM: A Method for Underground Pipeline Recognition Using 3-Dimensional GPR Images

Ground penetrating radar (GPR), as a non-destructive and rapid detection instrument, has been widely used for underground pipeline detection. However, as the interpretation of 3-dimensional GPR images is still manually performed, the process is inefficient. Aiming at solving the challenges of automatic recognition for underground pipelines, we propose a recognition method based on a deep learning algorithm, which uses 3-dimensional GPR images and the improved 3D depth-wise separable convolution block. In order to expand the number of samples in the dataset, we propose a data augmentation method based on three-dimensional matrix rotation and use a wavelet-based denoising method to filter out the direct wave interference. To prove the effectiveness and efficiency of our method, we compared the classification performance of the improved 3D depth-wise separable convolutional block with the traditional 3D convolutional block and the ordinary 3D depth-wise separable convolutional block under the same conditions. According to the experiment’s results, the number of parameters of the method we proposed is 66.9% less than that of the traditional 3D convolution method, while the classification performance is similar. Furthermore, compared with ordinary 3D depth-wise separable convolution, our method can significantly improve the classification and recognition ability of the neural network, while the number of calculations and the number of parameters remain almost the same. This study demonstrates the effectiveness of 3D-CNN in the field of GPR image interpretation. An improved 3D depth-wise separable convolutional block is also proposed. It greatly reduces the amount of calculation and parameters while ensuring classification performance. It is better than the existing algorithms in performance. At the same time, to obtain the position and direction of the pipeline, in this study, a conic fitting method using the Canny operator is proposed to locate the vertices of B-Scan images and record their horizontal and vertical coordinates. This method can estimate the direction of the pipeline and it lays the foundation for future work such as measuring the pipeline depth.

Experiment and numerical simulation of stress detection for oil and gas pipelines based on magnetic stress coupling of pipeline steel

In this study, a stress detection method is proposed to address the challenge of developing a nondestructive stress testing technique for oil and gas pipelines in real-time and online due to the complexity of the service environment and particularity of the transportation medium. The method is based on the magnetic mechanical properties of pipeline steel materials and the magnetic induction intensity stress coupling relationship. To evaluate the proposed method, six commonly used pipeline steels (X52, X56, X60, X65, X70, and X80) were selected as research objects, and their hysteresis curves were tested. The local magnetization key parameters of oil and gas pipelines were determined based on their magnetic properties. In addition, a finite element simulation model was established for oil and gas pipelines with coupling of magnetic field and stress field. This study appraise the distribution of magnetic field and magnetic induction intensity along the length and cross section height of the oil and gas pipelines simulation model after local magnetization, without being pressurized. Moreover, it examines the mechanism of magnetic induction intensity and stress variation of the simulation model under different internal pressures of the oil and gas pipelines. To this end, a coupling relationship of axial stress, circumferential stress, and magnetic induction intensity was separately established. Moreover, stress–strain test and magnetic induction intensity test systems were integrated to test the X80 pipeline steel. Through analyzing the test data and finite element simulation data, the validity of magnetic coupling simulation of oil and gas pipelines and the reliability of stress detection were verified. The results provide a theoretical basis for the key technologies of nondestructive online stress detection of oil and gas pipelines.

An Internal Detector Positioning Method in Oil Pipelines Using Vibration Signal

In-pipe inspection is an important means to ensure the safety of oil pipelines. External positioning technology of the internal detector is a vital component of the pipeline detection system. Due to the shielding effect of the metal tube wall,a great limit will be exerted on the application of general electromagnetic technology to the tracking and positioning of the internal detector. This paper proposes a positioning method of the internal detector in the pipeline based on the vibration signal. Aiming at the actual operation of the internal detection in the project site, a structural model of the internal detector positioning system based on the vibration signal is designed based on the idea of "coarse classification and exact regression". Through the vibration generated by the friction and collision between the detector and the tube wall, the feature extraction scheme of vibration signal based on wavelet packet decomposition is designed, and the precise positioning of the in-pipe detector is realized by using the two stage positioning network of Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN). Finally, the field experiments verify that the positioning method of internal detector designed in this paper has the advantages of high positioning accuracy and strong anti-interference ability.

Electromagnetic Imaging for Buried Conductors Using Deep Convolutional Neural Networks

In the past, many conventional algorithms, such as self-adaptive dynamic differential evolution and asynchronous particle swarm optimization, were used to reconstruct buried objects in the frequency domain; these were unfortunately time-consuming during the iterative, repeated computing process of the scattered field. Consequently, we propose an innovative deep convolutional neural network approach to solve the electromagnetic inverse scattering problem for buried conductors in this paper. Different shapes of conductors are buried in one half-space and the electromagnetic wave from the other half-space is incident. The shape of the conductor can be reconstructed promptly by inputting the received scattered fields measured from the upper half-space into the deep convolutional neural network module, which avoids the computational complexity of Green’s function for training. Numerical results show that the root mean square error for differently shaped—circular, elliptical, arrow, peanut, four-petal, and three-petal—reconstructed images are, respectively, 2.95%, 3.11%, 17.81%, 15.10%, 14.14%, and 15.24%. Briefly speaking, not only can circular and elliptical buried conductors be reconstructed; some irregular shapes can be reconstructed well. On the contrary, the reconstruction result by U-Net for buried objects is worse since it is not able to obtain a good preliminary image by processing only the upper scattered field—that is, rather than the full space. In other words, our proposed deep convolutional neural network can efficiently solve the electromagnetic inverse scattering problem of buried conductors and provide a novel method for the microwave imaging of the buried conductors. This is the first successful attempt at using deep convolutional neural networks for buried conductors in the frequency domain, which may be useful for practical applications in various fields such as the medical, military, or industrial fields, including magnetic resonance imaging, mine detection and clearance, non-destructive testing, gas or wire pipeline detection, etc.

Leak detection of a gathering pipeline based on a sensitive designed fiber Bragg grating pressure sensing system

Leak detection of a gathering pipeline based on a sensitive designed fiber Bragg grating pressure sensing system